Nematodes and microbial community affect the sizes and turnover rates of organic carbon pools in soil aggregates

Soil Biology and Biochemistry 119 (2018) 22–31

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Nematodes and microbial community affect the sizes and turnover rates of organic carbon pools in soil aggregates

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Yuji Jianga, Haiyan Qiana,b, Xiaoyue Wanga, Lijun Chena, Manqiang Liuc, Huixin Lic, Bo Suna,∗ a

State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China Center for Remote Sensing Information System in Jiangxi Province, Nanchang 330046, China c College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China b

A R T I C L E I N F O

A B S T R A C T

Keywords: Aggregate fractions Bacterivore predation Microbial community composition Organic carbon pools Size and turnover rate

Soil aggregates provide microhabitats for nematodes and microorganisms, but how nematodes and microbial community interactively drive the dynamics of soil organic carbon (SOC) pool remains unclear. Here, we examined the relationships between bacterivorous nematodes, microbial community, and the sizes and turnover rates of three SOC pools in a red soil under four fertilization regimes. The abundance and community composition of nematode and bacterial communities were examined within aggregate fractions, including large macroaggregates (LMA), small macroaggregates (SMA), and microaggregates (MA). The sizes of SOC pools in soil aggregates increased with decreasing aggregate size, while the turnover rates of SOC pools followed the opposite trend. The ratios of bacteria to fungi (B/F) and Gram-positive to Gram-negative bacteria (GP/GN) were higher in the MA fraction than in the SMA and LMA fractions. The assemblages of bacterivorous nematodes in the LMA fraction were significantly different from those in the MA and SMA fractions, primarily because of the higher abundance of the dominant genus Protorhabditis. Results of structural equation modelling indicated that the ratios of B/F and GP/GN showed stronger positive correlations with the sizes and turnover rates of SOC pools in the MA fraction compared with the LMA fraction. Conversely, bacterivores exhibited indirect relationships with the sizes and turnover rates of SOC pools through the B/F ratio in the SMA and LMA fractions. Taken together, these results highlight the functional role of nematodes and microbial community in controlling SOC pool dynamics at the aggregate scale.

1. Introduction Aggregation is a fundamental facet of soil structure, providing physical protection of organic matter and creating microhabitats for microorganisms and microfauna with non-uniformity in nutrient availability. Aggregated soil structure exerts important impacts on carbon sequestration (Six et al., 2000). An increase in the size of soil organic carbon (SOC) pool in soil aggregates requires a positive imbalance between inputs and outputs of soil organic matter stocks (Jastrow et al., 2007). To fully understand SOC dynamics within soil aggregates, it is necessary to separate SOC into its active, slow and stable pools (Denef et al., 2009). Three-pool models describing the persistence of SOC have received considerable attention because these pools are useful in evaluating the biochemical stability of SOC (Semenov et al., 2010). It has been widely accepted that the sizes and turnover rates of SOC pool increase with increasing aggregate size (Tian et al., 2015). Macroaggregates (> 250 μm) consist of a large active and

labile SOC pool that originates predominately from plant residues, fungal hyphea or fresh SOC inputs (Six et al., 2004). In marked contrast, microaggregates (< 250 μm) contain more recalcitrant SOC formed by microbial-induced bonding of clay particles and organometallic complexes (Davinic et al., 2012). However, Drury et al. (2004) noted that CO2 production is highest in microaggregates, suggesting that SOC mineralization in these aggregates occurs more rapidly than in macroaggregates. This contradictory evidence illustrates that the processes of SOC dynamics in soil aggregates remain highly uncertain because the mechanisms dominating the dynamics of the SOC pool are not thoroughly understood. Soil microorganisms are known to be functionally diverse, playing a pivotal role in SOC dynamics and the formation of aggregates (Schimel and Schaeffer, 2012; Liang et al., 2017). To further understand the mechanisms of SOC dynamics within soil aggregates, small-scale heterogeneity of microbial functional groups has been examined. Bacteria exude extracellular polysaccharides that bind soil particles and increase

∗ Corresponding author. State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, No. 71 East Beijing Road, Nanjing 210008, China. E-mail address: [email protected] (B. Sun).

https://doi.org/10.1016/j.soilbio.2018.01.001 Received 29 July 2017; Received in revised form 1 January 2018; Accepted 3 January 2018 0038-0717/ © 2018 Elsevier Ltd. All rights reserved.

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(OH)2 ha−1 3 y−1 (M3). Each treatment with three replicates was applied to 2 m long × 2 m wide × 1.5 m deep plots. The pig manure had an average water content of 75%, SOC content of 386.5 g kg−1, and total nitrogen (TN) content of 32.2 g kg−1 on a dry matter basis. The cropping system was monoculture corn (cultivar Denghai No. 11) that was planted annually in April and harvested in July. After manual harvesting, the aboveground crop residues and the roots were removed from the field.

