Divergent patterns of microbial community composition shift under two fertilization regimes revealed by responding species

Applied Soil Ecology 154 (2020) 103590

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Divergent patterns of microbial community composition shift under two fertilization regimes revealed by responding species

T

Tongyan Yaoa,b, Ruirui Chena, , Jianwei Zhanga, Youzhi Fenga, Miansong Huangc, Xiangui Lina ⁎

a

State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, 210008 Nanjing, PR China University of Chinese Academy of Sciences, 100049 Beijing, PR China c Beijing Capital Co., LTD, 100028 Beijing, PR China b

ARTICLE INFO

ABSTRACT

Keywords: Fertilization regimes Soil microbial community composition Bacteria Fungi Responding pattern

Using patterns of belowground biodiversity to predict ecological functions and to manage productivity has been a longstanding objective for agronomists and ecologists. However, inconsistent associations between microbial diversity and fertilization have been found in frequency, which can be partly due to the dilution effect of immense silent species in microbial communities. Therefore, we studied the responding pool of the microbial communities, which was determined by filtering out taxa that did not vary significantly between fertilization treatments and the control. With responding species, we hypothesized that the difference of microbial response to different fertilization regimes and between bacterial and fungal response to treatments can be enlarged. In this study, bacterial and fungal community compositions and networks were characterized from soils with chemical fertilization (F), the combined application of chemical fertilizers and organic residues (MRF) and without fertilization (Control) in a 7-year experiment located at the Fengqiu Agroecological Experimental Station. As a result, > 90% of the phyla, 75% of the genera and 92% of the OTUs were silent microbes. When filtering out these species, 9 phyla, 88 genera and 185 OTUs of bacteria responded significantly to the MRF treatment compared with those of the Control with an average abundance variation of 0.06% in responding OTUs. In contrast, only 2 phyla, 9 genera and 30 OTUs of bacteria significantly responded to the F treatment with an average amplitude of 0.11%. The higher connectedness and the lower average geodesic distance under the MRF treatment showed a more connective and closer bacterial network compared with the F treatment. Similar but stronger responses were detected in the fungal community composition and networks. To conclude, combined fertilization exerted a moderate impact on many microbial taxa (MM pattern), and chemical fertilization showed a substantial effect on a small amount of taxa (SS pattern). It was further inferred that a variation of the “MM” pattern can result in a more stable, harmonious and efficient ecosystem via maintenance of microbial diversity, stimulation of beneficial species, and exhibition of a more efficient microbial network. Our study showed explicit patterns of microbial communities under fertilization regimes revealed by responding species and highlighted the advantages of combined fertilization with organic matter in agroecosystems.

1. Introduction To feed the world's growing population, fertilization is a widely used and effective practice to maintain crop yields in agroecosystems (Tilman et al., 2011). Although chemical fertilization can effectively meet the requirement of crop yield (Vitousek et al., 2010), it increasingly leads to many environmental problems, e.g. soil acidification (Guo et al., 2010) and water eutrophication (Peng et al., 2011). There is also a concern that continuous fertilization is attributed to the loss of underground biodiversity and can ultimately cause degradation of terrestrial ecosystem (Hartmann et al., 2015). Organic fertilization

⁎

benefits soil ecosystems by improving soil fertility, aggregated structure and stability, bioactivity, microbial and earthworm biomass, and cooperation between crops and microbial communities (Mäder et al., 2002). However, the yields are normally lower than chemical fertilization (Seufert et al., 2012). Thus, the combined application of chemical and organic fertilizers can be a win-win practice in intensive agriculture. Soil microorganisms are essential components in terrestrial ecosystems. Due to their extreme sensitivity to environmental changes, the soil microbial response to fertilization can provide profound understanding of the inner relationship between fertilization and

Corresponding author. E-mail address: [email protected] (R. Chen).

https://doi.org/10.1016/j.apsoil.2020.103590 Received 12 April 2019; Received in revised form 20 December 2019; Accepted 14 March 2020 0929-1393/ © 2020 Elsevier B.V. All rights reserved.

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agroecosystem functions (Feng et al., 2017). Many experiments have investigated effects of fertilization on soil microbial diversity. Generally, the amendment of organic fertilizers has a positive effect on microbial diversity (Sun et al., 2016) or at least maintains the diversity compared to without fertilization (Feng et al., 2018a). The results are inconsistent with application of chemical fertilizers, which shows negative (Sradnicka et al., 2013) or positive effects (Hu et al., 2011) on soil microbial diversity. In more cases, diversity indices appeared with small changes but without significant variations (van der Bom et al., 2018). One possibility is that the internal diversity change, including the increase and decrease of certain taxa, may be offset in part or even in whole (Maestre et al., 2015). More often, most species in soils that do not respond to environmental disturbances, which we call silent species, will strongly dilute the effects of these disturbances on sensitive species. Therefore, regarding these sensitive species as an integer pool and focus on the behaviour of this responding pool can theoretically magnify microbial responses to environmental changes. Both bacteria and fungi are critical in terrestrial biogeochemical cycles. Fungi are mainly in charge of decomposing plant residues, e.g., lignin and lipid, whereas bacteria play an important role in the metabolism of carbohydrates and proteins (Lehmann and Kleber, 2015). The cooperation of bacteria and fungi completes the nutrient cycle. Fungi have a larger body size than that of bacteria, and their biomass often exceeds half of the microbial biomass in soils (Gulis and Suberkropp, 2003); at the same time, the species richness of fungi is always lower than that of bacteria (Gulis and Suberkropp, 2003). However, scant investigations found that fungal communities were more sensitive to the amendments of organic matter and chemical fertilizers (Phillips et al., 2013; Banerjee et al., 2016). Because most previous studies on bacterial and fungal communities in soils were separately investigated (Kaiser et al., 2016; Xu et al., 2016), differences in their responses to fertilization remain unclear. Therefore, a comparison of fungal and bacterial communities can give more evidence of the relationship between body size and species diversity in response to environmental disturbance, which is one of the central topics in the metabolic theory of ecology (Brown et al., 2004). In the current study, high-throughput sequencing was adopted to compare soil bacterial and fungal community composition and networks in response to 7-year continuous chemical fertilization (F), the combined application of chemical fertilizers and organic residues (MRF) and a Control without any fertilization. We aimed to 1) filter out the silent species and harvest responding species, and then based on the pool of responding species, 2) to present a more clarified response of microbial community composition and network to two fertilization regimes; and 3) to compare the responses of bacterial and fungal communities and their networks to fertilization.

