Soil microhabitats mediate microbial response in organic reduced tillage cropping

Applied Soil Ecology 137 (2019) 39–48

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Soil microhabitats mediate microbial response in organic reduced tillage cropping

T

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Viviana Loaiza Puertaa, , Engil Pujol Pereiraa,b, Ping Huanga,c, Raphaël Wittwerd, Johan Sixa a

Department of Environmental Systems Science, Swiss Federal Institute of Technology, ETH Zürich, 8092 Zürich, Switzerland School of Earth, Environmental, and Marine Sciences, University of Texas Rio Grande Valley, Edinburg, TX, United States c Key Laboratory of Reservoir Aquatic Environment, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Science, 400714 Chongqing, PR China d Research Division, Agroecology and Environment, Agroscope, Federal Office for Agriculture, 8046 Zürich, Switzerland b

A R T I C LE I N FO

A B S T R A C T

Keywords: Conservation tillage Cover crops Nitrogen Gene abundance nosZ amoA

We aimed to understand how crop practices such as tillage, crop management and cover crop use affect aggregate microhabitats for nitrogen (N)-cycling microorganisms. We quantified the abundance of five N-cycling functional genes: two genes related to ammonia oxygenation (amoA from archaea and bacteria), two nitrite reducers (nirS and nirK) and one for nitrous oxide reduction (nosZ) as well as total eubacteria (16S) in two soil aggregate fractions. These fractions were total (occluded and non-occluded) microaggregates (tMi) and total silt and clay (tS + C) from a field trial under conventional and organic crop management combined with intensive or conservation tillage and four different cover crops. Although we found no clear associations between abundances of different functional genes in aggregate fractions, abundance of nosZ and amoA AOA changed almost exclusively in the tMi, suggesting that this fraction may mediate soil microbial response to crop management practices. Terminal restriction fragment length polymorphism (T-RFLP) analysis of the nosZ gene indicated that aggregate fractions host different microbial communities, therefore providing distinct microhabitats.

1. Introduction Organic N becomes available to plants via mineralization, a microbial-mediated oxidation process resulting in inorganic N species. Both amoA gene-containing bacteria (amoA AOB) and Archaea (amoA AOA) are responsible for ammonia oxidation, the rate limiting step in nitrification (Levy-Booth et al., 2014). N losses from the soil system by denitrification, which reduces nitrite or nitrate into NO, N2O or N2, are caused by the activity of a number of different taxonomic groups (Zumft, 1997). Two different nitrite reductase enzymes (encoded by the nirS and nirK genes) reduce NO2− to NO, while further denitrification is catalyzed by nitrous oxide reductase (encoded by the nosZ gene). Although there are well-known controlling factors of denitrification, e.g. oxygen, temperature, nitrate and available carbon (C), a large proportion of activity happens in patches over brief time periods of intense activity (Burgin et al., 2010; Groffman, 2012; Groffman et al., 2009). This transient high denitrification activity can be caused by microbial hotspots, i.e. areas with faster than average soil process rates induced by the input of labile organics by plants (Kuzyakov and Blagodatskaya, 2015), nutrients being in limited supply yet high demand (Johnson et al., 2010), patchy distribution of particulate organic

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matter (Parkin, 1987), and heterogeneous soil architecture (Rabbi et al., 2016). Nonetheless, their precise mechanisms are unknown. Along this line, different-sized soil aggregates have been reported to affect ammonia oxidation (Jin et al., 2014), denitrification (Lensi et al., 1995) and N cycling enzymatic activity (Marx et al., 2005). Soil particles, in aggregated or unbound forms, constitute the physical environments where these microbial-mediated processes occur. As a component of soil structure, aggregation creates local heterogeneity in temperature, water, gas and nutrient dynamics critical for soil microbiota growth and activities (Chivenge et al., 2011; Monreal and Kodama, 1997; Sexstone et al., 1985; Six et al., 2004; Tiedje et al., 1984; Vos et al., 2013). Such variability in aggregate environments defines microhabitats that drive variation in community diversity, abundance and activity and ultimately impact ecosystem processes (Beauchamp and Seech, 1990; Mummey and Stahl, 2004). Denitrifying bacteria are relevant in N cycling, with consequences on plant N supply and climate regulation. Because of their wide distribution and general high diversity, they have been used as a proxy for microbial mediated ecosystem function (Hallin et al., 2012; Philippot and Hallin, 2005). Additionally, since they are sensitive to changing oxygen availability, potential increased anaerobic sites in microhabitats could affect this

Corresponding author. E-mail address: [email protected] (V. Loaiza Puerta).

https://doi.org/10.1016/j.apsoil.2019.01.009 Received 25 May 2018; Received in revised form 9 September 2018; Accepted 28 January 2019 Available online 07 February 2019 0929-1393/ © 2019 Elsevier B.V. All rights reserved.

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conventional and organic crop management with different tillage intensities in 4-year crop rotations. The field trial is located at the Swiss federal agricultural research station Agroscope, Reckenholz near Zurich, Switzerland (47° 26′20”N, 8° 31′40″E). The soil texture is a loamy Cambisol containing 23% clay, 34% silt, 43% sand on glacially deposited Pleistocene sediments (IUSS Working Group, WRB, 2014), where mean annual daily temperature is 9.4 °C (Swissmeteo), and annual precipitation averages 1054 mm (1981–2010 data). For a detailed description, please see Wittwer et al. (2017). The crop rotation representative of local Swiss farming practices began with winter wheat (Triticum aestivum cv. Titlis), followed by maize (Zea mays cv. Padrino), field bean (Phaseolus vulgaris cv. Fuego) and once again winter wheat (Triticum aestivum cv. Titlis). The field trial is set-up in a split-plot design with four cropping systems as main plots (6 m × 30 m) and four cover crop treatments as subplots (3 m × 15 m). The main plots are set-up as a randomized complete block design replicated four times, i.e. four blocks. The main plots test two types of crop management (organic and conventional) coupled with two types of tillage (intensive tillage and a type of conservation tillage). Intensive tillage was used for both organic and conventional crop management, while no tillage was used in conventional and reduced tillage in organic crop management. Although no tillage offers the least soil disruption, its use in organic crop management is impractical because of difficulties with weed control due to the prohibition of synthetic herbicides. The resulting four cropping systems were: conventional intensive tillage (C-IT), conventional no tillage (CNT), organic intensive tillage (O-IT) and organic reduced tillage (O-RT). O-IT and O-RT were fertilized with cattle manure slurry at a target level of 1.4 livestock unit, which reflects the practice in Swiss organic farms. This resulted in total amounts of 111 kg N ha−1 and 102 kg N ha−1, for the first and second crop of wheat, respectively, applied each in two separate applications, and 127 kg N ha−1 for maize also in two separate applications. Forty percent of total N applied was in the form of NH4+N, while the rest was organic N. C-IT and C-NT were fertilized with ammonium nitrate at 110 kg N ha−1 for the first wheat crop, 120 kg N ha−1 for the second wheat crop and 90 kg N ha−1 for maize following the Swiss fertilization recommendation (Richner and Sinaj, 2017). By the end of the crop rotation, organic had received 340 kg N ha−1 and conventional 320 kg N ha−1. For C-IT and O-IT, tillage was applied to 0.2 m depth using a moldboard plow (Menzi B. Schnyder, Switzerland) followed by a rotary harrow at 0.05 m (Amazone, H. Dreyer GmbH, Germany). For O-RT, we applied reduced tillage at 0.05 m depth using a disk harrow in the first year and a rotary harrow in successive years. All tillage operations were performed immediately before sowing. No tillage was applied on C-NT, where crops were direct-seeded without further soil disturbance. Cover crops were used in the first two years of the four-year crop rotation: in the first year they grew for 58 days before sowing winter wheat and then in the second year they grew for 256 days after harvesting winter wheat as a long intercrop before maize. They were terminated by mulching (Mulchy, Silent AG, Switzerland) in all treatments but C-NT, on which herbicide (glyphosate) was used. The cover crop treatments consisted of a control treatment (no cover crop), a non-legume (Sinapis alba), a legume (Vicia sativa before winter wheat and Vicia villosa before maize), and a mixture (Phacelia tanacetifolia, Trifolium resupinatum and Trifolium alexandrinum) before winter wheat and a different mixture (Phacelia tanacetifolia, Vicia villosa, Fagopyrum esculentum and Camelina sativa) before maize. Soil was sampled in August 2014 at the end of the fourth year of the cropping rotation. Four intact soil cores (5.5 cm diam. × 20 cm length) were taken at 3 m intervals from the center of each subplot using a Giddings hand sampler (Giddings Machinery Co, Windsor, Colorado, USA). Each 20 cm-length core was manually cut at 6 cm, separating the top 0–6 cm from the bottom 6–20 cm. Field-moist cores were sieved at 8 mm by gently crumbling to minimize aggregate disruption. Once the four cores from each plot were combined, approximately 300 g of fresh