inter-particle cohesion, while fungi can provide a large surface area for scavenging soil-bound nutrients and can significantly affect the formation of soil aggregates (Denef et al., 2001). In particular, arbuscular mycorrhizal fungi contribute substantially to SOC stabilization via their roles in soil macroaggregate formation (Jastrow et al., 2007). It has been extensively documented that manure application can improve SOC content and alter the microbial community composition through aggregate formation (Six et al., 2004). However, a profound insight into the quantitative effect of the microbial community on the dynamics of SOC pools with different turnover rates has not been achieved at the soil aggregate level. Nematodes are the most abundant group of soil metazoans, occurring at multiple trophic levels (Ferris, 2010). It is well known that bacterial abundance is inevitably subject to top-down regulation by nematode grazing in general (Wardle, 2006). Nematodes normally have special food preferences, and selective predation by nematodes can alter the bacterial community composition (Djigal et al., 2004). Accordingly, the biological linkages between nematodes and microorganisms affect microbial function related to SOC accumulation (Zhang et al., 2013). However, the magnitude and direction of nematodes vs. microbe control over SOC dynamics are greatly variable and depend on soil microenvironmental context. The relative large pore sizes of macroaggregates are more accessible to large-sized bacterivorous nematodes (Quénéhervé and Chotte, 1996). The higher density of bacterivore population in macroaggregates displays the vast and complex networks of the nematode-bacterial associations and strongly contributes to microbial abundance and activity (Jiang et al., 2015, 2017). Examining how nematode predation drives SOC pool dynamics will deepen our understanding of the biological processes regulating soil aggregation. Here, we examined the relationships between nematodes, microbial community and the dynamics of SOC pools at the aggregate level. Specifically, the overarching objectives of the present study were structured based on two questions: (1) how do the interactions between nematodes and microorganisms drive the dynamics of three SOC pools in soil aggregates? and (2) what is the contribution of soil properties, microbial community and nematode grazing to the sizes and turnover rates of SOC pools? For this purpose, we performed a 9-year field experiment under four fertilization regimes in red soils (Acrisol) with poor fertility. Soil samples were collected from three replicates of each treatment, and then separated into three aggregate-size fractions for physiochemical and biological analyses. The nematode assemblages were quantitatively detected under a microscope, while microbial community structure was examined using phospholipid fatty acid (PLFA) analysis. The sizes and mineralization rates of the active, slow and resistant SOC pools were measured in a microcosm incubation experiment. Our findings provide knowledge of the relationships between bacterivorous nematodes and microbial community that govern the sizes and turnover rates of SOC storage.

2.2. Soil sampling and aggregate fractionation Soils were sampled after harvest using a Dutch auger (5 cm diameter; Eijkelkamp, The Netherlands) in late July 2011. In each plot, 10 soil cores were collected from the surface layer (0–20 cm) using an S–type sampling strategy and were mixed to form a composite sample. All fresh soils were passed through a 4–mm sieve to remove roots and rocks, and then gently broken along natural fracture planes for determination of aggregate fractions. The aggregate fractions were determined by sieving soil as follows: 5000 g fresh soil was manually fractionated through a nest of 2 sieves (2000 and 250 μm) into three aggregate-size classes: large macroaggregates (LMA, > 2000 μm), small macroaggregates (SMA, 250–2000 μm) and microaggregates (MA, < 250 μm). Each aggregate fraction was divided into three sub-samples for soil physiochemical and biological analyses. Table S1 shows the distribution of aggregate size and main edaphic properties in soil aggregates. 2.3. Soil chemical properties Soil pH was measured with a glass electrode at a water-to-soil ratio of 2.5:1 (v/w). SOC and TN were determined by the Walkley-Black wet digestion method and the micro-Kjeldahl method, respectively (Jackson, 1964; Nelson and Sommers, 1982). Cation exchange capacity (CEC) was determined using ammonium acetate buffered at pH 7 (Sumner and Miller, 1996). Gravimetric water content was calculated as the ratio of the mass of water lost by oven drying and the mass of oven dried soil. 2.4. Phospholipid fatty acid (PLFA) analysis Phospholipid fatty acids were determined by a modified method described by Frostegård et al. (1993). Briefly, frozen soil samples were freeze-dried and then used for lipid extraction. Lipids were extracted using a one-phase chloroform/methanol/phosphate buffered solvent. Phospholipids were separated from nonpolar lipids and converted into fatty acid methyl esters prior to analysis. Quantification was performed using a HP 6890 Gas Chromatograph (Hewlett Packard, Wilmington, DE, USA) fitted with a 25–m Ultra 2 (5% phenyl)-methylopolysiloxane column (J&W Scientific, Folsom, CA, USA). Peaks were identified using bacterial fatty acid standards and Microbial Identification System software (Microbial ID Inc., Newark, DE, USA) and were quantified based on the addition of the internal standard methyl nonadecanoate (19:0). Individual PLFAs were named using standard nomenclature. The following PLFAs were representative markers of the specific groups: Gram-negative bacteria (cyclopropyl bacteria and unsaturated PLFAs; Zelles, 1999), Gram-positive bacteria (iso- and anteiso-branched PLFAs; Zelles, 1999), actinomycetes (10Me PLFAs; Mentzer et al., 2006), saprotrophic fungi (18:1ω9c and 18:2ω6,9c; Frostegård and Bååth, 1996), arbuscular mycorrhizal fungi (16:1ω5c; Olsson, 1999), methanotrophic bacteria (18:1ω7c; Kieft et al., 1997), anaerobic, Gram-negative bacteria (19:0 cyclo; Vestal and White, 1989). From these, the ratios of fungi to bacteria (F/B) and of Gram-positive to Gram-negative bacteria (GP/GN) were calculated. The ratios of cy17:0 to its precursor 16:1ω7c and cy19:0 to its precursor 18:1ω7c were denoted as two bacterial stress indexes, which indicated the microbial physiological status in response to environmental stress (Grogan and Cronan, 1997).

