Soil aggregate size and long-term fertilization effects on the function and community of ammonia oxidizers

Geoderma 338 (2019) 107–117

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Soil aggregate size and long-term fertilization effects on the function and community of ammonia oxidizers

T

Pei-Pei Lia,b, Yan-Lai Hana, Ji-Zheng Heb,d, Shui-Qing Zhangc, , Li-Mei Zhangb,d, ⁎

⁎⁎

a

College of Resource and Environmental Sciences, Henan Agricultural University, Zhengzhou 450002, China State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China c Institute of Plant Nutrition and Environmental Resource Sciences, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China d University of Chinese Academy of Sciences, Beijing 100049, China b

ARTICLE INFO

ABSTRACT

Handling Editor: Michael Vepraskas

Long-term field fertilization trials have suggested that reduced chemical nitrogen (N) plus organic fertilization can effectively reduce N loss without sacrificing crop yield, while the knowledge of how organic fertilizers regulate soil microorganisms and their function in N transformation are limited. In this study, the response of net nitrification rate and the ammonia-oxidizer community within soil aggregates to long-term combined organic N and reduced chemical N fertilization was evaluated to understand the underlying mechanism of the practice in mitigating soil N loss. The fertilization experiment included an unfertilized control; chemical N fertilizer (N), superphosphate (P) and potassium sulfate (K) fertilizer (NPK); NPK plus straw (NPKS); and NPK plus manure (NPKM). The results showed that the large macro-aggregates mass (> 2 mm) in soil significantly increased from 34.1% in NPK to 47.2% in NPKS (P < 0.05). NPKS and NPKM both had positive effects on soil moisture retention, total N (TN), soil organic carbon (SOC), ammonium (NH4+-N) and nitrate (NO3−-N) accumulation, particularly within micro- (< 0.25 mm) and small macro-aggregates. Compared with the NPK treatment, soil net nitrification rate (NNR) and ammonia-oxidizing bacterial (AOB) abundance decreased by 67.1% and 40.7% respectively under NPKS (P < 0.05), and the decrease mainly appeared in large macro-aggregate and microaggregates. The net nitrification rate was significantly correlated with ammonia-oxidizing archaea (AOA) abundance only in small macro-aggregates (r = 0.642, n = 12, P < 0.05). In contrast, NRR was positively correlated with AOB abundance within small macro- and micro-aggregate size classes (r values ranged from 0.654 to 0.813, P < 0.05). The community structure of AOB varied among different fertilization treatments, while AOA community differentiation was mainly dependent on aggregate size. The shift of the AOA and AOB communities corresponded with high moisture and lower ammonia content in the large macro-aggregates and were potentially responsible for the suppressed nitrification activity in the NPKS treatment. These results indicated that reduced chemical N plus organic fertilization is beneficial for increasing N concentration in large macro-aggregates.

Keywords: Combined organic and reduced chemical N fertilization Soil aggregation Net nitrification rate Ammonia-oxidizing bacteria and archaea N conservation

1. Introduction Application of chemical nitrogen (N) has greatly enhanced crop production (Robertson and Vitousek, 2009). In China, chemical N fertilizer use in the agricultural ecosystem increased by 191% between 1981 and 2007, and reached 32.6 billion kg per year (Guo et al., 2010). The excessive N fertilizer input in agriculture not only reduces the nitrogen use efficiency but also exacerbates the negative effects of N in the environment, such as degradation of soil fertility, soil acidification,

nitrate pollution and increased emissions of greenhouse gases (Guo et al., 2010; Ju et al., 2009). Therefore, management of N amendments in agricultural soils therefore presents a great challenge. In China, intensive double-crop wheat (Triticum aestivum L.)/maize (Zea mays L.) rotation is widely adopted on the North China Plain where chemical N inputs as large as 600 kg N per hectare per year are applied (Zhu and Chen, 2002). Nitrate leaching has been shown to be a main N loss pathway because of the calcareous characteristics of the fluvoaquic soil with high net mineralization and nitrification in the region

Corresponding author. Correspondence to: L.-M. Zhang, State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China. E-mail addresses: [email protected] (S.-Q. Zhang), [email protected] (L.-M. Zhang). ⁎

⁎⁎

https://doi.org/10.1016/j.geoderma.2018.11.033 Received 22 June 2018; Received in revised form 14 November 2018; Accepted 18 November 2018 0016-7061/ © 2018 Published by Elsevier B.V.

