Effect of tillage and static abiotic soil properties on microbial diversity

Applied Soil Ecology xxx (xxxx) xxx–xxx

Contents lists available at ScienceDirect

Applied Soil Ecology journal homepage: www.elsevier.com/locate/apsoil

Effect of tillage and static abiotic soil properties on microbial diversity ⁎

Fabienne Legranda,b, Adeline Picota, , José Francisco Cobo-Díaza, Matthieu Carofc, Wen Chend, Gaétan Le Flocha a

Université de Brest, EA 3882, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, IBSAM, ESIAB, Technopôle Brest-Iroise, 29280 Plouzané, France Lallemand SAS, 4 route de Beaupuy, 31180 Castelmaurou, France SAS, AGROCAMPUS OUEST, INRA, 35042 Rennes, France d Ottawa Research & Development Centre, Science & Technology Branch, Agriculture and Agri-Food Canada, 960 Carling Ave., Ottawa, ON K1A 0C6, Canada b c

A R T I C LE I N FO

A B S T R A C T

Keywords: Metabarcoding Soil microbiota Tillage Fertilization Bioindicators

Agricultural soil microbial communities are largely impacted by agronomic practices, soil physicochemical properties and climatic conditions. To understand how these factors induce changes in fungal and bacterial communities in soil, we used a metabarcoding approach to profile the microbial communities of 31 agricultural wheat fields with maize as previous crop, representative of usual crop rotation in Brittany, France. Our results clearly highlighted the importance of tillage for both bacteria and fungi, with species richness and evenness significantly higher in fields under minimum tillage practices than in fields under conventional tillage, despite that the core microbiota was similar between fields under these two practices. The functional diversity of the bacterial communities, predicted using FAPROTAX, was also significantly higher in fields under minimum tillage notably that involved in nitrogen cycling (denitrification, respiration). We also observed that animal manure increased bacterial richness and evenness compared to chemical fertilization only. Interestingly, fungal diversity was less sensitive to disturbance than bacterial communities. We also identified taxa groups as potential bioindicators of a specific agronomic practice, such as the strong association between animal manure application and Hydrogenophaga, Mycoplana and Sphingopyxis, as well as the enrichment of oligotrophic Acidobacteria under conventional tillage.

1. Introduction Soil is of primary importance for food security and considered as a non-renewable resource (Janvier et al., 2007; Stavi and Lal, 2015; Verbruggen et al., 2010). The diversity and composition of the soil microbiota is directly related to its function, structure and aggregation (Bender et al., 2016; Wakelin et al., 2008). Soil microbial communities are essential to vital biogeochemical cycles, with prokaryotes contributing to the cycling of all fundamental life elements, especially in nitrogen fixation and methanogenesis (Franche et al., 2009; Lin and Lu, 2015; Miransari, 2011; Serrano-Silva et al., 2014). Fungi also contribute to the biodegradation, biodeterioration and fermentation of organic matter (Qiu et al., 2018; Rineau et al., 2012). Moreover, the phytomicrobiota, which encompasses microbes associated with plants, plays beneficial or detrimental roles to the plants by affecting root development, nutrient requisition, growth and resistance to abiotic and biotic stresses (Adesemoye and Kloepper, 2009). In-depth understanding of the microbial diversity and soil function will assist in improving beneficial ecosystem services and reduce dependency on agro⁎

chemicals (Bakker et al., 2010; Compant et al., 2005). Studies aiming to understand the impact of the soil biodiversity on carbon cycling have been reviewed by Nielsen et al. (2011). The authors concluded that a shift in the microbial community composition and the loss of functional groups such as the C-cycling microorganisms is more likely to affect the carbon cycle than a decrease in species richness resulting from a global change. Similar observation was made by Philippot et al. (2013), who used soil dilution as a means of reducing overall species richness and resulted in a decreased denitrification process rates; which was attributed to the loss of microorganisms involved in the denitrification process. Such findings may be best explained by the functional redundancy in soil microbiota, meaning that several species take part in the same process and ultimately, changes in species richness may not necessarily affect the net ecosystem functioning (Loreau, 2004). These studies further suggest that the ecosystem process is determined by an equilibrium between species identity, community composition and species richness, and consequently environmental factors that can strongly affect the microbiota diversity and composition also influence the ecosystem process and functioning

Corresponding author. E-mail address: [email protected] (A. Picot).

https://doi.org/10.1016/j.apsoil.2018.08.016 Received 14 February 2018; Received in revised form 16 July 2018; Accepted 24 August 2018 0929-1393/ Crown Copyright © 2018 Published by Elsevier B.V. All rights reserved.

Please cite this article as: Legrand, F., Applied Soil Ecology, https://doi.org/10.1016/j.apsoil.2018.08.016

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the rotations. During 2012–2013, the antepenultimate crop in the rotation of AB04, AB09 and AB24 was potato whereas that of AB11 and AB30 was rape. Maize residues were always removed from the fields to feed livestock. The remaining stubbles and roots were incorporated to the soil by ploughing (referred to as conventional tillage) or by scraping the surface to a depth of 10 cm (referred to as minimum tillage). Brittany is a windy region surrounded by the Atlantic Ocean at the south and the English Channel at the north. The region is defined by different microclimates, and the fields were categorized in 3 climate types including coastal, inner and mountain climates. The coastal climate is characterized by windy, fresh summers and mild winters (mean annual temperature: 12–12.5 °C; rainfall: 900–1100 mm). The inner climate is characterized by ocean climate type with humid and fresh winters, mild summers (mean annual temperature: 12–12.5 °C; rainfall: 700–1100 mm). The mountain climate (Monts d’Arrée) is characterized by fresh summers, cold winters and higher rainfall (mean annual temperature: 11–12 °C; rainfall: 700–1100 mm). Soil parent material and soil type was determined using soil data available on GéoSAS (http://geowww.agrocampus-ouest.fr), an open geography portal developed by the Joint Research Unit “Soil Agro- and hydro-systems, Spatialization” (UMR SAS) of AGROCAMPUS OUEST (the French Institute for Life, Food and Horticultural Sciences and Landscaping) and INRA (the French National Institute for Agricultural Research). Soil data are available at the 1:250,000 scale as described in Lacoste et al. (2011). Information on the crop management practices in 2015, including soil tillage, fertilization, sowing date, previous crops over 3 years, and climate conditions are described in Table 1.