2. Materials and methods 2.1. Site description and design The study was conducted at the National Agro-Ecosystem Observation and Research Station in Yingtan, China (28°15′N, 116°55′E). The soil, which was derived from Quaternary red clay, belonged to Udic Ferralsols in Chinese Soil Taxonomy and Ferric Acrisols in the FAO classification system and contained 36.3% clay, 42.5% silt, 21.2% sand, 41.8 g kg−1 total iron, and 76.6 g kg−1 total aluminium. The average annual temperature and precipitation at the site are 18.1 °C and 1785 mm, respectively. The long-term field manure experiment was initiated in 2002, and four treatments were selected for the present investigation: (1) no manure (M0); (2) low manure with 150 kg N ha−1 y−1 (M1); (3) high manure with 600 kg N ha−1 y−1 (M2); (4) high manure with 600 kg N ha−1 y−1 and lime applied at 3000 kg Ca 23

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including soil properties, nematodes and microbial community. Soil properties included SOC, TN, C/N ratio, pH and CEC, nematodes variable included the abundance of bacterivores, and microbial variables included the ratios of B/F and GP/GN. Random Forest is an ensemble of unpruned classification or regression trees created using bootstrap samples of the training data and random feature selection in tree induction (Breiman, 2001). The importance of each predictor was determined by assessing the decrease in prediction accuracy (that is, increase in the mean square error (MSE) between observations and predictions) when the data for the predictor was randomly permuted (Breiman, 2001). This decrease was averaged over all trees to produce the final measure of importance. This accuracy of importance measure was calculated for each tree and averaged over the forest. These analyses were conducted using the randomForest package of the R statistical software (Liaw and Wiener, 2002). The significance of the model and the cross-validated R2 was evaluated with 999 permutations of the response variable using the A3R package (Fortmann-Roe, 2013). The significance of predictor importance on the sizes and turnover rates of three SOC pools was assessed by using the rfPermute package (Archer, 2013). Structural equation modelling (SEM) has been used to model complex relationships between directly and indirectly observed (latent) factors (Grace et al., 2012). In our study, SEM analysis was used to gain a mechanistic understanding of how soil variables, nematodes and microbial community mediate alterations in the sizes and turnover rates of SOC pools in aggregate fractions. Note that the latent variables for the sizes of SOC pools were indicated by Ca, Cs and Cr, and those for the turnover rates of SOC pools were indicated by Ka and Ks. The model was tested by the robust maximum likelihood evaluation method using the Amos 17.0 software package (Smallwaters Corporation, Chicago, USA). The χ2 values, P-values, goodness-of-fit index and the root mean square error of approximation (RMSEA) were adopted to evaluate the structural equation model fitness (Hooper et al., 2008). A best fitting and most parsimonious model was obtained after excluding all non-significant parameters.

2.5. Nematode assemblages Nematodes were extracted by a modified Baermann funnel method (Barker, 1985), and were visually examined using an inverted compound microscope. Bacterivorous nematodes from at least 100 total nematodes per sample were identified to the genus level based on their feeding habits or stoma and oesophageal morphology (Yeates et al., 1993). 2.6. Incubation experiment One hundred grams of each air-dried aggregate fraction were adjusted to 65% moisture content of field capacity and incubated in 250 ml jars for 100 days at 25 °C. The field capacity of undisturbed soil cores was determined at 10 kPa suction using pressure plate apparatus (Cresswell, 2002). Soil samples were replicated three times and three control jars (pure silica sand without soil) were used to measure the concentration of background CO2. A small absorbing bottle containing 0.5 mol l−1 NaOH was placed to trap the CO2 emitted from the soil. After a week of pre-incubation, the amount of CO2 trapped in the alkali solution was measured by titration at 1, 3, 5, 7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 85, and 100 d of incubation. The remaining alkali was titrated to pH 7 with 0.4 mol l−1 HCl after precipitating the carbonate with 20 ml of 1 mol l−1 BaCl2 solution. After each sampling, the incubation jars were left open for 1 h to reach ambient O2 level and NaOH was refreshed in the microcosms. The incubation jars containing wetted soil aggregates were weighed prior to incubation. Soil moisture was maintained by adding distilled water during the incubation period. Finally, mineralization rates were calculated on the basis of daily CO2 emission. Based on the SOC mineralization rates from the laboratory soil anaerobic incubation, the sizes and turnover rates of the active, slow, and resistant SOC pools in soil aggregates were simulated using a first-order kinetic model (Paul et al., 2001): Ct = Ca e−Kat + Cs e−Kst + Cr e−Krt, where Ct is the total SOC at time t; K is decomposition rate constant (1/MRT); Ca, Cs and Cr are the respective sizes of the active, slow and resistant SOC pools, and Ka, Ks and Kr are the corresponding turnover rates of three SOC pools. The CO2 emission rate during the incubation period was indicative of the size and turnover rate constant (1/MRT) of the active pool (Ca and Ka). Cr was determined by the acid hydrolysis method (Leavitt et al., 1996). Briefly, Cr was determined by refluxing 1 g soil with 10 ml 6 M of HCl at 116 °C for 18 h, and then rinsing three times with 100 ml of deionized water. The residue of acid hydrolysis SOC remaining after the acid treatment was considered Cr. Kr was calculated as the inverse of the mean residence time of the resistant C pool (MRTr). MRTr was assumed to be 1000 yr at 18 °C (Paul et al., 2001). In this study, MRTr was calculated as 620 yr at 25 °C (incubation temperature) using the equation Q10 = 2(25−MAT)/10 (Leavitt et al., 1996), and Kr was calculated as 4.4 × 10−6 d−1. Then, Ca, Ka and Ks were calculated using nonlinear regression analysis of the CO2 incubation data. Finally, Cs was calculated as Cs = Ct − CaeCr (Collins et al., 2000).