one-half of N came from urea and the other half was from mushroom residues. All P, K, and organic residues were applied as basal fertilizers, whereas urea was applied to the two fertilization regimes as both basal and supplementary fertilizers. Basal fertilizers were evenly broadcast onto the soil surface and were immediately incorporated into the ploughed layer before sowing in June for maize and in October for wheat. Supplementary fertilizer urea was surface applied by hand and then brought into the ploughed layer with irrigation water. 2.2. Soil sampling, analysis and DNA extraction In September 2017, after the maize harvest, soil samples were collected from plots of the Control, F and MRF treatments. From each plot, nine soil columns along the diagonals of each plot were taken at a depth of 0–15 cm using a 30-mm diameter gouge auger. Afterwards, these soil columns of a plot were mixed together, representing the sample of the plot. Visible roots and plant residues were then removed before the soil samples were homogenized. For each soil sample, approximately 10 g was stored at −40 °C for DNA extraction. The other portion was mixed, air dried and sieved to < 2 mm for further analysis. Soil pH was determined from soil-water suspensions (1:2.5 v/v) (Kabala et al., 2016). Soil organic carbon (SOC) was determined by the potassium dichromate oxidation-external heating method (ISSAS, 1978). Soil total N was determined by Kjeldahl digestion (Bremner, 1965). Soil total P and K were digested by hydrofluoric acid and perchloric acid and then detected by molybdenum-blue colorimetry and photometry, respectively (Sommers and Nelson, 1972; Logan and Miller, 1983). Soil inorganic N was firstly extracted with 2 mol l−1 KCl at a soil:solution ratio of 1:4 for 1 h, and then determined with Skalar SANplus Segmented Flow Analyzer (Chu et al., 2007a). Soil available P was extracted using sodium bicarbonate and then determined by molybdenum-blue method. Soil available K was extracted using ammonium acetate and then determined by flame photometry (Chen et al., 2011). Genomic DNA was extracted from 0.50 g fresh soil samples using a Fast DNA SPIN Kit for soil (MP Biomedicals, Santa Ana, CA) according to the manufacturer's instructions. The extracted DNA was dissolved in 80 μL TE buffer and then stored at −20 °C until further use. 2.3. High-throughput sequencing of 16S rRNA genes and the ITS region The bacterial and fungal communities of each soil sample were assayed by high-throughput sequencing performed with Illumina Miseq sequencing platform (Illumina Inc.). For bacteria, PCR amplification was conducted for the V4-V5 region of the bacterial 16S rRNA gene with a primer pair 519F (3′-CAGCMGCCGCGGTAATWC-5′) and 907R (3′-CCGTCAATTCMTTTRAGTTT-5′). Five-bp barcoded oligonucleotides were fused to the forward primer. PCR was carried out in a 50-μL reaction mixture containing the following components: 25 μL Premix Taq (TaKaRa Taq Version 2.0 plus dye) (TaKaRa, Japan), 0.5 μL (15 μM) forward and 0.5 μL (15 μM) reverse primers, 23 μL double distilled water (ddH2O) and 1 μL (50 ng) genomic DNA as a template. After pre-denaturing at 94 °C for 5 min, 30 cycles of PCR for 16S rRNA gene were performed (94 °C for 60 s, 55 °C for 60 s, 72 °C for 75 s) with a final extension at 72 °C for 10 min (Jing et al., 2017). The reaction products were purified and sequenced using the Illumina Miseq platform (Illumina, San Diego, USA). Bacterial sequences were processed using the Quantitative Insights Into Microbial Ecology (QIIME) pipeline for data sets (Caporaso et al., 2010). Sequences with a quality score below 25 and the length fewer than 200 bp were trimmed and then assigned to soil samples based on unique barcodes. Sequences were binned into operational taxonomic units (OTUs) using a 97% identity threshold and the most abundant sequence from each OTU was selected as a representative sequence. Taxonomy was then assigned to OTUs with reference to a subset of the SILVA 119 database (http://www.arb-silva.de/download/archive/qiime/). In

