Jorge Osman

Michael S Dubow
email: michael.dubow@u-psud.fr

Bacterial Communities of the Surface of Oligotrophic Nutrient-Poor Soils

Keywords: Bacterial diversity, pyrosequencing, rhizosphere, bacteria bacterial biodiversity, pyrosequencing, salinity, halophilic bacteria

Bacterial diversity of rice soils of the Camargue

Mediterranean Sea, is considered a saline ecosystem. In order to understand the soil bacterial ecology of the Camargue rice growing areas, we collected samples from six different sites, from the rhizosphere of two rice varieties (Arelate and Gageron) at late stages of growth, plus adjacent bulk soil samples, during 2013 and 2014. We used pyrosequencing of PCR amplified V3-V4 regions of the 16S rRNA gene from total extracted DNA to identify the bacterial communities present, and found that the principal bacterial phyla were composed of members belong to the Proteobacteria, Acidobacteria, Chloroflexi, Bacteoidetes and Gemmatimonadetes phyla. While the relative abundance of these phyla did not differ between the rhizosphere and bulk soil samples, the abundances varied significantly among sites and between the years of collection. The most abundant bacterial groups at both the phylum and genus levels were similar to those found in other rice growing soils, though only one halophilic genus was present in significant numbers.

Our work also suggests that the challenges of rice cultivation in proximity to relatively high-salt soils can likely be overcome by current irrigation and crop-rotation strategies.

List of Figures

List of tables

CHAPTER I (REVIEW ARTICLE):

Bacterial communities of oligotrophic soils

Abstract

Oligotrophic soil ecosystems can contain a wide variety of bacterial communities.

However, these communities vary in composition according to the mineral content and the chemical and physical properties of their environment. This creates a challenge for microbiologists intending to identify population patterns based on ecosystem characteristics. Advances in new technologies improve the identification of bacterial groups, which helps in understanding the role of microbial populations in different biomes. From studies of several sites, we have described the principal bacterial members involved in mineral transformations in the surface layers of nutrient-poor soil ecosystems as well as the bacterial groups present in various oligotrophic ecosystems We also assess factors that could shape the bacterial communities in these ecosystems, to better understand their distribution and their possible roles on mineral soil surfaces.

1-Introduction

Soil has been recognized as one of the most diverse microbial habitats on Earth (1, 2).

Soils are complex physical and chemical environments, where heterogeneous mixtures of minerals and nutrients, water, organic matter and biological species interact (3). Soil microorganisms play a fundamental role as principal drivers of biogeochemical cycles, through activities such as nutrient acquisition and cycling of nitrogen and carbon, and through their roles in soil formation and in soil plant and animal health (4, 5). The development of new sequencing technologies has enabled researchers in microbial ecology to understand how ecosystem components and microbes interact. Altogether, microbial populations, which participate in soil chemical cycling, are important for predicting future dynamics and consequences of global environmental changes (6, 7, 8).

The principal biological fluxes of the major elements of life (H, C, N, S, O and P) are driven by microorganisms, although in the case of P, volcanic activities and rock weathering are also significant contributors. These elements are transformed by redox cycles, where a chemical element is reduced or oxidized in a series of abiotic or biotic steps (6). The known principal bacteria that play preponderant roles in nutrient cycles are members of the genera Nitrosomonas and Nitrobacter (nitrification), Thiobacillus (sulfur and iron oxidation), Rhizobium and Frankia (N2 fixation), Bacillus and Clostridium (carbon cycling) and Caulobacter and Pseudomonas (manganese oxidation) (9, 10).

Terrestrial soil ecosystems are of many types, such as deserts (11), forests (12), caves (13) and grasslands (14). These ecosystems differ in nutrient availability and composition, soil structure and composition and species distribution. They are subject to interventions that change the original soil structure, such as agriculture and climate (15).

The consequent alterations in soil physicochemical properties can influence microbial community composition and diversity (16,5). The main factors influencing soil structure are both abiotic -pH, organic matter content, H2O content, O2 concentration, temperature -and biotic -plants and animals as well as microbe-microbe and microbe-organism interactions (17,18).

Here, we review the communities of soil-associated bacteria and their potential functions in terrestrial ecosystems, with a special focus on oligotrophic and/or hostile habitats. The term "oligotrophy" pertains to life in low-nutrient habitats (poor food) and describes a wide range of environments, including terrestrial, aquatic and aerial ecosystems (19,20).

Oligotrophic environments are characterized by limited supply of nutrients (carbon, nitrogen and phosphorus) and H2O (19,[START_REF] Kociolek | Oligotrophy : the forgotten end of an ecological spectrum[END_REF], and are distributed all over the Earth's surface.

2-Advances in microbial identification

Microbiology as a discipline began in the 17 th century with the invention by Antonie Van Leuwenhoek of the first microscope. It allowed him to observe single-celled organisms, which he named "animalcules". His contributions were essential for the later identification and classification of microorganisms, and thus crucial for research in microbiology [START_REF] O'malley | The nineteenth century roots of everything is everywhere[END_REF].

The Earth is estimated to contain approximately 4-6 x 10 30 prokaryotic cells (23).

Microorganisms constitute approximately 60% of the Earth's biomass, with microbial cell numbers estimated to be approximately 1.2 x 10 29 in aquatic environments and 4-5 x 10 30 in terrestrial ecosystems (24).

In nature, prokaryotes are key players in most ecological processes and require sources of energy, nutrients and proper physicochemical conditions for growth [START_REF] Alain | Cultivating the uncultured: limits, advances and future challenges[END_REF].

Culture based-methods Culture-based approaches for identifying microorganisms was indispensable for the study of microbial ecology, but it biases estimation of biodiversity, as different microorganisms require different concentrations and types of nutrients and growth conditions and most are not cultivable [START_REF] Pham | Cultivation of unculturable soil bacteria[END_REF]. Over the course of the 20 th century, microbiologists designed a wide variety of selective growth conditions by varying pH, nutrient concentration and composition, oxygen gradients and temperature, in an effort to expand the cultivable fraction of microbes. These included in vitro reproductions of natural environments such as sea water and soil [START_REF] Zengler | High-throughput cultivation of microorganisms using microcapsules[END_REF]. However, studies of the taxonomic relationships among cultivated microorganisms based on DNA-DNA hybridization (28)

led to the realization that it is difficult to culture the vast majority (>95%) of microorganisms under laboratory conditions (29, 30), owing to ignorance of the factors required for their growth (25).

Culture-independent methods Advances in DNA, RNA and protein sequencing techniques have enhanced the ability to discern phylogenetic relationships among microbes. In the 1970's, Carl Woese pioneered the study of molecular phylogenetics based on 16S rRNA sequences, with the purpose of reconstructing the tree of life. On this basis, he proposed that life comprises three primary evolutionary domains: Eucarya, Bacteria and Archaea [START_REF] Woese | Phylogenetic structure of the prokaryotic domain: The primary kingdoms[END_REF][START_REF] Woese | Bacterial evolution[END_REF].

Norman Pace used recombinant DNA techniques to reveal that the Bacteria domain contains over 40 bacterial divisions (28, 33). Research on the Archaea revealed that this group comprises most known microbial inhabitants ("extremophiles") of environments generally too hostile for Eucarya and Bacteria, and enlarged our view of habitats compatible with life. (34).

Metagenomics and the 16S rRNA gene as molecular marker

The earliest sequencing methods, based on chemical cleavage and synthesis termination [START_REF] Sanger | DNA sequencing with chain-terminating[END_REF][START_REF] Maxam | A new method for sequencing DNA[END_REF], have been supplanted by techniques based on hybridization and on ligation and cleavage (37).

Such high-throughput sequencing techniques have enabled the development of metagenomics, the derivation of microbial genome sequences from mass sequencing of environmental samples. [START_REF] Brenner | Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays[END_REF]. The collective genome of coexisting microbes (both cultivable and non-cultivable) is sampled from the environment and subsequently sequenced [START_REF] Handelsman | Molecular biological access to the chemistry of unknown soil microbes: a new frontier for natural products[END_REF][START_REF] Handelsman | Metagenomics : application of genomics to uncultured microorganisms metagenomics : application of genomics to uncultured microorganisms[END_REF]. Metagenomics enables derivation of functional gene comparisons, genetic diversity, species composition and interaction with the environment [START_REF] Schloss | Biotechnological prospects from metagenomics[END_REF][START_REF] Cowan | Metagenomic gene discovery : past, present and future[END_REF][START_REF] Vieites | Metagenomics approaches in systems microbiology[END_REF][START_REF] Thomas | Metagenomics -a guide from sampling to data analysis[END_REF][START_REF] Myrold | The potential of metagenomic approaches for understanding soil microbial processes[END_REF], and can lead to discovery of new taxa and genes [START_REF] Béjà | Bacterial rhodopsin : evidence for a new type of phototrophy in the Sea[END_REF][START_REF] Nicol | Ammonia-oxidising Crenarchaeota: important players in the nitrogen cycle[END_REF]. Several metagenomic surveys have been performed with the aim of understanding the functioning of ecosystems such as oceans [START_REF] Delong | The microbial ocean from genomes to biomes[END_REF][START_REF] Williamson | Metagenomic exploration of viruses throughout the Indian Ocean[END_REF][START_REF] Bork | Tara oceans studies plankton at planetary scale[END_REF], cold and hot deserts (5), soils in France (12), permafrost soils in Alaska (51) and submerged sediments in Brazil [START_REF] Andreote | The Microbiome of Brazilian mangrove sediments as revealed by metagenomics[END_REF]. Most studies of bacterial and archaeal diversity in environmental samples have used the 16S rRNA gene, a component of the 30S small subunit of prokaryotic ribosomes. This gene is very important in the study of microbial ecology and evolution owing to its universality, its extreme sequence conservation coupled with distinct regions of genetic variability, and the rarity of its transfer between taxa [START_REF] Woese | Bacterial evolution[END_REF][START_REF] Langille | Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences[END_REF][START_REF] Clarridge | Impact of 16S rRNA gene sequence analysis for identification of bacteria on clinical microbiology and infectious diseases[END_REF][START_REF] Tringe | A renaissance for the pioneering 16S rRNA gene[END_REF]. However, the 16S rRNA gene also has limits in taxonomy studies, especially when low sequence divergence restricts distinction between organisms (56) and when variation in 16S rRNA gene copy number distorts abundance estimates (57).

Next Generation DNA Sequencing (NGS) and bioinformatic tools Next generation

sequencing platforms allow analysis of hundreds of environmental samples in a single sequencing run (58).

Various DNA sequencing platforms have been implemented in the past decade. They share three steps: template preparation, sequencing, and data analysis. The different platforms produce different types of data [START_REF] Shendure | Next-generation DNA sequencing[END_REF][START_REF] Metzker | Sequencing technologies -the next generation[END_REF]. GS FLX pyrosequencing (454 by Roche), the first to use sequencing by synthesis, generates about 200 000 reads (20 Mb) of 110 base-pairs (bp). HiSeq 2000/2500 and MiSeq (Illumina/Solexa), also using sequencing by synthesis, reached approximately 180 million reads yielding about 600 Gb of data (61). SOLiD (Life Technologies), generated more reads than 454 but of only 35 nt in length (62). Newer technology being incorporated by Ion Torrent does not rely on the optical detection of incorporated nucleotides using fluorescence and camera scanning.

This platform has lower cost, is smaller and has higher throughput than 454, but as in 454, the sequence templates are generated on a bead or sphere via emulsion PCR (emPCR). Other NGS technologies have been established, such as Helicos BioSciences, using a single molecule detection system and PacBIO (Pacific Biosciences), released in 2010, and these may be ideal for de novo genome assemblies, based on the detection of DNA synthesis by a single DNA polymerase (63).

Interpretation of the data generated by NGS technologies requires complex bioinformatics analyses. A typical pipeline process includes sequence cleaning steps, followed by alignment to database reference sequences. The cleaning steps, essential for accurate downstream analysis, involve filtering to remove low quality reads, contaminating sequences and primer sequences [START_REF] Schmieder | Fast identification and removal of sequence contamination from genomic and metagenomic datasets[END_REF]. Several open source pipeline algorithms are available for these procedures, including Qiime (65) and Mothur (66). The alignment to a known 16S rRNA reference database is extremely important, as this step enables identification of taxa found in each sample from different environments. The databases, SILVA (67), GreenGenes (68) and RDP (69), are the most frequently used for 16S rRNA gene annotation.

The 16S rRNA should be clustered into OTUs (Operational Taxonomic Units) using a cut-off, generally of 97% (species level at taxonomy identification) sequence identity, with algorithms such as UCLUST (70) or UPARSE (71). This step delineates operational species that then allows calculations of alpha and beta diversity, respectively the number of species in each sample and the degree of dissimilarity among samples (72). The pH is commonly lower on the surface than deeper in the soil, as a result of interaction with organic matter (85). However, in deserts surface soil, the pH is commonly alkaline, with pH values higher than 8.5 being observed (83). It has been previously

shown that phylum composition is influenced by changes in soil pH, particularly for the Acidobacteria, which are commonly found, though not restricted, to low pH soils, and for the Actinobacteria phyla that are found in soils with high pH in arid or semiarid ecosystems. In contrast, diversity has been reported to be higher in neutral pH value soils

(86

Climate and Mechanical erosion

Local climate effects are often directly related to temperature and runoff. Depending on the ecosystem (i.e., tropical, mountainous, etc.) temperature may influence weathering and its fluctuation may do so through effects on activation energies (100). However, runoff will produce a direct interaction with the rock and, depending on soil characteristics (nature, thickness, porosity and root systems) and rain intensity, will induce soil weathering. Soils are often thick at low elevation and thin at high elevation (93). The role of physical erosion in mineral weathering is also related to the aging of the mineral surface and present-day climatic conditions (101, 102) that may contribute to clay mineralogy and thus the microorganism populations present in these soils. It has been reported that the basalt age is also a factor that can influence the bacterial communities associated with basaltic rock (103).

Organisms and organic matter

From a physical perspective, the vegetation and, in particular, plant root systems can decrease the mechanical erosion of soils because they facilitate water circulation and increase the surface contact between solutions and soil minerals (104). Vegetation also induces the evapotranspiration that can decrease runoff but generate local rainfall (96). Chemically, plants produce organic acids in their root exudates, which promote solubilization of insoluble and immobilized minerals from the deep soil layers [START_REF] Viers | Chemical weathering rates, CO2 consumption, and control parameters deduced from the chemical composition of rivers[END_REF][START_REF] Schoebitz | Effect of immobilized Serratia sp. by spray-drying technology on plant growth and phosphate uptake[END_REF]. Bacteria also help accelerate mineral weathering though the decomposition of litterfall that acidifies the upper soil (104) (Figure 1).

6-Bacterial adaptations and roles in mineral soil surfaces

Bacterial communities in soils may cause mineral complex transformations such as biological weathering (92 7-Bacteria living in mineralized surface soil ecosystems: Which genera are the most well reported? Some bacteria transform mineral complexes by various mechanisms. These bacteria interact with mineral complexes in soil surfaces. Ecosystems such as deserts or caves have low levels of nutrients [START_REF] Pointing | Microbial colonization and control in dryland systems[END_REF]119) Others, such as forest soils, have high levels of nutrients as well as surface layers composed of a variety of mineral complexes, creating oligotrophic niches for bacterial communities, which depend on mineral weathering for their growth and survival (105). In this section we describe the bacteria that play a role in mineral soil surfaces. The principal elements released by microorganisms from the mineral complex of biotite and granite are Al, Fe, Ni and Si (92, 120). Table 1 is a list of the principal bacterial genera playing a role in mineral transformation, in particular mineral weathering. Different bacteria colonize rock and sand surfaces (121), endholitic and epilithic bacterial communities being different (122). In deep mineral soils, bacteria are principally involved in complexolysis, whereas on the surface they weather minerals by acidolysis (111). Surface soils mostly harbor Burkholderia, Collimonas, Pseudomonas, Bacillus and Arthrobacter genera (see Table 1). Wang et al. (123) studied the differences in weathering ability of bacteria in upper and deeper soils from red soil in China. Their results suggest that the elements released from mineral surfaces are Fe, Si and Al, and that the bacterial diversity was higher in upper soils than in deeper soils. Some Burkholderia, Bacillus and Lysinibacillus are highly efficient weathering bacteria, while some Burkholderia were the most well reported in mineral transformations (Table1). The latter association was also seen in surface soils of forests (12, 124).

8-Bacterial communities of oligotrophic ecosystems: Which bacterial groups predominate?

Many different ecosystems are oligotrophic on the basis of low nutrient availability.

These ecosystems are usually represented by soils composed of a complex of mineral elements, for example desert soil crusts, caves, ice and rocks. However, several ecosystems, where the availability of nutrients (in particular C, N, and P) is higher than in oligotrophic ecosystems, can also represent specific niches and be considered as oligotrophic, for example, acid forest soils, grasslands, sandy dunes and other mineralized surface soils. Bacteria living in oligotrophic soil ecosystems adapted to drastic climatic changes are likely to be adapted also to conditions of carbon scarcity, suggesting that the study of these ecosystems may offer an opportunity to discover novel metabolic products, such as proteases (125). Desert ecosystems make up more than 30% of the Earth's land mass (90) and, because of interest in the impact of micro-organisms on desertification, are one of the most studied oligotrophic ecosystems in relation to its bacterial communities (11,[START_REF] Gombeer | The influence of surface soil physicochemistry on the edaphic bacterial communities in contrasting terrain types of the Central Namib Desert[END_REF]126). In deserts, surface soils are composed of complex mineral structures formed principally by sandy loam and load sand, which are formed by weathering of the surface soils and have a very low percentage of water retention and thus less vegetation. Sandy structures are formed principally of rocks such as granite, quartz and limestone, as also observed in soils from coastal forest and grasslands (127), where bacterial populations able to weather rock have been described (128). Biological soil crusts (BSC) are complex microbial communities, which colonize intersticial soil surfaces and can be distributed in arid and cold soil ecosystems, representing a niche for various microbial organisms, including both photosynthetic and heterotrophic bacteria (90). BSC's also exist in other biomes such as grasslands, forest soils, permafrost soils and Polar regions (129), where Cyanobacteria, phototrophic microorganisms of high radiation and salt resistance were the first colonizers and thus created niches in which subsequent microorganisms are integrated (130). Certain Cyanobacteria are able to fix atmospheric nitrogen in the presence of CO2 (80). A study of BSC's in the surface of pasture soils (131) showed that the soil surface (0-3mm) and bulk soil (3-12mm) are exposed to different light conditions. They also observed that the influence of light on microbial communities was restricted to the soil surface, and that nutrients such as extractable P and K were altered at the soil surface as a result of growth of these phototrophic communities. Numerous studies of bacterial diversity in arid ecosystems (e.g. deserts) have been performed. The most abundant groups observed are members belonging to the Actinobacteria, Proteobacteria and Bacteroidetes phyla (132) while, in lower proportions, members belonging to the Firmicutes, Acidobacteria, Gemmatimonadetes and Cyanobacteria phyla (11, 133) were also found. However, the bacterial diversity in surface desert soils differs significantly from that of other terrestrial biomes (5). Bacterial association with minerals from sandy soils have been studied, revealing that members of the Proteobacteria, Actinobacteria and Acidobacteria phyla create better associations with quartz, magnetite and pyroxene, though differences were observed between types of minerals and bacterial types (128). Microbial abundance has been explored, for other non arid-desert oligotrophic ecosystems, such as polar deserts (McMurdo Dry Valley, Antarctica) (134). Here, Acidobacteria and Actinobacteria phyla were present in higher proportions in high pH soils, while Acidobacteria phyla are commonly found in acidic soils (86, 135). However, pH is not the only factor that can influence bacterial composition (85). Ganzert et al. (136) studied bacterial diversity of permafrost soils in Greenland, and showed that soil bacterial communities depend significantly on soil pH. Members of the Acidobacteria, Bacteroidetes and Proteobacteria phyla were found to predominate in the soils, while low pH supported Acidobacteria and Proteobacteria but not Bacteroidetes phyla, whose members predominate at higher soil pH. Proteobacteria phyla were found principally in soils with high nitrogen and carbon availability (137).