(Peter et al., 2011; Tilman et al., 1997). For example, several studies, described below, investigated the importance of crop rotations and crop management systems, including soil tillage, intercropping and fertilization, in the modulation of soil physicochemical properties and shifts in microbial communities, thus affecting microbial communities and soil functionality. It was reported that organic fertilization and reduced tillage promote microbial diversity (Wang et al., 2017), while an increase of soil pH was reported to be associated with the enrichment of Acidobacteria and the depletion of Alpha-proteobacteria (Lauber et al., 2008). Long-term fertilization with animal waste, such as poultry litter, was shown to induce changes in soil chemistry, concurrently with an increase in bacterial diversity (Jangid et al., 2008) and specifically Bacteroidetes taxa (Ashworth et al., 2017). In the latter study, the authors also observed the importance of the crop rotation with a reduced bacterial diversity and activity in fields under cotton monoculture compared to other continuous monocultures and corn-soybean rotations. However, this result was attributed to a heavy use of pesticides to protect cotton crops. Overall, monitoring microbial biomass as well as community and functional diversity, thus, helps improve our understanding of the impact of specific factors on soil ecosystems. It is also of interest to identify bioindicators, e.g. specific microbes or functional genes, which are strongly associated with soil health and fertility, or soil recovery after a disturbance (Bouchez et al., 2016). The objective of this study was to investigate the influence of local agronomic practices and soil physicochemical properties on the soil microbiota of agricultural fields in Brittany, France. Soil samples were collected from 31 agricultural fields; of which, the diversity and composition of the bacterial and fungal communities as well as bacterial functions were determined using a metabarcoding approach (Illumina MiSeq PE300). With the development of bioinformatics tools and databases (Louca et al., 2016; Xu et al., 2014; Langille et al., 2013), metabarcoding data have been widely used for not only profiling microbial community compositional structures, but also for predicting the functionality of community members in various environmental niches (Bouchez et al., 2016; Tedersoo et al., 2015; Langille et al., 2013; Louca et al., 2016). Our study focused on the changes of the bacterial and fungal communities under the impacts of climate type, soil tillage (conventional vs. minimum tillage), the type of fertilization (chemical fertilizers and/or animal manure), soil physicochemical properties (structure, pH and metal contents) and nutrient availability (carbon, nitrogen, cation-exchange capacity, soil saturation). The ultimate objective was to identify which of these factors may play a key role in determining the composition and functionality of the soil microbiota in agricultural fields under intensive management.

2.2. Soil sampling A composite sampling method was used with 20 soil samples per field for DNA extraction and 10 soil samples per field for soil physicochemical analysis, collected at different times (April 2015 for DNA sequencing and August 2015 for soil analysis). For DNA sequencing, 20 samples were collected from the bulk topsoil (0–5 cm; Ø 5.5 cm) along a 100 m by 100 m square, every 20 m. Soil samples were homogenized with animals, stones, roots and plant residues being manually removed, and then stored at −80 °C before DNA extraction. For physicochemical analysis, 10 samples were collected in each field around a 100-m diameter circle at 30 cm depth. Samples were homogenized and 500 g were transported to the laboratory the same day for physicochemical analysis performed by Capinov (Landerneau, FR) according to the French standards NF ISO 11464, NF ISO 10,390 (pH), NF ISO 10,694 (carbon) NF ISO 13,878 (nitrogen) and NF X 31–107 (soil texture). After soil homogenization, 3 repetitions of DNA extraction and soil physicochemical analysis were performed per field. Table 2 summarizes the soil physicochemical properties of each field.

2. Material and methods 2.1. Sampling fields All agricultural fields (n = 31) (Tables 1 and 2) belonged to farmers who breed livestock (either pig, cattle or poultry), except for field AB04 which was an experimental field of a research center. A two-level design was used to study the influence of soil tillage (conventional vs. minimum tillage) on soil microbial communities. Additional factors were also taken into account, including type of fertilization, climate, wheat cultivar, previous crops, sowing date (Table 1) and soil physicochemical characteristics (Table 2). The effect of each of these factors was studied independently. Wheat was cultivated the year of the study (2015) with maize as previous crop (2014), except for field AB28, in which previous crop was oat. All plots were mostly under exclusive cereal (wheat/maize) rotation except in 7 fields, including AB01, AB04, AB09, AB11, AB21, AB24 and AB30, which also included a non-cereal crop in their rotation. In brief, during the 2013–2014 growing season, a vegetation cover was cultivated in AB01 (mixed cover) and AB04 (phacelia) and a mix of spinach and beans were cultivated in AB21 as the penultimate crops in

2.3. DNA extraction and Illumina MiSeq sequencing Total DNA of each soil sample was extracted in triplicate from 3 × 1 g fresh soil using the NucleoSpin® Kit for Soil (Macherey-Nagel, Dueren, De) according to the manufacturer’s instructions. DNA Quality and concentration were determined using a UV Spectrophotometer (Nanodrop1000, Thermo Scientific, US) and DNA was stored at −20 °C until use. Preparation of 16S and ITS libraries, and Illumina MiSeq 300PE sequencing were performed at the McGill University and Génome Québec Innovation Centre, Montreal, Canada. The variable regions V3-V4 of the bacterial 16S rRNA gene and the Internal Transcribed Spacer (ITS) region of fungi were amplified using primer pairs 341F/805R (Herlemann et al., 2011) and ITS1F/ITS4 (Gardes and Bruns, 1993; White et al., 1990), respectively.