2. Materials and methods 2.1. Study site and experimental design The 7-year plot experiment was sited at Fengqiu Agroecological Experimental Station (35° 00′ N, 114° 24′ E), Henan province, China. The soil, possessing a sandy loam texture, contained 5.8 g kg−1 organic C, 0.56 g kg−1 total N, 0.88 g kg−1 total phosphorus (P2O5), 29.3 g kg−1 total potassium (K2O), and pH (H2O) of 8.5 at the onset of the experiment in 2011. The plot experiment was conducted under a rotation of winter wheat (Triticum aestivum L.) and summer maize (Zea mays L.). Four replicated plots of fertilizer regimes were established randomly in the field. Each plot covers of 30 m2. The treatments included NPK fertilizers (F), the combined application of NPK fertilizers and mushroom residues (MRF), and a Control without fertilizers. For the F treatment, N, P, and K were applied in the form of urea (200 kg N ha−1), superphosphate (80 kg P2O5 ha−1), and potassium sulphate (150 kg K2O ha−1), respectively. The F and MRF treatments were designed to supply the same rate of total N. In the MRF treatment, 2

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total, we obtained 245,648 sequences of the bacterial 16S rRNA gene, and between 10,872 to 31,647 sequences per sample. To meet the demand of an even depth of sampling for alpha and beta diversity comparisons (Shaw et al., 2008), all samples were rarefied to 10,000 sequences per sample. For fungi, PCR amplification was conducted for ITS1 region of fungi gene with primer ITS1 (5’-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2 (5’-GCTGCGTTCTTCATCGATGC-3′) (Schoch et al., 2012). Eight-bp barcoded oligonucleotides were fused to the forward primer. The PCR was carried out in the same 50-μL reaction mixture (above mentioned). After 94 °C for 2 min, 25 cycles of PCR for the ITS region were performed (94 °C for 20 s, 55 °C for 30 s, 72 °C for 60 s), with a final extension at 72 °C for 10 min. Then, sequencing was performed by the same procedure as bacterial 16S rRNA gene. Taxonomic information was assigned to OTUs with reference to the UNITE database (https:// unite.ut.ee/). In total, we obtained 1,207,622 sequences of fungi ITS sequences, and between 48,815 to 148,522 sequences per sample. All samples were rarefied to 47,000 sequences per sample to evaluate alpha and beta diversities.

obtain the p value for every treatment. An absolute value of SparCC correlation coefficients (ρ) over 0.90 with statistical significance (p < .01) was used in the network analysis. The nodes of the co-occurrence networks corresponded to the OTUs, whereas the edges represented strong (ρ > 0.90 or < −0.90) and significant (p < .01) correlations between the nodes. To quantify the topological properties of the networks, a set of network parameters, including the numbers of nodes and edges, the average degree, modularity, connectedness and average geodesic distance were calculated, and the networks were explored and visualized using the interactive platform Gephi (Version 0.9.2) (Bastian et al., 2009). The average degree represented edges per node of a network. The connectedness was calculated by w Con = 1 n (n 1) / 2 , where w is the number of pairs of nodes that are not reachable and n is the number of nodes (Deng et al., 2012). This parameter elucidated the connection conditions among the subgraphs of a network. A lower average geodesic distance indicated the closer distance between the nodes.

2.4. Calculations and statistical analysis

Sequence data have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) with the accession number SRP171658.

2.6. Accession numbers

The Shannon index (Shannon, 1948), Chao1 index (Chao, 1984) and Pielou's evenness index (Pielou, 1966) were calculated in Qiime to describe alpha diversity of the microbial communities. Non-metric multidimensional scaling (NMDS) was performed using Bray-Curtis distance based on the OTU table to visualize the dissimilarities among microbial (bacterial and fungal) communities via the “vegan” package in R. A permutational multivariate analysis of variation (PERMANOVA) test was performed by the “vegan” package in R to determine the significance of dissimilarity among fertilization regimes. We set the Control as a reference to obtain the responding microbes. Three indices were defined to describe the patterns of microbial communities' variation in the F treatment and the MRF treatment: (1) count of responding taxa (CRT), respectively at the level of phylum (CRTp), genera (CRTg) and OTUs (CRTOTU); (2) variation of their relative abundance (VRA); and (3) variation amplitude per taxon (VRA/CRT). A comparison of the taxonomic groups was conducted between one treatment and the reference (the Control) by the likelihood ratio test (Wang and Ethier, 2004), and then the p value was corrected by the false discovery rate (fdr). This process was performed in R using package “edgeR” (McCarthy et al., 2012; Robinson et al., 2010). The taxon with p < .05 was selected as the taxon responding to the treatment. CRT was the count of all the responding taxa. The VRA was calculated by Eq. (4):

VRA =

CRT i=1

Ti

Ri

3. Results 3.1. Soil chemical properties Fertilization regimes exerted significant effects on soil chemical properties (Table 1). Fertilization significantly improved SOC content (p < .05), with 5.57 g kg−1, 6.88 g kg−1 and 14.32 g kg−1 in the Control, F and MRF treatments, respectively. After 7-year fertilization, the F treatment increased SOC by 24% and the MRF treatment increased SOC by 157%. Fertilization significantly decreased soil pH (p < .05) compared to the Control and this effect was stronger in the MRF treatment than in the F treatment. Contents of total N, total P, inorganic N, available P and available K were all observed maximal in the MRF treatment, significantly higher than the F and Control treatments (p < .05). F treatment significantly increased the contents of total N, inorganic N and total P compared with Control (p < .05), but slightly increased the contents of available P and available K. Contents of total K ranged from 9.58 to 9.66 g kg−1 without significant difference among the fertilization regimes. 3.2. Microbial community composition

(4)

2

The high-quality sequences were assigned to 28 phyla of bacterial communities and 6 phyla of fungal communities, except unclassified sequences. Eleven dominant phyla of bacterial communities with a relative abundance > 1% are shown in Fig. 1A, including Proteobacteria (31.85–34.52%), Acidobacteria (20.33–24.99%), Bacteroidetes

th

where Ti was the relative abundance of the i responding taxon in the treatment, and Ri was the relative abundance of the taxon in the reference. The quotient of VRA over CRT was calculated to describe the average responding amplitude of the responding taxa. Significance statistics were performed by Tukey's test, following the homogeneity of variance, the tests of assumptions of normal distribution and ANOVA, using version 20.0 of the IBM Statistical Product and Service Solutions (SPSS) Statistics for Windows.