3. Results 3.1. Sizes and turnover rates of different SOC pools in soil aggregates Our previous results revealed that fertilization treatments significantly influenced the proportion of large macroaggregates (LMA) and small macroaggregates (SMA) (P < .05). The proportion of the LMA fraction was significantly increased 39.6% by the high manure treatments (M2 and M3), while that of the SMA fraction was significantly decreased 34.2% compared with the M0 treatment (P < .05, Supplementary Table S1). Soil organic carbon (SOC) increased as a result of manure application, ranging from 3.34 g kg−1 to 11.99 g kg−1. The microaggregates (MA) showed significantly higher nutritional substrates than the LMA fractions in terms of SOC, total nitrogen (TN) and cation exchange capacity (CEC) (P < .05, Supplementary Table S1). The sizes of the active, slow and resistant SOC pools (Ca, Cs and Cr) were individually and interactively affected by manure treatments and aggregate size classes (P < .001). The active and slow SOC pools comprised 3.3–6.4% and 14.4–32.2% of SOC kg−1 individual aggregate fractions, respectively. Compared with M0, manure application significantly increased all three SOC pools. The MA fraction had significantly larger Ca, Cs and Cr than the SMA and LMA fractions (Fig. 1, P < .05). The turnover rate of active SOC pool (Ka) was much higher than that of the slow SOC pool (Ks). Similar to the size of SOC pools, Ka and Ks significantly increased under manure application (Fig. 2, P < .05). However, Ka and Ks increased with increasing aggregate size, such that the MA fraction (5.15 × 10−2 mg g−1 d−1 and 0.84 × 10−4 mg g−1 d−1) exhibited the lowest turnover rates relative to the SMA (5.59 × 10−2 mg g−1 d−1 and 1.16 × 10−4 mg g−1 d−1) and LMA

2.7. Statistical analyses Two-way analysis of variance (ANOVA) followed by Bonferroni's post-hoc test was used to test the effects of fertilization, soil aggregates, and their interactions using SPSS software (SPSS, Chicago, IL, USA). To assess the influence of soil aggregates on the beta diversity of nematode community composition, we calculated the Bray-Curtis distances and performed canonical analysis of principal coordinates (PCoA) constrained by the factor of interest and conditioning by the remaining variables (Anderson and Willis, 2003). We employed 'capscale' and 'permutest' permutation-based testing functions for PCoA and the calculation of the significance values, respectively. Random Forest was performed to quantitatively evaluate the important predictors to the sizes and turnover rates of three SOC pools 24

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Fig. 1. The sizes of the active (Ca, a), slow (Cs, b) and resistant (Cr, c) organic carbon pools in soil aggregates under four manure treatments. Bars with different lowercase letters indicate significant differences (P < .05) revealed by Bonferroni's post hoc test. M0, no manure; M1, low manure; M2, high manure; M3, high manure plus lime. LMA, large macroaggregates; SMA, small macroaggregates; MA, microaggregates.

(5.94 × 10−2 mg g−1 d−1 and 1.28 × 10−4 mg g−1 d−1) fractions (Fig. 2). Lime addition significantly deceased Ka in the LMA fraction but increased Ks in the MA fraction (Fig. 2, P < .05).

the ratios of cy17:0/16:1ω7c (r = 0.337, P = .044) and cy19:0/ 18:1ω7c (r = 0.411, P = .013). 3.3. Bacterivorous nematode assemblages in soil aggregates

3.2. Microbial community in soil aggregates The total number of nematodes increased significantly in manured soils (Fig. S3a). On average, bacterivorous nematodes (46.6%) were the most abundant trophic group in nematode assemblages (Jiang et al., 2013). The bacterivore assemblages were dominated by Protorhabditis (31.5%), followed by Rhabditis (5.6%) and Cephalobus (4.4%) (Fig. 4a). The average number of bacterivores was significantly higher in the LMA fraction than in the SMA and MA fractions (Fig. S3b). Bray-Curtis distances derived from PCoA were used to estimate the differences in the composition of bacterivore community between three aggregate fractions. The bacterivore community composition of the LMA fraction was clearly separated from those of the MA and SMA fractions, mainly because of the significantly higher abundance of the dominant genus Protorhabditis (Fig. 4b, P < .05).