microbial community. The sensitivity of soil microorganisms to aggregate conditions underscores the importance of practices which change aggregate proportion and composition (Blackwood et al., 2006; Nie et al., 2014). Tillage and fertilization are widespread practices with documented impacts on soil structure. In comparison with conventional tillage, reducing tillage intensity increases aggregation (Bronick and Lal, 2005; Kravchenko et al., 2011; Six et al., 1999) and may enrich C stocks (Cooper et al., 2016; Oorts et al., 2007; Six et al., 2000). Likewise, organic amendments can improve soil structure by promoting particle assemblage (Abiven et al., 2009; Six et al., 2004) through increased microbial activity (Lori et al., 2017; Wawrik et al., 2005). Another strategy is the use of cover crops, i.e. plant species grown between cash-crop growing seasons (Keene et al., 2017). As reported benefits of cover crop use include enhancing processes where soil aggregation and/or microbial activity play a key role, such as protection against erosion and disease (Hartwig and Ammon, 2002; Hossain et al., 2015), increased soil organic C content (Poeplau and Don, 2014; Sainju et al., 2007), reduced nitrate leaching (Thorup-Kristensen et al., 2001; Tonitto et al., 2006; White et al., 2017), increased soil water storage and quality (Basche et al., 2016; Qi et al., 2011), and yield maintenance through additional N input provided by legumes (Campiglia et al., 2014; Wittwer et al., 2017). Increased soil aggregation mediated by cover crops has been documented under different conditions; for example, in a winter wheat-grain sorghum rotation under no tillage on a silt loam (Blanco-Canqui et al., 2011), using non-legume cover crop after three years of tomato followed by eggplant in the U.S., and after four years using cereal rye in a no tillage corn-soybean rotation in the U.S. (Rorick and Kladivko, 2017; Villamil et al., 2006). Studying legacy effects, i.e. whether any changes persist after cover crops are discontinued, is relevant for optimizing cover crop management. Soil structure improvements observed when introducing cover crops, reduced tillage and/or organic management (Blanco-Canqui et al., 2011; Bottinelli et al., 2017; Jiao et al., 2006) change aggregate quantity and quality, altering microhabitat conditions for the microbial community active in N transformations. Since active microorganisms in the soil matrix control mineralization, nitrification and denitrification process rates (Braker and Conrad, 2011; Groffman, 2012), soil aggregation may affect these functions by influencing the availability of constraining resources. For example, if microaggregates rather than unaggregated silt and clay harbor greater abundance of denitrifying organisms due to higher moisture availability, practices leading to higher aggregation may lead to an increased potential for denitrification. Given this possibility, we aimed to investigate how tillage type, combined with organic and conventional crop management as well as different cover crop treatments affect soil aggregates; and how this in turn affects the N-cycling microbial community abundance and diversity within these soil aggregates. We hypothesized that (i) the use of cover crops, reduced tillage and organic management would increase MWD through an increase in macroaggregate abundance, (ii) tMi would harbor a higher amount of nosZ, amoA and nirS/K gene copies than tS + C and (iii) organic management would lead to a higher nosZ gene diversity. To do this, we used a field experiment testing three types of tillage, combined with organic and conventional crop management as well as four cover crop treatments. Following a 4-year crop rotation including cover crops during the first 2 years, we measured soil aggregate quality (total C and total N content) and quantity, quantified gene abundance in different microhabitats using real-time PCR and evaluated diversity of the nosZ gene using terminal restriction fragment (t-RFLP) diversity analyses. 2. Materials and methods 2.1. Field site and soil sampling The Swiss Farming System and Tillage experiment (FAST) combines 40

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soil were stored immediately at −20 °C and the remaining soil was airdried at room temperature. The hand sampler was wiped clean after each core, and sieves were washed with ultrapure water to minimize cross-contamination.

Considering that in our study 93–96% of the total occluded and nonoccluded S + C (tS + C) particles originated from occluded S + C, i.e. constituent of a macroaggregate, it is assumed that the response measured (community composition, TC, TN, gene abundances) reflects that of macroaggregate-occluded S + C. In the case of tMi, approximately 65% originated from occluded Mi. The possibility of whether occluded and non-occluded Mi and S + C contain different microbial communities is acknowledged but not addressed in the present study. We considered that the risk of compromising DNA integrity with the additional procedure required to separate occluded from free tMi outweighed possible additional input in comparing these two origins of Mi.

2.2. Soil aggregate isolation and characterization Wet-sieving has been shown to be preferred over dry sieving in determining N functional gene abundance among soil fractions (Blaud et al., 2017). We used two types of soil aggregate physical fractionation. The first type (Elliott, 1986) separated the bulk soil into four aggregate size classes: large macroaggregates (LM) > 2000 μm, small macroaggregates (SM) 2000–250 μm, microaggregates (Mi) 250–53 μm and silt-and-clay (S + C) < 53 μm, as well as organic residues. The purpose of this fractionation scheme was to characterize soil structure in terms of its distribution of macroaggregates (LM and SM which contain occluded Mi and S + C particles), microaggregates and non-aggregated particles of the soil matrix (free S + C and organic residues). This allows calculating mean weight diameter (MWD), used as an index for mean aggregate size, which relates to soil structure. 161The second type of soil physical fractionation isolated the total amount of Mi (tMi) and S + C (tS + C) of the whole soil, i.e. all those occluded inside macroaggregates as well as non-occluded free standing in the soil matrix. This method was preferred for quantifying gene abundance, as it allowed comparing microbial differences between aggregated (tMi) and un-aggregated (tS + C) microhabitats. For the first type of soil aggregate physical fractionation, 80 g of airdried soil were placed on a 2000 μm sieve and slaked for 5 min in deionized water. The sieve was submerged fully in a basin of deionized water and lifted out of the water 50 times over the course of 2 min. Aggregates remaining on the sieve were backwashed into pre-weighed aluminum tins and oven-dried at 60 °C, while those left in the water solution were sieved on a 250 μm sieve as described previously. This procedure was repeated with sequentially smaller mesh-sized sieves to separate all aggregate size classes. MWD was calculated by adding the product of each aggregate size class proportion in soil by its median size class diameter, as defined in Eq. (1):

2.3. Total carbon and nitrogen quantification Approximately 2 g of 8 mm sieved dry soil from each fraction, except organic residue, were finely ground using a mortar and pestle. Next, about 200 mg was subsampled for total C (TC) and total N (TN) quantification by combustion on an elemental analyzer (LECO Corporation, United States). 2.4. DNA isolation and real-time PCR Because nitrification and denitrification activity has been found to be greatest at the surface soil (Banning et al., 2015; Luo et al., 1998), we only sampled soil from 0 to 6 cm depth for gene abundance and nosZ diversity analysis. To eliminate humic acids before DNA extraction, 0.5 g of freeze-dried fraction was incubated in 2 ml of 0.1% Na4P2O7 in 10 mM Tris–HCl buffer (pH 8.0) - 1 mM ethylenediaminetetraacetic acid (EDTA) for 30 min and centrifuged at 8500 ×g for 10 min at room temperature. DNA was extracted from the remaining humic acid-washed pellet using FastDNA Spin Kit for Soil (MP Biomedicals, Illkirch, France) following manufacturer's instructions. DNA concentration and quality was measured spectrophotometrically (NanoDrop 1000, Thermo Fisher Scientific, Waltham, MA, USA). Gene abundance of total eubacteria, ammonia oxidizers and denitrifiers was quantified with real-time PCR (qPCR) of 16S, amoA AOA and amoA AOB, nirS, nirK and nosZ genes, respectively. Each reaction was run in triplicate using the 7500 Fast Real-Time PCR System (Applied Biosystems, Grand Island, NY, USA) for 16S, nirS, nirK and nosZ genes. qPCR of amoA ammonia oxidizing bacteria and archaea genes were run on a LightCycler® 480 Instrument (Roche Diagnostics, Switzerland). Each 25 μl reaction mixture for nirK and nirS assays was composed of 0.5 μl at 25 μM of both forward and reverse primer, 12.5 μl Power SYBR Green master mix (Applied Biosystems, Foster City, CA, USA), 6.5 μl H2O and 5 μl of DNA template. For nosZ, 20 μl reactions held 10 μl Power SYBR Green master mix, 0.8 μl of each primer (12.5 μM), 3.4 μl water and 5 μl DNA template. Reactions for Archaea amoA used 5 μl KAPA SYBR® FAST qPCR Kit Master Mix (PEQLAB Biotechnologie GmbH, Erlangen, Germany), 0.2 μl ROX low, 1 μl of each primer (5 μM), 1.8 μl water and 1 μl DNA template. For Bacterial amoA, reactions contained 5 μl KAPA SYBR FAST master mix, 0.2 μl ROX low, 2.3 μl water and 0.75 μl of each primer (5 μM). Using a hydrolysis probe, reactions to quantify the universal bacterial 16S transcript copies carried 4 μl template DNA, 10 μl TaqMan Universal PCR Master Mix (Applied Biosystems, Grand Island, NY, USA), 0.4 μl probe (TM1389, 200 nM), 4 μl water, 0.8 μl of each primer at 800 nM and 4 μl DNA template. A melt curve at the end of each SYBR Green and KAPA Green chemistry qPCR run controlled for reaction specificity. Supplemental material 1 details primer type and thermocycler settings used for each gene. Absolute quantification was done with a 6-point 10-fold dilution standard curve of target-gene containing plasmids of defined copy numbers, run in parallel with the samples of interest.

n

MWD =

∑ i=1

χi ωi

(1)

where χi is the mean diameter of the particle range of each size class, ωi is the weighted abundance of each aggregate fraction in whole soil and n is the number of aggregate size classes used. For the second type of soil physical fractionation, frozen whole soil was separated into three size fractions: organic residue (> 250 μm), total silt and clay (tS + C, < 53 μm) and total microaggregates (tMi, 53–250 μm). Following the procedure outlined in Six et al. (2000), approximately 20 g of frozen field-moist soil and 50 4-mm diameter glass beads were placed atop a 250 μm mesh set on a reciprocal shaker and immersed in deionized water. The soil was shaken at 165 oscillations per minute under a continuous steady flow of deionized water. A clear output flow indicated that only organic residue and sand remained on the mesh and all macroaggregates had been broken down into their constituent occluded Mi and S + C. TMi and tS + C fractions were separated from the collected output flow through wet-sieving by rhythmically submerging a 53 μm sieve 50 times over 2 min (Elliott, 1986). Excess water in the tS + C, tMi and organic residue fractions collected in suspension was eliminated by decanting after centrifugation for 15 min at 8245 ×g, 4 °C. Individual fractions were then transferred to plastic containers and frozen prior to lyophilization (Labconco, USA) and storage at −20 °C. It is possible that the wetsieving procedure washed off microbes loosely attached to the surface of the microaggregate, however as we were mostly concerned with comparing the microbial community inside the microaggregate compared to that of silt and clay, this potential loss was negligible.