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(Huang et al., 2017). As part of a traditional agronomic practice, organic fertilizers confer various benefits in increasing soil fertility compared with chemical fertilizers (Zhang et al., 2016; Mäder et al., 2002). Long-term 30% chemical N replaced by organic N significantly increased soil N storage and reduced N loss to approximately 20%, in comparison with 60% of N loss from 100% chemical N in a double-crop wheat/maize system (Duan et al., 2016). Soil N cycle processes are predominantly driven by soil microorganisms. In soils receiving long-term applications of different fertilizers different microbial communities can develop (Li et al., 2014; Habteselassie et al., 2013). However, knowledge of how organic fertilizers regulate soil microorganisms and their respective N transformation capability are limited. The conversion of ammonia to nitrate, driven by ammonia oxidizing bacteria, archaea and the recently discovered comammox (van Kessel et al., 2015; Santoro, 2016), is a central step in the N cycle. Understanding how the partial replacement of chemical N by organic N influences nitrification and nitrifiers is of considerable interest due to their impact on nitrate leaching/runoff. Numerous molecular ecology surveys have confirmed the widespread distribution of AOA and their numerical dominance over AOB in agricultural soils (He et al., 2007; Shen et al., 2008, 2012; Prosser and Nicol, 2008). While a shift in the AOB community composition has been observed, only a slight change in the AOA community occurred after 16 years of fertilization practices in the alkaline soils of the North China Plain (Shen et al., 2008). Further studies have suggested that AOB were active nitrifiers in neutral N-rich grassland soil and agricultural soil (Di et al., 2009; Jia and Conrad, 2009). By using the DNA stable-isotope probing (DNA-SIP) technique, Xia et al. (2011) demonstrated that AOB dominated the nitrification process over AOA in a fluvo-aquic soil of the North China Plain. However, evidence from mRNA and DNA-SIP in both field surveys and soil microcosm incubations suggests that AOA are functionally dominant in most low-pH and low-N input soils (Zhang et al., 2010, 2012). These suggested niche separation between AOA and AOB (Prosser and Nicol, 2008). Moreover, AOA was suggested to be mixotrophic and able to assimilate organic substrates (Walker et al., 2010; Palatinszky et al., 2015). It also has been observed that organic N addition significantly increased AOA abundance in some agricultural soils (Jiang et al., 2014; Wessén et al., 2010) and induced the shift of AOA community structure (Tao et al., 2017; Muema et al., 2015). Although the effect of chemical N fertilizers on ammonia oxidizers has been well studied in agricultural systems, there are few studies focusing on the response of AOA and AOB and nitrification to combined organic N and reduced chemical fertilizer inputs in calcareous soil with high soil pH. Soil encompasses a wide range of environments that sustain distinct microbial communities (Fierer, 2017), and ammonia oxidizers may change with soil temperature, moisture content, and other physical and chemical properties. Organic fertilization such as with straw and manure inputs, can strongly affect soil aggregation by increasing soil organic cementing substances, thereby promoting the linkage between soil particles and the stabilization of aggregates (Mäder et al., 2002). Variation in soil aggregation represents chemical and physical heterogeneity, which may cause differences in soil microbial communities and activities (Bailey et al., 2013; Briar et al., 2011; Fierer, 2017). Understanding the allocation of ammonia oxidizers and nitrification within soil aggregate size fractions and their response to organic fertilization and aggregate size change, induced by organic fertilization at the micron scale, would be helpful in revealing the underlying mechanism of how organic fertilization benefits nitrogen use efficiency, and would allow for the development of effective N management practices. Therefore, soils from a long-term field trial, initiated in 1990, with combined organic and reduced chemical N fertilization, were selected to test the hypotheses that aggregation may be more prevalent under combined organic and chemical N application, thus resulting in the differentiation of nitrification, the amoA gene abundance and composition of ammonia oxidizers within soil aggregates, in a calcareous

fluvo-aquic soil. Thus, the abundance and community structure of ammonia oxidizers in separated soil aggregates from different fertilization practices was analysed with the following aims (1): to understand the effects of the combined organic and reduced chemical N on soil physicochemical properties, NNR and ammonia oxidizers within aggregates; (2) to identify physiochemical factors determining the distribution of AOA and AOB in different soil aggregate sizes and their correlation with soil nitrification rate under different N fertilization practices; and (3) to compare the influence of fertilization and aggregation on the community structure of AOA and AOB. This information will be useful for understanding the mechanisms of lower N losses in combined organic and reduced chemical N fertilization and will shed light on the management of N fertilization in the intensive double-cropping systems in the North China Plain. 2. Materials and methods 2.1. Site description and experiment design This long-term field trial was located in Yuanyang County, Henan Province, China (35 °C 00′ 28″ N, 113 °C 41′48″ E). The study area is characterized by a temperate and monsoonal climate with an annual mean precipitation of 645 mm, an average annual air temperature of 14.8 °C, and near to the Yellow River with winter wheat and summer maize cropping systems. The surface soil particle size was measured by the rapid sieving procedure (Kettler et al., 2001) and had a sand: silt: clay ratio of 20.3: 67.2: 11.5. The soil is mainly composed of Yellow River sediments and is classified as a calcaric cambisol according to the FAO soil classification system (IUSS Working Group WRB, 2007) with an initial alkaline pH (water: soil = 2.5:1) of 8.6. The wheat maize rotation system with different fertilization practices was established in 1990. Each treatment included three replicate plots with an area of 51 m2 (8.5 × 6 m). All plots were randomly arranged in the field. Four treatments were selected including: (1) unfertilized control (Control); (2) fertilization with chemical N (urea), superphosphate (P) and potassium sulfate (K) (NPK); (3) NPK plus maize straw (NPKS); and (4) NPK plus cattle manure compost (NPKM). All P and K fertilizers, manure and maize straw and 60% of the N fertilizers (including all organic N and part chemical N fertilizer) were used as the base fertilizer before sowing, and the remaining urea was used at the stem-elongation stage. The total N input was equal in each fertilization treatment was equal. The application quantity of organic N was based on the concentration of N in those organic materials, and the organic N to mineral N ratio was 3:7. The average N concentrations in straw and manure were 8.5 and 13.1 g kg−1 N, respectively. The application of N, P and K for wheat was 165, 36 and 68.5 kg ha−1, and for maize was 187.5, 41 and 78 kg ha−1 year−1, respectively. 2.2. Soil sampling and aggregate sieving Topsoil (0–15 cm depth) from the three replicate plots of each treatment was sampled at the maize-seedling stage in June 2016. Four soil cores (100 mm in diameter) were collected randomly from each plot and stored at approximately 4 °C before laboratory analysis. Large roots and rocks were gently removed from each core by hand, and an optimal-moisture sieving approach modified from Dorodnikov et al. (2009) was used for soil aggregate fractionation. Briefly, field moist samples were gently broken apart along the natural points of weakness and spread out into a thin layer using sterile vessels and dried to an optimal moisture at room temperature for 1 week (gravimetric water content of approximately 10%). Then the soils were gently passed through an 8-mm sieve and transferred to a nest of sieves (2 and 0.25 mm) and shaken for 4 min. Soil was gently removed from each sieve and weighed to determine the mass distribution of aggregates into the following fractions: large macro-aggregates (> 2 mm), small macroaggregates (0.25–2 mm) and micro-aggregates (< 0.25 mm). According 108

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to Bach and Hofmockel (2014), the optimal-moisture fractionation technique has a minimal mechanical impact on the biological properties of the aggregate fractions. Soil samples without aggregate-separating (bulk soil) and aggregates were divided into three parts: stored at −20 °C for molecular analysis, at 4 °C for enzyme activity and NH4+/ NO3−-N extraction, and they were air-dried for additional chemical analysis.