Other terrestrial oligotrophic soil ecosystems such as grassland soils (138) have been studied, revealing that oligotrophic and acidophilic organisms such as Acidobacteria and Proteobacteria decrease in abundance with high pH, total C and N, while copiotrophic and alkaplophilic bacteria (Firmicutes, Chloroflexi, Actinobacteria and Bacteroidetes) are more abundant in soils rich in organic matter and nutrients. In the surface soil of an acid forest ecosystem, similar results were found; Proteobacteria and Acidobacteria phyla were found in low pH soils, suggesting that these bacteria could be a microbial indicator of soil quality improvement (139).

9-Conclusions and perspectives

A variety of studies have provided new insights into the bacterial communities present in oligotrophic or hostile conditions, such as those of arid deserts (140), rock surfaces (141), shallow biofilms in mountains (142) and coastal saline sandy soils (143).

The response of bacteria in oligotrophic environments, in particular in surface layers, is the formation of biofilm complexes, which protect the microorganisms from environmental fluctuation (144). Such ecosystems may provide keys for understanding the evolution of these microorganisms under harsh environmental conditions (145).

The study of bacterial communities living in terrestrial surface soil ecosystems is a challenge, owing to the fact that fewer than 5% of bacterial species are cultivable under laboratory conditions (30). Oligotrophic ecosystems are not well characterized, and new technological approaches to bacterial identification will facilitate the study of bacterial communities in different ecosystems subject to varied environmental factors.

Thesis objectives

The principal objectives of my thesis research were to identify and characterize, where possible, the bacterial populations present in three unusual and oligotrophic ecosystems using genomic approaches via pyrosequencing of PCR amplified 16S rDNA genes from total extracted DNA:

1. The surface soil, sediment and rhizosphere of rice plants, in the Camargue (Rhone delta) region of southern France. This site, where the Rhone River empties into the Mediterranean Sea, is a very saline environment. Here, I sampled 17 sites of soils and sediments from marshes and canals over the course of two years. I also sampled the rhizosphere of two varieties of French rice growing in the area in five different sites, plus nearly non-cultivated soil. We also obtained physical/chemical parameters to better understand possible correlations for understanding the composition of the bacterial population.

2. The eroded soil of the Padza de Dapani site of the island of Mayotte. This region is not a true (hot) desert, but resembles one due to extensive soil erosion. This tropical soil is also rich in iron and aluminum, and surround by accacias.

3. The surface sands and rhizosphere of selected sites in the Jizan desert region of Saudi Arabia that can support the growth of specific vegetation in a sandy flat desert terrain.

Bacterial diversity of the rhizosphere and nearby surface soil of rice (Oryza sativa) growing in the Camargue (France) Jorge R. Osman1 , Gustavo Fernandes 1 and Michael S. DuBow 1 *

Introduction

The study of bacterial diversity using culture independent methods is now widely accepted, due to the fact that ≤ 1% of bacteria are cultivable under laboratory conditions (Amann, 1995). The development of high throughput nucleic acid sequencing technologies has facilitated the analysis of microbial populations in nature, and frequently uses DNA sequencing of PCR amplified 16S rRNA genes from total extracted DNA as a reference marker in prokaryote identification (Kim et al., 2013).

The rhizosphere is one of the most diverse and complex soil environments on Earth [START_REF] Mendes | The rhizosphere microbiome: significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms[END_REF][START_REF] Pii | Microbial interactions in the rhizosphere: beneficial influences of plant growthpromoting rhizobacteria on nutrient acquisition process. A review[END_REF]. It is the part of soil most influenced by plant root activities and metabolism of root secretions [START_REF] Saharan | Plant growth promoting rhizobacteria : A critical review[END_REF][START_REF] Berendsen | The rhizosphere microbiome and plant health[END_REF]. This zone can contain >10 8 bacterial cells/g of root soil [START_REF] Egamberdieva | High incidence of plant growth-stimulating bacteria associated with the rhizosphere of wheat grown on salinated soil in Uzbekistan[END_REF]. Microbial groups existing in the rhizosphere include Bacteria, Fungi, Nematodes, Protozoa and Algae, with bacteria being the most abundant. The plant rhizosphere environment is highly selective, and identifying the bacteria present in the rhizosphere environment is crucial for understanding beneficial versus deleterious plant-microbe interactions. In addition, other biotic and abiotic factors, such as the physico-chemical soil characteristics, can affect plant and microbial physiology and thus bacterial rhizosphere populations [START_REF] Hinsinger | Rhizosphere: biophysics, biogeochemistry and ecological relevance[END_REF][START_REF] Saharan | Plant growth promoting rhizobacteria : A critical review[END_REF][START_REF] Philippot | Going back to the roots: the microbial ecology of the rhizosphere[END_REF]. Plant growth-promoting rhizobacteria (PGPR) are beneficial for plant health and development. They can act by inhibiting soilborne pathogens, principally via three kinds of interactions (competition, antagonism and hyperparasitism) [START_REF] Raaijmakers | The rhizosphere: a playground and battlefield for soilborne pathogens and beneficial microorganisms[END_REF][START_REF] Pii | Microbial interactions in the rhizosphere: beneficial influences of plant growthpromoting rhizobacteria on nutrient acquisition process. A review[END_REF]. The predominant bacterial groups recognized as promoting plant growth belong to the Rhizobium, Pseudomonas, Azospirillum, Burkholderia and Enterobacter [START_REF] Lagos | Current overview on the study of bacteria in the rhizosphere by modern molecular techniques : a minireview[END_REF]. PGPR can enhance the plant's acquisition of nutrients, using the organic compounds produced by the plants as growth substrates [START_REF] Schenk | Unraveling plant-microbe interactions: can multi-species transcriptomics help[END_REF]. Bacteria can participate in the biogeochemical cycling of the rhizosphere, principally in organic matter mineralization, nitrogen fixation and phosphorus uptake [START_REF] Philippot | Going back to the roots: the microbial ecology of the rhizosphere[END_REF][START_REF] Vacheron | Plant growth-promoting rhizobacteria and root system functioning[END_REF][START_REF] Chaudhary | Nutrients, microbial community structure and functional gene abundance of rhizosphere and bulk soils of halophytes[END_REF].

Rice is one of the major crop plants worldwide, providing food for more than half of the world's population, and rice lagoons represents a significant source (10 to 20%) of atmospheric methane (CH4) emissions [START_REF] Ahn | Characterization of the bacterial and archaeal communities in rice field soils subjected to long-term fertilization practices[END_REF][START_REF] Breidenbach | Seasonal dynamics of bacterial and archaeal methanogenic communities in flooded rice fields and effect of drainage[END_REF][START_REF] Edwards | Structure, variation, and assembly of the root-associated microbiomes of rice[END_REF]. Methane is produced principally by microbial methanogenesis, carried out by anaerobic archaea and methanotrophic bacteria. Controlling natural emissions of global methane by human activities (rice cultivation, landfill, fossil fuel extraction and farming) is crucial for global warming efforts [START_REF] Singh | Microorganisms and climate change: terrestrial feedbacks and mitigation options[END_REF]. Rice plant nutrition can also require large amounts of chemical fertilizers, especially nitrogen and phosphorus, which in high concentrations to considerably affect microorganism biomass and population composition [START_REF] Chen | Differentiated response of denitrifying communities to fertilization regime in paddy soil[END_REF]. The use of PGPR as inoculants in plants can play a beneficial role in the yield and growth of commercial agricultural plants (Schoebitz et al., 2013), while also diminishing fertilizer use.

Studies concerning the bacterial communities present in rice fields under different conditions have been performed. The bacterial community structure of rice fields under long-term fertilization in Suwon (South Korea) via pyrosequencing of PCR amplified 16S rDNA revealed that members of the Chloroflexi phylum were the most abundant, followed by members of the Proteobacteria and Actinobacteria phyla [START_REF] Ahn | Characterization of the bacterial and archaeal communities in rice field soils subjected to long-term fertilization practices[END_REF].

A similar type of study, performed on soils cultivated for >120 years in Chongming Island (China), showed that the predominant members of the bacterial communities were associated to the Proteobacteria and Verrucomicrobia phyla [START_REF] Cui | Bacterial succession during 500 years of soil development under agricultural use[END_REF].

Several studies of the bacterial communities of the rhizosphere of rice were performed in Nishitokyo (Japan) [START_REF] Doi | Analysis of rhizosphere bacteria of rice cultivated in Andosol lowland and upland fields using molecular biological methods[END_REF][START_REF] Doi | Study on rhizosphere bacterial community in lowland rice grown with organic fertilizers by using PCR-denaturing gradient gel electrophoresis[END_REF] and in Sacramento, Davis and Arbuckle (California) in the USA [START_REF] Edwards | Structure, variation, and assembly of the root-associated microbiomes of rice[END_REF]. However, little is known about the bacterial diversity in the rhizosphere of rice under apparent high salinity conditions, such as those seen in the Camargue area of France.

The Camargue is a natural delta triangle of 750 km 2 formed by the two branches of the Rhône river as it flows into the Mediterranean Sea in southern France. This area contains a wide variety of environments, including lakes and marshlands, as well as brackish lagoons and saline soils called "sansouires" (Tamisier and Grillas, 1994;[START_REF] Whyte | A Dictionary of Environmental History[END_REF].

Human activities in the Camargue concern principally salt production (160 km 2 ) near the coast by evaporating seawater, and rice cultivation (150 km 2 ) which represents 30% of France's rice consumption [START_REF] Whyte | A Dictionary of Environmental History[END_REF]. Rice production in the Camargue has a privileged climate, with relatively low temperature variation and much sunlight.

Camargue rice production became a modern reality during WW2 (1942) under the leadership of farmers in the area, with the leveling of the land, the building of a network of irrigation and drainage canals and pumping stations, plus facilities for the storage and processing of the harvested paddy rice [START_REF] Whyte | A Dictionary of Environmental History[END_REF]. In order to control, in part, the potentially adverse effects of salinity that could affect rice cultivation, irrigation systems were constructed to allow leaching of the salt as well as the introduction of other plant species for use in crop rotation [START_REF] Chauvelon | Satellite remote sensing and GIS used to quantify water input for rice cultivation (Rhône delta, France)[END_REF]Chauvelon et al., 2003).

High salinity in agricultural soils is a global problem because high salt concentrations generate osmotic stress in the plant rhizosphere, thus negatively affecting plant development, quality and productivity [START_REF] Paul | Osmotic stress adaptations in rhizobacteria[END_REF][START_REF] Shrivastava | Soil salinity: A serious environmental issue and plant growth promoting bacteria as one of the tools for its alleviation[END_REF].

However, the use of sustainable management agricultural practices, annual crop plant rotation and techniques for water irrigation such as micro-injection in root plant areas, can reduce soil salinity and enhance crop growth and yield [START_REF] Manchanda | Salinity and its effect on the functional biology of legumes[END_REF].

Here, we used pyrosequencing of PCR amplified 16S rDNA genes from total extracted DNA to examine the bacterial biodiversity present in the rhizosphere of rice from the Centre Français du Riz (CFR) in the Camargue. The principal objectives of this work were to identify the bacterial diversity present in the rhizosphere of two cultivated varieties of rice, and nearby surface soil, from five different rice fields in the Camargue region sampled in two different years.

Materials and methods

Sampling area

Plant rhizosphere soils were collected from the Camargue region, situated south of Arles in southern France (43º40 '36"N 4º39'49"E). Five and six sites, respectively, were examined in the Camargue area during October 1 and2, 2013 (Tº=16-26ºC; precipitation (10 days preceding) =0mm) andSeptember 25 and26, 2014 (Tº=11-25ºC; precipitation (10 days preceding) =0mm) (Figure 1). Three different points, located 5 meters apart for each rhizosphere rice variety and surface soil samples, were sampled for each location and then mixed. Control samples (surface soil (5-10 cm)) were taken 5 m away from any vegetation by surface scooping at each site, while plant soil rhizosphere were taken at a depth of 15 to 20 cm carefully separated from the roots. Samples were stored in 50 mL sterile polyethylene conical centrifuge tubes. In the first year of collection, 5 different rice fields from the Centre Français du Riz (CFR), containing "Arelate" (long rice) and "Gageron" (round rice) rice varieties, plus a control (surface soil), were sampled. In the second year of collection, the same 5 sites from 2013 were sampled, but both rice varieties were only growing in one, so an additional site in the southern Camargue area was added (Table 1). The sampling period occurred when the plants were at their late stage of vegetative phase of growth, one week before harvest. Physicochemical analyses of the six sites were performed by the Laboratoire de Developpement Mediterranee (Arles, France) (Table 2).

Genomic DNA extraction

Total DNA was extracted from each sample as previously described (An et al., 2013).

Briefly, two grams of plant rhizospheric soil were resuspended in 1ml of 1/4 TS (Tryptic Soy Broth) for 1 h at 30ºC. To extract DNA, 13.5 ml of extraction buffer (0.1M Tris/HCl pH 8, 0.1M EDTA pH 8, 0.1M Na2PO4, 1.5M NaCl, 1% [w/v] CTAB (Cetyltrimethylammonium bromide), containing 50 μl of self-digested Pronase (20 mg/ml) and 10 μl of boiled and cooled RNAse A (10 mg/ml), were added and incubation continued for 2 h at 37ºC with 220 rpm shaking. Subsequently, 2.5 ml of a 20% SDS [w/v] solution in water were added and a further incubation for 2 h at 65ºC was performed.

The supernatant fluid was collected after a 20 min centrifugation at 6000×g at room temperature. A further extraction was performed on the pellet with 4.5 ml of extraction buffer plus 0.5 ml of 20% (w/v) SDS, mixed by vortexing for 10 s, followed by incubation for 10 min at 65ºC and centrifugation for 15 min at 6000×g at room temperature. The supernatant fluids were then combined. The nucleic acids were extracted by the addition of an equal volume of chloroform/isoamyl alcohol (24:1) and, after centrifugation for 20 min at 6000×g at room temperature, the aqueous phase, containing the nucleic acids, was removed. Then, 0.6 volumes of isopropanol were added and a 1 h incubation at room temperature was performed to precipitate the DNA. Following this, centrifugation for 20 min at 13000×g at room temperature was performed. The DNA pellet was washed with 1 ml of 70% ethanol and subjected to centrifugation at 11000×g for 5 min at 4ºC. The DNA pellets were then dried and resuspended in 50 μl 1/10 TE buffer (1mM Tris-HCl pH 8, 0.1 mM Na EDTA pH 8), incubated overnight at 4ºC gently mixed and then stored at -20ºC.

16S rDNA library preparation and pyrosequencing

The 16S rRNA genes of bacteria were PCR amplified from 1-10 ng of total extracted DNA. PCR reactions (25 μl each) were performed using the universal 16S rDNA bacterial primers 357F (5'-Ad.A + xxxxxxxxxx + ACTCCTACGGGAGGCAGCAG-3') and 802R Start II DNA Polymerase (Thermo Scientific, Sweden) 98ºC for 2 min, followed by 30 cycles of 98ºC for 10 s (denaturation), 56ºC for 10 s (annealing), 72ºC for 15s (polymerization), and a final extension at 72ºC for 2 min; (II) AccuPOL TM DNA Polymerase (Ampliqon, Denmark) 95ºC for 2 min followed by 30 cycles of 95ºC for 30 s (denaturation), 54ºC for 30 s (annealing), 72ºC for 1 min (polymerization), and a final extension at 72ºC for 10 min; (III) Phusion High-Fidelity DNA Polymerase (Thermo Scientific, Sweden) 98ºC for 2 min followed by 30 cycles of 98ºC for 30 s (denaturation), 56ºC for 20 s (annealing), 72ºC for 20s (polymerization), and a final extension at 72ºC for 10 min. The 25 μl PCR reactions were performed using 1-10 ng DNA, 0.1 μM of each primer (Sigma-Aldrich, MO, USA), 0.2 mM dNTP mix (Fermentas, ON, Canada), 1.25 units of thermostable DNA polymerase for I and II and 0.25 units of thermostable DNA polymerase for III, using the corresponding buffers with each polymerase. The PCR products were then combined by sample and polymerase used, and the amplified 16S rDNA fragments were then gel purified after electrophoresis through a 1% agarose gel run in 1X TAE buffer (recipe) using the NucleoSpin Extraction Kit (Macherey-Nagel, Hoerdt, France) according to the manufacturer's instructions. Twenty nanograms of purified PCR product from each sample were mixed for pyrosequencing on a Roche/454 GS Junior Pyrosequencer (Department of Biology, University of Oulu, Finland) for the 2013 set of samples and on a Roche/454 FLX Titanium Pyrosequencer (Microsynth, Switzerland) for the 2014 set of samples.

Bioinformatic analyses

Raw sequences obtained from the GS Junior (2013) and FLX Titanium (2014) pyrosequencing runs were trimmed and selected by their length (>200 nt and <600 nt).

The sequences were retained if they contained no ambiguous base, no homopolymers ≥8 and no more than one mismatch to the sequencing primer, using Mothur software (Schloss et al., 2009). The 5' and 3' adaptors and primers remaining from the sequencing process were removed from the sequences using Cutadapt (Martin, 2011). A second trimming was performed using an 80% quality score with Condetri version 2.2 (Smeds and Künster, 2011). The sequences were screened for potential chimeric sequences detected by both UCHIME (Edgar et al., 2011) and DECIPHER (Wright et al., 2012) algorithms.