Table 1 Soil chemical properties under fertilization regimes.a

pH SOC (g kg−1) Total N (g kg−1) Inorganic N (mg kg−1)a Total P (g kg−1) Available P (mg kg−1) Total K (g kg−1) Available K (mg kg−1)

2.5. Network analysis A non-random co-occurrence analysis for each fertilization regime was conducted to explore the variations in the interactions among OTUs using SparCC (Friedman and Alm, 2012). First, the OTU with a relative abundance of < 0.1% in every fertilization regime was discarded. Then, the checkerboard score (C-score) (Stone and Roberts, 1990) was used to assess the co-occurrence in Mothur (Schloss et al., 2009). Random selections from the data table of 9999 permutations were conducted to

Control

F

MRF

8.63 a 5.57 c 0.51 c 1.85 b 0.34 c 0.01 b 9.59 a 55.97 b

8.43 b 6.88 b 0.61 b 8.62 a 0.42 b 2.40 b 9.66 a 82.35 b

8.30 c 14.32 a 1.13 a 12.68 a 0.54 a 45.56 a 9.58 a 170.9 a

Different letters in a row indicate significant difference (p < .05). a Inorganic N, including NH4+-N and NO3−-N. 3

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Fig. 1. Relative abundance of soil bacterial (A) and fungal (B) communities under fertilization regimes at the phylum. Control, without fertilizers; F, NPK fertilizers; MRF, the combined application of NPK fertilizers and mushroom residues. The displayed bacterial phyla are phyla with a relative abundance > 1% in three fertilization regimes. Bacterial phyla with lower abundance (< 1%) are labelled as “Others”. All observed fungal phyla are shown, and unclassified fungi are labelled as “Others”.

(8.16–13.93%), Actinobacteria (6.05–7.48%), Planctomycetes (5.45–5.63%), Firmicutes (2.18–5.02%), Cyanobacteria/Chloroplast (1.36–4.97%), Chloroflexi (3.26–4.73%), Nitrospirae (1.20–1.34%) and Gemmatimonadetes (1.36–1.66%). Among these dominant bacterial phyla, Acidobacteria, Bacteroidetes, Firmicutes, Proteobacteria were significantly enriched by 1.45%, 3.80%, 2.72%, and 2.67%, respectively, and Cyanobacteria was significantly depleted by 3.61% in the MRF treatment compared with those of the Control (p < .05, Fig. 1A). Only one phylum, Bacteroidetes was observed to be significantly enriched by 5.77% under the F treatment compared with that of the Control. Six phyla of fungal communities were detected, including Ascomycota (63.43–75.79%), Zygomycota (10.81–17.04%), Basidiomycota (8.23–10.17%), Glomeromycota (2.96–7.21%), Chytridiomycota (1.03–1.24%) and Cercozoa (0.92–1.12%). An increased relative abundance of Ascomycota and declined trends of Zygomycota, Glomeromycota and Basidiomycota were observed in fungal communities of the MRF treatment compared with those of the Control but without significant difference (p > .05, Fig. 1B). An increased relative abundance of Ascomycota and Basidiomycota and declined trends of Zygomycota and Glomeromycota were observed under the F treatment compared with those of the Control but without significant difference (p > .05, Fig. 1B). The alpha diversity of bacterial and fungal communities was characterized by the Shannon index, Chao1 index and Pielou's evenness. Except that Chao1 of the fungal communities was significantly greater under the MRF treatment than in the Control (p < .05, Table 2), none of these indices was observed to be significantly different among treatments (p > .05), which indicated an insignificant effect of fertilization on the alpha diversity of bacterial and fungal communities. Dissimilarity of the microbial community composition in response to fertilization regimes was visualized by the NMDS plot based on BrayCurtis distance (Fig. 2). Both bacterial and fungal communities were distributed into three separated clusters according to the three treatments (Control, F and MRF). The PERMANOVA pairwise test confirmed significant dissimilarities of soil microbial communities among the three treatments (p < .05, Fig. 3). A pairwise comparison showed that the Bray-Curtis distance was greatest for the MRF vs. Control and shortest for the F vs. Control, regardless of the bacterial and fungal