determined using GeneMapper 2.0 software (Applied Biosystems). 2.7. Clone library and phylogenetic analysis To further characterize the AOA and AOB community compositions in soil aggregates, clone libraries of AOA and AOB amoA genes were constructed for the three aggregate-size classes of the fertilizer treatments and subjected to sequencing analysis. As the RDA analysis based on T-RFLP showed that TRs of NPKS and NPKM were similar, only the NPKS treatment was selected as a representative of combined organic and chemical N fertilization, and NPK was selected as the complete chemical N input treatment for comparison within aggregates. A total of 18 DNA extracts (two treatments, three aggregate size classes, and three replicates) under NPK and NPKS treatments were used for clone library construction. Approximately 1800 archaeal and bacterial (randomly selected 50 clones per DNA extract) amoA gene sequences were obtained from AOA and AOB clone libraries of the three aggregate fractions of NPK and NPKS treatments. The same primer pairs for qPCR reaction were used for normal PCR amplification. Purified PCR amplifications were ligated into the pEasy-T3Vector (Beijing TransGen Biotech Co., Ltd., Beijing, China) and then the resulting ligation products were transformed into Escherichia coli JM109. Sequence analyses were performed as previously described by Zhang et al. (2017a). Briefly, randomly selected positive clones were sequenced by sending them to Shanghai Majorbio Bio-pharm Technology Co., Ltd. All sequences were analysed with MEGA 7.0 software and compared with public data in GenBank. Quality-checked sequences were clustered into operational taxonomy units (OTUs, 97% similarity) with QIIME software, and then neighbor-joining tree construction was performed with MEGA. AOA and AOB amoA gene sequences retrieved in this study have been submitted to GeneBank under the accession numbers of MK212119 to MK212130 for AOA and MK214746 to MK214758 for AOB, respectively.

2.3. Soil physicochemical properties Soil pH was determined with a soil to water ratio of 1:2.5. Soil inorganic N (exchangeable NH4+-N and NO3−-N) was extracted with 1 M KCl (soil to water ratio of 1:5) and measured with a Continuous Flow Analyser (SAN++, Skalar, Breda, Holland). The dried aggregate soil and bulk-soil samples were sieved through a 100-mesh and used for soil organic carbon (SOC) and total N (TN) analysis. Soil organic carbon was determined using the K2Cr2O7 oxidation method (Nelson and Sommers, 1996), and TN was determined using a semi-micro Kjeldahl method (Bremner, 1996). Approximately 5 g were dried to a constant weight at 105 °C for gravimetric moisture determination. 2.4. Soil net nitrification rate Nitrification net rate (NNR) was calculated as the change in extractable NO3−-N concentration between initial and final measurements during a 14-day incubation without N substrate. Briefly, approximately 50 g of fresh soil at 60% water-holding capacity (WHC) was incubated in the dark at 28 °C for 14 days, and exchangeable NH4+N and NO3−-N were determined at the beginning of the 14-day incubation period. 2.5. DNA extraction and real-time PCR (qPCR) Soil deoxyribonucleic acid (DNA) was extracted from 0.35 g samples with the MoBio Power Soil™ DNA Kit (MO BIO Laboratories, Inc., Carlsbad, CA, USA), according to the manufacturer's protocol. The concentrations of extracted DNA were measured using a Nanodrop ND1000 UV–Vis spectrophotometer (NanoDrop Tech-nologies, Wilmington, DE, USA) and the quality of DNA was determined on 0.8% agarose gel. Quantitative polymerase chain reaction (qPCR) of AOA and AOB amoA genes was performed using an iCycler iQ 5 thermocycler (Bio-Rad Laboratories, Hercules, CA, USA). Primers for AOA and AOB amoA gene amplification were Arch-amoAF/Arch-amoAR (Francis et al., 2005) and amoA-1F/amoA-2R (Rotthauwe et al., 1997), respectively. Polymerase Chain Reaction (PCR) reactions and thermal protocols used for the quantification of functional genes were according to Wang et al. (2017a). Standard curves were generated for AOA and AOB following He et al. (2007). Efficiencies of the qPCR reactions ranged from 83 to 90% for the AOA amoA gene, 85–86% for the AOB amoA gene, with R2 values ranging between 0.989 and 0.997. The amplification specificity of qPCR was confirmed by the melting-curve analysis with a single peak and by gel electrophoresis with a single band at approximately 500 bp and 650 bp for AOB and AOA, respectively.

2.8. Statistical analyses Statistical analyses were conducted in SPSS 19.0 software (IBM Co., Armonk, NY, USA). The Duncan test of two-way ANOVA was performed to test the differences in soil aggregate mass, pH, SOC, NH4+-N and NO3−-N concentrations, NNR and amoA gene copy numbers among the treatments (Control, NPK, NPKS and NPKM); P < 0.05 level was considered significant. The main effects and interactions of fertilization and aggregate-size class on soil properties and microorganisms were analysed by general liner model analysis. Pearson's correlations were performed among soil properties, NNR, and functional gene copies. The relationships between relative abundances of TRFs and soil properties were determined by Redundancy Analysis (RDA) using R software (version 3.2.2) based on the “RDA” function in the “vegan” package with the Bray-Curtis distance matrices plot calculation. 3. Results 3.1. Soil aggregate mass distribution and properties within aggregates Soil aggregate masses ranged from 34.2–47.2%, and 37.0–45.2% in large macro- and small macro-aggregates, respectively (Fig. 1). Significant interactions of fertilization and aggregate size on mass distribution were observed (P < 0.05, Table 2). Compared with Control and NPK treatments, the proportions of large macro-aggregates in the NPKS treatment increased by 14.6% and 38.3%, respectively, and were significantly greater than in the other three treatments (P < 0.05) (Fig. 1). However, the proportion of small macro-aggregates was decreased from 38.3% in the Control to 37.0% in the NPKS treatment, which was significantly lower than that in the NPKM (44.4%) and NPK (45.6%) treatments; the NPKM and NPK treatments did not differ between the latter two treatments (P < 0.05). In the two organic

2.6. Terminal restriction fragment length polymorphism (T-RFLP) The primer pairs and thermal conditions of PCR amplification for TRFLP analysis were the same as above described above for qPCR, except that the forward primer was fluorescently labelled (6-FAM). The gelpurified PCR products were digested with 5 U RsaI restriction endonuclease for AOB and HpyCH4V restriction endonuclease for AOA, respectively, at 37 °C for 50 min and then at 60 °C for 20 min. After restricting-enzyme digestion, the products were analysed using an ABI 3730xl DNA analyzer (Applied Biosystems, Foster City, CA) and the relative abundance of terminal restriction fragments (TRFs) was 109

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Soil aggregate mass of each fraction (%)

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55 50 45 40

Control

a bc

NPK

abc

NPKS

Table 1 Soil pH, moisture, NH4+-N, NO3−-N, SOC and TN within different aggregate size classes under different fertilizer treatments.