Contaminant sequences (Archaea, Chloroplast and Mitochondria) were also removed.

The remaining sequences were assigned into Operational Taxonomic Units (OTUs) using the CD-HIT-OTU method (Wu et al., 2011); and classified using the Silva NGS website with Silva database release 123 (Quast et al., 2013;Yilmaz et al., 2014), with 100% sequence similarity for clustering and 90% as a classification similarity threshold. In order to compare the bacterial richness, diversity and communities among the different samples, we also normalized the number of sequences for each sample by randomly selecting 5074 sequences and 10,142 sequences for the set of samples from 2013 and 2014, respectively, using the sub.sample script in Mothur (Schloss et al., 2009).

Statistical analyses

All statistical analyses were conducted using R version 3.1.2 (R core Team, 2014) using 0.05 as the significance threshold. The Phyloseq package (McMurdie and Holmes, 2013) was used to estimate the diversity and richness indices (Chao1 and Shannon) and to calculate the distance based on Bray-Curtis metrics. Pearson's test correlations, Principal Component Analysis (PCA) and Unweighted Pair Group Method with Arithmetic mean (UPGMA) were based on relative abundances and on Bray-Curtis distances, respectively, using the "ade4" R package (Chessel et. al., 2004). Venn diagrams were performed using the "VennDiagram" R package [START_REF] Chen | Consistent increase in abundance and diversity but variable change in community composition of bacteria in topsoil of rice paddy under short term biochar treatment across three sites from South China[END_REF].

Permutation analyses of variance (PERMANOVA) were conducted using the adonis function from the vegan package (Oksanen et al., 2013) with 1000 permutations based on Bray-Curtis dissimilarity between samples, for the most abundant (>1%) phyla/genera, and controlling the permutations to not permutate between samples from different sampling sites. Explaining variables were composed of the rhizosphere variety (Arelate and Gageron) and surface soil samples; geographical location; year of collection and physical-chemical parameters. Effects of significant variables on different phyla/genera were accessed by ANOVA analysis. All the multiple tests were corrected using the false discovery rate (FDR).

Samples comparison with other rice fields

We compared the bacterial communities found in the Camargue CFR samples with published descriptions of bacterial communities in the rhizosphere of rice and nearby surface soil from other studied areas by selecting three different regions where the late stage of vegetative growth of rice plants were analyzed in a manner similar to the one we used. We examined the relative abundance of bacterial phyla of samples from three different regions which were also analyzed using PCR amplified 16S rDNA pyrosequencing for bacterial identification. The first samples corresponds to those from a surface soil (1.surface) and rice rhizosphere (2.Rhizo) at 20 cm depth from a rice paddy in Sacheon, South Korea [START_REF] Lee | High resolution depth distribution of Bacteria, Archaea, methanotrophs, and methanogens in the bulk and rhizosphere soils of a flooded rice paddy[END_REF]. The second region corresponds to a rice rhizosphere (3.rhizo) and surface soil sample (4.surface) from an experimental farm of Montemor-o-Velho, in central Portugal [START_REF] Lopes | Bacterial community variations in an alfalfa-rice rotation system revealed by 16S rRNA gene 454-pyrosequencing[END_REF]. The third region corresponds to samples from three different areas in California (Arbuckle, Davis and Sacramento,) [START_REF] Edwards | Structure, variation, and assembly of the root-associated microbiomes of rice[END_REF]. The samples were named 5.Surface, 6.Rhizo for the Arbuckle samples, 7.Surface, 8.Rhizo for the samples from Davis and 9.Surface, 10.Rhizo for the samples from Sacramento.

Samples Database deposition

The raw DNA sequences from all the samples were deposited in the Sequence Read Archive (SRA) GenBank database, under the accession number SRP074948

Results

Physical and chemical properties of the Camargue CFR sites

The 6 different sites from the rhizosphere of rice plants, plus nearby surface soil samples, from the Camargue CFR were analyzed for selected physical and chemical properties (Table 2). These results show that the texture of the soil sites were composed principally of fine silt (26.2-46%), coarse silt (5.7-26%) and clays (16.7-35.7%) and, in a lower proportion, of coarse sand (0.9-15.6%). The chemical composition of nutrients varies among the different sites and were found to range from 22 to 37 g/kg (organic matter), 13-22 g/kg (organic carbon) and from 0.39 to 1.61 g/kg (total N). Soils were found to contain higher proportions of CaO than P2O5, MgO and K2O (Table 2). The pH values do not show significant variability among the samples, ranging from 8.2 to 8.6. Salinity levels (Na2O) vary by almost 10-fold among the samples, depending on the geographical location. We observed that sites 5 and 6 contain higher salinity (from 0.142 to 0.73g/kg) concentrations in their soils than the other samples, where the salinity varied from 0.056 to 0.11 g/kg, with the exception of site 3 in 2014 (0.251 g/kg). The first two axes of a PCA explained 63.1% of the total variance (Figure 2) and we observed that the principal properties which influenced the differences among the sites were the proportions of organic carbon and organic material (higher in site 1 of 2013), Na2O and chlorides (higher in site 5 from 2014), coarse silt (higher in site 4 from 2014), coarse sand and P2O5 (higher in site 1 of 2014). The other physical and chemical properties do not appear to have a strong influence in the physical and chemical properties distribution of the other sites from the Camargue CFR.

Pyrosequencing data

Total DNA was extracted from all soil samples, followed by pyrosequencing of PCR amplified V3-V4 regions of the 16S rRNA gene. For samples collected in 2013 we obtained 147,850 sequences from a total of 15 samples using the GS Junior pyrosequencing. For the 11 samples collected in 2014 we used the FLX Titanium sequencing and obtained 393,019 sequences (Table S.1). After quality control trimming, a total of 138,547 sequences remained for the set of samples from 2013 and 325,834 from those of 2014. Chimeric sequences were detected using two different programs (see Materials and Methods) and removed from all samples, with their proportions varying between 6%-16%. Non-bacterial sequences (Archaea, chloroplast and mitochondria) detected by the Silva database release 123 were also removed, representing < 0.1 % of the total sequences, with the exception of sample C5_2014 which contained ~ 8% of these type of sequences.

Richness and diversity of rhizospheric and surface soil samples from the

Camargue CFR

OTUs were defined by a 97% similarity level cutoff and we found between 1193 and 4630 detectable OTUs per sample. Richness, calculated by the Chao1 estimator, suggests that we are able to identify between 49.6% and 76.7% of the total species in the samples, while samples from 2014 represented at least 67% coverage (Table 3). It is interesting to note that no differences were observed between the rhizosphere and nearly surface soil samples concerning the richness estimators (OTUs, Chao1), even when comparing the set of samples from 2013 and 2014. Shannon diversity index, varied from 6 and 7.2 for the samples from 2013 and 5.9 and 7.4 for the samples from 2014 (Table 3). Normalization of the sequences to 5,074 and 10,142 for the set of samples from 2013 and 2014, respectively, did lower the OTUs and Chao1 richness estimators, however, Shannon diversity index remained relatively stable.

Phyla composition in the Camargue CFR samples

Normalized sequences were classified using the Silva 123 database with the Silva NGS website at a 90% threshold. We were able to classify >98% of the total sequences at the phylum level. We found that >90% of the sequences were able to be classified into 15 phyla, with their proportions varying among the different samples and years of collection (Figure 3). Phyla present in all samples are belong to the Proteobacteria (26.9-47.4%), Acidobacteria (4-45.1%), Bacteroidetes (3.3-17.4%) and Chloroflexi (4-16.1%) phyla.

Generally, less abundant phyla present in the samples were found to belong to the Gemmatimonadetes (0.8-9.8%), Firmicutes (0.4-19.8%) and Planctomycetes (0.7-2.8%) phyla. A UPGMA tree, based on the phyla communities, was constructed using the Bray-Curtis index to better understand the population relationships among the samples (Figure 4). It can be seen that most of the samples cluster by year. In addition, the most similar samples were collected from nearby surface soils C5 and C6 (2014), which present highly similar proportions of phyla composition, followed by samples C1, 6G and 1A (2014), 4G, 3A and C4 (2013) while the most dissimilar sample was found to be 3G _2013, which differs from all the other samples in the relatively high proportions of members belonging to the Firmicutes phylum (~ 20%) (Figure 3). The differences in the phyla communities do not seem to be influenced by the different rhizosphere soils from Arelate, Gageron rice varieties and nearby surface soil samples. The first two axes of the PCA (Figure 5), which explained 54.6% of the total variance observed on the UPGMA tree where we detect that the samples are clustered by year of collection into different groups, influenced principally by the proportions of members belonging to the Acidobacteria, Actinobacteria, Chloroflexi and Gemmatimonadetes phyla for the samples collected in 2014 and by members belonging to the Proteobacteria, Bacteroidetes and Firmicutes phyla for the samples collected in 2013. The exception is sample 5G from 2014, which is similar based on the phyla proportions of samples 5G and 5A from 2013, which are present in the same physical site.

Phyla comparisons of the Camargue CFR and other regions

Bacterial phyla communities of the samples are shown in Figure 3. We constructed an UPGMA tree of all the samples together (Figure 4). The three different regions were found to contain distinct bacterial phyla communities. The South Korea samples (sample 1.Surface and 2.Rhizo) clustered together having lower abundance of Acidobacteria and Proteobacteria than the Camargue samples. These samples clustered together with sample 3G_2013 from Camargue. We also found that members belonging to the Chloroflexi phylum are present in significant proportions. The second site, from Portugal (3.Rhizo and 4.Surface) clustered together with some Camargue CFR samples, in particular with sample C2_2014. The three different locations from the USA in California clustered together and in particular by location, showing no significant differences between the rhizosphere of rice and the surface soil samples.

Abundant genera in the Camargue CFR samples

We identified the major genera (≥1%) of all samples present in the soil rhizosphere of the Arelate and Gageron rice varieties and nearby surface soil samples (Figure 6). The most abundant genera present in the soil rhizosphere (Arelate and Gageron) and nearby surface soil samples belong to the Sphingomonas, Blastocatella, Thiobacillus, Anaeromyxobacter and Bryobacter genera. However, less abundant genera, belonging to the Pontibacter, Flavobacterium and Pseudomonas genera, were only detected in nearby surface soil samples. Members belonging to the Haliangium genus, generally considered halophilic [START_REF] Fudou | Haliangium ochraceum gen. nov., sp. and Haliangium tepidum sp. nov.: novel moderately halophilic myxobacteria isolated from coastal saline environments[END_REF], were also detected in the surface soil samples, with their abundance being > 1% in sample C6 (Figure 6C). Using a Venn diagram (Figure 7), we found that 339 and 349 genera in 2013 and 2014, respectively in the Camargue CFR samples were shared among either the rice rhizosphere or nearby surface soil samples. In 2013 the Gageron rice variety rhizosphere had the highest number (115) of only found associated with that variety (here after called "unique") genera, while in 2014 the nearby surface soil samples was highest, with 110 members. The numbers of genera present only in the Arelate rice variety samples were 53 and 46 in 2013 and 2014, respectively.

Changes of bacterial communities by year, type of samples and sites

Using a PERMANOVA analysis, we compared the nearby surface soil samples between 2013 and 2014, and the bacterial communities among the rice varieties and nearby soil surfaces from 2013. For the nearby soil surface samples, we found that samples varied significantly between years at phylum (r 2 =0.489, n=9, p-value=0.0069) and genus (r 2 =0.3122, n=9, p-value=0.0159) level. No significant differences were found among the two rice varieties and nearby soil surface samples in 2013 at phylum level (r 2 =0.1048, n=15, p-value=0.699), however at the genus level their abundances were found to vary between the sites (r 2 =0.1864, n=15, p-value=0.0259) and the type of sample (Arelate, Gageron and/or surface soil) (r 2 =0.3812, n=15, p-value=0.0259). Bacterial communities of Arelate and Gageron rice variety were not compared between years because of the samples missing in 2014.

Discussion

We described the rhizosphere bacterial communities [START_REF] Philippot | Going back to the roots: the microbial ecology of the rhizosphere[END_REF] of 2 varieties of rice in the Camargue region of southern France. The Camargue region is a saline wetland area of 750 km 2 , which is formed by the division of the Rhône River into two branches 40 km upstream from the coast (De Montety et al., 2008). In the Camargue, soil salinity is attempted to be controlled by a series of pumping stations and canal networks, bringing fresh Rhône river water to the agricultural lands and returning the waste to the Vaccares central lagoon (Chauvelon et al., 2003). The crop is flooded prior to the sowing period (April) and dried over time until the harvest in late September and early October (Tourenq et al., 2001), when we collected our samples. We explored the bacterial richness and diversity in the soil rhizosphere from two rice varieties (Arelate and Gageron), plus nearby surface soil from five sites from the Centre Français du Riz (CFR) fields in 2013 and six sites in 2014. The soil rice rhizosphere samples were collected at the late stage of plant growth, as it has been reported that the rhizosphere bacterial communities do not significantly change across different plant growth stages [START_REF] Breidenbach | Seasonal dynamics of bacterial and archaeal methanogenic communities in flooded rice fields and effect of drainage[END_REF].

Selected physical and chemical properties of the studied sites were also analyzed. No significant trends were observed among the sites, while the salinity (chlorides and Na2O) was found to be relatively higher in sites 5 and 6, while a basic pH (average = 8.4) was seen among the sites in both years of sample collection (2013 and 2014). In contrast, lower pH values were found in rice fields from the Philippines, also using flooded regimes, but with added N-fertilization [START_REF] Breidenbach | Seasonal dynamics of bacterial and archaeal methanogenic communities in flooded rice fields and effect of drainage[END_REF]. In rice fields from South Korea under long term fertilization practices, a lower pH of the soils subjected to long term inorganic fertilization was also observed [START_REF] Ahn | Characterization of the bacterial and archaeal communities in rice field soils subjected to long-term fertilization practices[END_REF]. It has been reported that nitrogen fertilization can lead to a decrease in soil pH by increasing acids in the soil via nitrification [START_REF] Ramirez | Consistent effects of nitrogen fertilization on soil bacterial communities in contrasting systems[END_REF], and soil pH can have a strong influence on the composition of bacterial communities (Lauber et al., 2009).

The richness and diversity of each sample was estimated and we observed that samples from site 5 contain a higher bacterial richness (Chao1) and diversity (Shannon) than the other sites, in particular, in the rhizosphere of Arelate from 2013 and Gageron from 2014, as well as in the bulk soil from 2014. Altogether, site 5 was found to contain a relatively high salinity compared to the other sites, along with site 6. Salinity was found to be an important determinant of microbial community composition (Lozupone and Knight, 2007), as was also observed in the rhizosphere of halophyte plants [START_REF] Yang | Salinity altered root distribution and increased diversity of bacterial communities in the rhizosphere soil of Jerusalem artichoke[END_REF], where salt stress was found to increase the diversity of rhizosphere bacterial communities.

Thus, agricultural practices can influence the bacterial population richness and diversity of the rhizosphere of rice fields, via changes in soil physical and chemical properties [START_REF] Faoro | Influence of soil characteristics on the diversity of bacteria in the Southern Brazilian Atlantic forest[END_REF][START_REF] Postma-Blaauw | Soil biota community structure and abundance under agricultural intensification and extensification[END_REF].

The proportions of bacterial phyla found in the rhizosphere and nearby surface soil samples were found not to show significant differences in 2013 and the major phyla present in both belonged to the Acidobacteria, Proteobacteria, Chloroflexi, Bacteroidetes Gemmatimonadetes and Firmicutes (∼ 80%) (Figure 3). Members belonging to the Proteobacteria phylum, one of the most ubiquitous and diverse bacterial groups in soils [START_REF] Youssef | Comparison of species richness estimates obtained using nearly complete fragments and simulated pyrosequencing-generated fragments in 16S rRNA genebased environmental surveys[END_REF] occupied the highest abundance in our rhizosphere and nearby surface soil samples. Members belonging to this phyla, as well as members belonging to Acidobacteria phylum, are abundant in soils (Janssen, 2006;[START_REF] Youssef | Comparison of species richness estimates obtained using nearly complete fragments and simulated pyrosequencing-generated fragments in 16S rRNA genebased environmental surveys[END_REF], also found in high proportions in our samples. Proteobacteria members were described to be widespread in many soil ecosystems, including the rhizospheres, wetland soils and peat soils [START_REF] Spain | Abundance, composition, diversity and novelty of soil Proteobacteria[END_REF]. Their relative abundance increases under high organic carbon availability in soils (Fierer et al., 2007;[START_REF] Eilers | Shifts in bacterial community structure associated with inputs of low molecular weight carbon compounds to soil[END_REF]. Members of this phylum were found to diminish among the sites in the 2 different years of collection, in particular in sites 1, 2 and 3, where their proportions were found to decrease between 5-10% in 2014 (Figure 3). Members belonging to the Acidobacteria phylum were also found to vary in their proportions by collection year and sampling site (Figure 3) and have been described as a group sensitive to the addition of bioavailable carbon (Fierer et al., 2007). However, due to their low capacity for growth under laboratory conditions, little is known about their physiology and metabolism [START_REF] Eilers | Shifts in bacterial community structure associated with inputs of low molecular weight carbon compounds to soil[END_REF]. In contrast to what we observed, members of this phyla are commonly found in soil ecosystems with lower pH values (Fierer and Jackson, 2006), though this is not a determining characteristic of all members of the phylum [START_REF] Ward | Three genomes from the phylum Acidobacteria provide insight into the lifestyles of these microorganisms in soils[END_REF].

Members belonging to the Chloroflexi phylum represent 5-15 % of the classified sequences in our samples. The variation of the proportions of this phyla was also observed between the two years of samples collection (higher abundance in 2014), however, the sites geographical locations do not seem to affect their abundances (Figure 3). Members of this phyla are commonly found in the rhizosphere and nearby surface soil of rice fields [START_REF] Ahn | Characterization of the bacterial and archaeal communities in rice field soils subjected to long-term fertilization practices[END_REF][START_REF] Zhao | Effects of organicinorganic compound fertilizer with reduced chemical fertilizer application on crop yields, soil biological activity and bacterial community structure in a rice -wheat cropping system[END_REF][START_REF] Breidenbach | Seasonal dynamics of bacterial and archaeal methanogenic communities in flooded rice fields and effect of drainage[END_REF] and their proportions appears to increase with depth in rhizosphere and nearby surface soil samples [START_REF] Lee | High resolution depth distribution of Bacteria, Archaea, methanotrophs, and methanogens in the bulk and rhizosphere soils of a flooded rice paddy[END_REF]. Members belonging to this phylum were reported to be the primary degrader of polysaccharides in the anoxic zones of rice fields [START_REF] Ahn | Characterization of the bacterial and archaeal communities in rice field soils subjected to long-term fertilization practices[END_REF].