2

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Table 1 Location, climate type, farm and field characteristics, main crop management practices in 2015 and previous crops over 3 years for the 31 fields. Field number

†

Climate

Farm type

Soil parental material

Soil tillage

†

Wheat cultivar

Sowing date

Type of fertilisers

Number of nitrogen applications

Previous crops 2014

2013

2012 Barley Maize Maize

Normal

Manure

3

Maize

CT‡ MT

Cellule/ Armada Cellule Cellule

Normal Normal

Chemical CM§

2 2

Maize Maize

Mixed Cover Wheat Wheat

CT MT

Cellule Fluor

Normal Normal

Chemical CM

2 3

Maize Maize

Phacelia Wheat

Potato Maize

CT CT CT CT CT

Fluor Boregar Fluor Barok Expert

Normal Normal Later Later Normal

CM CM CM CM Manure

2 3 2 3 2

Maize Maize Maize Maize Maize

Wheat Wheat Wheat Wheat Cereal

Maize Maize Maize Potato Cereal

MT

Normal

CM

3

Maize

Wheat

Rape

MT CT MT

Cellule/ Rubisko Hyfi Matheo Unknown

Normal Normal Normal

Chemical CM CM

3 3 3

Maize Maize Maize

Barley Cereal Cereal

Oats Cereal Cereal

MT CT CT CT MT

Matheo Fluor Hybrid Fluor Bergamo

Earlier Normal Later Later Normal

CM ?# CM CM ?

3 ? 2 3 ?

Maize Maize Maize Maize Maize

Wheat Cereal Maize Wheat Cereal

Maize Cereal Maize Maize Cereal

Cambisol Cambisol

CT CT

Palledor Rubisko

Later Normal

CM Chemical

3 3

Maize Maize

Maize Maize

Cambisol

CT

Barok

Normal

CM

3

Maize

Wheat Spinash/ Bean Barley

Cambisol Cambisol

CT CT

Cellule Cellule

Earlier Later

CM CM

? 2

Maize Maize

Cereal Wheat

Cereal Potato

CT

Fluor

Earlier

Chemical

2

Maize

Wheat

Maize

Granite/gneiss

Luvic Cambisol Cambisol

CT

Normal

CM

4

Maize

Maize

Maize

Granite/gneiss Granite/gneiss Granite/gneiss Granite/gneiss Aeolian silt

Cambisol Cambisol Cambisol Cambisol Cambisol

CT CT CT MT CT

Rubisko/ Bergamo Cellule Cellule Tulip Barok Rubisko

Later Later Normal Normal Normal

Chemical Chemical Chemical CM CM

3 1 2 3 3

Maize Oats Maize Maize Maize

Maize Maize Cereal Maize Wheat

Maize Maize Cereal Rape Maize

AB01

Coastal

Pig

Aeolian silt

AB02 AB03

Mountain Inner

Pig Pig

Granite/gneiss Aeolian silt

AB04 AB05

Mountain Inner

Research Pig

Granite/gneiss Aeolian silt

AB06 AB07 AB08 AB09 AB10

Mountain Coastal Coastal Mountain Mountain

Pig Pig Pig Pig Pig

AB11

Inner

Pig

Granite/gneiss Granite/gneiss Granite/gneiss Granite/gneiss Schist with quartzite Aeolian silt

AB12 AB13 AB14

Mountain Inner Inner

Poultry Pig Pig

Slate Granite/gneiss Aeolian silt

AB15 AB16 AB17 AB18 AB19

Inner Inner Coastal Mountain Inner

Pig Cattle Pig Pig Pig

AB20 AB21

Mountain Mountain

Pig Pig

Granite/gneiss Granite/gneiss Granite/gneiss Granite/gneiss Schiste briovérien Granite/gneiss Micaschist

AB22

Mountain

Pig

AB23 AB24

Coastal Coastal

Pig/Cattle Pig

AB25

Coastal

Pig

Schist with quartzite Granite/gneiss Schist with quartzite Aeolian silt

AB26

Coastal

Pig/Cattle

AB27 AB28 AB29 AB30 AB31

Mountain Mountain Mountain Inner Mountain

Pig Cattle Pig Pig Pig

MT: Minimum Tillage;

Soil type

Luvic Cambisol Cambisol Luvic Cambisol Cambisol Luvic Cambisol Cambisol Cambisol Cambisol Cambisol Cambisol Luvic Cambisol Cambisol Cambisol Luvic Cambisol Cambisol Cambisol Cambisol Cambisol Cambisol

MT

Wheat

‡C

T: Conventional Tillage; §CM: Chemicals and Manure; #?: Unknown.

ITSx (version 1.0.11) (Bengtsson-Palme et al., 2013) and concatenated using a homemade script. This new fasta file, with ITS1 and ITS2 concatenated, was utilized for OTU picking using the QIIME script pick_open_reference_otus.py, with BLAST (Altschul et al., 1990) as taxonomic assignment method and a modified database (ITS1 and ITS2 sequence region extracted by ITSx and concatenated) from UNITE plus INSD non-redundant ITS database version 7.1 (Kõljalg et al., 2013). The taxonomy for fungi known to have both sexual and asexual stages was replaced by accepted names using customized perl script (Chen et al., 2018). Singletons were removed from both ITS and 16S OTUs. Unassigned OTUs and OTUs assigned to archaea, chloroplast, mitochondria and plants were also removed.

2.4. Processing of sequencing data Sequencing data were processed using QIIME (Quantitative Insights Into Microbial Ecology, version 1.9.1) (Caporaso et al., 2010). For 16S rDNA V3-V4 amplicons, the forward (R1) and reverse (R2) paired-end sequences were joined using multiple_join_paired_ends.py, followed by multiple_split_libraries_fastq.py for demultiplexing. Chimera sequences were removed using UCHIME algorithm (Edgar et al., 2011) implemented in vsearch v1.1.3 (https://github.com/torognes/vsearch) against the ChimeraSlayer database (Haas et al., 2011). Pick open strategy was used to cluster the sequences into Operational Taxonomic Units (OTUs) at 97% similarity cut-off using pick_open_reference_otus.py. The taxonomic assignment was performed using UCLUST algorithm (Edgar, 2010) against GreenGenes v13_8 database preclustered at 97% similarity cutoff (McDonald et al., 2012). The R1 and R2 paired-end sequencing reads of ITS amplicons were processed independently using multiple_split_libraries_fastq.py. Chimeric sequences were identified and removed using UCHIME algorithm (Edgar et al., 2011) implemented in vsearch v1.1.3 (https://github. com/torognes/vsearch) against the UNITE/INSDC referenced database version 7.0 (Nilsson et al., 2015). The ITS1 and ITS2 regions were extracted from forward (R1) and reverse (R2) paired-end sequences using