NPKM

ab

Soil properties

cd d

d

35

pH

Control NPK NPKS NPKM

Soil moisture (%)

Control NPK NPKS NPKM

NH4+-N (mg kg−1)

Control NPK NPKS NPKM

NO3−-N (mg kg−1)

Control NPK NPKS NPKM

SOC (g kg−1)

Control NPK NPKS NPKM

TN (g kg−1)

Control NPK NPKS NPKM

30

e e

25 20

f

f

15 10

>2mm

Treatments

d

0.25-2mm

<0.25mm

Soil aggregate size Fig. 1. The percentage mass of each aggregate fraction under different fertilizer treatments. Control: No fertilizer; NPK: N, P and K mineral fertilizers; NPKS: 70% mineral N, 30% straw N, P and K mineral fertilizers; NPKM: 70% mineral N, 30% manure N, P and K mineral fertilizers. Data presented were the means of three replicates. Error bars indicate standard error. Lower case letters indicate significant differences among soil aggregates and fertilization (P < 0.05).

replacement treatments (NPKS and NPKM), the proportions of microaggregate mass significantly decreased by 29.7–32.3% compared with the Control (P < 0.05), and decreased by 36.1%–38.7% compared with the NPK treatment (P < 0.05), respectively. Soil pH in the Control was 8.4–8.5, but ranged from 8.2 to 8.3 in the fertilization treatments (NPK, NPKS and NPKM) (Table 1). ANOVA analysis suggested that soil pH was significantly affected by fertilization (P < 0.001), but not by aggregate size (P > 0.05) (Table 2). Soil moisture in aggregate-size classes increased significantly in the following order as follows: micro-aggregates < small macro-aggregates < large macro-aggregates, and macro-aggregates of NPKS and NPKM treatments contained greater water contents compared with other smaller aggregates. Total N and SOC concentration were greater in fertilizer treatments than in the Control, and greater in small macroand micro-aggregates. Furthermore they were significantly influenced by fertilization and aggregate size (P < 0.01 for both, Table 2). As the result of fertilization, NH4+-N and NO3−-N concentration in all fertilizer treatments (NPK, NPKS and NPKM) were greater compared to the Control, NO3−-N concentration showed no significant differences among aggregates, while NH4+-N concentration was greater (P < 0.05) in micro-aggregates than in macro-aggregates, with a significant interaction between fertilization and aggregates (P < 0.05, Table 2). Generally, two-way ANOVA analyses suggested that all six soil property parameters were significantly influenced by fertilization (Table 2), with a significant lower soil pH and greater moisture, NH4+N, NO3−-N, SOC and TN concentration were observed in almost all soil aggregate fractions of NPKS and NPKM treatments, compared to Control.

Aggregate size

Means

> 2 mm

0.25–2 mm

< 0.25 mm

8.5a 8.3b 8.3b 8.3b B 8.2b 9.2b 11.1a 10.7a A 4.9e 6.4abc 5.6de 6.0 cd B 10.8c 14.4b 23.8a 28.3a A 5.9b 7.4b 9.5a 9.6a B 0.7c 0.9b 1.1a 1.2a B

8.5a 8.3b 8.2b 8.3b B 6.5b 7.6ab 9.2a 9.2a B 4.9e 5.5de 6.6abc 5.9 cd B 10.0c 17.4b 26.6a 32.4a A 6.4b 8.7a 9.3a 10.0a A 0.8c 1.0b 1.2a 1.2a A

8.4a 8.3b 8.2b 8.3b B 5.0a 6.4a 7.8a 7.6a C 5.4de 6.1bcd 7.2a 6.8ab A 19.1b 22.8a 22.1a 26.7a A 6.4c 8.2b 9.9a 9.8a A 0.8c 1.0b 1.2ab 1.3a A

8.5A 8.3B 8.2C 8.3B 6.6C 7.7B 9.4A 9.2A 5.0B 6.0A 6.4A 6.2A 13.3B 18.2B 24.2A 29.1A 6.2C 8.1B 9.6A 9.8A 0.8C 1.0B 1.2A 1.2A

Within column variable, means with different lowercase letters (abc) indicate significant differences among fertilization treatments within the same aggregate fraction, and means with different capital letter (ABC) represent significant differences among the four fertilizer treatments; Different capital letter (ABC) below each property parameter represents significant differences among aggregate size of all treatments; as determined by LSD significant difference test, P < 0.05. Control: No fertilizer; NPK: N, P and K mineral fertilizers; NPKS: 70% mineral N, 30% straw N, P and K mineral fertilizers; NPKM: 70% mineral N, 30% manure N, P and K mineral fertilizers. SOC, soil organic matter; TN, total nitrogen. SOC, soil organic matter; TN, total nitrogen. Table 2 Results (P values) of two-way ANOVA analysis for the effects of fertilizer treatments and soil aggregate size on soil physiochemical parameters and amoA gene copy numbers.

Aggregate mass pH Soil moisture NH4+-N NO3−-N SOC TN NNR AOA amoA gene AOB amoA gene

3.2. Nitrification of bulk soil and aggregate fractions Compared with NPK, soil NNR was significantly lower in NPKS (Fig. 2). Significant main effects of fertilization (P < 0.01, Table 2) and aggregates size on NNR were observed (P < 0.01, Table 2). The NNR was greatest in the NPK treatment with a value in bulk soil of 1.48 μg NO3−-N g−1 d−1, which was significantly greater than the Control and NPKS treatments. The lowest value was also found in the NPKS treatment (0.52 μg NO3−-N g−1 d−1). Additionally, a significant effect of aggregates size on NNR was observed (P < 0.05, Table 2). However, there were no significant interactions between fertilization and aggregate size (P > 0.05). NNR in micro-aggregates with 0.85 and

F

AS

F*AS

1.000 0.000 0.000 0.000 0.002 0.000 0.000 0.000 0.050 0.000

0.000 0.829 0.000 0.003 0.191 0.009 0.000 0.012 0.002 0.878

0.000 0.977 0.847 0.044 0.567 0.052 0.150 0.233 0.000 0.140

Significant effects (P < 0.05) are highlighted in bold. SOC, soil organic matter; TN, total nitrogen; NNR, net nitrification rate. F, Fertilization; AS, aggregate size.