Members belonging to the Firmicutes phylum shows a marked variability among the samples, and in particular in sample 3G_2013, where their proportion is higher (∼ 20%) than other samples (∼ 1 to 8 %). Members of this phyla were described to present a stochastic distribution compared to the other bacterial phyla (Andrew et al., 2012).

Members belonging to the Bacteroidetes and Gemmatimonadetes phyla were also present in our samples (Figure 3) and were found to vary in their proportions between the two years of collection (Figure 3). Members belonging to the Bacteroidetes phylum have been found to be widespread in several different ecosystems, where they can function as degraders of polymeric organic matter (Fierer et al., 2007;[START_REF] Thomas | Environmental and Gut Bacteroidetes: The Food Connection[END_REF]. They have been observed to be the major phyla living under anoxic conditions present in rhizosphere and nearby surface soil ecosystems (Martinez-Alonso et al., 2010). Members of the Gemmatimonadetes phylum have not been well studied due to their difficulty of laboratory cultivation. However, they have been shown to be present in different soil ecosystems, especially dried soils, while at low abundance (DeBruyn et al., 2011). Less abundant phyla were also detected in our samples as Nitrospira phylum which members are known to participate in soil nitrification [START_REF] Baker | Community transcriptomic assembly reveals microbes that contribute to deep-sea carbon and nitrogen cycling[END_REF]. Members belonging to the Cyanobacteria phylum were found mostly (>1%) in samples C1_2013 (∼7%) and 3G_2013 (∼2.8%). Members belonging to this phylum were known to be present in soil crust in arid ecosystems and are recognized as primary producers, along with their capacity of nitrogen fixation [START_REF] Powell | Desert crust microorganisms, their environment, and human health[END_REF].

At the genus level, > 50% of the sequences could not be classified using the Silva database (release 123, July 2015). The most abundant genera in all the rhizosphere and nearby surface soil samples belonged to the Sphingomonas, Thiobacillus, Anaeromyxobacter and Bryobacter genera. Members belonging to the Sphingomonas and Lysobacter genera were also found in the rhizosphere of rice fields from Jinju, South Korea under no tillage practices [START_REF] Aslam | Diversity of the bacterial community in the rice rhizosphere managed under conventional and notillage practices[END_REF] and the rhizosphere of Jerusalem artichokes from Jiangsu, China, suggesting their presence in the rhizosphere of halophytic plants [START_REF] Yang | Salinity altered root distribution and increased diversity of bacterial communities in the rhizosphere soil of Jerusalem artichoke[END_REF]. Members of the Sphingomonas genus, also found in a rice rhizosphere sample from Goiás, Brasil, are recognized for their capacity for nitrogen fixation and denitrification [START_REF] Videira | Occurrence and diversity of nitrogen-fixing Sphingomonas bacteria associated with rice plants grown in Brazil[END_REF] and Lysobacter for their capacity to inhibit plants pathogens [START_REF] Islam | Suppression of damping-off disease in host plants by the rhizoplane bacterium Lysobacter sp. Strain SB-K88 is linked to plant colonization and antibiosis against soilborne peronosporomycetes[END_REF]. Some genera found in the rhizosphere or nearby surface soil samples from the Camargue have also been observed in other rice field studies. For example, members belonging to the Anaeromyxobacter genus were found to be present in rice fields in Suwon (South Korea) [START_REF] Ahn | Characterization of the bacterial and archaeal communities in rice field soils subjected to long-term fertilization practices[END_REF] as well in the rhizosphere of rice at the vegetative stage of growth in Los Banos (Philippines) [START_REF] Knief | Metaproteogenomic analysis of microbial communities in the phyllosphere and rhizosphere of rice[END_REF][START_REF] Breidenbach | Seasonal dynamics of bacterial and archaeal methanogenic communities in flooded rice fields and effect of drainage[END_REF] and rice in Vercelli (Italy) [START_REF] Breidenbach | Microbial community structure in the rhizosphere of rice plants[END_REF]. This genus, as well as members of the Thiobacillus genus, was also found in rice fields from Suwon in South Korea, and have been recognized for their capacity as iron reducers and role in carbon cycle dynamics in the rhizosphere of rice due to their capacity to be active in zones where organic and inorganic electron donors are present [START_REF] Treude | Strain FAc12, a dissimilatory iron-reducing member of the Anaeromyxobacter subgroup of Myxococcales[END_REF][START_REF] Ahn | Characterization of the bacterial and archaeal communities in rice field soils subjected to long-term fertilization practices[END_REF]. Members belonging to the Geobacter genus, found principally in the rhizosphere of the Gageron rice variety, were reported as abundant in the late plant stage rhizosphere of rice from Vercelli (Italy) [START_REF] Breidenbach | Microbial community structure in the rhizosphere of rice plants[END_REF] and they are recognized as an iron reducing [START_REF] Hori | Identification of iron-reducing microorganisms in anoxic rice paddy soil by 13C-acetate probing[END_REF] and denitrifying bacteria [START_REF] Chen | Consistent increase in abundance and diversity but variable change in community composition of bacteria in topsoil of rice paddy under short term biochar treatment across three sites from South China[END_REF].

Members of the Bradyrhizobium genus, found in the rhizosphere of the Gageron rice variety and nearby surface soil samples from the Camargue, are known as N2-fixing and reducers of N2O to N2 [START_REF] Anderson | Biochar induced soil microbial community change: Implications for biogeochemical cycling of carbon, nitrogen and phosphorus[END_REF] and they have been previously observed rice rhizosphere samples from Los Banos, Philippines [START_REF] Knief | Metaproteogenomic analysis of microbial communities in the phyllosphere and rhizosphere of rice[END_REF] and as part of the bacterial rhizosphere diversity in paddy rice top soils from southern China (Chen et al., 2015).

We found that members belonging to the Syderoxydans genus were found in the rhizosphere of Arelate rice variety and nearby surface soil samples, varying in a significant proportion between the two years of collection (Figure S.2). Members of this genus have been described as Fe (II) oxidation [START_REF] Kanaparthi | Chemolithotrophic nitrate-dependent Fe (II)-oxidizing nature of actinobacterial subdivision lineage TM3[END_REF] and have been reported as major members in the rhizosphere of soil associated with rice roots, where they participate in the iron coating formations on rice roots in paddy soils from China (Schmidt and Eirckhost, 2013). However, we were unable to observe iron coatings on our rice root samples (data not shown). We were able to detect members belonging to the Haliangium genus, whose members are composed of halophilic bacteria and have been previously isolated from saline soil environments [START_REF] Fudou | Haliangium ochraceum gen. nov., sp. and Haliangium tepidum sp. nov.: novel moderately halophilic myxobacteria isolated from coastal saline environments[END_REF]. This genus was found to be more abundant (>1%) in the surface soil from the site 6 (Figure 6C) in the southern Camargue area, where soil salinity was the highest.

Bacterial communities present as part of the native diversity of the rhizosphere and nearby surface soil samples from the Camargue were found to significantly vary in their abundance between the two years of collection as well as by geographical location.

However, only one group of halophilic bacteria was detected in the soil rhizosphere of one of our samples, suggesting that irrigations efforts made in the 1950´s in the Camargue to control soil salinity, were able to create a suitable environment for cultivation (Chauvelon et al., 2003;[START_REF] Whyte | A Dictionary of Environmental History[END_REF].

Figure 7

Venn diagrams of the total bacterial genera communities of the rhizosphere and surface soil samples from the Camargue CFR sites. This figure was generated using the CRAN package in R software

Introduction

Saline environments are widely distributed on Earth and represented by both saline lakes and soils [START_REF] Ventosa | Halophilic and Halotolerant Micro-Organisms from soils[END_REF]. The Camargue region in the south of France is a delta triangle of 1450 km 2 formed by the Rhône River as it flows into the Mediterranean Sea.

The Camargue area sustains a wide diversity of organisms, such as aquatic birds (the most famous of which are pink flamingos), horses, invertebrates and plants. This region is composed of numerous wetlands, represented principally by freshwater marshes and hyper-saline lagoons, with average salinity decreasing from south to north from 320 g liter -1 to 37g liter -1 of NaCl [START_REF] Britton | Inventory and classification of the wetlands of the Camargue (France)[END_REF][START_REF] Britton | An ecological account of a Mediterranean salina: the salin de Giraud, Camargue (S. France)[END_REF]Tamiser and Grillas, 1994).

The Camargue has been recognized by UNESCO as a Biosphere Reserve since 1977, though human activities such as rice growing and salt harvesting are still present [START_REF] Heurteaux | Modifications du régime hydrique et salin des étangs du système de Vaccarès (Camargue, France) liées aux perturbations anthropiques des cinquante dernières années[END_REF][START_REF] Heurteaux | Essai de quantification des termes du bilan hydrique des étangs du système Vaccarès (Camargue, France)[END_REF][START_REF] Giraud | An innovative modelling concept for integrated water resources management linking hydrological functioning and socio-economic behaviour -The Hérault catchment case study, south of France[END_REF]Chauvelon et al., 2003).

Moreover, intensive agriculture, principally rice farming, can pose potential ecological risks for the Camargue area due to the presence of pesticides, fertilizers and metal based anti-fouling paints [START_REF] Comoretto | Pesticides in the Rhône river delta (France): basic data for a field-based exposure assessment[END_REF][START_REF] Comoretto | Runoff of pesticides from rice fields in the Ile de Camargue (Rhône river delta, France): field study and modeling[END_REF][START_REF] Briant | Trace elements in the sediments of a large Mediterranean marina (Port Camargue, France): levels and contamination history[END_REF].

Salinity can be an important determinant of soil microbial community composition (Lozupone and Knigth, 2007). In nature, salinity is directly proportional to alkalinity, and microorganisms living in saline soils are often halotolerant and halophilic, as they require high salt levels to maintain their membrane integrity and enzyme stability (20)[START_REF] Kociolek | Oligotrophy : the forgotten end of an ecological spectrum[END_REF][START_REF] O'malley | The nineteenth century roots of everything is everywhere[END_REF][START_REF] Whitman | Perspective Prokaryotes: The unseen majority[END_REF][START_REF] Singh | Soil genomics Soil genomics[END_REF][START_REF] Alain | Cultivating the uncultured: limits, advances and future challenges[END_REF][START_REF] Pham | Cultivation of unculturable soil bacteria[END_REF][START_REF] Zengler | High-throughput cultivation of microorganisms using microcapsules[END_REF][START_REF] Hugenholtz | Identifying microbial diversity in the natural environment: a molecular phylogenetic approach[END_REF][START_REF] Torsvik | High diversity in DNA of soil bacteria[END_REF][START_REF] Amann | Phylogenetic identification and in situ detection of individual microbial cells without cultivation[END_REF]. Most halophilic bacteria isolated from soils are heterotrophic. In hypersaline waters, the predominant bacterial groups are Gram-negative, while in hypersaline soils they appear to be predominantly Gram-positive [START_REF] Ventosa | Halophilic and Halotolerant Micro-Organisms from soils[END_REF]. However, the microbiota of hypersaline soils are more similar to those found in non-saline soils than to those of hypersaline waters. Thus, microbial biodiversity may be more closely determined by the habitat than by other environmental factors such as high salinity [START_REF] Ventosa | Halophilic and Halotolerant Micro-Organisms from soils[END_REF]. Many studies have been performed concerning the microbial composition and function of saline environments, such as the Piana del signore, a semiarid Mediterranean location in Italy [START_REF] Canfora | Salinity and bacterial diversity: to what extent does the concentration of salt affect the bacterial community in a saline soil?[END_REF], a saline lake in the Monegros desert in Spain [START_REF] Casamayor | Microbial biodiversity in saline shallow lakes of the Monegros Desert, Spain[END_REF], La Sal del Rey in southern Texas, (USA) [START_REF] Hollister | Shifts in microbial community structure along an ecological gradient of hypersaline soils and sediments[END_REF], aquatic samples across the Baltic Sea area in Europe [START_REF] Dupont | Functional tradeoffs underpin salinity-driven divergence in microbial community composition[END_REF], ponds in Eilat, Israel (Sorensen et al., 2009), and the Pearl River in southern China [START_REF] Xie | Salinity-dominated change in community structure and ecological function of Archaea from the lower Pearl River to coastal South China Sea[END_REF], among others.

The microbiology of complex biofilms, such as microbial mats, that are vertically laminated and stratified microbial communities compressed into a few millimeters of sedimentary structures, are also relevant to study [START_REF] Van Gemerden | Microbial mats: A joint venture[END_REF][START_REF] Berlanga | Phylogenetic diversity and temporal variation in the Spirochaeta populations from two Mediterranean microbial mats[END_REF]. These ecosystems, represented as a complex laminae system, are exposed to diverse environmental conditions such as salinity, pH, temperature, UV radiation, water availability, and hydrodynamic conditions [START_REF] Grötzschel | Effect of oxygen concentration on photosynthesis and respiration in two hypersaline microbial mats[END_REF][START_REF] Des Marais | Biogeochemistry of hypersaline microbial mats illustrates the dynamics of modern microbial ecosystems and the early evolution of the biosphere[END_REF][START_REF] Decker | Mathematical simulation of the diel O, S, and C biogeochemistry of a hypersaline microbial mat[END_REF]. Fourçans et al. ( 2004) studied the microbial composition and bacterial populations of microbial mats in the Camargue region, but were restricted to the hypersaline area called the "Salin de Giraud" containing water salinity of more than 70% (w/v). They showed, by DGGE (denaturing gradient gel electrophoresis) analysis of PCR amplified 16S rDNA fragments and measures of local light and oxygen, that the microbial mat is mainly composed of Cyanobacteria in the first few millimeters and sulfur reducing bacteria (SRB) in the underlying microlayers. They also studied the migration of phototrophic bacterial populations with abiotic and biotic factors in a diel cycle, showing that the oxygen concentration, pH and biomass influenced the distribution of these microorganisms across the mat [START_REF] Fourçans | Vertical migration of phototrophic bacterial populations in a hypersaline microbial mat from Salins-de-Giraud (Camargue, France)[END_REF]. They also showed that there were less SRBs at higher oxygen levels [START_REF] Fourçans | Molecular analysis of the spatio-temporal distribution of sulfate-reducing bacteria (SRB) in Camargue (France) hypersaline microbial mat[END_REF]. Other patterns of specific microbial communities present in saline mats from the Camargue have been examined, such as Spirochaeta genus diversity [START_REF] Berlanga | Phylogenetic diversity and temporal variation in the Spirochaeta populations from two Mediterranean microbial mats[END_REF], denitrifier communities [START_REF] Desnues | Seasonal and diel distributions of denitrifying and bacterial communities in a hypersaline microbial mat (Camargue, France)[END_REF], halophilic archaea capable of degrading hydrocarbons [START_REF] Tapilatu | Isolation of hydrocarbon-degrading extremely halophilic archaea from an uncontaminated hypersaline pond (Camargue, France)[END_REF], the evolution of bacterial communities after contamination by heavy fuel oil [START_REF] Bordenave | Effects of heavy fuel oil on the bacterial community structure of a pristine microbial mat[END_REF] and microbial diversity composition based on day-time measures [START_REF] Villanueva | Analysis of diurnal and vertical microbial diversity of a hypersaline microbial mat[END_REF]. However, most microbes are not cultivable under laboratory condition (Amaan et al., 1995), and these prior studies did not use high throughput sequencing to discern total bacterial communities.

Here, we examine the bacterial diversity present in saline soils, lagoons and marsh sediments from the Camargue area using pyrosequencing of the V3-V4 regions of the 16S rRNA gene. The principal objectives of this research were to examine spatial and temporal heterogeneity in bacterial communities from sediment and soil samples from 17 sites of the Camargue with two sampling campaigns one year apart.

Materials and Methods

Sampling

Soil and sediment samples were collected from 17 sites in the Camargue area during the 1 st and 2 nd of October, 2013 (Tº=16-26ºC; precipitation=0mm) and 25 and 26 September, 2014 (Tº=11-25ºC;precipitation=0mm). During the 10 days preceding the sampling dates, the mean temperature range varied between 15ºC and 26ºC for 2013 and between 17ºC and 26ºC for 2014. The last precipitation for 2013 occurred on September 29 th ( 23mm) and for 2014 on September, 24 th (40 mm). Samples were collected from different types of environments: wetlands such as salt-saturated lagoons, man-made canals, temporarily dry and/or flooded brackish swamps zones called "sansouires" and sandy soils (Table S.2). Salinity levels varied depending on the geographical location (Table 1 and Figure 1). At each site, three different points, located several meters apart, were sampled and then mixed. Sediments were sampled on the surface, from 0.5 to 5 m from the shore, at water depth ≤ 40 cm, and both soils and sediments were sampled by scooping, into a 50 ml sterile polypropylene centrifuge tube, from the uppermost 5 cm.

Total DNA extraction

Total DNA was extracted from each sample using the protocol from An et al., 2013 ml of extraction buffer plus 0.5 ml of 20% (w/v) SDS, mixed by vortexing for 10 s, followed by incubation for 10 min at 65ºC and centrifugation for 15 min at 6000×g. The supernatant fluids were then combined. The nucleic acids were extracted by the addition of an equal volume of chloroform/isoamyl alcohol (24:1) and, after centrifugation for 20 min at 6000×g, the aqueous phase containing the nucleic acids was removed. Then, 0.6 volumes of isopropanol were added and 1 h incubation at room temperature was performed to precipitate the DNA. Following this, centrifugation for 20 min at 13000×g was performed. The DNA pellet was washed with 1 ml of 70% ethanol and centrifuged at 11000×g for 5 min at 4ºC. The DNA pellets were then dried and resuspended in 50 μl 1/10 TE buffer (1mM Tris-HCl pH 8, 0.1 mM Na EDTA pH 8), incubated overnight at 4ºC and then stored at -20ºC.