We determined the microbial community structure in soil aggregates by PLFA analysis. The abundance of PLFA biomarkers varied strongly among soil fractions in terms of the biomass of arbuscular mycorrhizal fungi (AMF, 16:1ω5c, P = .001), saprotrophic or ectomycorrhizal fungi (18:1ω9c, P < .001), anaerobic bacteria (cy19:0, P < .001), and methanotrophic bacteria (18:1ω7c, P = .002). The AMF biomass was the largest in the LMA fraction, while the biomasses of saprotrophic or ectomycorrhizal fungi, anaerobic bacteria and methanotrophic bacteria were the highest in the MA fraction (Fig. S1). The interaction between fertilization and aggregate fraction was statistically significant for PLFAs 16:1ω5c (P = .045), 18:1ω9c (P = .038) and cy19:0 (P = .009) except for 18:1ω7c (P = .230). The distribution of microbial PLFA biomarkers as an index of community structure responded remarkably to fertilization and aggregate fraction (P < .001). The B/F ratio increased considerably but the GP/GN ratio decreased in response to manure addition. Furthermore, soil aggregates yielded significant differences in the ratios of B/F (P < .001) and GP/GN (P = .008). The B/F ratio was the highest in the MA fraction (2.17 ± 0.05), followed by the SMA (2.06 ± 0.06) and LMA (1.98 ± 0.09) fractions (Fig. 3). The pattern for the B/F ratio was mainly driven by greater shifts in bacterial compared with fungal biomass. Similar to the B/F ratio, the GP/GN ratio was the highest in the MA fraction (1.95 ± 0.09) followed by the SMA (1.89 ± 0.07) and LMA (1.59 ± 0.08) fractions. The ratios of cyclopropyl fatty acids to their precursors (cy17:0/16:1ω7c and cy19:0/ 18:1ω7c) as two bacterial stress indexes were evaluated in the microbial communities. These ratios were significantly lower under manure addition compared to the M0 treatment (Fig. S2). Additionally, these stress indexes in the MA were approximately twice as high as in the LMA fraction. The GP/GN ratio showed positively relationships with

3.4. SOC pool dynamics affected by soil properties, nematodes and microbial community We performed random forest modelling to determine the potential important factors of SOC pool dynamics. The models for the sizes and turnover rates of SOC pools were significant at the 0.01 level with R2 = 0.61–0.89. Overall, we found that SOC and TN were the two most important determinants for the sizes and turnover rates of SOC storage. According to random forest modelling, the mean square error (MSE) increased 13.7–18.5% when removing the predictor of SOC, and increased 10.8–14.9% when removing the predictor of TN. The MSE only increased 8.69–12.51% and 7.53–10.93% when removing the predictor of the B/F ratio and pH, respectively (Table 1). Notably, bacterivore grazing exerted a significant impact on the sizes and turnover rates of the active and slow SOC pools. We predicted the direct and indirect effects of soil edaphic properties, bacterivores and microbial community composition on the sizes Fig. 2. The turnover rates of the active (Ka, a) and slow (Ks, b) carbon pools in aggregates under different manure treatments. Bars with different lowercase letters indicate significant differences (P < .05) revealed by Bonferroni's post hoc test. M0, no manure; M1, low manure; M2, high manure; M3, high manure plus lime. LMA, large macroaggregates; SMA, small macroaggregates; MA, microaggregates.

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Fig. 3. The ratios of bacteria to fungi (B/F, a) and Gram-positive to Gram-negative bacteria (GP/GN, b) in aggregates under different manure treatments. Bars with different lowercase letters indicate significant differences (P < .05) revealed by Bonferroni's post hoc test. M0, no manure; M1, low manure; M2, high manure; M3, high manure plus lime. LMA, large macroaggregates; SMA, small macroaggregates; MA, microaggregates.

soil texture and chemical characteristics. The role of organic materials as major binding agents in the hierarchical development of soil aggregation is believed to be important in soils characterized by permanently charged clays and coarse texture (Oades and Waters, 1991). In Ultisols and Oxisols, sesquioxides can solely stabilize large macroaggregates, which are very dense and resistant to mechanical stress but are susceptible to hydraulic stress (Barthès et al., 2008). Although Fe/Al oxides are the predominant binding agents in Ultisols, the role of soil organic matter (SOM) in aggregation can be improved by increasing SOC input with manure addition (Peng et al., 2015). We found that the MA fraction consistently contained higher Ca, Cs and Cr compared with two other aggregate sizes. At the first glance, these results appear to contradict the soil aggregate hierarchy. However, our previous results reported that the proportions of aggregate fractions ranged from 46.6% to 66.8% for the LMA, 22.4%–36.3% for the SMA, and 10.8–17.1% for the MA (Jiang et al., 2013). In fact, the physical fractionation approach (dry sieving) recovered greater absolute values of protected SOC in the LMA fraction. The dynamic model for aggregate turnover (AggModel) corresponds to SOM dynamics (Segoli et al., 2013). SOC accumulation on mineral surfaces and the binding of small-size aggregates by inorganic agents contributed to the observed increases in three SOC pools in soil aggregates. It is well established that the SOC turnover rate is lower in microaggregates than in macroaggregates (John et al., 2005). Ka and Kc declined with decreasing aggregate size, suggesting that SOC associated with microaggregates was more physically protected and biochemically recalcitrant. More recently, Peng et al. (2017) demonstrate that new SOC preferentially incorporates in macroaggregates but persists for a shorter time than in microaggregates using a combined tracing approach consisting of 13C labelling and rare earth oxides. A range of conditions affects 13C decomposition in microaggregates, including SOC quality, water and oxygen status, the soil biotic community and physical