2.5. Terminal restriction fragment (t-RFLP) diversity analyses NosZ diversity was evaluated in tMi and tS + C fractions of all main 41

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Fig. 1. Mean weight diameter of soil aggregates of cropping systems under each cover crop at 0–6 cm and 6–20 cm soil depth. Error bars represent standard deviation of the arithmetic mean. Different letters indicate significant differences between cropping systems within cover crop and depth (p < 0.05).

the adonis function of the vegan package. To facilitate statistical analysis and interpretation of nosZ diversity, we calculated the Shannon diversity index (Shannon, 1948) as effective number of species, also known as Hill numbers (Chao et al., 2014; Jost, 2006). Differences in Shannon effective number of species were calculated using a 2-way ANOVA, with cropping system and fraction as independent variables. Linear correlation among gene copies and soil properties, as well as between nosZ gene copies with the diversity index and soil properties were assessed using Spearman correlation coefficients.

treatments, i.e. all cropping systems without cover crop, at 0–6 cm. We used an initial PCR with the same primers and reaction setup as detailed for qPCR, except for the use of a different master mix purchased from Promega (Madison, WI, USA) and the use of forward primer nosZ2R (Henry et al., 2006) labelled with 6-FAM. Digestion of PCR products was done with restriction enzyme HhaI (recognition site: GCG/C, New England Biolabs, Pickerington, ON, Canada) and then the resulting fragments were purified with Sephadex G-50 (GE Healthcare, Glattbrugg, Switzerland). Restriction fragments were analyzed using ABI 3130XL DNA analyzer (Applied Biosystems, Foster City, USA), with size comparison to a 500 bp internal standard (GeneScanTM-500 LIZ® Size Standard; Applied Biosystems). Resulting peak patterns were analyzed with GeneMapper v3.7 (Applied Biosystems). Noise filtering and T-RF alignment (at a 2.5 bp clustering threshold) was performed using TREX (Culman et al., 2009).

3. Results 3.1. Microhabitat composition of whole soil Macroaggregate content across all treatments ranged between 55 and 66% at 0–6 cm depth (LM + SM, Supplemental material 2). Under mixture cover crop at 0–6 cm depth, MWD in O-RT and C-NT were higher than C-IT, while at 6–20 cm O-RT had a higher MWD than in CIT when no cover crop was used (Fig. 1). The overall percentage of tS + C (43–56%) was not significantly higher than that of tMi (33–50%), except in the case of C-IT (no cover crop and non-legume cover crop) and C-NT (mixed and non-legume cover crop), at 0–6 cm (Table 1). At the deeper 6–20 cm soil depth, however, only O-IT showed a greater percentage of tS + C than tMi (Supplemental material 3). Non-occluded Mi and S + C represented 29–39% and 4–7% of whole soil, respectively (Supplemental material 2). TC contents of bulk soil across cropping systems ranged between 16.0 and 23.4 g kg−1, without any significant differences across cropping systems or depths (data not shown). TC for tS + C and tMi spanned from 19.6 to 28.8 g kg−1 dry fraction and 10.6–20.0 g kg−1 dry fraction, respectively (Table 1). At 0–6 cm depth, TC was greater in tS + C than in tMi across all cropping systems and cover crops except C-IT no cover crop (Table 1). At the deeper 6–20 cm depth, only O-RT had higher TC in S + C than tMi (no cover crop) (Supplemental material 3). When comparing non-occluded and total (occluded + non-occluded) fractions, TC content in tS + C was on average 5% higher than nonoccluded S + C. On the contrary, TC in tMi was on average 12% lower than that in non-occluded Mi.

2.6. Statistical analyses All statistical testing was performed in R 3.3.2 (R Development Core Team, 2017). Differences in gene abundance (per gram fraction and per gram whole soil), aggregate proportion (i.e. percentage abundance in whole soil), MWD, TC and TN were analyzed using linear mixed effects models with R package lmerTest (Kutznetsova et al., 2016) with restricted maximum likelihood and type I ANOVA with Satterthwaite approximation. The afex package (Singman et al., 2018) was used to calculate model p values for all fixed effects. Block, cropping system, cover crop and/or fraction type were used as fixed effects, while cropping system within block, and cropping system and cover crop within block were used as random effects with a varying intercept. Response variables were log10 transformed to meet model assumptions. Homogeneity of residuals was assessed visually using quantile-quantile probability plots. Post-hoc tests using least squared means were Tukeyadjusted for multiple testing and run with the R package LSMEANS (Lenth, 2016) with statistical significance tested at p < 0.05. A significant difference among fractions of a cropping system within cover crops is noted with “f”, while uppercase letters identify significantly different means among cover crops, within fractions and cropping systems. Ordination of the six genes quantified was visualized using PCA. The relativized fragment height profile of T-RFs between 50 and 267 bp (size of nosZ fragment) was used for operational taxonomic unit (OTU) representation. Two-dimension nonmetric multidimensional scaling (nMDS) plots were used for community composition visualization using Bray-Curtis distance with the vegan package (Oksanen et al., 2017; Rees et al., 2005). In comparison to Euclidean distance, Bray-Curtis allows for management of joint absent T-RFs (Clarke, 1993). T-RF differences between cropping systems and fractions were assessed using a permutational analysis of variance (PERMANOVA) at 9999 permutations with

3.2. Microbial gene abundances in microhabitats Of the six genes studied, we found significant differences in gene abundance per fraction among cropping system and cover crop treatments in the tMi fraction for nosZ and amoA AOA (Table 2 and Supplemental material 4). Regarding the tS + C fraction, only cover crop significantly altered gene abundance for amoA AOA, and only under CIT. Abundance of nosZ associated with tMi in C-NT was the lowest compared to the other cropping systems under no cover crop. This 42

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Table 1 Mean distribution, TC and TN of the total microaggregates (tMi) and total silt and clay (tS + C) fractions at 0–6 cm depth. Values are arithmetic means followed by standard deviation. “a” and “b” represent differences (p < 0.05) between least-squares of cropping systems within cover crop and fraction, while “f” represents differences between fractions, within cover crop and cropping system. Cover crop/Cropping system

No cover crop C-IT C-NT O-IT O-RT Non-legume C-IT C-NT O-IT O-RT Legume C-IT C-NT O-IT O-RT Mixture C-IT C-NT O-IT O-RT

TC (g kg−1 dry fraction)

Proportion (%)

TN (g kg −1 dry fraction)

tS + C

tMi

tS + C

tMi

tS + C

56 49 47 51

(6) (8) (5) (8)

33 (1) f 42 (5) 39 (10) 40 (6)

21.2 20.9 23.0 24.0

(3.2) (1.9) (1.5) (2.0)

16.6 (6.3) 13.9 (0.5) f 12.6 (5.3) f 15.6 (6.1) f

2.4 2.6 2.7 2.7

(0.2) (0.4) (0.0) (0.2)

1.5 1.4 1.3 1.4

(0.6) (0.1) (0.5) (0.4)

f f f f

47 (8) 55 (4) 49 (5) 49 (13)

35 (1) f 37 (4) f 40 (7) 46 (12)

21.9 26.1 24.0 25.5

(3.5) (6.0) (0.4) (3.7)

14.9 14.6 14.2 15.9

2.4 2.8 2.8 2.8

(0.4) (0.2) (0.2) (0.3)

1.4 1.5 1.5 1.6

(0.3) (0.1) (0.1) (0.1)

f f f f

49 (10) 47 (3) 48 (5) 44 (8)

41 (10) 43 (6) 40 (8) 42 (9)

23.7 27.2 19.6 28.8

(3.3) (4.9) (1.7) (3.6)

14.0 (1.0) fab 20.0 (5.5) fa 10.6 (1.8) fb 18.9 (4.3) fa

2.8 2.8 2.6 2.8

(0.4) (0.3) (0.3) (0.2)

1.4 1.8 1.3 1.6

(0.1) (0.2) (0.3) (0.2)

f f f f

50 (8) 58 (20) 46 (4) 48 (8)

50 (10) 43 (11) f 41 (7) 40 (8)

21.3 27.9 21.8 28.1

(7.2) (9.7) (5.3) (1.5)