communities. These results indicated that long-term fertilization resulted in a shift of the microbial community composition, and the amendments of the organic residues showed a larger influence than the chemical fertilizers. 3.3. Microbial community variation based on responding microbes To clarify the variation of microbial communities in response to different fertilization regimes, three indices, the count of responding taxa (CRT), the total variation of their relative abundance (VRA) and the variation amplitude per responding taxon (VRA/CRT) were calculated based on the pairwise comparison between the treatments and the Control. The number of responding bacterial taxa was greater in the MRF treatment than that of the F treatment. In comparing the bacterial communities of the F vs. Control, 2 phyla, 9 genera and 30 OTUs were observed with significant variation; i.e., only 6.45% of the phyla, 1.71% of the genera and 0.54% of the OTUs were observed as responding microbes (Table 3). > 90% of the phyla, 98% of the genera and 99% of the OTUs were silent microbes with chemical fertilization. The total relative abundance variation of all these responding OTUs (VRAOTU) accounted for 3.30%, and the average variation per responding OTU (VRA/CRTOTU) was 0.11%. The responding genera contain Galbibacter, Azoarcus, Crocinitomix, Ferruginibacter, Parachlamydia, Nitrosospira, and Arenibacter. Comparing the bacterial communities of MRF vs. Control, 9 phyla, 88 genera and 185 OTUs significantly responded, accounting for 29.03% of the phyla, 16.76% of the genera and 3.33% of the OTUs, respectively (Table 3). The bacterial VRAOTU of MRF vs. Control was 11.37%, which was significantly higher than that of F vs. Control (p < .05, Table 3), whereas VRA/CRTOTU was 0.06%, which was significantly lower than that of F vs. Control (p < .05, Table 3). Among these responding taxa, some genera of the MRF treatment were plant growth-promoting bacteria, including Paenibacillus, Lysinibacillus, Planococcus, Sporosarcina and Staphylococcus. The same pattern was also observed for the fungal communities. Seventy genera and 223 OTUs were significantly varied with a relative CRT of 25.55% and 7.44% in the MRF vs. Control, respectively, both of which were higher than those of the F vs. Control (Table 3). The fungal VRAOTU of the MRF vs. Control was 24.31%, which was significantly higher than that of the F vs. Control (p < .05, Table 3), whereas VRA/ CRTOTU was 0.11%, significantly lower than that of the F vs. Control (p < .05, Table 3). The largest variations of relative abundance among the genera in the MRF treatment were 1.4% (bacteria) and 1.7% (fungi), which were lower than those of the F treatment: 1.6% (bacteria) and 2.3% (fungi). In the F treatment, the three responding genera with maximal variation were Bipolaris, Ilyonectria and Mycosphaerella, which were all pathogens. The most responding genus in the MRF treatment was Talaromyces, which can occupy plant rhizosphere and reduce pathogens.

Table 2 Alpha diversity indices of microbial communities. Shannon index Bacteria Fungi

Control F MRF Control F MRF

4.78 4.94 5.05 4.78 4.94 5.05

a a a a a a

Chao1 index 3010 a 2954 a 2911 a 1069 b 1152 ab 1207 a

Pielou's evenness 0.86 0.85 0.86 0.70 0.71 0.72

a a a a a a

Different letters in a column indicate significant difference (p < .05). 4

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Fig. 2. Non-metric multidimensional scaling (NMDS) plots of bacterial communities (A) and fungal communities (B) under fertilization regimes using Bray-Curtis distance. PERMANOVA (pairwise comparison) showed Bray-Curtis distance of soil microbial communities among three regimes. Control, without fertilizers; F, NPK fertilizers; MRF, the combined application of NPK fertilizers and mushroom residues.

3.4. Microbial networks

F treatment (0.62) and the Control (0.62), suggesting a more connective network of bacteria in the MRF treatment. The average geodesic distance of the MRF treatment (5.66) was lower than that of the F treatment (6.00), indicating a more closely connected network. Module hubs and connectors with high-stress centrality (bacteria: OTU62, fungi: OTU23) and (or) betweenness (bacteria: OTU28, fungi: OTU23) were observed under the F treatment (Deng et al., 2012). None of these were observed under the MRF treatment. In the fungal networks, the average degree was 4.77, 4.56 and 7.25 for the Control, F and MRF treatments, respectively. This indicated a clearer effect of fertilization on the fungal networks. The fungal connectedness followed the order of MRF (0.83) > F (0.70) > Control (0.64). The order was similar with bacterial networks, but the scores were all higher than those of bacteria. The average geodesic distance scored 3.37 in the MRF, which was shorter than 4.33 in the F treatment.

Non-random co-occurrence networks were individually generated for three treatments of fertilization regimes (Fig. 3); the fundamental topological parameters are listed in Table 4. Specifically, the networks of all fertilization regimes led to modularity ranging from 0.50 to 0.71. All values were above a threshold value of 0.4 (Newman, 2006), indicating that the partition produced by the modularity algorithm can be used to detect distinct communities within the network. Among all of the bacterial OTUs with relative abundance over 0.1% in the Control, 145 nodes and 288 edges were generated in the network. The average degree of the bacterial network was quite similar in response to fertilization regimes, with 3.97, 3.90 and 3.88 in the Control, F and MRF treatments, respectively. The bacterial connectedness of the MRF treatment was 0.67 (Table 4), which was greater than those of the

Fig. 3. Network co-occurrence analyses of bacterial and fungal phylotypes under fertilization regimes. Control, without fertilizers; F, NPK fertilizers; MRF, the combined application of NPK fertilizers and mushroom residues. Each node represents a phylotype (an OTU clustered at 97%). An edge stands for statistically significant (p < .01) SparCC correlation with a magnitude > +0.90 or < −0.90. The size of each node is proportional to degree. Red nodes are OTUs responding to fertilization, and blue nodes are OTUs silent to fertilization. A red edge stands for connection with at least one incident node responding to fertilization, and a blue edge stands for connection with two incident nodes silent to fertilization. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 5