and turnover rates of SOC pools using structural equation modelling (SEM) (Figs. 5 and 6). Compared to the LMA fraction, the B/F ratio showed a larger positive correlation with the sizes and turnover rates of SOC pools in the MA fraction (Figs. 5a and 6a). The GP/GN ratio was significantly linked to the sizes and turnover rates of SOC pools in the MA fraction. However, bacterivores had indirect relationships with SOC pool dynamics through the B/F ratio in the SMA and LMA fractions (Figs. 5c and 6c). Additionally, the comprehensive effects of the B/F and GP/GN ratios on the sizes and turnover rates of SOC pools were stronger in the MA fraction than in the LMA fraction (Figs. 5d and 6d). However, the comprehensive effects of bacterivores on the sizes and turnover rates of SOC pools increased in the LMA compared with the MA fraction (Figs. 5f and 6f). 4. Discussion 4.1. SOC pool dynamics in soil aggregates The application of easily decomposable substrates rapidly stimulates soil organisms and determines the proportions of aggregate fractions, resulting in a significant improvement in soil structure (de Gryze et al., 2005; Jiang et al., 2014). In the current study, the sizes and turnover rates of three SOC pools in soil aggregates increased significantly with manure application. Our previous findings confirmed that SOC in surface soils increased sharply until the seventh year when it stabilized at different levels according to the rates of manure application (Long et al., 2015). Incorporation of manure into soil organic matter is viewed as a key attribute determining SOC sequestration and turnover. The aggregate hierarchy model predicts a higher SOC content in macroaggregates compared with microaggregates (Tisdall and Oades, 1982; Six et al., 2000). The inconsistent relationships between aggregate size and SOC content may be attributed to the difference in

Fig. 4. Taxonomic compositions of the bacterivore assemblages in soil aggregates. (a) The abundance of bacterivores is calculated based on bacterivorous guilds. (b) Aggregate fractions alter the bacterivore assemblages based on Bray-Curtis distances using principal coordinate analysis. The arrows point to the centroid of the constrained factor. Circle sizes correspond to the abundance of bacterivorous guilds, and colours are assigned to different genera. M0, no manure; M1, low manure; M2, high manure; M3, high manure plus lime. LMA, large macroaggregates; SMA, small macroaggregates; MA, microaggregates.

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Table 1 The importance of soil properties, nematodes, microbial community on the sizes and turnover rates of SOC pools.a

Ca Cs Cr Ka Ks

SOC

TN

C/N

pH

CEC

B/F

GP/GN

Ba

R2

P

17.60** 14.24** 13.67** 14.33** 15.59**

14.93** 14.24** 13.21** 11.17** 10.80**

4.28 4.52 2.25 3.18 3.11

8.98* 8.69* 10.31** 7.53* 10.93**

4.52 4.28 5.70 3.18 5.21

12.51** 10.30** 8.69* 10.92** 11.99**

6.33 6.27 6.86* 6.16 6.74*

9.13** 7.42* 6.23 8.71* 6.90*

0.89 0.72 0.88 0.61 0.84

.01 .01 .01 .01 .01

a The importance of each predictor was determined by assessing the decrease in prediction accuracy when the data for the predictor was randomly permuted. Models for the sizes (Ca, Cs and Cr) and turnover rates (Ka and Ks) of active, slow and resistant SOC pools were both significant at the 0.01 level with 999 trees. SOC, soil organic carbon; TN, total nitrogen; C/N, the ratio of SOC to TN; CEC, cation exchange capacity; B/F, the ratio of bacteria to fungi; GP/GN, the ratio of Gram-positive bacteria to Gram-negative bacteria; Ba, the abundance of bacterivorous nematodes. Significant levels of each predictor are as follows: *P < .05 and **P < .01.

Fig. 5. The effects of soil properties, nematodes and the microbial community on the sizes of SOC pools (C) estimated in the MA (a), SMA (b) and LMA (c) fractions using structural equation modelling. The latent variables for the sizes of SOC pools (C) are indicated by Ca, Cs and Cr. Blue lines indicate positive effects, while red lines indicate negative effects. The width of arrows indicates the strength of significant standardized path coefficients (P < .05). Paths with non-significant coefficients are presented as grey lines. The comprehensive influence of the factors is depicted for the MA (d), SMA (e) and LMA (f) fractions. SOC, soil organic carbon; TN, total nitrogen; B/F, the ratio of bacteria to fungi; GP/GN, the ratio of Gram-positive bacteria to Gram-negative bacteria; Ba, the abundance of bacterivorous nematodes. LMA, large macroaggregates; SMA, small macroaggregates; MA, microaggregates. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

different functional groups. Observed shifts in the composition of microbial community in aggregate fractions indicated that the abundance of functional groups was unevenly influenced by the substrate microsite heterogeneity of soil aggregates. Incorporating multiple microbial functional groups allow to better predict the dynamics of SOC pools. The substantial changes of the B/F ratios in soil aggregates indicated that microbial community structure was significantly shaped by aggregate fractions. We observed a shift towards bacterial dominance in soil microaggregates as mirrored by the significant increase in the B/F ratio. High SOC and TN in the MA fraction could provide an advantage for bacteria to compete with fungi for resources. SEM quantitatively described the stronger effects of the B/F ratio on the sizes and turnover rates of SOC pools in the MA fraction compared to the LMA and SMA

protection (Cosentino et al., 2006; Davinic et al., 2012). The presence of unstable C in the macroaggregates is beneficial for microbial decomposition and functioning involved in SOC pool turnover.