−0.48 μg NO3−-N g−1 d−1 in Control and NPKS treatments, was significantly lower than in large macro- and small macro-aggregates, respectively (P < 0.05). However, there was no significant difference between them (P > 0.05).

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aggregate-size fractions were approximately ten times lower in the Control than in the three fertilizer treatments, which ranged from 6.55 × 105 to 1.08 × 106 copies g−1 dry soil in the former, and ranged from 6.47 × 106 to 1.45 × 107 copies g−1 dry soil in the latter. Neither AOA nor AOB amoA gene abundances in the Control differed among aggregate size classes. The maximum values of AOB abundance were observed in the small macro-aggregates of the NPKS and NPKM treatments. A significant effect of aggregate size on AOA and a significant effect of fertilization on AOB abundance were observed, respectively (P < 0.05, P < 0.01, Table 2). Compared with the lack of variation of AOB abundance among aggregates of the three N fertilizer treatments, AOA ranged from 1.55 × 108 to 7.72 × 108 copies g−1 dry soil and decreased in micro-aggregates of the NPKM and NPK treatments compared to those in macro-aggregates with 1.55 × 108 and 2.31 × 108 copies g−1 dry soil, respectively. Generally, AOA abundances in the three fertilizer treatments decreased with decreasing aggregate size, which differ from the even distribution of AOA abundance among aggregate fractions in the Control. The two amoA gene abundances were significantly positively correlated with soil NH4+-N, but only AOB abundance was significantly correlated with NNR (r = 0.670, n = 12, P < 0.05, Table 3). Ammonium and pH were positively correlated with NNR in the bulk soil. AOA abundance was significantly positively correlated with soil TN and SOC (P < 0.05, Table 3) and were negatively correlated with soil pH (P < 0.05, Table 3) within large macro-aggregates (> 2 mm), while no significant positive correlation between NNR and AOA abundance was detected in the macro-aggregate size class. Ammonia oxidizing archaea abundance was significantly positively correlated with NNR only within small macro-aggregates. By contrast, AOB abundance was positively related with NNR in all aggregate-size classes, and significant correlations existed in the small macro-aggregate (P < 0.05, Table 3) and micro-aggregate (P < 0.01, Table 3) classes. Interestingly, soil TN, SOC and pH, NNR and the two amoA gene abundances were significantly correlated with each other in the small macro-aggregate class.

Net nitrification (NO3- mg kg-1d-1)

2.5

a

2

a

a

a

a

a a

a

1.5

a b

ab

b

a a

1

b

0.5

b

0 -0.5

Control

NPK

NPKS

0.25mm CK NPK NPKS NPKM

0.25mm 2mm 0.25-2mm

0.25mm 2mm 0.25-2mm

0.25mm 2mm 0.25-2mm

2mm 0.25-2mm

-1

NPKM

Bulk soil

Fig. 2. Net nitrification rate (NNR) among three aggregate size classes and bulk soil under four treatments. Control: No fertilizer; NPK: N, P and K mineral fertilizers; NPKS: 70% mineral N, 30% straw N, P and K mineral fertilizers; NPKM: 70% mineral N, 30% manure N, P and K mineral fertilizers. Data presented were the means of three replicates. Error bars indicate standard error. Lower case letters indicate significant differences among fertilizer treatments (P < 0.05).

3.3. Abundance of AOA and AOB For bulk soil, AOA amoA gene copies ranged from 1.38 × 109 copies g−1 dry soil to 5.52 × 109 copies g−1 dry soil in four treatments (Control, NPK, NPKS and NPKM). The AOA abundances in all fertilizer treatments (NPK, NPKS and NPKM) were four to five times greater than those in the Control, but did not differ among NPK, NPKS and NPKM treatments (Fig. 3). The highest AOB amoA gene abundance was observed in the NPK treatment with 1.89 × 107 copies g−1 dry soil, which was significantly greater than in the NPKM (8.27 × 106) and Control (1.79 × 106 copies g−1 dry soil) treatments. Ammonia oxidizing archaea were consistently two to three orders of magnitude more abundant (P < 0.001) than AOB and the ratio of AOA/AOB decreased in all fertilizer treatments compared to Control. Ammonia oxidizing bacteria (AOB) abundances in all three AOA

3.4. Community profiling of AOA and AOB within soil aggregates Redundancy analysis, based on the TRs matrix and soil properties for individual samples, showed that AOA compositions differed among aggregate fractions (Fig. 4a). Additionally, the Control treatment without N input harboured distinct microbial assemblages from the

AOB

a

9

a

a

a

a

a

a

ab

b

b

a

8.5

b

a a

c

b

8 7.5

a

a a

a

ab

6.5

a 6

a

b

a b

ab

b

b

7

a

c

a

Control

NPK

NPKS

NPKM

Bulk soil 111

NPKM

NPKS

NPK

Control

0.25mm

0.25-2mm

2mm

0.25mm

0.25-2mm

2mm

0.25mm

0.25-2mm

2mm

0.25mm

0.25-2mm

5.5

2mm

amoA gene copies g-1 soil log10 scale

9.5

Fig. 3. The amoA gene abundances of ammonia oxidizers among three aggregate size classes and bulk soil under four nitrogen treatments. Control: No fertilizer; NPK: N, P and K mineral fertilizers; NPKS: 70% mineral N, 30% straw N, P and K mineral fertilizers; NPKM: 70% mineral N, 30% manure N, P and K mineral fertilizers. Data presented were the means of three replicates. Error bars indicate standard error. Lower case letters indicate significant differences among fertilizer treatments (P < 0.05).