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Table 3 Variation of bacterial and fungal communities based on responding microbes. Bacteria

CRTa Relative CRTb VRAc VRA/CRTOTU

Phylum Genus OTU Phylum Genus OTU

Fungi

F vs. Control

MRF vs. Control

F vs. Control

MRF vs. Control

2 9 30 6.45% 1.71% 0.54% 3.30% d 0.11% b

9 88 185 29.03% 16.76% 3.33% 11.37% b 0.06% c

0 35 46 0.00% 12.77% 1.53% 6.51% c 0.14% a

0 70 223 0.00% 25.55% 7.44% 24.31% a 0.11% b

Different letters in a row indicate significant difference (p < .05). a CRT: Count of responding taxa. b Relative CRT: CRT divided by number of taxonomic groups in all treatments. c VRA: Variation of relative abundance.

Fig. 4. Conceptual figure showing variation patterns of responding microbes under combined fertilization and chemical fertilization. The x-axis is taxa and the y axis is variation of the relative abundance. Basically, the red lines depict that less taxa varied, but the relative abundance of some taxa largely changed, compared to the blue lines. The blue curves: variation pattern of responding microbes under combined fertilization; the red curves: variation pattern of responding microbes under chemical fertilization. The solid curves: variation pattern of responding fungi; the dashed curves: variation pattern of responding bacteria. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Table 4 Topological properties of bacteria and fungi networks under fertilization regimes. Network metrics

Number of nodes Number of edges Average degree Modularity Connectedness Average geodesic distance

Bacteria

Fungi

Control

F

MRF

Control

F

MRF

145 288 3.97 0.69 0.62 5.64

154 300 3.9 0.71 0.62 6.00

148 287 3.88 0.71 0.67 5.66

93 222 4.77 0.61 0.64 3.64

126 287 4.56 0.66 0.70 4.33

112 406 7.25 0.50 0.83 3.37

treatment appeared with a substantial variation in a small number of bacteria, which can be visualized as the “SS” pattern (red line, Fig. 4). The observed “MM” pattern in the MRF treatment was possibly due to the effects of applied organic matter on the entire microbial community. In our study, the SOC content in the MRF treatment was 14.32 g kg−1, which was significantly higher than that in the F and the Control treatments (Table 1). As circumstantial evidence, Cyanobacteria, a phylum of bacteria that can conduct photosynthetic carbon fixation, significantly decreased under the MRF treatment. Organic residues supplied both labile and recalcitrant carbon to soils (Rovira and Vallejo, 2002). Labile carbon was an effective energy source and metabolic substrate to most soil microbes, which can promptly enhance microbial activity and improve the decomposition of added and native organic matter (Banerjee et al., 2016). The relative content of labile carbon under the MRF treatment significantly increased according to our prior study (Yao et al., 2019), indicating a more active microbial community and a more rapid C cycling. Although recalcitrant carbon is hard to degrade, it can also improve the growth of abundant fungi, such as Ascomycota, Basidiomycota (Voříšková and Baldrian, 2012) and mycorrhizal fungi (Lindahl et al., 2010), as well as bacterial taxa from the phyla Acidobacteria and Actinobacteria (DeAngelis et al., 2015). High levels of recalcitrant organic matter had a positive correlation with the abundance of oligotrophs (Trivedi et al., 2017). Recalcitrant carbon, as an essential component of soil aggregates, improved soil structure (Bronick and Lal, 2005) and benefited most microbes by providing them with better habitats and more niches. Thus, more microbes can be stimulated by the organic residues in a relatively moderate way, shaping the “MM” pattern. That the “SS” pattern could have occurred was probably due to the selective stimulation of certain microbes by chemical fertilization. Our results showed that one-third of 9 responding bacterial genera (p < .05) under the F treatment participated in soil N and P cycling, e.g., Nitrosospira, nitrifying bacteria produced nitrous oxide by denitrification (Shaw et al., 2006); Azoarcus, endophytes fixed nitrogen (Hurek et al., 2002). Similarly, chemical fertilization has been reported to stimulate specific microbes that are involved in N cycling (Chu et al., 2007b), including Bacteroidetes, Gemmatimonadetes and Fusarium oxysporum, participated in denitrification (Hu et al., 2015), and Zygomycota participated in nitrification (Hayatsu et al., 2008), as well as ammonia-oxidizing bacteria and archaea (Strauss et al., 2014).

The variation in the geodesic distance between fertilization treatments and the Control in fungal networks was larger than that in bacterial networks. These indices suggested that fungal networks were more sensitive to fertilization than bacterial networks were. It also showed that the MRF treatment generated more connective and closer microbial networks. 4. Discussion 4.1. “MM” pattern vs. “SS” pattern No significant effects of fertilization on the alpha diversity of soil microbial communities were detected (Table 2), except that the MRF treatment caused an increase in fungal richness. However, after extracting the responding microbes from the fertilization regimes in comparison to the Control and then investigating them as a new pool, different patterns of variation in the microbial communities in response to the F and MRF treatment emerged (Table 3 and Fig. 4). This finding supported our hypothesis that the microbial response to environmental changes can be enlarged by removing silent species. In the F treatment, only 1.17% of the bacterial genera and 12.77% of the fungal genera significantly responded compared with those of the Control, whereas 16.76% of the bacterial genera and 25.55% of the fungal genera significantly responded under the MRF treatment (Table 3). For both bacteria and fungi, the responding amplitude per OTU (VRA/CRT) was significantly higher for the F treatment than for the MRF treatment (Table 3). Data from a previous study also showed a sharp variation in the relative abundance of certain microbes after chemical fertilization, compared to combined fertilization (Francioli et al., 2016). In simple terms, the responding microbes under the MRF treatment (compared with the Control) appeared with a moderate variation in the many taxa, which can be visualized as the “MM” pattern (blue line, Fig. 4). In contrast, the responding microbes under the F 6