2.5. Data analysis Statistical analyses and plotting were carried out in R environment (R Development Core Team, 2008). Alpha-diversity analysis was carried out using OTU.diversity function in RAM package (version 1.2.1.7, Chen et al., 2016). Pairwise test for multiple comparisons of mean rank sums (Kruskal Nemenyi Tests) was performed to determine significant differences in OTUs abundance and alpha-diversity indices. OTUs were Hellinger-transformed using decostand function in vegan package for 3

4

11.46 14.78 12.49 14.19 15.29 13.52 18.43 14.35 11.97 16.72 16.86 18.74 15.20 17.05 14.18 12.33 12.66 14.50 17.48 13.39 18.35 18.15 14.76 19.28 14.32 15.99 17.17 17.23 14.36 14.94 14.24

%

Clay†

17.19 19.11 18.42 22.46 16.13 24.46 21.50 18.19 21.57 29.39 37.30 45.18 22.85 36.63 18.19 17.58 17.70 23.45 33.51 20.72 20.63 25.60 17.36 27.34 17.22 17.24 21.60 14.60 21.86 22.40 22.87

Fine silt‡

43.44 28.62 46.51 43.11 23.04 40.24 25.11 46.15 34.09 34.19 32.67 14.24 24.12 28.74 37.82 15.22 24.79 36.64 30.59 44.31 15.64 37.78 38.88 28.98 37.86 29.36 17.82 12.71 39.12 23.05 41.53

Coarse silt§

15.63 11.72 18.83 10.28 27.85 9.01 14.19 13.72 9.49 6.82 6.95 9.53 8.61 7.05 19.42 11.79 14.06 8.29 10.51 10.10 10.75 9.44 17.57 11.87 16.60 19.44 20.84 17.96 10.72 7.25 9.92

Fine sand#

12.29 25.77 3.75 9.96 17.70 12.77 20.76 7.59 22.87 12.89 6.22 12.31 29.22 10.53 10.38 43.09 30.79 17.12 7.91 11.48 34.64 9.02 11.43 12.53 14.00 17.97 22.57 37.50 13.93 32.36 11.43

Coarse sand††

6.70 6.50 6.80 6.40 7.40 6.40 6.90 6.90 6.20 6.30 6.40 7.00 6.50 6.50 6.70 5.80 6.20 6.50 6.40 6.40 6.70 6.30 7.20 6.60 7.20 6.30 6.20 6.80 6.30 6.70 6.30

pH −1

13.60 20.20 13.90 21.60 17.50 29.50 23.60 14.80 26.40 24.20 22.90 20.80 21.20 21.70 10.60 23.60 24.80 23.10 17.00 25.20 27.60 19.60 13.40 36.20 13.30 15.80 27.60 18.60 25.50 29.30 23.10

g kg

C

1.47 2.13 1.54 2.17 1.72 2.74 2.63 1.62 2.63 2.63 2.31 2.56 2.07 2.25 1.15 2.05 2.57 2.38 1.77 2.71 2.46 2.00 1.51 3.33 1.49 1.80 2.74 1.94 2.53 2.55 2.42

N

9.25 9.48 9.03 9.95 10.17 10.77 8.97 9.14 10.04 9.20 9.91 8.13 10.24 9.64 9.22 11.51 9.65 9.71 9.60 9.30 11.22 9.80 8.87 10.87 8.93 8.78 10.07 9.59 10.08 11.49 9.55

C/N

Clay: < 0.002 mm; ‡Fine silt: 0.002–0.02 mm, §Coarse silt: 0.02–0.05 mm; #Fine sand: 0.05–0.2 mm;

†

AB01 AB02 AB03 AB04 AB05 AB06 AB07 AB08 AB09 AB10 AB11 AB12 AB13 AB14 AB15 AB16 AB17 AB18 AB19 AB20 AB21 AB22 AB23 AB24 AB25 AB26 AB27 AB28 AB29 AB30 AB31

Field number

††

3.50 4.60 4.80 7.10 11.00 6.70 7.00 7.60 5.00 8.20 9.00 5.20 4.30 7.10 3.80 6.10 8.60 6.50 6.00 7.10 5.50 7.00 14.00 5.30 4.50 9.10 7.00 7.00 6.00 9.30 7.50

meq kg

K2O

71.50 60.70 65.90 56.00 121.30 66.30 122.40 81.70 48.90 60.10 61.60 100.20 66.80 80.20 62.90 33.90 53.90 66.40 57.20 73.50 71.80 71.20 106.90 104.90 83.20 66.70 62.10 106.50 53.60 86.40 55.30

−1

CaO

Coarse sand: 0.2–2 mm;

0.12 0.16 0.04 0.07 0.17 0.11 0.14 0.25 0.08 0.22 0.09 0.17 0.12 0.20 0.05 0.33 0.16 0.13 0.12 0.25 0.09 0.21 0.16 0.12 0.17 0.13 0.15 0.17 0.11 0.09 0.21

g kg

−1

P Olsen

‡‡

1.30 0.90 0.80 0.60 1.20 1.20 1.70 1.00 1.60 1.00 1.00 1.00 0.70 0.70 1.10 0.40 2.00 1.20 1.10 1.40 0.70 1.20 1.20 1.20 0.90 0.90 0.70 0.80 1.00 0.80 1.30

Na2O

95 77 98 72 100 72 100 100 52 63 73 92 63 78 100 36 63 73 87 76 62 81 100 87 100 93 55 100 60 75 64

%

Soil saturation

87 97 84 98 119 118 131 97 121 124 107 124 123 121 74 124 115 112 81 121 136 108 97 137 94 96 141 118 116 143 117

meq kg

CEC‡‡

CEC: cation-exchange capacity.

5.90 7.90 10.60 7.10 13.50 10.10 9.80 13.20 6.80 8.90 7.00 7.20 6.60 6.70 8.00 4.10 7.50 8.10 6.30 9.20 5.90 8.30 8.90 8.20 6.40 12.20 8.00 10.30 9.20 10.20 10.40

MgO

Table 2 Soil physicochemical properties in the top 30 cm of soil sampled after harvest in 2015 and analyzed by Capinov (Landerneau, FR).