Quantification of bacterial abundance by quantitative PCR (qPCR)

Bacterial 16S rRNA gene quantification was performed using a PikoReal 24 Real-Time PCR System (Thermo Fisher Scientific Inc.), on 24 well plates, with the primer sets listed in Table 2. The 25 µL reaction mixture contained 12.5 µL Maxima SYBR Green/ROX qPCR Master Mix 2X (Thermo Fisher Scientific Inc.), 0.3 µM of forward and reverse primers and 1 µL of DNA template. The temperature cycling program was: denaturation at 95°C for 10 min, 40 cycles of denaturation at 95°C for 15s, annealing at the appropriate temperature (Table 2) for 15s and extension at 72°C for 30s and a final extension at 72°C for 5 min. SYBR green fluorescence was measured at the end of each cycle. Amplification specificity was checked with a melting curve. Because no significant differences were observed between linear versus circular plasmids as templates for the establishment of standard curves (Oldham and Duncan, 2012), non-linearized plasmids containing the 16S rRNA gene cloned from Escherichia coli were used as controls. For each plate, five duplicate standard dilutions, ranging from 22,500 to 9×10 7 for bacterial quantification were prepared in 25 µL reaction mixtures. Only results showing amplification efficiencies product from each sample were mixed for pyrosequencing using a Roche/454 FLX Titanium Pyrosequencer (Microsynth, Switzerland).

Bioinformatic data analyses

Raw DNA sequences obtained from pyrosequencing were extracted and grouped by their MID sequences, trimmed and selected by their length (>200 nt and <600 nt). The sequences were retained if they contained no ambiguous base, no homopolymers ≥8 and no more than one mismatch to the sequencing primer, using Mothur software (Schloss et al., 2009). Adaptors remaining from the sequencing process were removed from the sequences using Cutadapt (Martin, 2011). A second trimming based on quality score was performed using a 80% quality score with >25 using Condetri version 2.2 (Smeds and Künster, 2011). Potential chimeric sequences found by both UCHIME (Edgar et al., 2011) and DECIPHER (Wright et al., 2012) algorithms were removed. Contaminants (Archaea, chloroplasts and mitochondria) were also removed.

The remaining sequences were assigned to Operational Taxonomic Units (OTUs) using the CD-HIT-OTU method (Wu et al., 2011) with a 97% threshold; and individually classified using the Silva NGS website with Silva database release 123 (Quast et al., 2013;Yilmaz et al., 2014), with 100% sequence similarity for clustering and 90% as a classification similarity threshold. In order to compare the bacterial richness, diversity and communities among the different samples, we also normalized the number of sequences for each sample by randomly selecting 14,040 sequences using the sub.sample script in Mothur (Schloss et al., 2009).

Statistical community analyses

All statistical analyses were conducted using R version 3.1.2 (R core Team, 2014) using 0.05 as the significance threshold. The Phyloseq package (McMurdie and Holmes, 2013) was used to estimate the richness and diversity indices (Chao1 and Shannon) and to calculate the distance based on Bray-Curtis metrics. Pearson's test correlations, Principal

Component Analysis (PCA) and Unweighted Pair Group Method with Arithmetic mean (UPGMA) were based on relative abundances and on Bray-Curtis distances, respectively, using the "ade4" R package (Chessel et. al., 2004).

Permutation analysis of variance (PERMANOVA) were conducted using the adonis function from the vegan package (Oksanen et al., 2013) with 1000 permutations based on Bray-Curtis dissimilarity between samples, for the most abundant (>1%) phyla, and controlling the permutations to not permute between samples from different sampling sites. Explanatory variables included type of sample (soil or sediment) geographical location, year of collection and salinity (only soils). Significance of effects on different phylum were accessed by ANOVA analysis. All the multiple tests were corrected using the false discovery rate (FDR).

Samples Database deposition

The raw DNA sequences from all the samples were deposited in the Sequence Read Archive (SRA) GenBank database, under the accession number SRP074539

Results

Samples and environmental characteristics

We collected 24 sediment and soil samples from 17 different sites from the Camargue region in 2013 and 23 sediment and soil samples from 16 different sites in 2014 (Table 1). Sites sampled in 2013 and 2014 were collected from the same geographical locations (with the exception of the SED 17 sample, sampled only in 2013).

Salinity levels in the Camargue area vary depending on the geographical location. Salinity concentrations in the sampling soils were found to be higher than 5g/kg of Na2O and the values were assigned according to the "Parc Naturel Régional de Camargue" data (Table 1). The pH of soil samples varied from 7.5 (SOIL6_2013) to 9.9 (SOIL8_2014), that of the sediment samples from 7.6 (SED1_2013) to 9.4 (SED13_2014) (Table 1).

16S rDNA pyrosequencing data

Roche FLX Titanium DNA pyrosequencing yielded 16) in soils collected in 2014 (Figure 2;

Table S.2). A statistical t-student test was performed using a 95% confidence level and showed there were no significant differences between the 2013 and the 2014 measures of bacterial abundances from the same sites (t = 1.43, df = 43.3, p-value = 0.161). However, significant differences were observed among the sediment and soil samples (t = 2.106, df = 43.3, p-value = 0.041).

Seasonal variation of bacterial richness and diversity indices

We found between 1,731 and 5.941 OTUs per sample in 2013 and between 1,389 and 5,301 in 2014. The Chao1 estimations in all our samples ranged from 2247 to 7779, suggesting that > 50% of the total OTUs were able to be identified (Table 3). The Shannon diversity index did not differ between years. The highest diversity was found in sample SED 12 with 6.76 (2013) and6.94 (2014), as well as the sample SED 13 with 6. 83 (2013) and 6.92 (2014) .The lowest value was found in sample SED 3, with 5.46 (2013) and 5.41

(2014) (Table 3). Normalization of the sequences to 14,040 did lower the OTUs and Chao1 richness estimators. However, the Shannon diversity index remained relatively stable.

Dominant phyla of the Camargue sediment and soil samples

The phylum composition of bacterial communities is shown in Figure 3. Over 99% of the sequences from the samples were able to be classified to the phylum level. Members belonging to the Proteobacteria, Bacteroidetes, Chloroflexi and Firmicutes phyla dominated sediment samples, while, the soil samples were more varied, including dominant Proteobacteria, Bacteroidetes, Gemmatimonadetes, Actinobacteria, Firmicutes and Acidobacteria phyla. In order to explore patterns in the composition of the bacterial communities of the soil and sediment samples, we constructed a UPGMA cluster tree, using the Bray-Curtis index via the "ade4" package in R on all samples. The similarities among the samples were calculated based on their percentage of similarity (red numbers). Groups with ≥ 95 are considered as highly similar. The most similar soil samples (Figure 4) are represented by SOIL 12_2013 and SOIL12_2014. A clear trend of the cluster tree is a separation of the sediment and soil samples into two different groups, with the exception of the samples SOIL9_2013 and SOIL 4_2014, which cluster with sediment sample types. Samples differed little between the year of collection.

Salinity levels ( 2(5-15 g/kg), 3 (15-50 g/kg) or 4 (40-200 g/kg), see Table 1) of the soil samples showed that those with salinity level 2 clustered together (SOIL8, SOIL12 and SOIL14), with the exception of SOIL14_2014. The bacterial communities of samples with salinity levels 3 and 4 do not appear cluster to closely.

Dominant genera of the Camargue sediment and soil samples

To better compare the bacterial communities at the genus level, we divided their member composition into five different categories according to known properties as well as their isolation source (List of Prokayotic names with Standing in Nomenclature, www.bacterio.net). The categories were assigned as halophilic bacteria, sulphate reducing bacteria, coliforms, nitrate reducing bacteria, and other bacterial types (Figure 5; Table S.3).

In sediment samples, the most abundant genera the halophilic bacteria category are represented by members belonging to the Roseovarius, Robiginitalea, Halanaerobium and Marinicella genera, while in soil samples the most abundant genera were members belonging to the Fodinibius, Salinomicrobium and Halomonas genera (Table S.3A). The coliforms Escherichia-Shigella were found in 9 of 27 sediment samples, representing < 8% of relative abundance in each sample, with the exception of sample SED7_2013, where bacterial communities contained ∼15% of these genera. In sites with only soil samples, these genera represent < 0.5% of total bacterial abundance. However, in sites containing soil and sediment samples, the SOIL5_2014 and SOIL12_2014 contained ∼ 18% and ∼ 6%, respectively, of coliform relative abundance (Figure 5; Table S

Members belonging to the

Effect of variation in the abundance of bacterial communities

At the phylum level, the PERMANOVA showed that the principal variables affecting the abundance of the bacterial phyla communities are the salinity levels (only in soil samples), geographical location and type of sample (soil or sediment). However, the year of collection was found not to affect the distribution of the bacterial phyla composition of the samples (Table S.4A). At the genus level, we found that the variables influencing significant changes in the bacterial genera abundance are principally salinity level (for soils), geographical location and type of sample. However, the year of sampling does not show significant differences for the bacterial community genera (Table S.4B).

Discussion

The Camargue area is located to the south of the city of Arles (43º40 '36"N 4º39'49"E) in southern France. The climate of the Rhône Delta is Mediterranean: a rainy season occurs during the autumn and winter (average 600 mm/year) followed by a dry hot season in spring and summer (average 23ºC) with intense evaporation generated by strong winds called "Le mistral" (average 150 days/year with speeds >90 km/h). Salinity levels are unevenly distributed in the Camargue, depending on the distance from the sea, the level of saline groundwater, topography, soil characteristics and human activity affecting the management of the ground water. Climate parameters can also reduce or increase the salt concentrations (e.g. dilution after precipitation or saturation during the high evaporation season) (Tourenq et al., 2001;Chauvelon et al., 2003, de Montety et al., 2008).

The Camargue region is divided into two parts, the northern part (from Arles to the Vaccares lagoon) which is principally used for rice cultivation, and the southern Camargue, which contains numerous lagoons of brackish water (salinity=50g/L in summer) and high saline soils (Tamisier and Grillas, 1994;de Montenty et al., 2008), with vegetation belonging mostly to Salicornia Spp. and Inula Spp., which can resist the presence of high salt concentrations [START_REF] Brochet | Plant dispersal by teal (Anas crecca) in the Camargue: duck guts are more important than their feet[END_REF][START_REF] Mesléard | Effects of seasonal flooding and grazing on the vegetation of former ricefields in the Rhône delta (Southern France)[END_REF]. This area is one of the most important wintering sites for water birds in Europe [START_REF] Health | Important bird areas in Europe: priority sites for conservation[END_REF], in particular because of intensive rice cultivation, which provides suitable habitats for them, as well as in the lagoons and marshes [START_REF] Fasola | The value of rice fields as substitutes for natural wetlands for waterbirds in the Mediterranean Region[END_REF]. Wind can play an important role in the Camargue ecosystem, as when it is blowing from the north, it can induce fresh water circulation from the central Vaccares lagoon to small estuaries and lagoons towards the southern part of the Camargue. However, when the wind blows from the south, seawater is transported to the small estuaries and lagoons, and as a consequence increases water salinity (Chauvelon et al., 2003).

Due to its unusual ecosystem, the Camargue presents an interesting are to study. In this study, we used pyrosequencing of PCR amplified 16S rDNA fragments from total extracted DNA to examine the bacterial biodiversity from different 14 sediment sites and 10 soil sites from the southern part of the Camargue region, sampled twice separated by one year (Table 1).

Bacterial abundance in the Camargue were found to be in the same broad range observed in other soils or saline sediments [START_REF] Keshri | Microbial population index and community structure in saline-alkaline soil using gene targeted metagenomics[END_REF][START_REF] Pereira E Silva | Spatial and temporal variation of archaeal, bacterial and fungal communities in agricultural soils[END_REF].

Abundance comparisons among soils and sediments can be problematic because of their potential different DNA extraction efficiencies. The disparate number of 16S rRNA copies per bacterial genome also make it difficult to convert environmental 16S rRNA copy numbers into cell numbers. We therefore chose to express our results in 16S rDNA copy number per gram of sediment or soil. Bacterial abundance varied across samples and sites. Soils had less 16S rDNA abundance than sediment samples, with no clear differences observed between the soil bacterial abundances from 2013 versus 2014.

Bacterial 16S rDNA abundance does not appear to significantly vary between the different types of Camargue soil and sediment samples. These values are similar to these observed in saline sediments and soils from la Sal del Rey in Texas, USA [START_REF] Hollister | Shifts in microbial community structure along an ecological gradient of hypersaline soils and sediments[END_REF].

Differences were observed in the bacterial communities based on salinity levels (in soil samples), type of sample (soil or sediment) and geographical location. Differences of bacterial diversity and/or community composition were observed on near-coastal sea or ocean or lake locations based on different variables such as pH, sodium, organic carbon, among others [START_REF] Hollister | Shifts in microbial community structure along an ecological gradient of hypersaline soils and sediments[END_REF][START_REF] Gilbert | Defining seasonal marine microbial community dynamics[END_REF], salinity concentration in soils, (Campbell et al., 2013;[START_REF] Canfora | Salinity and bacterial diversity: to what extent does the concentration of salt affect the bacterial community in a saline soil?[END_REF], abiotic factors such as phosphorus and temperature [START_REF] Langenheder | Temporal variation of β-diversity and assembly mechanisms in a bacterial metacommunity[END_REF] and geographical location [START_REF] Ding | Spatial distribution of bacterial communities driven by multiple environmental factors in a beach wetland of the largest freshwater lake in China[END_REF].

Soil samples clustered together, as did sediment samples (Figure 4). Sediment samples were richer in Proteobacteria, Latescibacteria and Spirochaetae phyla (Figure 3), and these last two phyla were less common in the soil samples. Members belonging to the Spirochaetae phylum have been found in saline microbial mats from the Ebro delta from Northeastern Spain and the Camargue delta [START_REF] Villanueva | Analysis of diurnal and vertical microbial diversity of a hypersaline microbial mat[END_REF][START_REF] Berlanga | Phylogenetic diversity and temporal variation in the Spirochaeta populations from two Mediterranean microbial mats[END_REF]. Here, we also found members of the Spirochaeta genus in higher proportions in the sediments samples (Table S .3 B).

Members belonging to the Gemmatimonadetes phylum were usually only found in soil samples. Members belonging to this phylum have been associated with the presence of dried soils (DeBruyn et al., 2011), as well hypersaline soils [START_REF] Zhang | Gemmatimonas aurantiaca gen. nov., sp. nov., a gramnegative, aerobic, polyphosphate-accumulating micro-organism, the first cultured representative of the new bacterial phylum Gemmatimonadetes phyl[END_REF] in a wide variety of ecosystem types such as air, soils, rivers, lakes and plant surfaces [START_REF] Zeng | Metagenomic evidence for the presence of phototrophic Gemmatimonadetes bacteria in diverse environments[END_REF].

The major bacterial genera found to vary in proportion to salinity concentrations belonged to the Fodinibius, Halanaerobium, Halomonas, Marinicella, Salinibacter and Thiohalorhabdus genera (sites 5 and 6 which presented salinity level "4").

Members belonging to the Proteobacteria phylum were found to be the major phyla present in saline soils [START_REF] Gilbert | Defining seasonal marine microbial community dynamics[END_REF][START_REF] Ma | A meta-analysis of the publicly available bacterial and archaeal sequence diversity in saline soils[END_REF]. We found that the dominant (>1%) bacterial genera belonging to this phylum are represented by halophilic bacteria of the Hamolomonas, Marinicella, Marinobacter and Robiginitalea genera.

Members belonging to the Marinicella and Halomonas genera have been isolated

principally from hyper-saline sources [START_REF] Ventosa | Halophilic and Halotolerant Micro-Organisms from soils[END_REF].

Sulphate reducing bacteria (SRB) genera members belonging to the Proteobacteria phylum (Desulfotignum, Desulfovermiculus and Sulfurovum genus) are predominant in sediment samples and studied by [START_REF] Fourçans | Molecular analysis of the spatio-temporal distribution of sulfate-reducing bacteria (SRB) in Camargue (France) hypersaline microbial mat[END_REF] in order to detect oxygen tolerant SRB's from a mat in a solar saltern from South Camargue.

Ardenticatena genus (Chloroflexi phylum) were found in an iron rich hydrothermal fields and shown to be resistant to high levels of salinity [START_REF] Kawaichi | Ardenticatena maritima gen. nov., sp. nov., a ferric iron-and nitrate-reducing bacterium of the phylum "Chloroflexi" isolated from an iron-rich coastal hydrothermal field, and description of Ardenticatenia classis nov[END_REF].

Members belonging to the Acidobacteria phylum are abundant in soils with low salinity concentration (Soil 8) as well as members belonging to the Actinobacteria phylum (Site 8, 12 and 14, principally), which were previously detected in saline and non-saline soils [START_REF] Canfora | Salinity and bacterial diversity: to what extent does the concentration of salt affect the bacterial community in a saline soil?[END_REF]. However, members belonging to the Parcubacteria phylum (ex.

OD1), which we found principally in sediment samples from the Camargue, were previously detected exclusively in anoxic environments and described to be principally parasites of other organisms (Nelson and Stengen, 2015). Members belonging to the Acetothermia (ex. OP1) and Atribacteria (ex OP9) phylum are thermophilic bacteria, found mostly in geothermal systems [START_REF] Hedlund | Uncultivated thermophiles: current status and spotlight on "Aigarchaeota[END_REF].

Our results show that in sites with higher levels of salinity in the Camargue, high proportions of halophilic bacteria can be observed. Sediment samples may contain higher bacterial diversity than soils, however no significant differences were found in their bacterial communities between 2013 and 2014. The Camargue area thus represent a heterogeneous saline environment that nonetheless can be used for agricultural purposes.

Its continued study can likely help other saline coastal areas to be productive. (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) g/kg; 3= g/kg, 4= (50-200) g/kg. (Salinity results calculated according to the "Parc Naturel regional de Camargue" data; de Montety et al., 2008;[START_REF] Wieland | Carbon pools and isotopic trends in a hypersaline cyanobacterial mat[END_REF][START_REF] Villanueva | Analysis of diurnal and vertical microbial diversity of a hypersaline microbial mat[END_REF][START_REF] Fourçans | Characterization of functional bacterial groups in a hypersaline microbial mat community (Salins-de-Giraud, Camargue, France)[END_REF] b. Soil pH value (mean of 3 replicates) measured dissolving 5g of soil in 25 ml of distilled water (Sun et al., 2014). c. Only sampled in 2013. N/D (not done).

Introduction

Soils have been found to contain diverse microbial populations [START_REF] Roesch | Pyrosequencing enumerates and contrasts soil microbial diversity[END_REF]. One gram of soil can contain > 10 9 bacterial cells (Whitman et al. 1998) representing > 10 3

different species [START_REF] Gans | Computational improvements reveal great bacterial diversity and high metal toxicity in soil[END_REF]. The study of the microorganisms present in soils is importance due to the need to identify them and their role in the regulation, dynamics and functions of terrestrial ecosystems [START_REF] Harris | Soil microbial communities and restoration ecology: facilitators or followers?[END_REF]. To overcome the fact that > 95% of microorganisms are non-cultivable under laboratory conditions (Amann et al. 1995), the study of microbial populations has been made possible by the use of high throughput DNA sequencing, often using the 16S rRNA gene as a reference sequence. This gene contains both variable and conserved regions, permitting an accurate microbial identification (Kim et al. 2013). For example, microbial communities present in soils from forests [START_REF] Banning | Soil microbial community successional patterns during forest ecosystem restoration[END_REF][START_REF] Zhang | An integrated study to analyze soil microbial community structure and metabolic potential in two forest types[END_REF], grasslands (Cruz-Martínez et al.