11.0 (2.2) fb 15.9 (9.5) fab 12.2 (6.3) fab 18.0 (1.1) fa

2.4 2.8 2.6 3.0

(0.9) (0.4) (0.4) (0.1)

(2.6) (0.2) (1.5) (2.1)

f f f f

tMi

1.1 (0.1) fb 2.0 (1.4) fa 1.2 (0.3) fab 1.7 (0.2) fa

Aggregate fractions “tS + C” and “tMi” include total (both macroaggregate-occluded and non-occluded) portions from whole soil.

abundance in C-IT (tMi and tS + C) and O-RT (tMi) compared to all other cover crops. When comparing amoA AOA abundances associated to soil fractions, there were no differences between tS + C and tMi for most treatment combinations, except for O-RT under legume cover crop where tS + C held more copies than tMi. Abundance of amoA AOA correlated with that of 16S in the tMi fraction across all cropping systems (r = 0.62, p < 0.01). Gene copies for nitrite reducers nirS and nirK spanned from 7.57 to 9.04 log copies g−1 fraction and from 7.23 to 9.02 log copies g−1 fraction respectively, without any cropping system or fraction effects (Supplemental material 4). When scaling gene abundance to the whole soil level, differences in fraction percentage in whole soil had a higher influence than gene

difference was mitigated by the addition of cover crops, with leguminous cover crop in C-NT promoting the highest nosZ abundance within cover crops. When comparing nosZ abundance associated to soil fractions, tMi had a greater gene abundance than tS + C in C-IT (no cover crop) and C-NT (non-legume cover crop), while tS + C hosted more abundance than tMi in O-IT (non-legume cover crop). We found TC was related to microbial abundance (16S copies) in tS + C (r = 0.36; p < 0.05), but not tMi. Copy numbers of amoA AOA (7.02–8.46 log copies g−1 fraction) were lower than those of amoA AOB (8.72–9.05 log copies g−1 fraction), the latter of which did not show any significant differences among treatments. Legume cover crop had the lowest amoA AOA gene

Table 2 Log-transformed mean and standard deviation of nosZ and amoA AOA gene abundances in soil fractions of each cropping system at 0–6 cm depth. Uppercase letters represent significant differences between cover crops within cropping system and fraction, “a” and “b” indicate differences between cropping systems within cover crop and fraction, while “f” represents differences between fractions, within cropping system and cover crop (p < 0.05). Cover crop/Cropping system

Functional gene (log copies g−1 dry fraction) nosZ

No cover crop C-IT C-NT O-IT O-RT Non-legume C-IT C-NT O-IT O-RT Legume C-IT C-NT O-IT O-RT Mixture C-IT C-NT O-IT O-RT

amoA AOA

tS + C

tMi

tS + C

6.00 6.19 6.92 6.36

(0.82) (0.87) (0.64) (0.75)

7.23 (0.60) fa 5.66 (0.86) bB 6.96 (0.36) ab 6.90 (0.75) ab

8.13 8.11 8.02 8.03

(0.26) (0.19) (0.15) (0.10)

AB

8.46 (0.17) A 8.07 (0.07) 8.44 (0.38) 7.82 (0.24) A

6.31 6.40 6.99 7.05

(0.65) (0.22) (0.53) (0.21)

6.84 (0.40) ab 7.66 (0.51) faA 5.60 (0.40) fb 6.40 (0.13) ab

8.40 7.66 8.24 8.08

(0.54) (0.31) (0.34) (0.54)

A

8.05 (0.22) A 8.04 (0.73) 7.81 (0.10) 7.94 (0.29) A

6.69 7.43 6.31 7.05

(0.16) (0.29) (0.90) (1.23)

5.91 (1.30) b 7.33 (0.56) aA 6.59 (0.36) ab 6.45 (0.25) ab

7.45 8.33 8.04 8.32

(0.51) (0.20) (0.31) (0.21)

B

7.02 (1.53) bcB 8.42 (0.31) a 7.82 (0.76) ab 6.87 (1.33) fcB

6.20 6.66 6.83 6.78

(1.43) (0.66) (0.08) (0.72)

6.74 (0.69) 6.79 (0.24) AB 5.96 (1.32) 7.27 (0.33)

8.19 7.92 7.97 8.19

(0.12) (0.32) (0.33) (0.18)

AB

43

tMi

8.05 (0.13) A 7.90 (0.50) 7.90 (0.08) 8.19 (0.32) A

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Fig. 2. Denitrifier gene abundance means for cropping systems in all cover crops and both soil fractions, corrected per corresponding gram fraction distribution in whole soil. “f” notes a significant difference between fractions within cropping system and cover crop. Error bars indicate standard deviation of the mean. Cropping systems indicated are: C-IT (conventional intensive tillage); C-NT (conventional no tillage); O-IT (organic intensive tillage); O-RT (organic reduced tillage). Fractions tS + C and tMi represent total silt and clay and total microaggregates, respectively.

Fig. 3. Nitrifier and bacterial 16S mean gene abundances for all cover crops and soil fractions at 0–6 cm depth corrected per corresponding gram fraction distribution in whole soil. “f” indicates a significant difference across fractions, within cover crop and cropping system, while uppercase letters identify different means between cover crops, within cropping system and fraction. Error bars show standard deviation of the mean. Cropping systems indicated are: C-IT (conventional intensive tillage); C-NT (conventional no tillage); O-IT (organic intensive tillage); O-RT (organic reduced tillage). Fractions tS + C and tMi represent total silt and clay and total microaggregates, respectively.

3.3. Community profiling of nitrous oxide reducing bacteria

abundance per fraction. As a result, significant differences in gene abundance scaled to whole soil were seen across fractions, rather than cropping systems or cover crops. The only exception was 16S copies, where mixture was more abundant than legume (in the tS + C fraction under C-NT, (Fig. 3)). At the whole soil level, C-IT (no cover crop and non-legume cover crop) and C-NT (non-legume and mixture cover crops) had higher copy numbers of amoA AOA, amoA AOB and 16S in tS + C than in tMi (Fig. 3). In the case of denitrifiers, nirS had more copies in tS + C than tMi in C-IT (no cover crop) and C-NT (non-legume cover crop) (Fig. 2). Greater copy numbers in tS + C than tMi were also found for nosZ in O-IT (non-legume cover crop) and C-NT (mixture cover crop) (Fig. 2). PCA ordination indicated that cropping systems and cover crop did not influence gene assemblage in fractions (data not shown).

Diversity of nosZ gene assessed using T-RFLP revealed a profile of 43 OTUs (operational taxonomic units). A stress value (variation not explained by all 2 dimensions) of the non-metric multidimensional scaling (nMDS) ordination between 0.05 and 0.10 is considered good (Clarke, 1993). Fraction as a main effect varied along different axes of the nMDS where stress was 0.08, indicating a good fit (Supplemental material 6). PERMANOVA revealed dissimilar community composition between fractions (p = 0.0475). Fraction as a main effect was significant for Shannon effective diversity (p = 0.038) and evenness (p = 0.004), where tS + C was greater than tMi (data not shown).

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4. Discussion

evaluate the outcome of different management practices. There were few differences in aggregate fraction nosZ abundance among cropping systems and cover crops. C-NT was the only cropping system that increased nosZ abundance with the use of any cover crop. Higher nosZ abundance could imply a higher potential for complete denitrification, a process preferable over incomplete denitrification as it results in the emission of N2 rather than N2O, which would lower the environmental impact of N gaseous losses (Krauss et al., 2017). Further research is needed in this area, as the relationship between gene detection and gene expression remains unclear (Levy-Booth et al., 2014; Rocca et al., 2015). We had expected a pronounced effect of the organic fertilizer in the organically managed cropping systems based on reported enhanced nosZ abundance in soil amended with liquid manures (Miller et al., 2009). Reports of long-term effects of certain cover crops on the availability of soil N on a second cash crop following their destruction pointed towards the possibility of cover crop legacy effects (Campiglia et al., 2014). However, neither management type nor cover crop alone affected gene abundance. An increase in nosZ abundance with the use of cover crops because of cover crop root deposition of organic compounds was similarly observed by Gao et al. (2016), who reported for a laboratory incubation of soil amended with crop residues individually or in combination with N fertilizer, higher abundance of nosZ with crop residue addition (compared to a control or N-only enrichment). However, this was the case only for C-NT. Since C-IT had higher nosZ abundance than C-NT without cover crop, we cannot attribute this difference to a possible increase in anaerobic sites in C-NT but rather other factors outside the scope of our study. TC was related to microbial abundance (16S copies) in tS + C, but not tMi, meaning that other variables besides C as energy source had a stronger effect on bacterial growth in tMi. In contrast to our results, increased carbon sequestration with the cultivation of cover crops was found in a meta-analysis from 139 plots in 37 different sites (Poeplau and Don, 2014), and Blanco-Canqui et al. (2011) reported increased C for no tillage under cover crops. Although root-derived C has been shown to be preferentially stabilized over aboveground residue-derived C in no tillage systems (Kong and Six, 2010), we did not measure any legacy effects after 2 years without cover crops on TC content. Most cover crop C, if any had been accumulated, was lost in our field experiment. Consistent with this explanation, a recent study using 13CO2 pulse labeling to trace plant C from cover crop into C pools, reported substantial C decline after 2 years following cover crop termination in a no tillage continuous corn system in northern U.S. (Austin et al., 2017), indicating that cover crops may need to be sowed yearly for C accumulation. In contrast to Kong et al. (2010), who found higher abundances of ammonia oxidizing, denitrifier and total bacteria in tMi than tS + C in both conventional and organic maize-tomato cropping systems, we found higher abundance only for nosZ in tMi compared to tS + C in C-IT and C-NT (no cover crop and non-legume cover crop, respectively). Besides differences in climate, organic matter input, as well as clay mineralogy with variable adsorption capacity within sites (Lützow et al., 2006; Six et al., 2002), the cause of these differences is mostly unknown. The opposite, greater abundance in tS + C than tMi, was measured for amoA AOA in C-IT (no cover crop), O-IT (non-legume cover crop), as well as O-RT (legume cover crop) for amoA AOA. AmoA AOB copies did not change across any of the treatments, indicating no influence from TN content. Although legume cover crop was expected to provide additional N, TN values did not vary among cover crops at the sampling time. Any possible legume-fixed N could have been denitrified or absorbed by crops in the 2 years following the last cover crop growth. It possible that, as is the case with C, cover crops may need to be sowed yearly for N accumulation. Considering that the rhizosphere can affect nitrification dynamics more strongly than fertilization practices (Rudisill et al., 2016), it is Iikely that lower amoA AOA copies in tMi O-RT and C-IT, as well as in tS + C C-IT under legume cover crop can be related to a higher sensitivity of amoA AOA to changes in