4.2. The mechanism responsible for microbial community affecting SOC pools in soil aggregates Growing evidence suggests that microbial community composition influences the “broad” processes of decomposition and SOC turnover in soils because mineralization is determined by microbial biomass size, community structure and specific activity (Schimel and Schaeffer, 2012). Our results showed that management regimes and soil aggregates greatly altered the microbial community structure and selected 27

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Fig. 6. The effects of soil properties, nematodes and the microbial community on the turnover rates of SOC pools (K) estimated in the MA (a), SMA (b) and LMA (c) fractions using structural equation modelling. The latent variables for the turnover rates of SOC pools (K) are indicated by Ka and Ks. Blue lines indicate positive effects, while red lines indicate negative effects. The width of arrows indicates the strength of significant standardized path coefficients (P < .05). Paths with non-significant coefficients are presented as grey lines. The comprehensive influence of the factors is depicted for the MA (d), SMA (e) and LMA (f) fractions. SOC, soil organic carbon; TN, total nitrogen; B/F, the ratio of bacteria to fungi; GP/GN, the ratio of Gram-positive bacteria to Gram-negative bacteria; Ba, the abundance of bacterivorous nematodes. LMA, large macroaggregates; SMA, small macroaggregates; MA, microaggregates. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

with actinomycetes were involved in the conversion of relatively more new C inputs into stable microbial-derived SOC pools. Therefore, it is possible that Gram-positive bacteria specialized in microaggregates maintain high SOC storages. The larger cy17:0/16:1ω7c and cy19:0/ 18:1ω7c ratios suggested that nutritional availability was more limiting to the microbial population in the MA fraction than in the SMA and LMA fractions. The cy/pre ratios have been linked to nutritional stress and pH (Moore-Kucera and Dick, 2008). Higher values of these ratios are associated with decreased bacterial growth rate and increased SOC limitation (Sampedro et al., 2009). Macroaggregates typically associate with alkyl and aliphatic groups contain large abundance of Alphaproteobacteria (Janik et al., 2007). However, microaggregates are characterized by the great phenolic and aromatic groups (recalcitrant C), with the predominance of Rubrobacteriales (Actinobacteria). Mummey et al. (2006) report that Rubrobacteriales are enriched in the protected inner-aggregate microaggregates, suggesting they may play a crucial role in microaggregate reformation and SOC sequestration. AMF generally colonize the pores of large macroaggregates as they prefer growing in soils with high porosity and low bulk density, and consequently facilitate SOC stabilization and protection by enhancing well-developed soil aggregation (Harris et al., 2003). The high abundance of AMF in the LMA fraction can be explained by their known role in producing abundant hyphae that bind soil aggregates. The contribution of AMF hyphae to carbon cycling involves extra-radical hyphae as well as hyphal exudates. Increased fungal hyphal length and the concomitant increased deposition of extracellular protein facilitate the

fractions. Microorganisms can promote SOC accumulation by the direct incorporation of microbial residues (cellular components from living and senesced biomass) into the stable SOC pool (Liang et al., 2017). Bacteria are frequently abundant in the microaggregates, leading to the deposition of microbial-derived C into the SOC reservoir by biomass turnover and necromass accumulation (Benner, 2011). Furthermore, the strong physical protection for microorganisms against desiccation or predation provided by soil microaggregates will increase the transformation of microbial-derived C and microbial biomass C into SOC pool (Six et al., 2006). Similar to the B/F ratio, SEM revealed that the GP/GN ratio significantly correlated with the sizes and turnover rates of SOC pools in the MA fraction. The data for PLFAs indicated that Gram-positive bacteria and actinomycetes had strong ecological functions controlling the sizes and turnover rates of SOC pools. Microbial changes reflect differences in the content and chemical composition of aggregate SOC due to microbial preference for different substrates and microbe-specific contributions to distinct SOC pools (Smith et al., 2014). Gramnegative bacteria preferentially use fresh manure as SOC sources, whereas Gram-positive bacteria are thought to prefer older and more recalcitrant SOC (Kramer and Gleixner, 2006). Gram-negative bacteria are typically characterized by more rapid growth rates but lower SOC use efficiency compared with slowly growing Gram-positive bacteria (Beardmore et al., 2011). Gram-negative bacteria require high nutrition level to support their growth, causing considerably consumption in SOC pools (Elfstrand et al., 2008). The 10Me16:0 and 10Me17:0 associated

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formation of macroaggregates (Rillig and Mummey, 2006). The active SOC pool is composed of hyphae with rapid turnover rates (days or weeks) related to the hyphal architectural type (Staddon et al., 2003), while the slow SOC pool is composed of glomalin-related soil proteins produced by AMF hyphae (González-Chávez et al., 2010). Glomalin is considered a significant pool of decomposition-resistant or recalcitrant SOC with a residence time of 6–42 years in soils (Rillig et al., 2001).

bacterivores and bacteria, and consequently make more contribution to the sizes and turnover rates of SOC pools. Changes in predator abundance substantially alter the diversity and population dynamics of prey communities. Increased predator abundance is thought to have a positive effect on important microbial species (e.g. keystone species) within a community and regulate nutrient cycling (Duffy et al., 2003; Jiang et al., 2015).