2009

) and hot deserts [START_REF] Prestel | An examination of the bacteriophages and bacteria of the Namib desert[END_REF][START_REF] Neilson | Life at the hyperarid margin: novel bacterial diversity in arid soils of the Atacama Desert, Chile[END_REF]An et al. 2013;[START_REF] Prestel | The bacteria and bacteriophages from a Mesquite Flats site of the Death Valley desert[END_REF]) have been previously examined. Oligotrophic hot desert soil environments generally present limitations in carbon levels, extreme fluctuations of surface temperature, a low mean annual rainfall and high UV exposure (Cary et al. 2010;Ortiz et al. 2014;[START_REF] Tebo | Microbial communities in dark oligotrophic volcanic ice cave ecosystems of Mt. Erebus, Antarctica[END_REF]. The role of these communities in soil depends on many factors (Fierer and Jackson 2006) and can include symbiotic interactions and/or infections of plants and insects (Hirsch et al. 2010).

As in Baja California, Mexico. They found a greater diversity of microbial communities in the scrublands than those from soils under agricultural use. [START_REF] Banning | Soil microbial community successional patterns during forest ecosystem restoration[END_REF] studied the northern jarrah (Eucalyptus marginata) forest region of Western Australia, and found differences in the structure of the microbial communities due principally to soil variables such as pH, C, N and P, as has been previously observed [START_REF] Cookson | Nitrogen dynamic in an Australian semiarid grassland soil[END_REF]Fierer and Jackson 2006;[START_REF] Kvist | Archaeal diversity in Icelandic hot springs[END_REF]Rousk et al. 2010). Other studies have proposed differences in soil microbial communities due to different environmental factors, such as geochemical and soil mineralogy, including the biogeochemical cycles of a range of elements such as Fe and S (Gleeson et al. 2006).

Mayotte, a French overseas department of the Comoros archipelago, is the easternmost of four islands (Anjouan, Comoros, Mayotte and Moelí) located in the tropical South West Indian Ocean in the northern part of the Mozambique Channel (12º 50'S, 45º10'E).

Mayotte covers 379 km 2 and is composed of two islands, named Large Island ("Grande

Terre") and Little Island ("Petite Terre"). The two islands are surrounded by one large lagoon (1,100 km 2 ) that is bordered by a barrier reef. The maximum elevation on the islands is 660 m, represented by Benara Peak on the Large Island [START_REF] Zinke | Systems tracts sedimentology in the lagoon of Mayotte associated with the Holocene transgression[END_REF][START_REF] Kull | Invasive Australian acacias on western Indian Ocean: a historical and ecological perspective[END_REF][START_REF] Audru | Major natural hazards in a tropical volcanic island: A review for Mayotte Island, Comoros archipelago, Indian Ocean[END_REF]. The climate of Mayotte is tropical, humid and hot from November to March (the hot season: average 28ºC) with a cooler dry season between April and October (average 24ºC). The estimated annual rainfall is in the range of 150-250 cm [START_REF] Safford | Important Bird Areas in Africa and associated islands -Mayotte[END_REF][START_REF] Zinke | Systems tracts sedimentology in the lagoon of Mayotte associated with the Holocene transgression[END_REF].

The islands of Mayotte had their most recent volcanic event during the Holocene. The soil structure is largely dominated by erosion, vegetation disappearance and underlying rock exposure [START_REF] Zinke | Systems tracts sedimentology in the lagoon of Mayotte associated with the Holocene transgression[END_REF]. The erosion has, as a consequence, led to the appearance of eroded badlands, called "padzas", that are natural alterites that occupy the crest of hills. These padzas are much more abundant near the domes of the volcanic magmatic rock, called phonolites, which surround the zones of alterites riches in kaolinite (Al2Si2O5 (OH)4) [START_REF] Audru | Superficial formations and natural hazards in Mayotte Island[END_REF]. The original forest cover has mostly disappeared and the top soils are thus directly exposed to frequent torrential rains, and thus storm

Materials and Methods

Site and Sample collection

Five samples were collected from the surface of five different sites of sandy soil in the Dapani area, on the large island of Mayotte, on September 9, 2012 (Tº:26ºC, wind: 18 km/h, humidity: 73%). The samples are named "Padza 1" (12º59'05 S; 45º09'06 E), "Padza 2" (12º59'07 S; 45º09'07 E), "Padza 3" (12º59'06 S; 45º09'05 E), "Padza 4" (12º59'07 S; 45º09'05 E) and "Padza 5" (12º59'07 S; 45º09'08 E) (Fig. 1). Three replicates of 50 g of surface soil were collected by scooping surface soil into 50 mL sterile conical polyethylene centrifuge tubes, and half of the contents were subsequently mixed to make a representative sample for each site. This area is surrounded by the invasive shrub Acacia mangium, especially near the Padza 4 and 5 samples. Physicochemical analysis of these sites were performed by the Laboratoire d'Analyses de Sols (INRA Arras, France) (Table 1).

Total DNA extraction

Genomic DNA was extracted from each sample as previously described (An et al. 2013).

Briefly, five grams of each soil sample were rehydrated in 1ml of 1/4 was added and a further incubation for 2H at 65ºC was performed. The supernatant fluid was collected after a 20 min centrifugation at 6,000×g at room temperature. A further extraction was performed on the pellet with 4,5 ml of extraction buffer plus 0,5 ml of 20% SDS, mixed by vortexing for 10 s followed by incubation for 10 min at 65ºC and centrifugation for 15 min at 6,000×g at room temperature. The supernatant fluids were then combined. The nucleic acids were extracted by the addition of an equal volume of chloroform/isoamyl alcohol (24:1), and, after centrifugation for 20 min at 6,000×g at room temperature, the aqueous phase containing the nucleic acids was removed. Then, 0.6 volume of isopropanol was added and a 1H incubation at room temperature was performed to precipitate the DNA. Following this, centrifugation for 20 min at 13000×g at 20ºC was performed. The DNA pellet was washed with 1 ml of 70% ethanol and subjected to centrifugation at 11000×g for 5 min at 4ºC. The DNA pellet were then dried and resuspended in 30 μl 1/10 TE buffer (1mM Tris-HCl pH 8, 0.1 mM Na EDTA pH 8), incubated overnight at 4ºC and then stored at -20ºC.

Quantification of bacterial abundance by quantitative PCR (qPCR)

To prepare optimal qPCR primers for Bacteria and Archaea, two primer pairs for each kingdom that generate an amplification product of approximately 200 pb with bacterial and archaeal 16S rRNA genes were prepared using the probeBase online resource [START_REF] Loy | probeBase--an online resource for rRNAtargeted oligonucleotide probes: new features 2007[END_REF]). The coverage of each primer set was assessed in silico using two different databases, RDP using the Probe Match program (Cole et al. 2014) and Silva using the Silva Test Prime program (Klindworth et al. 2013) (data not shown). The primer sets used are listed in Table 2 and16S 2) for 15s and extension at 72°C for 30s and a final extension at 72°C for 5 min. SYBR green fluorescence was measured at the end of each cycle and the amplification specificity was checked by a melting curve. Because no significant differences were observed between linear versus circular plasmids as templates for the establishment of standard curves (Oldham et al. 2012), non-linearized plasmids containing the 16S rRNA gene cloned from Escherichia coli (bacteria), or the 16S rRNA genes cloned from Thermococcus gammatolerans (archaea), were used as controls. For each qPCR plate, five duplicate standard dilutions, ranging from 9,000 to 9×10 7 copies of the 16S rRNA gene, were prepared in 25 µL reaction mixtures. Only results showing amplification efficiencies (E = 10 1-s -1, with s = slope of the standard curve) between 85% and 105% were analyzed.

Each sample was quantified at two different dilutions (Bustin et al. 2009). If the differences between the two did not represent coherent qPCR reactions, further dilutions were performed and tested.

16S rDNA preparation and pyrosequencing

The 16S rRNA gene of bacteria was amplified from 10 to 20 ng of total extracted DNA.

PCR reactions (25 μl each) were performed using the universal 16S rDNA bacterial primers 27F (5'-Ad.A + GAGTTTGATCMTGGCTCAG-3') and 518R ( 5 μM of each primer (Sigma-Aldrich, USA), 0.2 mM dNTP mix (Fermentas, Canada), 0.5 units of thermostable DNA polymerase for I and II using the corresponding buffers with each polymerase. The PCR products were then combined by sample and polymerase used, and the amplified 16S DNA fragments were then purified from a 1% agarose gel after electrophoresis in TAE buffer (An et al. 2013) using a NucleoSpin Extraction Kit (Macherey-Nagel, France) according to the manufacturer's instructions. Twenty nanograms of purified PCR product from each sample were mixed for pyrosequencing using a Roche/454 GS Junior Pyrosequencer (Department of Biology, University of Oulu, Finland).

Bioinformatic analyses

Raw DNA sequences obtained from pyrosequencing were extracted and grouped by their MID sequences, trimmed and selected by their length (>200 nt and <600 nt). The sequences were retained if they contained no ambiguous base, no homopolymers ≥8 and no more than one mismatch to the sequencing primer, using Mothur software (Schloss et al. 2009). Adaptors remaining from the sequencing process were removed from the sequences using Cutadapt (Martin 2011). A second trimming based on quality score was performed using a 90% quality score with >25 using Condetri V_2.2 (Smeds and Künster 2011). The sequences were screened for potential chimeric sequences with the Decipher web tool (http://decipher.cee.wisc.edu) (Wright et al. 2012). In order to compare the bacterial richness and diversity estimators among the different samples, we also normalized the number of sequences for each sample by randomly selecting 2195 sequences from the fasta file using the sub.sample script in Mothur software (Schloss et al. 2009).

The sequences were assigned into Operational Taxonomic Units (OTUs) using the CD-HIT-OTU method (Wu et al. 2011) with a 97% threshold; and individually classified using the Silva NGS website with Silva database release 123 (Quast et al. 2013;Yilmaz Less abundant genera are represented by members belong to Crossiella in Padza 1 (5%)

and Padza 3 (4%) and Ramlibacter in Padza 2 (5%). Genera members representing <5% relative abundance were represented by the Jatrophihabitants (0-4%), Blastococcus (0-4%), Phyllobacterium (0-4%), Noviherbaspirillum (0-3%), Geodermatophilus (0-3%), Acidiphilium (0-3%) and Flavisolibacter (0-3%) genera.

We were able to discern bacteria belonging to 23 genera in common among the five Dapani Padza samples (Figure 4B). The samples with the highest number of genera in 2015). However, a 15-cm deep grassland soil sampled in the Chihuahua desert area of Mexico, containing similar proportions of silt and clays as our samples in the Padza de Dapani, were found to be basic in pH, with values ranging from 8. 4 -8.9 (Tapia-Torres et al. 2015).

Bacterial abundance and richness of the Dapani Padza samples

We calculated the bacterial and archaeal abundances by qPCR. Each sample was analyzed at two different dilutions but also with an amplification of two different regions of the 16S rRNA genes (Table 2). This procedure can aid in reducing bias encountered when using only one primer set, such as amplification preference or lack of amplification among mixed target genes. Based on the conclusions of [START_REF] Suzuki | Bias caused by template annealing in the amplification of mixtures of 16S rRNA genes by PCR[END_REF] and Lloyd et al. (2013) that certain "universal" primer pairs targeting the 16S rRNA genes can over-or underestimate the actual abundance, we tested primer coverage in silico using two different databases (see Materials and Methods) and found that the sequence coverage is between 1 and 2% higher for almost all phyla when two different primer sets are used compared to only one (data not shown). Additionally, as a primer sets can overor underestimate quantification, the average between both results was calculated. An estimation of the 16S rRNA Archaeal copies number was not possible, as the primer sets used (A967F -A1060R or AB789F -AR9R) yielded a quantification below the lowest dilution of the standard (≤ 9000 copies of the 16S rRNA genes/g of soil).

We investigated the soil surface bacterial biodiversity and richness via an analysis of the V1-V3 regions of the 16S rRNA gene using PCR amplification from total extracted DNA, followed by 454 pyrosequencing. The V1-V3 variable regions of the 16S rRNA gene were selected based on their utility for bacterial identification, as previously shown for OTU estimates, and large phylogenetic resolution [START_REF] Kim | Evaluation of different partial 16S rRNA gene sequence regions for phylogenetic analysis of microbiomes[END_REF]. We classified the total sequences to the genus level (71.3 % of the total sequences) using the Silva NGS website with the Silva database (release 123). OTUs were calculated at >97% sequence similarity, and we were able to identify >77% coverage of the OTUs, calculated by the Chao1 estimator.

Dominant phyla in the Dapani Padza samples

The principal bacterial members were found to belong to the Actinobacteria (29-44%)

and Proteobacteria (27-36%) phyla, commonly found in soil (Lauber et al. 2009;[START_REF] Russo | Bacterial community structure of contrasting soils underlying Bornean rain forests: Inferences from microarray and next-generation sequencing methods[END_REF]). In the Padza 1 and Padza 3 samples, many members belonging to the Chloroflexi phyla were found, and in lesser proportions in the Padza 2, 4 and 5 samples.

Members belonging to the Firmicutes phyla were founded in high proportions (27%) only in the Padza 2 sample, and in lower proportions (2%, 4%) in the Padza 4 and 5 samples, respectively, while members belonging to the Bacteroidetes phyla, one of the largest groups within bacteria after Proteobacteria, Firmicutes and Actinobacteria [START_REF] Cole | The Ribosomal Database Project (RDP-II): previewing a new autoaligner that allows regular updates and the new prokaryotic taxonomy[END_REF] were found at high levels in the Padza 4 (8%) and Padza 5 (12%) samples. Members belonging to the Bacteroidetes phyla are often one of the major phyla observed under anoxic conditions and present principally in the rhizosphere, though they can also be found in bulk soils (Martínez-Alonso et al. 2010). The results from the Dapani area confirm that different sites from the same region can contain distinct bacterial communities, as was also observed in bacterial soil crusts from dry lake beds in southern Australia [START_REF] Abed | Microbial diversity of eolian dust sources from saline lake sediments and biological soil crusts in arid Southern Australia[END_REF], as well as in eroded sediments from different soil types [START_REF] Gardner | Pyrosequencing reveals bacteria carried in different winderoded sediments[END_REF].

Variations among bacterial populations are usually related to the physical, chemical and biological characteristics of their ecosystems (Fierer et al. 2012). It has been previously

shown that phyla proportions can be strongly influenced by changes in soil pH, especially for members belong to the Acidobacteria phylum, which are generally found in soils with low pH's, and members belonging to the Actinobacteria phylum found in higher proportions in arid or semiarid ecosystems where the pH is relatively high (Fierer and Jackson 2006;Janssen 2006;Lauber et al., 2009;[START_REF] Russo | Bacterial community structure of contrasting soils underlying Bornean rain forests: Inferences from microarray and next-generation sequencing methods[END_REF]). In the Dapani Padza samples, we found a large proportion (>35%) of members belong to Actinobacteria phylum, even though the pH of the soil is acidic. However, pH is not the only factor that can influence bacterial community composition, as carbon availability, soil moisture, and temperature can also influence bacterial community structure in soils (Fierer and Jackson 2006;Rousk et al. 2010). The Padza 4 and 5 samples are located the closest to surrounding vegetation (Acacia mangium), and these samples contain the highest organic carbon levels as well higher proportions of members belong to the Actinobacteria, Bacteroidetes

and Proteobacteria phyla, and the proportions of the latter two phyla have been reported

to be strongly related to the higher availability of carbon (Fierer et al. 2007), though these samples also present the lowest pH (4.77 and 4.74, respectively). A similar situation has been reported in soils from a Hoosfield acid strip where the pH is below 4.5 and where members belonging to the Actinobacteria and Bacteroidetes phyla were found in relatively high proportions (Rousk et al. 2010).

Genus composition in the Dapani Padza samples

At the genus level, the bacterial communities of the Dapani Padza samples show that members of the Arthrobacter, Acinetobacter, Bacillus and Burkholderia genera appear to be the most abundant genera. Members of the genus Arthrobacter were found in higher proportions in the Padza 4 (27%), Padza 5 (11%) and Padza 2 (11%) samples, as well as

Acinetobacter in Padza 1 (12%) and Padza 5 (11%), Bacillus in Padza 2 (25%) and Burkholderia in Padza 3 (8%). In previous studies [START_REF] Lepleux | A short-term mineral amendment impacts the mineral weathering bacterial communities in an acidic forest soil[END_REF], members of the Arthrobacter, Bacillus and Burkholderia genera were reported as being important as bioindicators of weathering and the presence of complex minerals such as apatite, silicate, feldspar and basaltic rock. The predominance of members of the Burkholderia genus in forests and surrounding soils has been observed in other sites (Uroz et al. 2009;[START_REF] Uroz | Pyrosequencing reveals a contrasted bacterial diversity between oak rhizosphere and surrounding soil[END_REF]). These bacteria are described as efficient weathering (dissolution of minerals) bacteria, including their known efficiency to weather biotite from iron (Collignon et al. 2011). It is interesting to note that the Dapani Padza samples contain relatively high concentrations of iron (17.5g/100g). As well, members of the Burkholderia genus have been described as capable of influencing granite weathering [START_REF] Wu | Characterization of elemental release during microbe-granite interactions at T=28°C[END_REF], along with members of the genus Bacillus (Song et al. 2007). However, members of the Bacillus and Acinetobacter genera were also found in high proportions in non-acidic (pH=9) surface soils of Asian deserts (An et al. 2013), while Acinetobacter genera were found in lower proportions (3%) in soils related to mineral weathering functions (Wang et al. 2014),

suggesting that the relationships among soil bacterial communities and the soil physical and chemical parameters in these oligotrophic surface soils are not simple.