4.1. Management affects microhabitat distribution The high prevalence of macroaggregates indicated a well-structured soil across all cropping systems, cover crops and depths. MWD reflects the changes in the proportion of different sized aggregate fractions in whole soil. Higher MWD in C-NT and O-RT than C-IT was expected given known soil structure improvement through increased organic matter input (Abiven et al., 2009) and less physical disruption when reducing tillage intensity (Sheehy et al., 2015). Yet it is uncertain why this was the case only for mixed cover crop. No other legacy effects were found in MWD between cover crop treatments at 0–6 cm depth. It could be that at this depth, cover cropping frequency greatly affects C input effects (Brennan and Acosta-Martinez, 2017). Because of sampling only at the end of the crop rotation, we cannot affirm whether any improvements had diminished or not occurred at all. Differently, at 6–20 cm depth, using any cover crop led to similar MWD in O-RT and CIT, potentially related to soil particle binding effects of cover crop root rhizodeposition. The positive correlation of MWD with nosZ H′ diversity index (r = 0.51, p < 0.05) suggests that increased aggregation supports microbial diversity. TC was related to microbial abundance (16S copies) in tS + C (r = 0.36, p < 0.05), but not tMi. 4.2. N-cycling gene abundance in microhabitats Although the soil pre-wash could have extracted some DNA, any effect was equal across treatments and necessary to remove humic acids, which are known qPCR inhibitors. Principal components analysis (PCA) did not differentiate cropping systems, cover crops or fraction according to the abundance of the six genes tested. This was in contrast to our expectation that conservation tillage would lead to a proliferation of anaerobic sites which would contain higher abundances of denitrifying organisms, as reported in a meta-analysis by Zuber and Villamil (2016). Genes may vary in their response to factors that affect abundance, such as resource availability, microhabitat conditions and community composition beyond those measured in this study. It is also possible that the copies of active genes were obscured by relic DNA (Carini et al., 2016) or dormant organisms (Leite et al., 2017; Lennon and Jones, 2011). Nevertheless, stronger effects may become evident after successive cropping seasons as well as under more extreme conditions where growth is restricted for all but the most adapted microorganisms or microorganisms protected from variations by residing in a specific microhabitat. For example, in a study quantifying 16S and nosZ gene abundance in soil aggregate fractions from elevated CO2 and reduced soil moisture in a mesic Typic Endoaquoll, Pujol Pereira et al. (2013) attributed greater abundance of nosZ copies under reduced precipitation in the tMi fraction to aggregate protection from desiccation. Cropping system differences in gene copies per gram of fraction for a given cover crop were observed almost exclusively in the tMi fraction, suggesting that the tMi fraction was more sensitive than tS + C to management practices. Stronger responses to management practices in macroaggregate-occluded microaggregates have been reported before for C and, consequently, macroaggregate-occluded microaggregates have been proposed as a diagnostic fraction for changes in soil C content (Denef et al., 2007; Mayzelle et al., 2014). Furthermore, in a study combining qPCR of 16S, amoA and nosZ genes and potential gross N mineralization and nitrification to discern the impacts of different N management practices in a maize-tomato rotation, Kong et al. (2010) suggested that the tMi fraction is more favorable to nitrification and denitrification than tS + C. If certain processes occur preferentially in distinct soil fractions, measurements of gene diversity or abundance from less active fractions could confound the relationship between process rates and gene abundance and/or diversity. Targeting the specific fractions where changes are more likely to occur can help to 45

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management practices.

ammonium availability in the legume cover crop rhizosphere. Xu et al. (2012) showed that adding a plant root extract to amoA AOA enrichment culture increases the abundance of a particular clade of Archaea ammonia oxidizers. No change in copy numbers of nirS and nirK might reflect the relatively homogenous conditions at the end of the growing season.

Acknowledgements We thank Hans-Martin Krause for providing amoA standard plasmids for qPCR. We also thank Mattias Barthel, Christopher Mikita and Benjamin Wilde for field sampling assistance, as well as Derick Becker and Ramon Lang for laboratory support. DNA extraction and downstream applications occurred at the facilities of the Genetic Diversity Centre (GDC), ETH Zurich. The authors would like to thank the Mercator Research Program of the ETH Zurich World Food System Center and the ETH Zurich Foundation for financially supporting this project, as well as the National Natural Science Foundation of China (Nos. 41401243 and 41771266) for financially enabling the collaboration of Dr. Ping Huang.

4.3. Microhabitats change nosZ community composition We analyzed the T-RFLP fingerprint of the nosZ-bearing microbial community, which is responsible for the anaerobic complete reduction of nitrous oxide to dinitrogen. It bears noting that these results do not span the breadth of diversity of this gene, as we do not account for the recently identified cluster of atypical nosZ gene (clade II) which can be present in a similar range as clade I (Jones et al., 2013; Sanford et al., 2012). T-RF community profile did not vary between crop management and tillage type as expected (Dambreville et al., 2006; Dong et al., 2017; Enwall et al., 2005; Pastorelli et al., 2013). However, it was significantly different between fractions, confirming that microhabitats can select for different communities. Shannon diversity indices did not correlate with gene abundance, showing that numerically equal communities can differ in composition. The lower Shannon diversity found in tMi than tS + C may suggest that few bacterial genotypes account for the majority of activity. Along this line, in a study investigating denitrifier community size, composition and activity along a gradient of pasture to riparian soils, Deslippe et al. (2014) reported lower nosZ community richness and evenness at lower slope positions, where conditions favoring denitrification (e.g. limited oxygen availability due to water saturation) were likely to occur frequently and extensively. The constant favorable conditions for denitrification would encourage the growth of the most adapted organisms, thereby reducing the range of genotypes and thus decreasing community evenness. The effects of abundance and community diversity on denitrification rates remain, however, unclear. Higher TC content in tS + C than tMi is in agreement with Louis et al. (2016), who reviewed 50C and N models integrating soil microbial diversity, and found that on average, enhanced soil functions related to C cycling occurred with a gain of microbial richness and evenness. Total gene abundance at the whole soil level was driven by aggregate proportion rather than copies per gram fraction per se. Therefore, nitrification and denitrification at the whole soil level may depend on the microbial community response to the conditions of each microhabitat. Thus, aggregation-altering management practices may influence the N cycling microbial community activity (Ebrahimi and Or, 2016). Suggested niche partitioning between the two nosZ clades as well as a possible important role of clade II in soil N2O sink capacity warrant the inclusion of both clades in future studies (Hallin et al., 2017). Because of the limitations in taxonomic discrimination of T-RFLP, determining microbial diversity by sequencing is recommended for indepth analysis of soil microbial diversity (Schöler et al., 2017; Vestergaard et al., 2017). Further progress may include defining what level of microorganism taxonomy, functional guild or abundance characterization best represent functional interactions in models of terrestrial ecosystems and biogeochemical processes at different scales.