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Table 3 Correlations of soil properties, NNR and the amoA gene abundances of AOA and AOB in soil aggregate classes and bulk soil. NNR

NH4+ NO3−

TN SOC NNR pH

AOA

AOB

> 2 mm

2–0.25 mm

< 0.25 mm

Bulk soil

> 2 mm

2–0.25 mm

< 0.25 mm

Bulk soil

> 2 mm

2–0.25

< 0.25

Bulk soil

0.087 −0.725* −0.608* −0.695* – 0.457

0.467 0.762** 0.931** 0.913** – −0.910**

−0.069 0.389 0.564 0.833** – −0.901**

0.326 −0.317 −0.230 −0.453 – 0.262

0.153 0.455 0.657* 0.681* −0.217 −0.604*

0.170 0.272 0.557 0.597* 0.642* −0.766**

−0.256 −0.121 −0.488 0.272 −0.238 0.252

0.682* 0.581* 0.409 0.328 0.196 −0.429

0.731** 0.405 0.567 0.469 0.083 −0.717**

0.243 0.455 0.659* 0.685* 0.654* −0.843**

−0.322 0.480 0.325 0.558 0.813** −0.845**

0.824** 0.404 0.454 0.258 0.670* −0.335

Significant effects (P < 0.05) are highlighted in bold. SOC, soil organic matter; TN, total nitrogen; NNR, net nitrification rate.

three fertilizer-N treatments. Aggregates of with the same size in the NPK, NPKS and NPKM treatments were each grouped together, which showed a strong influence by aggregate size. Subtle alterations were detected among the N-fertilizer treatments, and the same aggregate-size classes of NPKS and NPKM treatments appeared more closely to each other compared to the NPK treatment, which separated from the NPK treatment. The analysis of the relationship between soil parameters and AOA community structures showed that axis1 and axis2 explained 60.7 and 22.6% of the total variation, respectively (Fig. 4a). Except for NH4+-N, soil pH and aggregate-size class the other soil factors were significantly associated with AOA community composition.

Similar to the AOA community, the AOB community from the same aggregate-size class in the NPKS and NPKM treatments were grouped together. Unlike AOA, the three aggregate-size classes of the NPK treatment were grouped together and were clearly separated from that in the NPKS and NPKM treatments. The relationships between soil parameters and AOB community structures analysed by RDA showed that axis1 and axis2 explained 55.0 and 30.5% of the total variation, respectively (Fig. 4b), and soil properties had a significant influence on AOB community, with pH and SOC as the primary factors.

1.5

a

1

RDA2 (22.6%)

0.5

NH4+

NO30

pH

SOC

-0.5

TN

-1 Aggregate size -1.5 -2

-1.5

-1

-0.5

0

0.5

1

1.5

RDA1 (60.7%) 1.5

b

Control

1

NPK NH4+

RDA2 (30.5%)

0.5

NPKS

2mm

NPKM

TN

2mm

2mm 2mm

Control 0.25-2mm NO3-

0

NPK 0.25-2mm NPKS 0.25-2mm

pH

-0.5

Aggregate size

SOC

NPKM 0.25-2mm Control NPK

-1

NPKS NPKM

-1.5 -3

-2

-1

0

1

0.25mm

0.25mm 0.25mm 0.25mm

2

RDA1 (55.0%) Fig. 4. Redundancy analysis between soil parameters and ammonia-oxidizing archaeal (a) and bacterial (b) communities across aggregate size classes under different fertilizer treatments. The length of each arrow indicates the contribution of the corresponding property to the structural variation. 112

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(caption on next page)

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Fig. 5. Neighbor-joining phylogenetic tree of archaeal amoA sequences (625-bp fragment) (a) and bacterial amoA sequences (492-bp fragment) (b) retrieved from three aggregate soils under NPK and NPKS treatments. Numbers in parentheses denote the total number of each clone detected in all aggregates.

Fig. 6. The relative abundance of AOA (a) and AOB (b) phylotypes in three aggregate soils under NPK and NPKS treatments based on amoA gene sequences. Error bars represent the standard error of three replicates.

3.5. Phylogeny of AOA and AOB

Table 4 Results (P values) of a two-way ANOVA analysis for the effects of fertilizer treatments and soil aggregate size on dominant phylotypes of AOA and AOB.

The quality-checked datasets were clustered into 87 OTUs for AOA and 75 OTUs for AOB; all AOA sequences were affiliated with Thaumarchaeota Group1.1b, while all AOB amoA gene sequences shared the most similarity with Nitrosospira by phylogenetic analysis (Figs. 5a and 6a). The AOA phylogenetic tree was grouped into four clades: clade B, clade Nitrososphaera, clade A and clade E according to the classification method of Alves et al. (2013). Clade A dominated AOA communities in all samples with a relative abundance ranging from 55.8 to 71.6% (Fig. 6a). Generally, the AOA community within each aggregate-size class did not differ except that Clade A had a greater proportion in large macro-aggregates of NPK than in the NPKS treatment, and Clade E had a greater proportion in small macro-aggregates of NPKS than in the NPK treatment. Among different aggregate-size classes, micro-aggregates had a relatively lower proportion of clade A and a greater proportion of Clade E than in the large macro- and small macro-aggregates, while the latter had a relatively lower proportion of clade B than in the large macroand micro-aggregates. The proportion of clade Nitrososphaera was rather stable, with no difference among all soil samples (Table 4). It seems that the AOA community structure is mainly influenced by aggregatesize class. The phylogenetic tree of AOB amoA gene sequences was further grouped into four clades: clade Nitrosospira multiformis (3a.2), clade Nitrosospira sp. Nsp2/Nsp17 (3a.1), clade N. briensis (3b) and clade Nitrosospira sp. NP 39-19 (Fig. 5b). Clade Nitrosospira multiformis dominated the AOB community in all soil samples, with the proportion ranging between 41.2 and 48.1%, and were lower in all three aggregate-size classes of NPKS than in the corresponding aggregate-size classes of NPK (Fig. 6b). Clades Nitrosospira sp. Nsp2/Nsp17 (3a.1), Nitrosospira sp. NP 39-19 and N. briensis (3b) accounted for 10.3 to 23.7%, 2.9 to 13.1% and 27.5 to 36.4% of the AOB sequences in all soil aggregates, respectively, and these values did not differ within microaggregate fractions in NPKS and NPK treatment. Compared with