Bacterial comparison between Dapani Padza samples and other selected environments

We used the published sequences of bacterial 16S rDNA pyrosequencing from four other locations and classified them using the same protocol as that used for the Dapani Padza samples. The results show that the bacterial communities of the Atacama desert samples (Atacama 1, 2 and 3), are composed of a relatively high proportion (58%-82%) of members belonging to the Actinobacteria phyla, in contrast with the samples from Saudi Arabia (Swh1, Swh2, Swh7 and Swh8) and Mendocino, California (John 6, 13, 14 and 15), which were found to contain higher proportions of bacteria belonging to the Proteobacteria and Acidobacteria phyla. Samples from the Finland Boreal forest (Pine-PP2 and Pine-MS) revealed relatively high proportions of members belonging to the Acidobacteria phyla, which is likely not surprising given the low pH in these soils (4.11 and 4.26) (Rousk et al. 2010;Fierer et al. 2012;[START_REF] Bevivino | Soil bacterial community response to differences in agricultural management along with seasonal changes in a Mediterranean region[END_REF]. The Dapani Padza samples were related to these found in samples from Saudi Arabia, which have a basic pH, but a higher concentration of organic matter than the Dapani Padza samples. In particular, samples Swh1 and Swh2 were most similar in phyla composition with that observed in the Padza 4 and Padza 5 samples, especially concerning the proportions of members belonging to the Bacteroidetes and Chloroflexi phyla. Further studies, including greater sampling as well as temporal studies, will be important to detect variations of the bacterial diversity of the Dapani Padza samples and potentially aid in understanding the variability of the bacterial composition versus soil erosion parameters, and hopefully provide suggestions to recover these soils for forestry and/or agricultural use.

be ideal for bacterial community analysis due to the risk of forming chimeric products with small DNA templates during PCR (4, 5).

Primer pair design

Primer pair design and tags (MID) for 16S rDNA library preparation were chosen according to Roche's pyrosequencing suggestions. There are two types of sequencing primers, termed "Primer A" and "Primer B". The Lib-L pyrosequencing chemistry supports sequencing only from the Primer A end (Figure 1). The 3'-portion of each primer is designed to anneal with a specific sequence on either side of the target of interest on the DNA sample, defining the margins of the amplicon that will be produced. This requires detailed knowledge of the target sequence, and in particular the sites targeted by the primers. These template-specific sequences are typically 20-25 nt in length, though these may vary; they are shown with {green} and {blue-green} background in Figure 1. For our analyses of the Jizan desert and Mayotte Padza de Dapani, we used the primer pairs 27F and 518R comprising the 16S rRNA gene regions V1 toV3 (6). However, we chose new primer pairs for somewhat improved taxonomic accuracy, using 357F and 802R primers (16S rRNA gene V3-V4 variable regions) (7) for our analyses concerning the Camargue.

Forward primer (Primer A): 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAG-{MID}-{template-specific sequence}-3'

Reverse primer (Primer B): 5'-CCTATCCCCTGTGTGCCTTGGCAGTCTCAG-{template-specific sequence}-3'

Quantitative PCR (qPCR)

For bacterial and archaeal quantification by qPCR, two different primer sets were used.

Each sample was therefore analyzed at two different dilutions and also with amplification of two different segments of the 16S rRNA gene. This procedure allowed us to reduce the known bias classically encountered when using only one primer set. In silico analyses showed that sequence coverage was between 1-2% higher for almost all phyla when two different primer sets were used (8). The coverage of each primer pair was assessed in silico using two different databases, RDP with the Probe Match program (9) and Silva with the Silva Test Prime program (10). Primer sets with the best coverage (with zero or one mismatch) were selected and are shown in Figure 2.

Figure 3.

In silico primer set coverages: In silico evaluation of the primer set coverages within the targeted domain (Bacteria or Archaea). Domain maximization was calculated with the maximum coverage of the different primer sets for each phylum using the Silva and RDP databases.

Pyrosequencing and data analysis

Many studies in microbial ecology have used pyrosequencing of 16S rDNA genes for bacterial identification, particularly Titanium technology, which produces approximately 500 nt of sequence per segment. The 454 pyrosequencing technology is based on sequencing by synthesis and employs a series of enzymes (DNA polymerase, ATP sulfurylase, luciferase and apyrase) to detect nucleic acid sequences during synthesis (11).

The large amount of DNA sequences obtained from this process can allow the identification of a great number of microbial taxa. However, the information generated by this process needs an important analyses based on bioinformatics tools.

We thus tested several available bioinformatic tools in order to improve bacterial population detection of our soil environmental samples. The schematic of this procedure is shown in Figure 3.

Depending on the approach used, diverse analyses can be performed. In our study, we used non-OTU based analyses, where the individual sequences obtained were analyzed, not just the most abundant or large OTUs. First, we used a cleaning step, based on sequence selection by size, along with primers and barcode mismatches, removal of homopolymers and ambiguous bases, all using MOTHUR (12). In this step, ∼ 2 % of total sequences were removed from the raw sequences. After the cleaning process, we detected and deleted chimeric sequences, formed due to errors of amplification during the PCR reactions (13). For this, we tested two different algorithms called UCHIME (14) and DECIPHER (15). We chose to use both of them, as we determined that DECIPHER has a higher rate of detection of short chimeric sequences (100-250 nt) and complex chimeras with multiple parent sequences, while UCHIME can detect chimeras formed from closely related sequences. After the cleaning process (Figure 3), the remaining high quality sequences were classified using a 16S rRNA gene reference database. We compared two different databases, SILVA (16) and GreenGenes (17), which are repositories for high quality sequences clustered to be non-redundant at a 99% similarity level. In Table 1, we show the number of sequences whose classification is only assigned down to the indicated taxonomic level in each database. We used the SILVA database for our analyses, with exception of the samples from Jizan. The SILVA database provides a broader collection of sequences classified down to the genus and species levels, together with sequences from other kingdoms, which are helpful to identify contaminant sequences. The SILVA database produced a higher percentage of sequences classified to the genus level, with all classifiers, when compared with the use of the GreenGenes database. Thus, the SILVA database seems capable of providing a higher resolution of the bacterial community under study and was chosen as the reference database for our studies on

Mayotte and the Camargue. The three classification methods showed very distinct behaviors over the range of identity/confidence thresholds tested. RDP was the most resistant to identity threshold changes and, had the highest number of classified sequences independent of the taxonomic rank. UCLUST is the most sensitive, being capable of classifying almost 85% of the sequences, at the genus level, with lower identity thresholds (80-85%), but it cannot classify >70% of the sequences when set to higher thresholds (95-99%). SILVA NGS shows a performance that is not as drastically affected by the identity threshold as UCLUST, and classifies more sequences down to the genus level than RDP.

Since SILVA NGS generates more sequences reliably classified to the genus level, even at mid-high thresholds (90-95%), we chose it as a more appropriate classification method.

When setting the identity threshold to 90%, the SILVA NGS method returned 80% of the sequences aligned at ≥ 95% identity, while UCLUST, using similar settings, only aligns 50% of the sequences with at least 95% identity.

3-Conclusions

3.1-Study reflections

During my thesis research, we examined the bacterial populations present in soil and sediment samples from three different regions in the world. For example, The Jizan desert in Saudi Arabia is a real true (hot) desert, which supports very limited vegetation, and is exposed to high UV radiation. The Padza de Dapani in Mayotte, with its sandy dunes where erosion plays a significant role due to deforestation and extensive rainfall, produces a soil that is not a true desert but resembles one due to the extensive erosion. The Camargue, a wide river delta, has a northern part that is dedicated to agricultural practices, in particular that of rice, which accounts for more than 30% of France's rice consumption.

The southern soils and marshes, are affected by high salinity levels, where halophytes such as Salicornia spp. vegetation grows.

3.2-Bacterial abundance challenges

In our microbial ecology studies, we were able to determine the presence and relative richness of the bacterial taxa, though we could not accurately relate their functions and determine many biotic and/or abiotic factors underlying the patterns of bacterial populations present in a particular site.

It is important to add here that a potential bias of microbial ecology studies is the DNA extraction from an environmental sample, as dead cells and free DNA present in these samples can also be collected, and thus the results may be an overestimation of the number of groups in the population.

In our studies, we used the 16S rRNA gene to determine the bacterial communities present in soils. This gene allows reasonable accuracy of taxonomic identification. However, there are limitations that need to be overcome when using sequence-based approaches, as different taxa can have different 16S rRNA gene copy numbers.

3.3-Bacterial populations across different ecosystems

It is important to link phylogeny with soil structure and function in order to understand the biogeochemical process of soil ecosystems. The principal bacterial members found in the Camargue belonged to the Proteobacteria, Actinobacteria, Acidobacteria and Bacteroidetes phyla. The rhizosphere of rice, revealed bacterial groups related to proper functioning of the rhizosphere, including the processes of nitrification, denitrification, oxidation, reduction and to plant survival and development. In the soil and sediment samples from the Camargue, we noted few genera related to halophiles though many sulfate reducers, likely due to the fact that certain sites contained higher concentrations of salt in their soils.

The Dapani samples from Mayotte show important erosion of the surface soils. The Turgidum" qui contient des proportions élevées (∼70%) des membres appartenant au genre Flavobacterium.

oxidation, as well as act as biocontrol agents. The bacterial populations were found to significantly vary by geographical location as well by year of collection.

C. We examined the surface soils from "Padza de Dapani" on the island of Mayotte off the east coast of Africa, as this region is not a true (hot) desert, but resembles one due to extensive soil erosion. In the acidic, oligotrophic and mineralized soil samples from Mayotte, members of the Actinobacteria, Proteobacteria and Acidobacteria phyla dominated the bacterial populations. Interestingly, members of the genera Acinetobacter, Arthrobacter, Burkholderia and Bacillus were found to be predominant in our samples, as is also observed in hot (Asian) deserts and may play roles in soil mineral weathering, thus helping to understand desertification processes.

D.

Earth's arid regions comprise >30% of the continental surface and the oligotrophic soils are subjected to harsh environmental factors such as low average annual rainfall, high UV exposure and large temperature fluctuations. We examined the bacterial populations present in the rhizosphere of pioneer plants and surface soils in the Jizan desert of Saudi Arabia. The most abundant bacterial phyla belonged to the Bacteroidetes, Proteobacteria and Firmicutes phyla that were different between the rhizosphere of plant versus these from surface sand, with the exception of the plant "Panicum Turgidum", which contain in its rhizosphere high proportions (∼70%) of members belonging to the Flavobacterium genus.

Chapter

  VI Discussion and general conclusions ....................................................... 1.Research challenge ................................................................................................. 2.Techniques and methods used ................................................................................ 2.1 Soil sampling .................................................................................................... 2.2 DNA extraction ................................................................................................ 2.3 Primer pair design ............................................................................................ 2.4 Quantitative PCR (qPCR) ................................................................................ 2.5 Pyrosequencing and data analysis .................................................................... 3.Conclusions ............................................................................................................. 3.1 Study reflections .............................................................................................. 3.2 Bacterial abundance challenges ....................................................................... 3.3 Bacterial populations across different ecosystems .......................................... References .................................................................................................................. Résumé .......................................................................................................................... Abstract ........................................................................................................................

Figure 1 .

 1 Figure 1. The mineralosphere and its ecological traits. .................................................

  Figure 1. The mineralosphere and its ecological traits. .................................................

Figure 2 .

 2 Figure 2. Representation of pyrosequencing primers design for 454 Lib-L chemistry pyrosequencing. ............................................................................................................

Figure 3 .

 3 Figure 3. In silico primer set coverages .......................................................................

Figure 4 .

 4 Figure 4. Schematic representation of the approaches used for soil sample analyses. .

3 - 4 -

 34 Bacterial biogeography: Are bacteria distributed ubiquitously? Biogeography, as applied to microbial ecology, comprises the study of microorganism distribution in space and time and interprets the mechanisms responsible, including selection, drift, dispersal and mutation on richness and composition of microbial communities (73). Endemic taxa are restricted to particular habitats and microorganisms living in extreme or oligotrophic environments appear to be endemic to distinct types of environments (74, 75), and non-randomly distributed in space (73). Taking into account the bacterial diversity at the micro-scale, studies based on the 16S rRNA gene show a large diversity of taxa in a few cm of soil. However, many of these taxa present may not be metabolically active, thus, genes for microbial identification may underestimate the bacterial diversity. Under unfavorable environmental conditions, some elements of bacterial communities may be in a dormant state, and then revive when conditions turn in their favor (76, 77). Different mechanisms can influence the distribution of bacteria, for example, colonization of new environments, in particular in soils where passive transport is limited by physical influences such as pore size and surface particle interactions (73, 78). However, bacterial dispersal is not always a restriction due to the fact that they can colonize different ecosystems via different dispersal mechanisms. In oligotrophic soil environments, diverse physical and chemical characteristics can play a role in the profile of native bacterial populations (79, 80) and are discussed in the next section. Principal factors affecting bacterial diversity in surface soils Microorganisms living in the surface soil of oligotrophic environments are exposed to various abiotic and biotic factors that can selectively influence the survival and resistance of particular groups (12). The factors known to influence bacterial composition in oligotrophic ecosystems include pH, salinity, temperature, plant and animal presence, fertilizer use, soil moisture, and water and nutrient availability. The structure of soils, in particular the size of aggregates, plays an important role in the distribution of bacterial communities (3, 81). For example, a study of mineral composition in artificial soils suggested that quartz, montmorillillonite and charcoal particles can affect the abundance and functional diversity of soil bacterial communities, notably those belonging to the Proteobacteria, Actinobacteria, Firmicutes and Bacteroidetes phyla (82). Soils can be characterized by different physical and chemical properties, and can contain a unique microbial community based on geographical location (11). The most important known physical factors shaping microbial communities in soils are pH (83) and salinity (84).

( 5 '

 5 -Ad.B + TACNVGGGTATCTAATCC-3'), where Ad. A and Ad. B represent the adaptors used for pyrosequencing (Roche/454). The xxxxxxxxxx represent ten nucleotide sequence MIDs (multiplex identifiers) for sample identification barcoding. Two different thermostable DNA polymerases, in 3 separate reactions, were used in the 16S rDNA PCR amplifications for each sample in order to reduce PCR bias. Enzymes I and II for the 2013 set of samples and Enzymes I and III for the 2014 set of samples were used: (I) Phire Hot

Figure 1

 1 Figure 1 Location of sampled rice field sites of the Centre Français du Riz (CFR) in the Camargue area in France.

Figure 2

 2 Figure 2 Plot of the physical and chemical properties of the Camargue sites on the first two principal components. This figure was generated using the ade4 package in R software.

Figure 3

 3 Figure 3 Relative abundance of the bacterial phyla populations of the rhizosphere and nearby surface soil samples from the Camargue CFR samples, and three other sites (for comparison). On the right of the graph: South Korea (1/Surface, 2/Rhizo), Portugal (3/Rhizo, 4/ Surface) and California, USA (5/ Surface, 6/Rhizo, 7/ Surface, 8/Rhizo, 9/ Surface, 10/Rhizo).

Figure 4

 4 Figure4UPGMA tree of the bacterial phyla populations of the rhizosphere and surface soil samples from the Camargue CFR samples, and three other selected (red box) sites for comparison, using the Bray Curtis similarity index. This figure was generated using the ade4 package in R software.

Figure 5

 5 Figure 5 Plot of the bacterial communities based on phyla of the rhizosphere and nearby surface soil samples from the Camargue area in France on the first two principal components.

Figure 6

 6 Figure 6 Relative abundance of the bacterial genera (>1%) communities of the rhizosphere A) Arelate rice variety, B) Gageron rice variety and C) surface soil samples from the Camargue area in France.

  . Two grams of sediment/soil sample were rehydrated in 1ml of 1/4 TS (Tryptic Soy Broth) for 1 h at 30ºC. To extract DNA, 13.5 ml of extraction buffer (0.1M Tris/HCl pH 8, 0.1M EDTA pH 8, 0.1M Na2PO4, 1.5M NaCl, 1% [w/v] CTAB (Cetyltrimethylammonium bromide), containing 50 μl of self-digested Pronase (20 mg/ml) and 10 μl of RNAse A (10 mg/ml), were added and incubation continued for 2 h at 37ºC with 220 rpm shaking. Subsequently, 2.5 ml of 20% SDS [w/v] was added and a further incubation for 2 h at 65ºC was performed. The supernatant fluid was collected after a 20 min centrifugation at 6000×g at room temperature. A further extraction was performed on the pellet with 4.5

  Halothiobacillus genus were more abundant in sediment samples from 2014, while members belonging to the Marinobacter and Thiohalorhabdus, were more abundant in soil samples from 2013. Members belonging to the sulfate reducing bacteria Desulfotignum, Desulfovermiculus and Sulfurovum genera were more abundant in sediment samples from 2013.

Figure 1 .

 1 Figure 1. Sampling site locations of the soil and sediment samples from the Camargue

Figure 2 .

 2 Figure 2. Quantitative PCR results of bacterial 16S rRNA genes copies in 1 gr of wet

Figure 3 .

 3 Figure 3. The relative abundance of bacterial phyla in sediment and soil samples from

Figure 4 .

 4 Figure 4. UPGMA clustering tree among the sediment and soil samples phyla

Figure 6 .