Declarations of interest None. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.apsoil.2019.01.009. References Abiven, S., Menasseri, S., Chenu, C., 2009. The effects of organic inputs over time on soil aggregate stability - a literature analysis. Soil Biol. Biochem. 41, 1–12. https://doi. org/10.1016/j.soilbio.2008.09.015. Austin, E.E., Wickings, K., McDaniel, M.D., Robertson, G.P., Grandy, A.S., 2017. Cover crop root contributions to soil carbon in a no-till corn bioenergy cropping system. GCB Bioenergy 9, 1252–1263. https://doi.org/10.1111/gcbb.12428. Banning, N.C., Maccarone, L.D., Fisk, L.M., Murphy, D.V., 2015. Ammonia-oxidising bacteria not archaea dominate nitrification activity in semi-arid agricultural soil. Sci. Rep. 5, 11146. https://doi.org/10.1038/srep11146. Basche, A.D., Kaspar, T.C., Archontoulis, S.V., Jaynes, D.B., Sauer, T.J., Parkin, T.B., Miguez, F.E., 2016. Soil water improvements with the long-term use of a winter rye cover crop. Agric. Water Manag. 172, 40–50. https://doi.org/10.1016/j.agwat.2016. 04.006. Beauchamp, E.G., Seech, A.G., 1990. Denitrification with different sizes of soil aggregates obtained from dry-sieving and from sieving with water. Biol. Fertil. Soils 10, 188–193. https://doi.org/10.1007/BF00336134. Blackwood, C.B., Dell, C.J., Smucker, A.J.M., Paul, E.A., 2006. Eubacterial communities in different soil macroaggregate environments and cropping systems. Soil Biol. Biochem. 38, 720–728. https://doi.org/10.1016/j.soilbio.2005.07.006. Blanco-Canqui, H., Mikha, M.M., Presley, D.R., Claassen, M.M., 2011. Addition of cover crops enhances no-till potential for improving soil physical properties. Soil Sci. Soc. Am. J. 75, 1471. https://doi.org/10.2136/sssaj2010.0430. Blaud, A., Menon, M., van der Zaan, B., Lair, G.J., Banwart, S.A., 2017. Effects of dry and wet sieving of soil on identification and interpretation of microbial community composition. In: Advances in Agronomy, 1st ed. Elsevier Inc.. https://doi.org/10. 1016/bs.agron.2016.10.006. Bottinelli, N., Angers, D.A., Hallaire, V., Michot, D., Le Guillou, C., Cluzeau, D., Heddadj, D., Menasseri-Aubry, S., 2017. Tillage and fertilization practices affect soil aggregate stability in a humic Cambisol of Northwest France. Soil Tillage Res. https://doi.org/ 10.1016/j.still.2017.02.008. Braker, G., Conrad, R., 2011. Diversity, structure, and size of N2O-producing microbial communities in soils-what matters for their functioning? Adv. Appl. Microbiol. 75, 33–70. https://doi.org/10.1016/B978-0-12-387046-9.00002-5. Brennan, E.B., Acosta-Martinez, V., 2017. Cover cropping frequency is the main driver of soil microbial changes during six years of organic vegetable production. Soil Biol. Biochem. 109, 188–204. https://doi.org/10.1016/j.soilbio.2017.01.014. Bronick, C.J., Lal, R., 2005. Soil structure and management: a review. Geoderma 124, 3–22. https://doi.org/10.1016/j.geoderma.2004.03.005. Burgin, A.J., Groffman, P.M., Lewis, D.N., 2010. Factors regulating denitrification in a riparian wetland. Soil Sci. Soc. Am. J. 74, 1826. https://doi.org/10.2136/sssaj2009. 0463. Campiglia, E., Mancinelli, R., Di Felice, V., Radicetti, E., 2014. Long-term residual effects of the management of cover crop biomass on soil nitrogen and yield of endive (Cichorium endivia L.) and savoy cabbage (Brassica oleracea var. sabauda). Soil Tillage Res. 139, 1–7. https://doi.org/10.1016/j.still.2014.01.003. Carini, P., Marsden, P.J., Leff, J.W., Morgan, E.E., Strickland, M.S., Fierer, N., 2016. Relic DNA is abundant in soil and obscures estimates of soil microbial diversity. Nat. Microbiol. 2. https://doi.org/10.1038/nmicrobiol.2016.242. Chao, A., Chiu, C.-H., Jost, L., 2014. Unifying species diversity, phylogenetic diversity, functional diversity, and related similarity and differentiation measures through hill

5. Conclusions We found that microhabitats can select for genetically diverse microbial communities, as indicated by differences between nosZ T-RFLP analysis. The high responsiveness of nosZ gene abundance in the microaggregate fraction may indicate that this fraction mediates soil microbial response to crop management practices. We propose that further research clarify whether these differences reflect in denitrification rates, to define whether microaggregates form a preferential microhabitat that should be maintained in agricultural soils through 46

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Jost, L., 2006. Entropy and diversity. Oikos 113, 363–375. https://doi.org/10.1111/j. 2006.0030-1299.14714.x. Keene, C.L., Curran, W.S., Wallace, J.M., Ryan, M.R., Mirsky, S.B., VanGessel, M.J., Barbercheck, M.E., 2017. Cover crop termination timing is critical in organic rotational no-till systems. Agron. J. 109, 272. https://doi.org/10.2134/agronj2016.05. 0266. Kong, A.Y.Y., Six, J., 2010. Tracing root vs. residue carbon into soils from conventional and alternative cropping systems. Soil Sci. Soc. Am. J. 74, 1201. https://doi.org/10. 2136/sssaj2009.0346. Kong, A.Y.Y., Hristova, K., Scow, K.M., Six, J., 2010. Impacts of different N management regimes on nitrifier and denitrifier communities and N cycling in soil microenvironments. Soil Biol. Biochem. 42, 1523–1533. https://doi.org/10.1016/j.soilbio. 2010.05.021. Krauss, M., Krause, H.M., Spangler, S., Kandeler, E., Behrens, S., Kappler, A., Maeder, P., Gattinger, A., 2017. Tillage system affects fertilizer-induced nitrous oxide emissions. Biol. Fertil. Soils 53, 49–59. https://doi.org/10.1007/s00374-016-1152-2. Kravchenko, A.N., Wang, A.N.W., Smucker, A.J.M., Rivers, M.L., 2011. Long-term differences in tillage and land use affect intra-aggregate pore heterogeneity. Soil Sci. Soc. Am. J. 75, 1658. https://doi.org/10.2136/sssaj2011.0096. Kutznetsova, A., Brockhoff, P.B., Christensen, R.H.B., 2016. lmerTest: Tests in Linear Mixed Effects Models. R Package Version 2.0-33. Kuzyakov, Y., Blagodatskaya, E., 2015. Microbial hotspots and hot moments in soil: concept & review. Soil Biol. Biochem. 83, 184–199. https://doi.org/10.1016/j. soilbio.2015.01.025. Leite, M.F.A., Pan, Y., Bloem, J., Berge, Hein Tein, Kuramae, E.E., 2017. Organic nitrogen rearranges both structure and activity of the soil-borne microbial seedbank. Sci. Rep. 7, 42634. https://doi.org/10.1038/srep42634. Lennon, J.T., Jones, S.E., 2011. Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat. Rev. Microbiol. 9, 119–130. https://doi.org/10.1038/ nrmicro2504. Lensi, R., Clays-Josserand, A., Jocteur Monrozier, L., 1995. Denitrifiers and denitrifying activity in size fractions of a mollisol under permanent pasture and continuous cultivation. Soil Biol. Biochem. 27, 61–69. https://doi.org/10.1016/0038-0717(94) 00132-K. Lenth, R.V., 2016. Least-Squares Means: The R Package lsmeans. https://doi.org/10. 18637/jss.v069.i01. Levy-Booth, D.J., Prescott, C.E., Grayston, S.J., 2014. Microbial functional genes involved in nitrogen fixation, nitrification and denitrification in forest ecosystems. Soil Biol. Biochem. 75, 11–25. https://doi.org/10.1016/j.soilbio.2014.03.021. Lori, M., Symnaczik, S., Mäder, P., De Deyn, G., Gattinger, A., 2017. Organic farming enhances soil microbial abundance and activity–a meta-analysis and meta-regression. PLoS One 12, e0180442. https://doi.org/10.1371/journal.pone.0180442. Louis, B.P., Maron, P.A., Viaud, V., Leterme, P., Menasseri-Aubry, S., 2016. Soil C and N models that integrate microbial diversity. Environ. Chem. Lett. 14, 331–344. https:// doi.org/10.1007/s10311-016-0571-5. Luo, J., Tillman, R.W., White, R.E., Ball, P.R., 1998. Variation in denitrification activity with soil depth under pasture. Soil Biol. Biochem. 30, 897–903. https://doi.org/10. 1016/S0038-0717(97)00206-X. Lützow, M.V., Kögel-Knabner, I., Ekschmitt, K., Matzner, E., Guggenberger, G., Marschner, B., Flessa, H., 2006. Stabilization of organic matter in temperate soils: mechanisms and their relevance under different soil conditions - a review. Eur. J. Soil Sci. 57, 426–445. https://doi.org/10.1111/j.1365-2389.2006.00809.x. Marx, M.C., Kandeler, E., Wood, M., Wermbter, N., Jarvis, S.C., 2005. Exploring the enzymatic landscape: distribution and kinetics of hydrolytic enzymes in soil particlesize fractions. Soil Biol. Biochem. 37, 35–48. https://doi.org/10.1016/j.soilbio.2004. 05.024. Mayzelle, M.M., Krusor, M.L., Lajtha, K., Bowden, R.D., Six, J., 2014. Effects of detrital inputs and roots on carbon saturation deficit of a temperate forest soil. Soil Sci. Soc. Am. J. 78, S76. https://doi.org/10.2136/sssaj2013.09.0415nafsc. Miller, M.N., Zebarth, B.J., Dandie, C.E., Burton, D.L., Goyer, C., Trevors, J.T., 2009. Influence of liquid manure on soil denitrifier abundance, denitrification, and nitrous oxide emissions. Soil Sci. Soc. Am. J. 73, 760. https://doi.org/10.2136/sssaj2008. 0059. Monreal, C.M., Kodama, H., 1997. Influence of aggregate architecture and minerals on living habitats and soil organic matter. Can. J. Soil Sci. 77, 367–377. https://doi.org/ 10.4141/S95-063. Mummey, D.L., Stahl, P.D., 2004. Analysis of soil whole- and inner-microaggregate bacterial communities. Microb. Ecol. 48, 41–50. https://doi.org/10.1007/s00248003-1000-4. Nie, M., Pendall, E., Bell, C., Wallenstein, M.D., 2014. Soil aggregate size distribution mediates microbial climate change feedbacks. Soil Biol. Biochem. 68, 357–365. https://doi.org/10.1016/j.soilbio.2013.10.012. Oksanen, J., Blanchet, G., Simpson, G., Solymos, P., Stevens, M.H.H., Szoecs, E., Wagner, H., 2017. Vegan: Community Ecology Package. R Package Version-2.4-5. (https://CR). Oorts, K., Bossuyt, H., Labreuche, J., Merckx, R., Nicolardot, B., 2007. Carbon and nitrogen stocks in relation to organic matter fractions, aggregation and pore size distribution in no-tillage and conventional tillage in northern France. Eur. J. Soil Sci. 58, 248–259. https://doi.org/10.1111/j.1365-2389.2006.00832.x. Parkin, T.B., 1987. Soil microsites as a source of denitrification variability. Soil Sci. Soc. Am. J. 51, 1194. https://doi.org/10.2136/sssaj1987.03615995005100050019x. Pastorelli, R., Vignozzi, N., Landi, S., Piccolo, R., Orsini, R., Seddaiu, G., Roggero, P.P., Pagliai, M., 2013. Consequences on macroporosity and bacterial diversity of adopting ano-tillage farming system in a clayish soil of Central Italy. Soil Biol. Biochem. 66, 78–93. https://doi.org/10.1016/j.soilbio.2013.06.015. Philippot, L., Hallin, S., 2005. Finding the missing link between diversity and activity