−1

6.77 6.11 3.61 2.15 9.51 8.79 6.51 12.16 2.96 5.60 6.67 3.95 3.72 8.83 5.96 5.35 1.74 6.70 7.08 8.38 3.33 11.77 8.56 6.62 7.94 7.96 6.13 6.39 3.48 3.04 7.05

mg kg

−1

Cu EDTA

17.86 16.22 31.61 10.37 29.00 6.59 21.40 32.26 4.65 12.31 14.68 12.17 12.68 18.21 37.46 13.85 8.08 16.81 30.51 12.32 7.15 40.37 56.08 10.40 39.89 63.80 9.07 44.47 7.55 6.72 12.10

Mn EDTA

5.54 5.18 4.13 1.86 8.79 6.40 6.85 15.89 4.08 6.68 4.92 4.80 3.60 7.20 3.32 7.96 5.49 7.04 4.40 14.35 2.55 10.38 6.59 7.52 8.61 5.94 7.65 4.58 6.26 6.00 10.62

Zn EDTA

168.97 207.81 107.33 101.55 161.20 166.71 178.16 184.82 108.29 163.70 114.69 114.56 140.80 147.12 126.23 367.43 227.33 174.07 203.01 193.07 86.02 253.04 196.64 168.75 203.84 395.83 195.26 266.85 148.76 146.70 136.19

Fe EDTA

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beta-diversity analysis. Factors significantly affecting bacterial and fungal communities were determined using multivariate homogeneity of groups dispersions based on Bray-Curtis distance matrices (vegdist and betadisper functions), ADONIS and Mantel tests in vegan package. Bacterial and fungal indicators associated with specific agronomic factors were predicted using group.indicators function in RAM package, which is a wrapper of indicator species analyses in R package indicspecies (De Cáceres et al., 2012). Metabolic and ecologically relevant functions were annotated by FAPROTAX (Louca et al., 2016) for the 16S rDNA OTU and FUNGuild v1.0 (Nguyen et al., 2016) for the ITS OTU. Finally, Spearman correlation was computed between soil physicochemical parameters and relative abundance of genera highlighted by our previous analyses (20 most abundant genera, genera more abundant in soil under conventional and minimum tillage, bioindicators and prevalent genera for putative functions). Only correlations with p-value lower than 0.05 and the absolute value of correlation coefficient higher than 0.35 were considered.

Fig. 1. Beta-diversity plot of bacterial communities based on multivariate homogeneity of group dispersions for soil tillage (minimum vs. conventional tillage). The plots were obtained using vegan R-package and the Hellingertransformed bacterial OTU abundance matrix.

sequences, 3.1% OTUs) (Fig. S2).

2.6. Accession numbers

3.3. Diversity and structure of bacterial communities

All the biosamples raw reads have been deposited at the NCBI website and are available under the project ID PRJNA429425 with the biosample accession numbers ranging from SAMN08339403 to SAMN08339433 for ITS amplicons and from SAMN08364905 to SAMN08364935 for 16S rRNA gene amplicons.

3.3.1. Influence of climate and agronomic practices The factors significantly affecting the diversity of bacterial communities were the climate type, soil tillage and the type of fertilization. Shannon and Chao indices were significantly higher in soils collected from fields in inner climate region compared to those in mountain climate region (p < 0.05, Table 3). Species richness and most of the other alpha-diversity indices were also significantly higher in fields under minimum tillage (p < 0.05, Table 3) and when animal manure was applied, with or without chemical fertilizer (p < 0.05, Table 3). The compositional structure of bacterial communities was also significantly different depending on soil tillage. A higher beta-diversity of the bacterial communities was observed in fields under conventional tillage when compared to fields under minimum tillage (Fig. 1). Phyla Proteobacteria, Bacteroidetes and Verrucomicrobia were more abundant in soil under minimum tillage; while Acidobacteria, Actinobacteria, Chloroflexi and Nitrospirae were more prevalent in soils under conventional tillage (Kruskal Nemenyi test, α = 0.05). We defined the core microbiota as OTUs recovered from 70% of the soil samples under each tillage treatment. Thus, there were 1822 OTUs, representing 85 genera, in the core microbiota of soils under conventional tillage, while 1720 OTUs, representing 105 genera, were found in the core microbiota of soils under minimum tillage (Table S2). Among all recovered bacterial genera, 83 were found in core microbiota of soils under both types of soil tillage. Edaphobacter and Geobacter were only recovered from soils under conventional tillage. Genera Kaistobacter, Salinibacterium, Ramlibacter, Flavisolibacter, Chthoniobacter, Phormidium and Ellin506 were significantly more abundant in the fields under minimum tillage whereas Edaphobacter, Bacillus and Candidatus Koribacter were more abundant in fields under conventional tillage (Kruskal Nemenyi test, α = 0.05) (Fig. S3). We also observed that genera Sphingopyxis, Mycoplana and Hydrogenophaga were more prevalent in the fields where animal manure was applied (Kruskal Nemenyi test, p-value < 0.001).