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Additionally, both our results and the results of Feng et al. (2013) found that Ferruginibacter, a phosphate solubilizing bacteria (Kamika et al., 2017), was significantly increased under chemical fertilization. Others also documented that P fertilization altered Actinobacterial communities (Tang et al., 2016), most of which were identified as polyphosphate-accumulating organisms (Kong et al., 2005). Recent investigations of microbial assembly processes supported that environmental selection contributed more to soil bacterial (Feng et al., 2018b) and fungal (Liu et al., 2015) assembly with chemical fertilization than combined fertilization did. The patterns of responding microbes can help understanding of the relationship of diversity-stability and further predict ecosystem stability. Since Charles Elton stated the positive relationship of diversitystability (Solans Vila and Barbosa, 2010), a growing amount of studies have demonstrated this relationship in various ecosystems (Ptacnik et al., 2008; Sul et al., 2013). The fact that no effects or small increases of microbial diversity in response to fertilization (Table 2) can be a big obstacle to predict the consequence of shifted microbial communities. However, when investigating the responding pool, microbial richness and evenness were visually greater with the “MM” pattern of the MRF treatment than those of the “SS” pattern of the F treatment. It can be furtherly inferred that the “MM” pattern, which contributes to the maintenance of species diversity (Feng et al., 2018a) and functional diversity (Gomez et al., 2006) in soil, will associate with a more stable agroecosystem. As a matter of fact, yield stability, an important index to estimate agroecosystem stability, was enhanced after long-term combined or organic fertilization (Zhang et al., 2016). It was also found that crop yield with long-term combined fertilization was more resistant to extreme weather. During the jointing stage in 2018, the local region where the current experiment was conducted encountered a severe cold damage, as a consequence the wheat yield was decreased by 45.38% with chemical fertilization comparing to the former three years, however, the yield of the MRF treatment decreased by 29.74% (Yao et al., in press). Additionally, the MRF treatment substantially improved soil fertility with the highest contents of SOC and nutrients (Table 1). The responding microbial communities with a higher diversity would actively resist disturbance and rapidly recover under stress (Girvan et al., 2005). This was in accordance with regional-scale findings that combined fertilization decreased the sensitivity of bacterial communities to anthropogenic and natural stress (Chen et al., 2017), enhancing agroecosystem stability and yield sustainability. The topological properties of bacterial and fungal networks showed that the connectedness was higher, and the average geodesic distances were lower under the MRF treatment than under the F treatment (Fig. 3 and Table 4). The higher connectedness suggested “a potential ecological niche shared by the organisms in a community” (Karimi et al., 2017), which indicated more harmonious microbial communities after combined fertilization. The shorter geodesic distance demonstrated higher community efficiency in energy consumption and matter transportation. The evenly and tightly connected microbial networks without brokers in the MRF treatment undoubtedly exhibited more stable, harmonious and efficient microbial relationships. In contrast, it was observed that several brokers in the F treatment had a higher centralization. Once the brokers in the F treatment declined or the functions of those brokers were lost, the whole microbial community can encounter an irreversible deterioration (Worden, 2010). Paenibacillus, Lysinibacillus, Planococcus, Sporosarcina and Staphylococcus were significantly increased under the MRF treatment. All of these genera have been reported to be plant growth-promoting bacteria. Paenibacillus and Planococcus can directly promote plant growth by solubilization of phosphorus, potassium and zinc (Yadav and Saxena, 2018). Lysinibacillus and Sporosarcina are served as P-solubilizers (Yadav et al., 2016). Sporosarcina and Staphylococcus benefit wheat yield via an association with wheat root (Verma et al., 2016). We also found that the fungi Talaromyces, which can occupy crop rhizosphere and reduce pathogen (Marois et al., 1984), was significantly