numbers. Annu. Rev. Ecol. Evol. Syst. 45, 297–324. https://doi.org/10.1146/ annurev-ecolsys-120213-091540. Chivenge, P., Vanlauwe, B., Gentile, R., Six, J., 2011. Organic resource quality influences short-term aggregate dynamics and soil organic carbon and nitrogen accumulation. Soil Biol. Biochem. 43, 657–666. https://doi.org/10.1016/j.soilbio.2010.12.002. Clarke, K.R., 1993. Non-parametric multivariate analyses of changes in community structure. Austral Ecol. 18, 117–143. https://doi.org/10.1111/j.1442-9993.1993. tb00438.x. Cooper, J., Baranski, M., Stewart, G., Nobel-de Lange, M., Bàrberi, P., Fließbach, A., Peigné, J., Berner, A., Brock, C., Casagrande, M., Crowley, O., David, C., De Vliegher, A., Döring, T.F., Dupont, A., Entz, M., Grosse, M., Haase, T., Halde, C., Hammerl, V., Huiting, H., Leithold, G., Messmer, M., Schloter, M., Sukkel, W., van der Heijden, M.G.A., Willekens, K., Wittwer, R., Mäder, P., 2016. Shallow non-inversion tillage in organic farming maintains crop yields and increases soil C stocks: a meta-analysis. Agron. Sustain. Dev. 36, 22. https://doi.org/10.1007/s13593-016-0354-1. Culman, S.W., Bukowski, R., Gauch, H.G., Cadillo-Quiroz, H., Buckley, D.H., 2009. TREX: software for the processing and analysis of T-RFLP data. BMC Bioinformatics 10, 171. https://doi.org/10.1186/1471-2105-10-171. Dambreville, C., Hallet, S., Nguyen, C., Morvan, T., Germon, J.-C., Philippot, L., 2006. Structure and activity of the denitrifying community in a maize-cropped field fertilized with composted pig manure or ammonium nitrate. FEMS Microbiol. Ecol. 56, 119–131. https://doi.org/10.1111/j.1574-6941.2006.00064.x. Denef, K., Zotarelli, L., Boddey, R.M., Six, J., 2007. Microaggregate-associated carbon as a diagnostic fraction for management-induced changes in soil organic carbon in two Oxisols. Soil Biol. Biochem. 39, 1165–1172. https://doi.org/10.1016/j.soilbio.2006. 12.024. Deslippe, J.R., Jamali, H., Jha, N., Saggar, S., 2014. Denitrifier community size, structure and activity along a gradient of pasture to riparian soils. Soil Biol. Biochem. 71, 48–60. https://doi.org/10.1016/j.soilbio.2014.01.007. Dong, W., Liu, E., Yan, C., Tian, J., Zhang, H., Zhang, Y., 2017. Impact of no tillage vs. conventional tillage on the soil bacterial community structure in a winter wheat cropping succession in northern China. Eur. J. Soil Biol. 80, 35–42. https://doi.org/ 10.1016/j.ejsobi.2017.03.001. Ebrahimi, A., Or, D., 2016. Microbial community dynamics in soil aggregates shape biogeochemical gas fluxes from soil profiles - upscaling an aggregate biophysical model. Glob. Chang. Biol. 22, 3141–3156. https://doi.org/10.1111/gcb.13345. Elliott, E.T., 1986. Aggregate structure and carbon, nitrogen, and phosphorus in native and cultivated soils. Soil Sci. Soc. Am. J. 50, 627. https://doi.org/10.2136/sssaj1986. 03615995005000030017x. Enwall, K., Philippot, L., Hallin, S., 2005. Activity and composition of the denitrifying bacterial community respond differently to long-term fertilization. Appl. Environ. Microbiol. 71, 8335–8343. https://doi.org/10.1128/AEM.71.12.8335-8343.2005. Gao, J., Xie, Y., Jin, H., Liu, Y., Bai, X., Ma, D., Zhu, Y., Wang, C., Guo, T., 2016. Nitrous oxide emission and denitrifier abundance in two agricultural soils amended with crop residues and urea in the North China Plain. PLoS One 11, 1–15. https://doi.org/10. 1371/journal.pone.0154773. Groffman, P.M., 2012. Terrestrial denitrification: challenges and opportunities. Ecol. Process. 1, 11. https://doi.org/10.1186/2192-1709-1-11. Groffman, P.M., Butterbach-Bahl, K., Fulweiler, R.W., Gold, A.J., Morse, J.L., Stander, E.K., Tague, C., Tonitto, C., Vidon, P., 2009. Challenges to incorporating spatially and temporally explicit phenomena (hotspots and hot moments) in denitrification models. Biogeochemistry 93, 49–77. https://doi.org/10.1007/s10533-008-9277-5. Hallin, S., Welsh, A., Stenström, J., Hallet, S., Enwall, K., Bru, D., Philippot, L., 2012. Soil functional operating range linked to microbial biodiversity and community composition using Denitrifiers as model guild. PLoS One 7, e51962. https://doi.org/10. 1371/journal.pone.0051962. Hallin, S., Philippot, L., Löffler, F.E., Sanford, R.A., Jones, C.M., 2017. Genomics and ecology of novel N2O-reducing microorganisms. Trends Microbiol. https://doi.org/ 10.1016/j.tim.2017.07.003. Hartwig, N.L., Ammon, H.U., 2002. Cover crops and living mulches. Weed Sci. 50, 688–699. https://doi.org/10.1614/0043-1745(2002)050[0688:AIACCA]2.0.CO;2. Henry, S., Bru, D., Stres, B., Hallet, S., Philippot, L., 2006. Quantitative detection of the nosZ gene, encoding nitrous oxide reductase, and comparison of the abundances of 16S rRNA, narG, nirK, and nosZ genes in soils. Appl. Environ. Microbiol. 72, 5181–5189. https://doi.org/10.1128/AEM.00231-06. Hossain, S., Bergkvist, G., Glinwood, R., Berglund, K., Mårtensson, A., Hallin, S., Persson, P., 2015. Brassicaceae cover crops reduce Aphanomyces pea root rot without suppressing genetic potential of microbial nitrogen cycling. Plant Soil 392, 227–238. https://doi.org/10.1007/s11104-015-2456-y. IUSS Working Group, WRB, 2014. IUSS: World reference base for soil resources 2014: international soil classification system for naming soils and creating legends for soil maps. In: World Soil Resources Reports. no. 106 (Rome). Jiao, Y., Whalen, J.K., Hendershot, W.H., 2006. No-tillage and manure applications increase aggregation and improve nutrient retention in a sandy-loam soil. Geoderma 134, 24–33. https://doi.org/10.1016/j.geoderma.2005.08.012. Jin, Z. Jiang, Li, L. Qing, Liu, X. Yu, Pan, G. Xing, Qaiser, H., Liu, Y. Zhuo, 2014. Impact of long-term fertilization on community structure of ammonia oxidizing and denitrifying bacteria based on amoA and nirK genes in a rice paddy from Tai lake region, China. J. Integr. Agric. 13, 2286–2298. https://doi.org/10.1016/S2095-3119(14) 60784-X. Johnson, D.W., Glass, D.W., Murphy, J.D., Stein, C.M., Miller, W.W., 2010. Nutrient hot spots in some sierra Nevada forest soils. Biogeochemistry 101, 93–103. https://doi. org/10.1007/s10533-010-9423-8. Jones, C.M., Graf, D.R., Bru, D., Philippot, L., Hallin, S., 2013. The unaccounted yet abundant nitrous oxide-reducing microbial community: a potential nitrous oxide sink. ISME J. 7, 417–426. https://doi.org/10.1038/ismej.2012.125.