3. Results 3.1. Agronomic practices and soil physicochemical characteristics Among the 31 fields, 9 were under minimum tillage practices (0–10 cm) whereas the other 21 were under conventional tillage. Crop management practices were determined by the feed requirements of the livestock and were similar among all 31 farms (Table 1). Field soils had analogous textures and were predominantly silt-loam (Table 2). Nutrient reserve, given by soil saturation, was below the optimal level in half of the fields (< 80%) and was especially low for field AB16 (soil saturation = 36%) (Table 2). 3.2. Sequencing data analysis A total of 1,447,185 16S rDNA sequences were clustered in to 9,028 OTUs and assigned to 41 phyla and 298 genera. More than 90% of the OTUs were assigned above the order level whereas 27.04% of the OTUs were assigned to the genus level (Table S1). Proteobacteria (41.8% of sequences, 35.7% of OTUs), Acidobacteria (19.9% of sequences, 14.3% of OTUs), Actinobacteria (11.1% of sequences, 11.8% of OTUs), Bacteroidetes (5.4% of sequences, 9.8% of OTUs), Chloroflexi (5.2% of sequences, 3.1% of OTUs), Planctomycetes (4.9% of sequences, 6.7% of OTUs) and Verrucomicrobia (3.3% of sequences, 4.6% of OTUs) represented more than 90% of the described bacterial phyla. At genus level, Kaistobacter, Rhodoplanes, Solibacter, Flavobacterium, Sphingomonas, DA101, Devosia, Pseudomonas, Nitrospira and Pedomicrobium were the most abundant groups (Fig. S1). For fungi, 1,795,150 ITS sequences were clustered in to 37,449 OTUs, which were assigned to 6 phyla and 1262 genera. The percentage of OTUs assigned at the genus level was 53.4%. Approximately 98% of the sequences were assigned to Ascomycota (90.6%, 90.0% OTUs) and Basidiomycota (8.0%, 5.8% OTUs) (Fig. 2). The ten most abundant genera were Gibellulopsis (7.9% sequences, 0.5% OTUs), Fusarium (7.2% sequences, 6.3% OTUs), Cryptococcus (6.4% sequences, 4.5% OTUs), Acremonium (4.3% sequences, 5.4% OTUs), Humicola (3.9% sequences, 4.0% OTUs), Exophiala (3.3% sequences, 8.1% OTUs), Schizothecium (2.4% sequences, 4.4% OTUs), Chaetomium (2.5% sequences, 2.6% OTUs), Fusicolla (2.4% sequences, 0.8% OTUs) and Microdochium (2.2%

3.3.2. Functional groups FAPROTAX assigned 20% of the OTUs (n = 1828) to 64 functional groups. Some OTUs were assigned to multiple functional groups. For example, OTU#1128234 (belonging to the genus Rhodoplanes) was considered being involved in denitrification, nitrate respiration, chemoheterotrophy and photoautotrophy. Thirty functions, such as chemoheterotrophy, denitrification, phototrophy and photoautotrophy, were recovered in all soil samples. However, the community compositional structure was significantly different under conventional and 5

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Fig. 2. Correlation diagrams showing positive (blue) and negative (red) correlations between soil physicochemical properties and 2A) 21 bacterial genera; and 2B) 18 fungal genera. The correlogram was drawn using ggplot2 and ggcorplot R-packages. Only correlation coefficients with significant p-value (p ≤ 0.05) are indicated. Non-significant correlations appear in white (p ≥ 0.05). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

p = 0.009). A Mantel r statistic of 0.2228 was obtained when clay, sand and silt percentages were taken together (p = 0.001). The variables that best correlated with the overall bacterial communities were pH (r = 0.5878, p = 0.001), calcium (r = 0.3825, p = 0.01), carbon (r = 0.3332, p = 0.001), nitrogen (r = 0.3445, p = 0.001) and manganese (r = 0.3321, p = 0.001). Dissimilarity matrices of all identified functional OTUs (n = 1828) and numeric soil physiochemical

minimum tillage for most of the functional groups, as represented by the distinct dispersion plots (beta-diversity) shown in Fig. S4A, B, C, D, E, F and G. 3.3.3. Influence of soil physicochemical properties The bacterial community compositional structure was significantly affected by the soil properties based on Mantel test (r = 0.5978, 6

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Alpha-diversity indices are reported as mean values ± standard deviation; †n: number of fields; ‡CM: chemicals and manure; * or different letters indicate significant differences between treatments (ANOVA, Kruskal Nemenyi test, p < 0.05).

0.6 ± 0.07b 0.639 ± 0.047a 0.579 ± 0.07b 4.36 ± 0.67b 4.83 ± 0.57a 4.17 ± 0.61b 2485 ± 1043b 3208 ± 1136a 2435 ± 1047b 3083 ± 326ab 3197 ± 233a 2925 ± 279b 2311 ± 192ab 2448 ± 186a 2259 ± 195b 8 9 14

0.9971 ± 0.0009 0.9973 ± 0.0004 0.9969 ± 0.0013

6.84 ± 0.12ab 6.90 ± 0.10a 6.81 ± 0.14b

0.884 ± 0.011 0.885 ± 0.009 0.882 ± 0.012

1515 ± 620b 2020 ± 744a 1470 ± 642b

0.9477 ± 0.0251b 0.966 ± 0.0142a 0.9147 ± 0.07b

0.625 ± 0.049 0.589 ± 0.073 4.49 ± 0.58 4.33 ± 0.7 2368 ± 1224 2677 ± 1032 2786 ± 255 3122 ± 264* 2167 ± 186 2381 ± 187* 8 21

0.9965 ± 0.0017 0.9973 ± 0.0006*

6.74 ± 0.14 6.89 ± 0.10*

0.878 ± 0.013 0.886 ± 0.001

1460 ± 765 1628 ± 642

0.9579 ± 0.0286 0.9276 ± 0.0597*

0.64 ± 0.049 0.59 ± 0.069* 4.72 ± 0.59 4.29 ± 0.67* 2780 ± 1034 2628 ± 1152

Soil Tillage Minimum Tillage Conventional Tillage Fertilization Chemicals CM‡ or Manure Climate Coastal Inner Mountain

3240 ± 236 2965 ± 288* 2476 ± 171 2266 ± 187* 9 22

0.9970 ± 0.0004 0.9969 ± 0.0012

6.95 ± 0.06 6.8 ± 0.13*

0.889 ± 0.007 0.881 ± 0.011*

1705 ± 705 1615 ± 707

0.9666 ± 0.0139 0.9264 ± 0.0591*

Shannon evenness Shannon Simpson Chao Nb of OTUs n†

Nb of OTUs

Chao

Simpson

Shannon

Shannon evenness

ITS 16S rDNA

Table 3 Alpha-diversity indices including the number of OTUs, Chao, Simpson, Shannon and Shannon evenness indices according to various treatments (soil tillage, fertilization and climate) for 16S rDNA and ITS dataset.