4.3. The mechanism responsible for nematode predation affecting SOC pools in soil aggregates

5. Conclusions In summary, fertilization regimes and aggregate fractions considerably affected the sizes and turnover rates of the active, slow and resistant SOC pools. The sizes of SOC pools (Ca, Cs and Cr) in the MA fraction were significantly larger than in the SMA and LMA fractions, while the turnover rates of SOC pools (Ka and Ks) followed the opposite trend. With regard to microbial community, the B/F and GP/GN ratios were the highest in the MA fraction compared to the SMA and LMA fractions. The substantial differences in the composition of bacterivore assemblages in soil aggregates were largely explained by the abundance of the dominant genus Protorhabditis. SOC and TN predominantly affected the sizes and turnover rates of three SOC pools, followed by the B/F ratio and soil pH. The ratios of B/F and GP/GN were positively correlated with the sizes and turnover rates of SOC pools in the MA fraction. Bacterivores showed indirect correlations with the sizes and conversion rates of SOC storage through the B/F ratio in soil macroaggregates. Our study highlighted the important role of nematodes and microbial community in governing SOC pool dynamics within soil aggregates. In future, presenting conclusions drawn from a range of experiments from open field to miniaturized controlled laboratory experiments will help improve the ecological understanding of the functional role of multitrophic interactions in SOC pool dynamics.

The population dynamics of soil animals are linked to SOC sequestration and soil food webs, wherein nematodes hold a central position (Zhang et al., 2013). In acidic red soil, the nematode assemblages are dominated by bacterivores, primarily or entirely feeding on soildwelling bacteria. The bacterial abundance and community composition is strongly top-down regulated via grazing by nematodes (Rønn et al., 2012). Bacterivores abundance was positively related to the B/F ratio, suggesting bacterivores predation substantially changed the composition of microbial community. Selective feeding trait of bacterivores likely accounted for a shift in microbial community composition, depending heavily on physical constrains of the feeding apparatus and on responses to chemical cues of bacteria. Bacteria are not equally susceptible to predation, since they evolve different means to resist nematodes grazing by physical (e.g. bacterial shape, filaments and biofilms) (Jousset, 2011; Bjørnlund et al., 2012) and chemical protections (e.g. pigments, poly-saccharides and specific toxins) (Jousset et al., 2009). Bacterial-feeding nematodes generally prefer to prey on Gram-negative bacteria (e.g. Pseudomonas, the typical rhizosphere colonizer) over Gram-positive bacteria due to their thinner cell walls are easier to digest (Rønn et al., 2002), which is supported by Salinas et al. (2007) who report that Cephalobus brevicauda displays a specific preference for Gram-negative bacteria. Taxonomically selective predation is thought to be crucial for bacterivores fitness and microbial community dynamics. The capability of bacterivorous nematodes to alter microbial communities can feed back on microbial activity and influence the size and turnover rate of SOC storage (Neher, 2010). More specifically, the associations between bacterivores and bacteria constitute the bacterial degradation pathway in soils, ensuring that energy flows to higher trophic levels through the bacterial energy channel (Bonkowski et al., 2009). The presence of bacterivores significantly stimulates microbial basal respiration contributing to SOC dynamics (Alphei et al., 1996). Our results clearly showed that the abundance and composition of bacterivore community varied between soil aggregate fractions. Manure applications increased the proportion of the LMA fraction, the intra-aggregate pore spaces of which were more facilitative for bacterivorous nematodes survival and predation. Nematodes are aquatic organisms that rely on thin water films to live and move through existing pathways of habitable soil pores with the neck diameter of 30–90 μm (Quénéhervé and Chotte, 1996). The high density of bacterivores in the LMA fraction promotes the formation of highly complex nematodes-bacteria relationships and their functions on nutrient turnover (Jiang et al., 2015, 2017). Bacterivores showed indirect correlations with the sizes and turnover rates of SOC pools through the B/F ratio in the SMA and LMA fractions. This result indicated that predation by bacterivores stimulated the sizes and turnover rates of SOC pools by altering microbial community structure in soil macroaggregates. The fixation of carbon in microbial biomass (MBC) is increased with the increasing abundance of bacterivores, suggesting that predation on microorganisms promote microbial-derived SOC retention. Our previous study reported that selective grazing of bacterivores on active bacteria decreased soil metabolic quotient and stimulated SOC accumulation in the LMA fraction (Jiang et al., 2013). The more positive coupling between bacterivores and microorganisms in the LMA fraction probably increase the mutualistic cooperating relationships between

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