47

Applied Soil Ecology 137 (2019) 39–48

V. Loaiza Puerta et al.

Sheehy, J., Regina, K., Alakukku, L., Six, J., 2015. Impact of no-till and reduced tillage on aggregation and aggregate-associated carbon in northern European agroecosystems. Soil Tillage Res. 150, 107–113. https://doi.org/10.1016/j.still.2015.01.015. Singman, H., Bolker, B., Westfall, J., Aust, J., 2018. afex: Analysis of Factorial Experiments. R Package Version 0.21-2. https://CRAN.R-project.org/. Six, J., Elliott, E.T., Paustian, K., 1999. Aggregate and soil organic matter dynamics under conventional and no-tillage systems. Soil Sci. Soc. Am. J. 63, 1350. https://doi.org/ 10.2136/sssaj1999.6351350x. Six, J., Elliot, E.T., Paustian, K., 2000. Soil microaggregate turnover and microaggregate formation: a mechanism for C organic under no-tillage agriculture. Soil Biol. Biochem. 32, 2099–2103. Six, J., Conant, R.T., Paul, E.a., Paustian, K., 2002. Stabilization mechanisms of soil organic matter: implications for C-saturation of soils. Plant Soil 241, 155–176. https:// doi.org/10.1023/A:1016125726789. Six, J., Bossuyt, H., Degryze, S., Denef, K., 2004. A history of research on the link between (micro)aggregates, soil biota, and soil organic matter dynamics. Soil Tillage Res. 79, 7–31. https://doi.org/10.1016/j.still.2004.03.008. Thorup-Kristensen, K., Magid, J., Jensen, L.S., 2001. Catch crops and green manures as biological tools in nitrogen management in temperate zones. Adv. Agron. 79, 227–302. https://doi.org/10.1016/S0065-2113(02)79005-6. Tiedje, J.M., Sexstone, A.J., Parkin, T.B., Revsbech, N.P., 1984. Anaerobic processes in soil. Plant Soil 76, 197–212. https://doi.org/10.1007/BF02205580. Tonitto, C., David, M.B., Drinkwater, L.E., 2006. Replacing bare fallows with cover crops in fertilizer-intensive cropping systems: a meta-analysis of crop yield and N dynamics. Agric. Ecosyst. Environ. 112, 58–72. https://doi.org/10.1016/j.agee.2005. 07.003. Vestergaard, G., Schulz, S., Schöler, A., Schloter, M., 2017. Making big data smart—how to use metagenomics to understand soil quality. Biol. Fertil. Soils 53, 479–484. https://doi.org/10.1007/s00374-017-1191-3. Villamil, M.B., Bollero, G.A., Darmody, R.G., Simmons, F.W., Bullock, D.G., 2006. No-till corn/soybean systems including winter cover crops. Soil Sci. Soc. Am. J. 70, 1936. https://doi.org/10.2136/sssaj2005.0350. Vos, M., Wolf, A.B., Jennings, S.J., Kowalchuk, G.A., 2013. Micro-scale determinants of bacterial diversity in soil. FEMS Microbiol. Rev. https://doi.org/10.1111/1574-6976. 12023. Wawrik, B., Kerkhof, L., Kukor, J., Zylstra, G., 2005. Effect of different carbon sources on community composition of bacterial enrichments from soil. Appl. Environ. Microbiol. 71, 6776–6783. https://doi.org/10.1128/AEM.71.11.6776-6783.2005. White, C.M., DuPont, S.T., Hautau, M., Hartman, D., Finney, D.M., Bradley, B., LaChance, J.C., Kaye, J.P., 2017. Managing the trade off between nitrogen supply and retention with cover crop mixtures. Agric. Ecosyst. Environ. 237, 121–133. https://doi.org/10. 1016/j.agee.2016.12.016. Wittwer, R., Dorn, B., Jossi, W., Van Der Heijden, M.G.A., 2017. Cover crops support ecological intensification of arable cropping systems. Sci. Rep. 7, 1–12. https://doi. org/10.1038/srep41911. Xu, M., Schnorr, J., Keibler, B., Simon, H.M., 2012. Comparative analysis of 16S rRNA and amoA genes from archaea selected with organic and inorganic amendments in enrichment culture. Appl. Environ. Microbiol. 78, 2137–2146. https://doi.org/10. 1128/AEM.06845-11. Zuber, S.M., Villamil, M.B., 2016. Meta-analysis approach to assess effect of tillage on microbial biomass and enzyme activities. Soil Biol. Biochem. 97, 176–187. https:// doi.org/10.1016/j.soilbio.2016.03.011. Zumft, W.G., 1997. Cell biology and molecular basis of denitrification. Microbiol. Mol. Biol. Rev. 61, 533–616.

using denitrifying bacteria as a model functional community. Curr. Opin. Microbiol. 8, 234–239. https://doi.org/10.1016/j.mib.2005.04.003. Poeplau, C., Don, A., 2014. Carbon sequestration in agricultural soils via cultivation of cover crops - a meta-analysis. Agric. Ecosyst. Environ. 200, 33–41. https://doi.org/ 10.1016/j.agee.2014.10.024. Pujol Pereira, E.I., Chung, H., Scow, K., Six, J., 2013. Microbial communities and soil structure are affected by reduced precipitation, but not by elevated carbon dioxide. Soil Sci. Soc. Am. J. 77, 482. https://doi.org/10.2136/sssaj2012.0218. Qi, Z., Helmers, M.J., Kaleita, A.L., 2011. Soil water dynamics under various agricultural land covers on a subsurface drained field in north-central Iowa, USA. Agric. Water Manag. 98, 665–674. https://doi.org/10.1016/j.agwat.2010.11.004. R Development Core Team, 2017. R: a Language and Environment for Statistical Computing. R Found. Stat. Comput., Vienna, Austria ISBN 3-900051-07-0. http:// www.R-project.org/. Rabbi, S.M.F., Daniel, H., Lockwood, P.V., Macdonald, C., Pereg, L., Tighe, M., Wilson, B.R., Young, I.M., 2016. Physical soil architectural traits are functionally linked to carbon decomposition and bacterial diversity. Sci. Rep. 6, 33012. https://doi.org/10. 1038/srep33012. Rees, G.N., Baldwin, D.S., Watson, G.O., Perryman, S., Nielsen, D.L., 2005. Ordination and significance testing of microbial community composition derived from terminal restriction fragment length polymorphisms: application of multivariate statistics. Antonie Van Leeuwenhoek 86, 339–347. https://doi.org/10.1007/s10482-0050498-5. Richner, W., Sinaj, S., 2017. Grundlagen für die Düngung landwirtschaftlicher Kulturen in der Schweiz (GRUD 2017). 8(6). Sepzialpublikation, Agraforsch. Schweiz, pp. 276 (S). Rocca, J.D., Hall, E.K., Lennon, J.T., Evans, S.E., Waldrop, M.P., Cotner, J.B., Nemergut, D.R., Graham, E.B., Wallenstein, M.D., 2015. Relationships between protein-encoding gene abundance and corresponding process are commonly assumed yet rarely observed. ISME J. 9, 1693–1699. https://doi.org/10.1038/ismej.2014.252. Rorick, J.D., Kladivko, E.J., 2017. Cereal rye cover crop effects on soil carbon and physical properties in southeastern Indiana. J. Soil Water Conserv. 72, 260–265. https:// doi.org/10.2489/jswc.72.3.260. Rudisill, M.A., Turco, R.F., Hoagland, L.A., 2016. Fertility practices and rhizosphere effects alter ammonia oxidizer community structure and potential nitrification activity in pepper production soils. Appl. Soil Ecol. 99, 70–77. https://doi.org/10.1016/j. apsoil.2015.10.011. Sainju, U.M., Schomberg, H.H., Singh, B.P., Whitehead, W.F., Tillman, P.G., LachnichtWeyers, S.L., 2007. Cover crop effect on soil carbon fractions under conservation tillage cotton. Soil Tillage Res. 96, 205–218. https://doi.org/10.1016/j.still.2007.06. 006. Sanford, R. a, Wagner, D.D., Wu, Q., Chee-Sanford, J.C., Thomas, S.H., Cruz-García, C., Rodríguez, G., Massol-Deyá, A., Krishnani, K.K., Ritalahti, K.M., Nissen, S., Konstantinidis, K.T., Löffler, F.E., 2012. Unexpected nondenitrifier nitrous oxide reductase gene diversity and abundance in soils. Proc. Natl. Acad. Sci. U. S. A. 109, 19709–19714. https://doi.org/10.1073/pnas.1211238109. Schöler, A., Jacquiod, S., Vestergaard, G., Schulz, S., Schloter, M., 2017. Analysis of soil microbial communities based on amplicon sequencing of marker genes. Biol. Fertil. Soils 53, 485–489. https://doi.org/10.1007/s00374-017-1205-1. Sexstone, A.J., Revsbech, N.P., Parkin, T.B., Tiedje, J.M., 1985. Direct measurement of oxygen profiles and denitrification rates in soil aggregates. Soil Sci. Soc. Am. J. 49, 645–651. https://doi.org/10.2136/sssaj1985.03615995004900030024x. Shannon, C.E., 1948. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x.

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