F

AS

F*AS

AOA CladeA CladeB Clade Nitrososphaera Clade E

0.215 0.282 0.836 0.838

0.058 0.045 0.224 0.033

0.962 0.309 0.645 0.140

AOB N. multiformis clade (3a.2) Nitrosospira sp. Nsp2/Nsp17 (3a.1) N. briensis clade (3b) Nitrosospira sp. NP39-19 clade

0.522 0.035 0.548 0.015

0.698 0.223 0.099 0.575

0.654 0.410 0.952 0.100

Significant effects (P < 0.05) are highlighted in bold. F, Fertilization; AS, aggregate size.

aggregates of the NPK treatment, Clade Nitrosospira sp. NP 39-19 clade decreased by 77.8% and 67.9% respectively within large and small macro-aggregates in the NPKS treatment, which accompanied a significant increase in clades Nitrosospira Nsp2/Nsp17 clade (3a.1) (from 11.1–11.6% to 15.0–23.7%) and N. briensis (from 27.4–30.9% to 30.7–36.4%) within the two aggregate-size classes, respectively. 4. Discussion 4.1. Effect of fertilizer practice on soil aggregation and physicochemical properties Consistent with our hypothesis, significant decreases in micro-aggregate mass were measured in NPKS and NPKM treatments compared to the Control and NPK treatment. A study with different rates of crop straw incorporation revealed that soil aggregate size distribution, soil aggregate stability and water content were significantly increased with the addition of straw (Zhang et al., 2014). In the combined organic N 114

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treatments, large macro-aggregates significantly increased with straw addition, while small macro-aggregates significantly increased with manure application. In contrast, chemical fertilizer alone increased micro-aggregates mass, which degraded soil physical structure. Lignin and cellulose in straw had a positive effect on soil carbon, macro-aggregates (Paula et al., 2013; Christensen, 1986) and water-holding capacity (Zhu et al., 2010). Numerous fertilization experiments have confirmed increased soil TN and SOC in aggregate fractions (Zhang et al., 2016; He et al., 2015; Wang et al., 2018). Greater NH4+-N in micro-aggregates could be explained by increased soil organic matter in the micro-aggregate size fractions, which could effectively retain soil base cations, such as NH4+-N (Oorts et al., 2003). Moreover, clay minerals in the micro-aggregates were higher than in the macro-aggregates especially in sandy, free-draining soils (Wang et al., 2017b), which also have a positive effect on soil base cation adsorption. The laboratory incubation for NNR also confirmed that the largest net NO3−-N accumulation occurred in small macroaggregates. Soil pore-size distribution influences soil water retention, which can give rise to a diverse range of microorganisms (Strong et al., 2004). The connected porosity of macro-aggregates increased with straw and manure additions, solving the competition between water and gas by improving pore conditions (Yu et al., 2018), which could enhance the interactions between microorganisms and their substrates. This would explain why there were greater nitrification rates in small macro- than in micro-aggregates. The lower NO3−-N accumulation in large macro-aggregates may be attributed to excessive moisture content. Macro-aggregates had relatively greater soil enzyme activity which could imply greater soil organic matter turnover compared with micro-aggregates (Dorodnikov et al., 2009). Moreover, previous studies have suggested that small macro-aggregates (0.25–2 mm size) might be an ammonia-oxidation hot spot, as the microenvironment provides more substrate, suitable oxygen and water for ammonia oxidizers (Chen et al., 2016). Compared with NPKM, a lower NNR was observed in the NPKS treatment indicating a relatively lower N-leaching potential, as straw contains a larger proportion of recalcitrant carbon than manure. Generally, almost 80% of the aggregate mass was dominated by macroaggregates including large and small macro-aggregates. The proportion of large macro-aggregates mass greater increased more in the NPKS compared to the other treatments, and the small macro-aggregates mass decreased, which likely resulted in less N turnover in the fertilizer treatment with straw.

Greater large macro-aggregate mass and moisture in the NPKS treatment may result in lower oxygen availability and limited nitrification, which could explain why there was lower AOB abundance in the NPKS treatment. The lack of a significant relationship between AOA amoA abundance and NNR in the bulk soil and some aggregate sizes may imply that AOA is not active in nitrification at bulk soil level. Contrastingly, the significant positive correlation between AOA abundance and NNR indicated that AOA had a significant effect on ammonia oxidation in the small macro-aggregates. Previous studies suggested that AOA was more tolerant to oxygen stress (Schleper and Nicol, 2010; Lu et al., 2016), and that oxygen diffuse in the core of small aggregate soils was generally occurred sooner (Ebrahimi and Or, 2016), which explained the observation that AOA abundance was significantly lower in micro-aggregates than in large and small macro-aggregates. A recent study also reported that AOA abundance increased with soil clay content of a paddy soil with smaller mean pore size and lower oxygen (Zhang et al., 2017a). All these indicated that AOA may function actively in oxygen-limited environments. Though no significant correlation between AOA abundance and NNR was detected in bulk soil in the present study, AOA may function alternatively in macro-aggregates where oxic conditions may vary and favour AOA activity. 4.3. Separation of AOA and AOB within aggregate size classes Cumulative data collected from this field trial showed that using 30% organic N to replace chemical N was environmentally and economically feasible with no grain yield reduction (Duan et al., 2016). Experiments with different forms of N suggested that the difference in N availability may control ammonium oxidation rates in soils through AOA and AOB community compositional shifts (Prosser and Nicol, 2008). In the present study, T-RFLP and sequencing analysis of amoA genes from aggregate fractions in NPK and NPKS treatments showed prominent changes in the abundances of amoA gene phylotypes in response to forms of N fertilizer and soil aggregate size. Nitrososphaera (group 1.1b) was the only genotype detectable across all soil samples, and it was known as the most abundant soil lineage (Zhang et al., 2017b). Nitrososphaera was further grouped into several distinct phylogenetic clades, and clade A was predominant with a relatively lower proportion within NPKS macro-aggregates in comparison to NPK. Clade A was shown by enrichment cultures with little or no nitrification activity, and clades B and Nitrososphaera could be linked to NH3 oxidation (Alves et al., 2013). The decrease of clade A in the NPKS treatment may imply that the AOA community contributes to an increase in soil nitrification due to straw application. Clone libraries for the effects of fertilization and aggregate size for the dominant clades of AOA indicated that aggregate size significantly influenced clade B and clade E community structure, but no clades were significantly influenced by the N-fertilization regime (Table 4). Clade B was shown to be the dominant group in a long-term fertilized acidic soil (Zhang et al., 2017b), and adapted to lower pH (Gubry-Rangin et al., 2011). The relative proportion of clade B was reduced in small macro-aggregates in both NPK and NPKS treatments, accompanying a significant increase in clade E in micro-aggregates. It can be concluded that AOA community structure responded mainly to strict aggregate size. Soil aggregate size contributed more to AOA community structure, which was consistent with the RDA analysis (Fig. 4a). Soil pH was a fundamental factor determining bacterial and archaeal community composition (Lauber et al., 2009; Fierer, 2017). The result was confirmed by both RDA analysis of the two functional microorganisms and soil factors in the present study (Fig. 4). However, a significant difference in soil pH was only detected between micro-aggregate and macro-aggregate sizes, but not between treatments of different forms of fertilizer-N. Most of the AOB amoA genes were affiliated with a frequently observed Nitrosospira cluster 3a in agricultural soils (Zhang et al., 2017a; Habteselassie et al., 2013). Moreover, most of the AOB from cluster 3a corresponded to N. multiformis clades (cluster 3a.2), and the proportion