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properties had a correlation of 0.306 (Mantel test, p = 0.001). The abiotic soil properties that best correlated with the identified functional community were soil saturation (Mantel r = 0.27, p = 0.001), manganese (Mantel r = 0.27, p = 0.001), cation-exchange capacity (C.E.C, Mantel r = 0.22, p = 0.001) and total carbon (Mantel r = 0.23, p = 0.001). Our previous analysis highlighted 56 genera of importance including the 20 most abundant genera, the genera significantly affected by soil tillage as well as the bioindicators of soil tillage and type of fertilization. Among these genera, 11 correlated with coarse sand (with particle size ranging from 0.2 to 2 mm), 13 with soil acidity (pH), 12 with soil saturation and 17 with total carbon and nitrogen content in soil (Fig. 2-A) (Spearman's rho > 0.35 or < −0.35, p ≤ 0.05). The genera Burkholderia, Candidatus Solibacter, Candidatus Koribacter, Dokodonella, and Rhodoplanes were all significantly positively or negatively correlated to calcium (CaO), carbon content (C), manganese (Mn) and soil saturation (Fig. 2-A). Multiple linear regression analysis showed that Rhodoplanes and Dokodonella were best explained by overall soil properties (cross validation coefficient of 0.68 and 0.71, respectively). Linear regression further showed that soil saturation was statistically significantly correlated with Burkholderia (Fig. 3A) and Candidatus Koribacter (R-squared = 0.3161 and 0.5045, respectively, p = 0.001), while soil nitrogen content was correlated with Rhodoplanes (R-squared = 0.3938, p = 0.001) and soil pH with Candidatus Solibacter (R-squared = 0.6039, p = 0.001) (Fig. 3). 3.4. Influence of climate, agronomic practices and soil physicochemical properties on the structure and diversity of fungal communities Similar to those of bacterial communities, average alpha-diversity indices of soil fungal communities were significantly higher in fields located in inner climate region compared to those in mountain climate and coastal climate regions (Kruskal Nemenyi test, p-value < 0.05, Table 3). Simpson and Shannon indices were also significantly higher in fields under minimum tillage than under conventional tillage (Table 3). However, chemical inputs or animal manure did not affect alpha-diversity indices of fungal communities, except for Simpson index (Table 3). Regarding beta-diversity, climate, soil tillage and sowing date did not account for the variations observed (Adonis test: R2 = 0.03, R2 = 0.02 and R2 = 0.03, respectively; α = 0.05). Crop rotation, however, explained 15.6% of the variation based on community distance matrix. Mantel test showed that the mycobiota composition was correlated to the overall soil properties (r = 0.2074, p = 0.001); texture, including clay, sand and silt percentage (r = 0.1717, p = 0.001); manganese (r = 0.2351, p = 0.001); water reserve (r = 0.2253, p = 0.001) and carbon (r = 0.2024, p = 0.001). A significantly higher abundance of Zygomycota was found in fields under conventional tillage (p = 0.00084) and Rozellomycota in fields in coast climate region (p = 0.0044). Among the 20 most abundant genera, Fusarium (p = 0.014), Cladosporium (p < 0.0001) and Pyrenochaetopsis (p = 0.0094) were significantly more abundant in fields under minimum tillage whereas Mortierella (p = 0.00047) and Pseudorotium (p = 0.02) were more abundant in fields under conventional tillage. Fusarium and Cladosporium were also more abundant in fields in inner climate region (p < 0.0001). Additional significant correlations between some fungal genera and soil physicochemical properties (Spearman rho < −0.35 or > 0.35, p < 0.05) were shown in Fig. 2B. When defining the core microbiota for soil tillage, 18 and 21 genera were found in 70% of the soils under conventional and minimum tillage. Four genera (Cladosporium, Pyrenochaetopsis, Dydimella and Ramularia) were recovered only from fields under minimum tillage and one (Pseudorotium) only found in fields under conventional tillage (Table S3). In addition, Lasiosphaeris was found significantly more abundant when wheat sowing date was normal (October 2014). An average of 32.66 ± 8.76% of the identified OTUs at the genus level 7

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Fig. 3. Linear regression analaysis between selected soil properties and the abundance of 4 bacterial genera: A) soil saturation vs. Burkholderia; B) soil pH vs. Candidatus Solibacter, C) soil nitrogen content vs. Rhodoplanes; and D) soil saturation vs. Candidatus Koribacter. The abundance of each genus was Hellingertransformed.

manure was applied in combination or not with chemicals. Apart from the influence of fertilization, soil physicochemical parameters and soil tillage are also known factors that greatly affect the structure and diversity of microbial communities (Bender et al., 2016; Lauber et al., 2008; Naveed et al., 2016). Our results suggested that climate, soil tillage and sowing date did not show significant impact on fungal community dynamics, despite that previous studies showed otherwise (Hu et al., 2017; CwalinaAmbroziak and Bowszys, 2009). In addition, soil parameters, either taken individually or altogether, did not seem to influence greatly the mycobiota composition. In contrast, bacterial communities were more sensitive under the impact of environmental stressors and were much more responsive to individual agronomic practices (e.g. soil tillage) and physicochemical properties. We found that soil pH as well as soil carbon and nitrogen content significantly explained the variation in compositional structure of bacterial communities. This is in congruence with the study of Liu et al. (2017), which showed the importance of soil pH, total nitrogen and total phosphorus as key factors determining bacterial community composition in bulk soil. Soil texture, particularly coarse sand, also seemed to influence the compositional structure of bacterial

was associated to plant pathogen based on FUNGuild. These fungal pathogens, however, did not show significant association with any agronomic practices or any particular fields. 4. Discussion Overall, our results suggest that under intensive agricultural production, geographic location, soil tillage and fertilization greatly influenced the diversity and the community structure of the microbiota in soil, with bacterial and fungal communities responded differently to these stressors. Fertilization, such as application of organic manure or chemical fertilizers, were described as an important factor driving microbial community dynamics (Bradley et al., 2006; He et al., 2008; Verbruggen et al., 2010). In our study, bacterial community diversity in soils receiving animal manure, in association with or without chemical fertilizers, was significantly higher than that in soils receiving only chemical fertilizers. Similar results were found by Zhong et al. (2010) in a longterm experiment, which suggested that in comparison with chemical fertilization only, a higher functional diversity was found when organic 8

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(Babujia et al., 2010) and enzyme activities (Mathew et al., 2012), as well as increased nitrogen and nutrient availability (Babujia et al., 2010; Gómez-Rey et al., 2012; Mathew et al., 2012). In our study, minimum tillage was shown to significantly increase both diversity and functions related to the nitrogen cycle. Therefore, our results suggest that conservative soil management is beneficial for sustaining soil functionality under maize/wheat rotations in agricultural fields in Brittany. Long-term sampling and metadata documenting, however, are required to better study the influence of soil management practices on the soil functioning.