increased under the MRF treatment. This plant growth-promoting effect of microbes might be proved by the crop yield, as the yield of the MRF treatment was significantly higher than that of the F treatment (Yao et al., in press). In contrast, some harmful fungi, e.g., Bipolaris, Ilyonectria and Mycosphaerella, were massively increased under the F treatment. Bipolaris is a pathogenic fungus that inhibits wheat and maize production (Dewey et al., 1988). Ilyonectria, which is also a pathogenic fungus, causes black foot disease in grapevines (Cabral et al., 2012). Mycosphaerella is a pathogen of leaf spot disease (Goodwin et al., 2011). These pathogens generally absorbed nutrients by attacking host cells, which in turn resulted in serious plant disease and damage of healthy soil microbial communities (Anthony et al., 2017). If the sharply increased species after chemical fertilization were unfortunately pathogenic, there is a large probability of plant disease and ecosystem collapse. 4.2. Stronger response of fungal communities to fertilization Our results suggested that fungal communities showed a stronger response to the application of fertilizers. The Chao1 index of fungi was significantly increased with organic fertilization and was slightly increased with chemical fertilization (Table 2). Although the responding patterns were the same, the amplitude was significantly higher in fungal communities than in bacterial communities, which was supported by both the total variation and the average variation of the responding taxa (Fig. 4). Topological properties of fungal networks, including the average degree, connectedness and average geodesic distance, were more sensitive than the topological properties of bacterial networks (Table 4). These results supported that fertilizer application, especially with organic residues, had a greater influence on fungal communities than on bacterial communities. Consistent with our results, fungal communities have been reported to be more responsive to environmental changes than bacterial communities are in diverse ecosystems, e.g., to fertilization in the agroecosystem, especially organic fertilization (Banerjee et al., 2016), to precipitation shifts in the subtropical forest (He et al., 2017) and to pH variations in the polar regions (Siciliano et al., 2014). Contrary results, stressing that fungal diversity and the community structure were more tolerant, were mainly reported in the investigations of drought (Williams, 2007). This was probably due to the strong cell wall of most fungi (Manzoni et al., 2012). However, when investigating the relative variation of richness and topological properties of the network, it is still consistent with our results (de Vries et al., 2018). The stronger response of fungal communities to fertilization could be due to their lower species diversity than that of bacteria. In our study, the Chao1 index of fungal communities ranged from 1000 to 2000, whereas that of bacterial communities was > 2800 (Table 2). Compared with bacteria, fungi have lower species diversity, probably because they generally exit with a larger size and higher cost of time, energy and materials to grow (Naveed et al., 2016). The equal or even less absolute value of variation in fungal communities could cause an enlarged response to fertilization than bacterial communities. Additionally, fungal communities have less function redundancy than bacterial communities (Wohl et al., 2004). That is, a lower level of functional redundancy can be related to the lower resistance of microbial communities to maintain both functional and species diversity in the face of environmental changes (Allison and Martiny, 2008; Konopka, 2009). This could be another reason why fungal communities respond more strongly to fertilization. As our results showed, fungi showed a stronger response to fertilization than bacteria; the functions of fungi cannot be neglected during microbial-mediated carbon cycling in agroecosystems, and their effects could possibly exceed bacteria after fertilization (Six et al., 2006). First, the rate-limiting step of litter decomposition in soil C turnover was generally conducted by fungi (Voříšková and Baldrian, 2012). Generally, fungi devoted themselves to disassemble more complex and 7

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stable organic compounds (Lehmann and Kleber, 2015), e.g., lignin, polycyclic aromatic hydrocarbons and humus (Lindahl and Tunlid, 2015; Kadri et al., 2017), while most bacteria favoured downstream labile biopolymers, such as protein, and broke them down into smaller biopolymers, which were ultimately monomers (Gramss et al., 1999). Romaní et al. (2006) reported that when grown alone, bacteria showed a low activity of organic matter decomposition, and when grown together with fungi, both bacterial growth and SOC degradation were greatly improved. The decomposition of SOC in soils induced by fungi was mostly limited by nutrients, especially phosphorus limitation (Vitousek et al., 2010). This limitation can be directly addressed by fertilization. It was supported by the significant increase of available P and K under the MRF treatment (Table 1) and the decrease in the relative content of recalcitrant organic carbon (Yao et al., 2019). Without nutrient limitation, fungi probably weighed more in the SOC turnover. Therefore, we call for more concentration on the response of fungal communities to fertilization when investigating soil microbes.

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5. Conclusions Our study provides insights into the responses of soil microbial community composition to long-term fertilization regimes, chemical fertilization (F) and chemical fertilizers plus organic residues (MRF). Different from previous relevant studies, the study filtered out “silent” species from the whole community and focused particularly on the “responding” species. In this way, only a minor part of the microbial communities was observed responded, but microbial response to environmental changes were enlarged and the responding pattern was more explicit than before. When focusing on the responding populations, chemical fertilization showed a substantial effect on a small amount of taxa (SS pattern), in contrast, combined fertilization exerted a moderate impact on many microbial taxa (MM pattern). A higher connectedness and shorter average geodesic distance observed in microbial networks under the combined fertilization illustrated a more connective and closer network, comparing to chemical fertilization. The variation of the “MM” pattern accompanied with the distinct improvement of soil fertility, was considered to result in a more stable, harmonious and efficient ecosystem. Acknowledgements We are grateful to Dr. Junli Hu for his useful discussion; to Ms. Junhua Wang for her support in lab work and Mr. Liangguo Bo for his support in the plot experiments. This work was supported by National Natural Science Foundation of China (41977045, 41430859), Key Program of the Chinese Academy of Sciences (KFZD-SW-112-03-04), Knowledge Innovation Program of Institute of Soil Science, CAS (Grant No. ISSASIP1639) and Research Program for Key Technologies of Guyuan Sponge City Construction and Management (Grant No. SCHM-2018). Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Allison, S.D., Martiny, J.B.H., 2008. Resistance, resilience, and redundancy in microbial communities. Proc. Natl. Acad. Sci. 105, 11512–11519. Anthony, M.A., Frey, S.D., Stinson, K.A., 2017. Fungal community homogenization, shift in dominant trophic guild, and appearance of novel taxa with biotic invasion. Ecosphere 8, e01951. Banerjee, S., Kirkby, C.A., Schmutter, D., et al., 2016. Network analysis reveals functional redundancy and keystone taxa amongst bacterial and fungal communities during organic matter decomposition in an arable soil. Soil Biol. Biochem. 97, 188–198.

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