4.2. AOA and AOB amoA gene abundance distributions Quantitative PCR data showed that AOB and AOA abundances significantly increased in fertilizer treatments compared with the Control, and greater amoA gene copy numbers were measured for AOB in bulk soils in NPK than in NPKS and NPKM treatments, suggesting that greater chemical N additions in NPK favoured the growth of AOB in bulk soil, but not AOA. Similarly, previous studies reported that AOB are strongly enriched after the application of inorganic N fertilization (Muema et al., 2015; Offre et al., 2009). In this study, AOB abundance was significantly correlated with NNR across small macro- and microaggregates, while a significant correlation between AOA abundance and NNR was only observed in the small macro-aggregates, but not in the other two size classes. Similar to the results of Jiang et al. (2014), aggregate size significantly influenced AOA abundance. Morever, AOA abundance and NNR decreased with larger NH4+ − N concentrations in micro-aggregates, showing a negative relationship as expected. Consequently, AOB likely had a major role in NNR in this study, and from this long-term fertilizer field trial, it was revealed that AOB are more functionally active than AOA in soils. The results of this study are consistent with previous findings that AOB dominate the ammoniaoxidation process in alkaline soils (He et al., 2018) and soils with large N concentration (Shen et al., 2012; Di et al., 2009). 115

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of N. multiformis clades was greater within micro-aggregates of NPK and NPKS treatments compared with other aggregate-size classes, which corresponded to the increased SOC, TN and NH4+-N. According to a previous study, the Nitrosospira sp. Nsp2/Nsp17 clade was dominant in neutral to alkaline soils of north China (Shen et al., 2012), and the greater proportion of Nitrosospira sp. Nsp2/Nsp17 in the aggregates of NPKS showed that the growth of this clade adapted to straw addition (Table 4). The N. briensis clade showed the largest relative abundance within large macro-aggregate of NPKS than within the same aggregate size of NPK and other aggregate sizes, indicating that the addition of straw favoured the N. briensis clade and suppressed part of the Nitrosospira sp. NP39-19 clade (Table 4). According to Webster et al. (2005), Nitrosospira 3a were sensitive to large ammonia concentrations, while N. briensis (3b) clade were tolerant, which could explain the increase of the N. briensis clade within large macro-aggregates of NPKS with relatively lower nitrification activity and ammonia concentration. The larger soil moisture, SOC and TN concentrations of the large and small macro-aggregates of the NPKS treatment corresponded to the suppressed clade of Nitrosospira sp. NP39-19 which was classified into cluster 3b in other studies (Shen et al., 2008; He et al., 2007). Redundancy analysis confirmed that soil pH and SOC have great influences on AOB community structure in comparison with other soil properties, coinciding with observations that the effect of N application on soil ammonia oxidizers originated from the increase in total organic carbon concentration (Muema et al., 2015; Tao et al., 2017). Soil properties, such as oxygen, water and nutrient availability vary among aggregate-size classes in unsaturated soil (Ebrahimi and Or, 2016; Leffelaar, 1993) and provide spatially heterogeneous habitats for AOA and AOB (Sexstone et al., 1985), which strongly influence nitrification within different aggregate fractions. The shift in community structure of AOA and AOB may result from microhabitat selection of ammonium oxidizers.

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5. Conclusions In conclusion, long-term combined organic and chemical N fertilizer application had a positive influence on soil physiochemical properties and improved soil aggregation. The positive correlations between reduced soil net nitrification rate and AOB abundance under combined organic and chemical N across all aggregate size classes illustrated that AOB were functionally important to nitrification in calcareous fluvoaquic soil. AOB abundance and composition were significantly affected by fertilization, while AOA abundance and composition were mainly affected by aggregate size. Relatively lower nitrification activity and AOB abundance in large macro- compared with small micro-aggregate indicated greater N conservation in the former. This well explained the lower N loss in combined straw and reduced chemical N fertilization in the North China plain. The shift of AOA and AOB communities under different fertilization practices also well accounted for the suppressed nitrification activity within large macro-aggregate of combined organic and reduced chemical nitrogen fertilization. These results give fundamental insights into the functional response of nitrifier dynamics to combined organic and reduced chemical N fertilization across small scales, which would be useful for the reduction of chemical N inputs in intensive double-cropping systems in the North China Plain. Acknowledgements This work was financially supported by National Key R&D Program of China (2017YFD0301103), the Natural Science Foundation of China (41401273), the Strategic Priority Research Program (XDB15020200) and Youth Innovation Promotion Association of the Chinese Academy of Sciences. We would like to thank Prof. Phillip Chalk for language polishing, Dr. Shaomin Huang for assistance in soil sampling, and Dr. Juntao Wang for technical support for data analysis. 116

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