communities because this factor was found to be significantly correlated, either positively or negatively, with 11 bacterial genera. It is generally agreed that soil texture determines its aggregation and porosity, hence consequently affects microbial activity (Vinhal-Freitas et al., 2017). A recent study suggested that coarse sandy soil may create micro-aggregates, of which, the rough outer crust may provide good niches for microbial activity (Paradiś et al., 2017). More strikingly, our results showed that bacterial communities had a lower alpha-diversity but higher beta-diversity in fields under conventional tillage compared to those under minimum tillage. In other words, the average species diversity is lower with a higher variability between fields under conventional tillage, suggesting that the responses of soil microbial communities to the stress imposed by conventional tillage were non-uniform. The phyla Acidobacteria, Actinobacteria, Chloroflexi, Verrucomicrobia and Nitrospirae were also more abundant in field under conventional tillage whereas Proteobacteria and Bacteroidetes were more abundant in fields under minimum tillage. Interestingly, Acidobacteria are oligotrophic bacteria usually present in biotopes with low levels of nutrients, as has been demonstrated in Koyama et al. (2014). These authors found that these bacteria were less abundant in fertilized soils. Taken together, these results suggest that conventional tillage may degrade the soil fertility or the soil nutrient availability. In agreement with the results of our study, Dong et al. (2017) and Lupwayi et al. (1998) also found a reduced diversity in soils under conventional tillage. In contrast, a couple of recent studies observed a higher diversity of microbiota in soils under conventional tillage than that of no-tillage or minimum tillage (Degrune et al., 2016; Wu et al., 2015). Such discrepancy between these studies may be attributed to the differences in the carbon and/or nitrogen inputs, temperature, climate or soil pH, which are known to influence the diversity and structure of microbial communities (Nivelle et al., 2016), making it difficult to compare conclusions withdrawn from these studies. Taxonomic diversity of microbial communities is a key to maintain functional diversity (Mendes et al., 2015) such as the taxa involved in soil biogeochemical cycles (Bender et al., 2016; Wakelin et al., 2008). Previous studies and reviews have suggested that keystone species such as specific pathogens or nitrogen fixers rather than the overall community diversity are the key determinants for the functioning of the agroecosystem (Heijden and Wagg, 2013; Orr et al., 2011; Compant et al., 2010; Verbruggen and Kiers, 2010). However, the contribution of the soil microbial diversity and/or specific species towards the functioning and sustainability of the agroecosystem is still poorly understood (Heijden and Wagg, 2013). Studying how the agricultural practices may affect microbial communities associated with soil health would help to reduce chemical inputs and increase resource-use efficiency. In our study, bacterial genera involved in nitrogen cycling were among the most abundant taxon groups in soil, thus it was logical to hypothesize that environmental factors affecting these genera would also affect the nitrogen cycling. Although the overall abundance and richness (number of OTUs) of bacterial taxa assigned to known functions, based on FAPROTAX results, were not affected by the soil management, some functional groups in fields under minimum tillage had a higher taxonomic diversity than those in fields under conventional tillage. It was notably the case for bacterial taxa involved in nitrogen cycling (denitrification, respiration). Minimum tillage is described as a practice that is able to reduce soil erosion and improve water conservation (Treonis et al., 2010; Hobbs et al., 2008; Montgomery, 2007; Holland, 2004). It requires less human and technical resources to plough a field thus has economic advantages (Derpsch et al., 2010; Stavi and Lal, 2015). It is also used as a means to achieve the “Zero Net Land Degradation” concept (Stavi and Lal, 2015). Effects of minimum tillage are difficult to assess due to the combination of variable agricultural practices and the diverse geographic location of agricultural fields. However, it has been demonstrated that reduced or minimum tillage may improve carbon sequestration (Gómez-Rey et al., 2012; Luo et al., 2010; Mathew et al., 2012; Virto et al., 2012), microbial biomass

5. Conclusions Soil fungal and bacterial communities were both affected by the climate, agronomic practices and soil properties, with bacterial communities being more responsive to such changes. Soil tillage was the predominant agronomic practice affecting soil bacterial diversity with minimum tillage leading to increased bacterial taxonomic and functional diversity in agricultural soils compared to conventional tillage. Our study supports the benefits of conservative management on soil functionality. In addition, the high similarity between fields, in terms of bacterial and fungal composition and diversity, suggests that similar intensive management practices might lead to an increased resemblance of the biological and functional diversity among such fields overtime. Nonetheless, we also identified taxa groups as potential bioindicators of a specific agronomic practice, such as the strong association between animal manure application and Hydrogenophaga, Mycoplana and Sphingopyxis, as well as the enrichment of oligotrophic Acidobacteria under conventional tillage. These results may offer interesting insight into the identification of biological indicators under similar crop rotation system. Acknowledgements We gratefully thank Triskalia as project partners and the farmers for kindly giving us access to their field and providing information about their field management. Funding This work was supported by the Brittany Region [Grant #13008022 Rhisotox] and the French Association for Research and Technology ANRT [Grant #2014/108]. It was certified by the Foodstuff Cluster of the Future (Valorial). The computational infrastructure was partially supported by Agriculture & Agri-Food Canada-funded projects (proposal ID #97, #1134 & #1136) and the Government of Canada's Genomics Research and Development Initiative (GRDI) Shared Priority Project – Metagenomics Based Ecosystem Biomonitoring (Ecobiomics) (J-001263). Declaration of interest None. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.apsoil.2018.08.016. References Adesemoye, A.O., Kloepper, J.W., 2009. Plant-microbes interactions in enhanced fertilizer-use efficiency. Appl. Microbiol. Biotechnol. 85, 1–12. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J., 1990. Basic local alignment search tool. J. Mol. Biol. 215, 403–410. Ashworth, A.J., DeBruyn, J.M., Allen, F.L., Radosevich, M., Owens, P.R., 2017. Microbial community structure is affected by cropping sequences and poultry litter under long-

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