 6 Figure 6. Camargue sediment and soil sample bacterial group composition according to

Figure 5

 5 Figure 5

  examples, Liao et al. (2015) studied the influence of environmental factors, such as the geographical location, soil physical and chemical properties and oil contamination, on the composition of the bacterial community in four oil field locations in Northern (Huabei and Shengli), Northeastern (Daqing) and Western (Kamarai) China. Ding et al. (2013) examined the influence of land use in the composition of the bacterial diversity of a semi-arid ecosystem comparing scrublands with field soils of alfalfa (Medicago sativa)

  rRNA gene quantification was performed using a PikoReal 24 Real-Time PCR System (Thermo Fisher Scientific Inc.) on 24 well plates. The 25 µL reaction mixtures contained 12.5 µL Maxima SYBR Green/ROX qPCR Master Mix 2X (Thermo Fisher Scientific Inc.), 0,3 µM of forward and reverse primers and 10 ng/µL of DNA template. The temperature cycling program was: denaturation at 95°C for 10 min, 40 cycles of denaturation at 95°C for 15s, annealing at the Tm (see Table

  '-Ad.B + xxxxxxxxxx + WTTACCGCGGCTGCTGG-3'), where the Ad. A and Ad. B represent the adaptors used for pyrosequencing (Roche/454). The xxxxxxxxxx represent ten nucleotide sequence MIDs (multiplex identifiers) for sample identification barcoding. Two different thermostable DNA polymerases were used in the 16S rDNA PCR amplifications for each sample in order to reduce PCR bias: (I) Phusion High-Fidelity DNA Polymerase (Thermo Scientific, Sweden): 98ºC for 2 min followed by 30 cycles of 98ºC for 30 s (denaturation), 56ºC for 20 s (annealing), 72ºC for 20s (polymerization), and a final extension at 72ºC for 10 min; (II) iProof TM High-Fidelity DNA Polymerase (Bio-Rad, USA): 98ºC for 2 min followed by 30 cycles of 98ºC for 30 s (denaturation), 54ºC for 20 s (annealing), 72ºC for 20 s (polymerization), and a final extension at 72ºC for 10 min. Three tubes of each 25 μl PCR reaction were performed for each thermostable DNA polymerase and for each sample, to reduce PCR bias, using 10 to 20 ng DNA, 0.1

  common are samples Padza 4 and Padza 5 (21 genera), followed by samples Padza 1 and Padza 3, sharing 11 genera. The most dissimilar samples, sharing the lowest number of genera, are samples Padza 2 and Padza 4, with 0 common genera. Padza 4 was found to present the highest number of unique genera and Padza 2 the lowest number, with 49 and 16 genera, respectively. Bacterial comparison between Padza of Dapani samples and other selected soil environments We compared our bacterial communities from Mayotte with those from other potentially similar (and different) soils (Figure 5A). The most abundant phyla in all the samples are represented by members belonging to the Actinobacteria, Chloroflexi, Acidobacteria and Proteobacteria phyla, with less abundant members belonging to the Bacteroidetes and Firmicutes phyla. In order to reveal differences in the composition of the bacterial communities among the different samples, a UPGMA clustering tree (Figure 5B) was constructed based on the Bray-Curtis distance at the phylum level. The set of samples from Dapani Padza and Saudi Arabia cluster together, especially sample Padza 5 with Swh2 and Padza 4 with Swh1. These samples are the most similar in relation to the number of Bacteroidetes and Firmicutes members. The samples from the Atacama desert and Finland boreal forest present more members belong to the Actinobacteria and Proteobacteria phyla, respectively. The Dapani Padza bacterial communities are more similar into those in the Saudi Arabia samples, followed by those of the Mendocino samples, based on the relative abundance of Actinobacteria, Bacteroidetes and Proteobacteria phyla. The first two axes of a PCA of the Dapani Padza and Saudi Arabia samples (Figure 5C) explained 73.3% of total variance among the samples and confirmed the results observed in Figure 5A and 5B. Padza 5 falls close to Swh2 and Padza 4 close to Swh1 in PCA space. Based on four soil properties (organic matter, nitrogen, pH and phosphorus) the first two axes of a PCA (Figure 5D) explained 95.7% of the total variance and revealed that the samples are divided principally by the content of organic matter, nitrogen and the pH values, which are higher in samples from Saudi Arabia.

Figure 1

 1 Figure 1 Padzas soil sampled from the Dapani location in Mayotte. Sampling sites are indicated by red dots in the photos and the Dapani location "sampling area" in the southern part of the "Large Island" of Mayotte.

Figure 2

 2 Figure 2 Principal Component Analyses (PCA) of the physical and chemical properties of the Dapani Padza samples from Mayotte. The figure was generated using the ade4 package in R software.

Figure 3

 3 Figure 3 Bacterial phyla composition of the Dapani Padza samples based on A) the relative abundance and B) UPGMA clustering tree using the Bray-Curtis distance.

Figure 4 A

 4 Figure 4 A) Bacterial genera composition of the Dapani Padza samples based on the relative abundance and B) Venn diagram representing the distribution of the number of genera detected in each sample and the overlap among the samples.

Figure 5

 5 Figure 5 Bacterial comparison of the Dapani Padza samples and other environments at the phylum level based on A) relative abundance composition, B) UPGMA clustering using the Bray-Curtis distance (a red box represents similar bacterial phyla composition among the samples) and Principal Component Analyses (PCA) among the Dapani Padza and Saudi Arabia samples using C) Phylum community levels and D) soil properties. Ellipses represent potential cluster relationships among the samples.

Figure 2 .

 2 Figure 2. Representation of pyrosequencing primers for 454 Lib-L chemistry pyrosequencing.

Figure 4 .

 4 Figure 4. Schematic representation of the approaches used for soil sample bacterial diversity analyses.

  principal bacterial phyla belonged to the Proteobacteria and Actinobacteria phyla, found as well in desert and soils associated with forest practices. We found that the bacterial populations in hot desert soils (Atacama and Saudi Arabia), and forest soil types (Finland boreal forest and Mendocino, California) contain distinct bacterial populations. The Dapani Padza samples were found to contain populations belonging to the Arthrobacter, Acinetobacter, Bacillus and Burkholderia genera. In the Jizan desert of Saudi Arabia, we found that plants living under harsh environmental disturbances can contain different bacterial populations in their rhizosphere from those from the soil surface, as well as dicotyledons versus monocotyledon plants, suggesting that the rhizosphere of desert plants can support specific bacterial members that may help the survival of plant vegetation. B. Les bactéries associées à la rhizosphère des plantes sont connues pour jouer un rôle essentiel dans les cycles biogéochimiques, la nutrition des plantes et la lutte biologique contre les maladies végétales. Nous avons examiné les populations bactériennes de la rhizosphère du riz (Oryza sativa) en fin de croissance dans la région de la Camargue en 2013 et 2014. Les populations bactériennes les plus abondantes se sont révélées être des membres appartenant au phylum Proteobacteria, Acidobacteria, Chloroflexi et Gemmatimonadetes. Les genres bactériens auxquels appartiennent ces différents phylums sont connus pour participer dans des processus biogéochimiques du sol, tel que la nitrification, la dénitrification, l'oxydation, ainsi que comme agents de control biologique. Les proportions bactériennes trouvées varient considérablement en fonction de leur localisation géographique et selon l'année d'échantillonnage. C. Nous avons examiné les sols de surface de "Padza de Dapani" situés sur l'île de Mayotte au large de la côte est de l'Afrique, car cette région n'est pas un vrai désert, mais y ressemble due à l'érosion du sol. Les sols de Mayotte sont acides, oligotrophes et minéralisées, et leur population bactérienne principale appartient aux phylums des Actinobactéries, Proteobacteria et Acidobacteria. Un fait intéressant, les membres des genres Acinetobacter, Arthrobacter, Burkholderia et Bacillus sont prédominants dans nos échantillons, comme observé dans des déserts (asiatiques) chauds et jouant probablement un rôle dans la minéralisation des sols, expliquant la désertification. D. Les régions arides de la Terre constituent > de 30% de la surface continentale et les sols oligotrophes sont soumis à des facteurs environnementaux difficiles tels que la faible pluviométrie moyenne annuelle, l'exposition aux UV et les grandes fluctuations de température. Nous avons examiné les populations bactériennes présentes dans la rhizosphère des plantes pionnières et les sols de surface du désert de Jizan d'Arabie Saoudite. Les phylums bactériens les plus abondants appartiennent aux groupes des Bacteroidetes, Proteobacteria et Firmicutes qui diffèrent entre la rhizosphère des plantes étudiées par rapport à la surface du sol, à l'exception de la plante "Panicum

Table 1

 1 Bacterial members associated to inorganic mineral transformations ...............

Table 2 .

 2 GreenGenes and SILVA databases ................................................................

89, 90). 5-Biogeochemistry: How soils get mineralized? Definition:

  Mineralization in soils is the effect of the dissolution of minerals, which can influence the physicochemical structure of the soils and, as a consequence, soil fertility (91). Mineralization occurs by the generation of new rock material, generally by tectonics, by sedimentation of organic and inorganic particles, and by rock erosion (92).

	Composition and processes The minerals in surface soils are the result of weathering and
	erosion of rocks exposed on the Earth's surface. These minerals are classified into three
	primary types: silicates (granite, basalt and shale), carbonates (limestone and dolomite)

). Salinity may decrease bacterial biomass and enzyme activity (87), though some groups, such as halophilic bacteria, can resist osmotic stress and desiccation

[START_REF] Lozupone | Global patterns in bacterial diversity[END_REF][START_REF] Nemergut | Patterns and processes of microbial community assembly[END_REF]

.

Other factors such as temperature, UV radiation, wind, altitude, and nutrients also affect bacterial community composition. Bacteria can confront these challenges through different tolerance strategies such as biofilm formation, stress responses or dormancy (and evaporates (halite and gypsum) (93). Silicates compose more than 90% of the Earth's land surface (94). The chemical weathering of rock is an irreversible process brought about by temperature and pressure changes, precipitation, erosion and runoff (94).

Weathering of continental rocks is a major cause of soil mineralization: it determines the formation, evolution, chemical and physical properties and fertility of soil. Weathering of silicate rocks (which increases with temperature and runoff) consumes CO2, an important factor in climate regulation, and produces chemically mobile elements as Na and Ca and immobile elements such as Al or Fe that are constituents of clays (e.g., Kaolinite) and metallic oxyhydroxides (e.g., goethite) (93). The dissolved material is derived from rock by chemical (i.e., production of secondary minerals) and physical (i.e., breakdown of the rocks) weathering (92, 93). Biotic factors that contribute to soil weathering include plant roots and microorganisms, which, by releasing CO2 and organic acids, produce an acid environment that improves nutrient uptake and mineral dissolution (95). The principal parameters controlling chemical weathering in soils are lithology (nature of the rocks), climate, mechanical erosion, biotic activity and organic material

[START_REF] White | Natural weathering of silicate minerals[END_REF][START_REF] Hartmann | Global chemical weathering and associated p-release -the role of lithology, temperature and soil properties[END_REF]

.

Lithology

The major elements present on the Earth's surface resulting from soil weathering processes are SiO2, cations (Na, K, Ca and Mg) and anions (HCO3, SO4 and Cl) (97). Rock properties such as mineralogy, crystal sizes, surface and fracture density can influence the lithology and, as a consequence, chemical weathering (96). The dissolution rates vary among the different mineral types and depend on the soil matrix and the environmental factors of different locations, and are essential for specific microbial group colonization (98, 99).

Table 1

 1 Microbes can also contribute to biomineralization forming minerals such as calcium carbonates, silicates, iron oxides and sulfides. Bacterium-mineral interactions may also affect bacterial growth and metabolic activity. The principal mechanisms of bacterial survival in the presence of minerals are redox transformations, productions of proteins and peptides for binding minerals, precipitation and active mineral transport Bacterial members associated with inorganic mineral transformations

	Bacterial members associated to inorganic mineral transformation in surface soils
	Bacteria	Mineral dissolution (or released)	Reference
	Sphingomonas spp.	Biotite, phosphate	111
			111, 123, 146, 147, 148
	Burkholderia spp.	Biotite, phosphate, granite, apatite
			111, 146, 147, 148
	Collimonas spp.	Apatite, Biotite
	Janthinobacterium spp.	Biotite	147
	Geobacter spp.	Iron	149
	Gallionella spp.	Iron	115
	Leptothrix spp.	Iron	115
	Acidithiobacillus spp.
	Leptospirillum spp. Sulfolobus spp.	Sulfides oxidation	107
	Acidianus spp.
	Pseudomonas spp.	Biotite, phosphate, kaolinite, vermiculite	120
	Arthrobacter spp.	Apatite, biotite	123, 147
	Bacillus spp.	Granite, apatite, bentonite	147, 151
	Paenibacillus spp.	Biotite, bauxite	150
	Erwinia spp.	Biotite	120
	Pedobacter spp.	Apatite	147
	Chinitophaga spp.	Apatite	147
	Shewanella spp.	Nontronite	152

). However, determining whether such a process results from activity of specific microbes (105) can be difficult, owing to low cultivability of copiotrophic microorganisms (106). Bacteria interact with minerals by various mechanisms, depending on mineral type, organism and environmental conditions, while mineral composition and bacterial metabolic activity influence mineral solubility, mobility and bioavailability (107). Bacteria promote mineral release from silicates by colonizing the mineral surfaces, where they release hydrogen ions, low molecular-weight organic molecules such as siderophores and organic acids (108). Abiotic and biotic factors can modify the original chemical structure of the soil surface and, as a consequence, influence changes in the taxonomic distribution of bacterial groups present on mineral surfaces (92). The products of dissolution from mineral complexes by bacterial activity depends on bacterial cell wall structure, outer layers and products of secondary metabolism such as exopolymers that can adsorb to minerals such as clays, colloids or oxides (107). Several studies of bacteria associated with minerals report that in silicate mineral weathering processes, the principal dynamics are associated with the production of hydrogen ions, hydroxyl ions or metal chelating metabolic products (98). Different mineral types may be associated with a wide variety of bacterial types, as found by Glesson et al. (109), who demonstrated that distinct bacterial communities present on the surfaces of mineral types are determined principally by the chemical composition of the mineral substrate, suggesting that chemically different substrates are colonize by different bacterial communities, as are different sized particles (110). Ding et al. (82), suggested that bacterial dissemination in artificial soils is not a stochastic process, and that the abundance of bacterial communities is determined by the type of clay mineral, metal oxides and the presence of charcoal. Under aerobic conditions, bacterium-mineral interactions generate hydrogen ions and ligands that contribute to acidolysis, complexolysis and iron immobilization mechanisms of minerals by specific bacterial members (111).

(112). Energy generation, nutrient acquisition, cell adhesion and biofilm formation can all respond to toxicity of minerals. Micro-topography, surface composition, surface charge and hydrophobicity can also play an important role in the ecology of bacterial communities associated with mineral surfaces (113). This is particularly the cases with Al and Fe oxides, which are among the most reactive components on the surface of acidic and neutral soils, and play a role in the mineral catalysis of humic substances (114). The principal elements forming inorganic mineral complexes under the influence of bacterial activity are Al, Fe, Si, Mg, Mn, S and P (107). Specific bacterial groups are involved in these geochemical transformations, mainly those that oxidize and reduce iron and manganese and that reduce sulfur and sulfate. (107). The common minerals transformed by bacteria are bauxite, whose major constituents are Al2O3, Fe2O3 and SiO2, where the weathering is promoted by bacteria that mobilize oxides of aluminum, iron and silicates, and that reduce iron under anaerobic conditions (115). Carbonates, which represent a significant portion of insoluble minerals, are also mineralized by bacterial biofilms.

Cyanobacteria participate in global carbon cycling and photosynthesis (116). Sulfatereducing bacterial groups have a role in carbonate deposition due the production of extracellular polymeric substances which nucleate carbonate (116, 117). Phosphates can also be found on the surface of mineral soils, and bacteria can solubilize inorganic phosphate complexes (FePO4, AlPO4) by producing organic or mineral acids or by chelation (95). Silicates compose up to 90% of the Earth's surface, and are not stable to environmental change (115, 118), while their bond structures may be altered by bacterial mechanisms through the release of organic acid products, ligands of cations and acidic polysaccharides (115).

Table 1

 1 Camargue CFR sample site locations collected in 2013 and 2014.

			Geographical Location	Samples collected
	Sites	Site name
			N	E	2013	2014
	Site 1	Mas de Sonnailler	43° 40' 48'' 4° 36' 08 '' 1A 1 , 1G 2 , C1 3 1A,1G,C1
	Site 2	Mas Blanc	43° 42' 27'' 4° 33' 57 ''	2A, 2G, C2	C2
	Site 3	Mas D'eymini 43° 40' 30'' 4° 32' 14 ''	3A, 3G, C3	C3
	Site 4	Mas de Signoret 43° 36' 40'' 4° 30' 16 ''	4A, 4G, C4	C4
	Site 5	Mas de Vedeau 1	43° 26' 27'' 4° 42' 07 ''	5A, 5G, C5	5G, C5
	Site 6	Mas de Vedeau 2	43° 25' 60'' 4° 43' 06 ''	N/A	6A, 6G, C6

1. A= Soil rhizosphere from Arelate rice variety, 2. G= Soil rhizosphere from Gageron rice variety, 3. C= Surface soil from each site.

Table 2

 2 Physical and chemical properties of the six sampled sites of the CFR from the Camargue region in France.

	N/A= not available

Table 3

 3 Richness and diversity indices of the Camargue rhizosphere and surface soil samples from 2013 and 2014

	Samples	Raw data		Normalized data**
	OTUs* Chao1 % coverage Shannon OTUs Chao1 % coverage Shannon
	1A_2013 1690 3088	54.73%	6.55	1587 2834	56.00%	6.52
	1G_2013 2289 3922	58.36%	6.69	1509 2863	52.71%	6.53
	2A_2013 1915 3285	58.30%	6.58	1482 2866	51.71%	6.47
	2G_2013 2578 4247	60.70%	6.76	1530 2909	52.60%	6.57
	3A_2013 1718 3399	50.54%	6.72	,1696 3325	51.01%	6.71
	3G_2013 1359 2540	53.50%	5.95	1031 2093	49.26%	5.85
	4A_2013 2298 3925	58.55%	6.67	1558 2939	53.01%	6.55
	4G_2013 1589 2922	54.38%	6.56	1589 2922	54.38%	6.56
	5A_2013 2825 5077	55.64%	7.17	1970 3926	50.18%	6.99
	5G_2013 1875 3784	49.55%	6.89	1870 3768	49.63%	6.89
	C1_2013 1713 2856	59.98%	6.27	1314 2540	51.73%	6.16
	C2_2013 1508 2671	56.46%	6.49	1487 2652	56.07%	6.48
	C3_2013 1592 2840	56.06%	6.42	1420 2561	55.45%	6.38
	C4_2013 2665 4538	58.73%	6.82	1648 3128	52.69%	6.60
	C5_2013 1906 2942	64.79%	6.44	1494 2700	55.33%	6.32
	1A_2014 3513 4832	72.70%	6.98	2299 3605	63.77%	6.84
	1G_2014 2058 2682	76.73%	6.29	1602 2405	66.61%	6.22
	5G_2014 4632 6333	73.14%	7.35	2823 4644	60.79%	7.17
	6A_2014 3619 4948	73.14%	7.06	2359 3616	65.24%	6.94
	6G_2014 2984 4193	71.17%	6.89	2099 3154	66.55%	6.76
	C1_2014 3756 4966	75.63%	7.08	2324 3684	63.08%	6.93
	C2_2014 2298 3180	72.26%	6.47	1675 2246	74.58%	6.37
	C3_2014 1193 1711	69.73%	5.87	1193 1711	69.73%	5.87
	C4_2014 3574 4721	75.70%	7.13	2411 3639	66.25%	6.99
	C5_2014 4512 6225	72.48%	7.27	2633 4303	61.19%	7.09
	C6_2014 2935 4319	67.96%	7.12	2372 3640	65.16%	7.04

*Abbreviation, OTU (Operational Taxonomic Unit) **The sequence numbers of each sample were normalized to 5074 (2013) and 10142 (2014) using sub.sample in Mothur.

Table 1 .

 1 Samples sites, location and physical characteristics

	Sample	Sample	Sample	Geographic coordinates Salinity a	pH b
	Nature	name	site
				North	East		2013	2014
			Sites with sediment and soil samples
	Sediment	Sed_ 3	3	43°22'60'' 4° 45' 41''	N/D	9.0	9.2
	Soil	Soil_3				3	8.5	8.7
	Sediment	Sed_4	4	43°24'31'' 4° 41' 79''	N/D	8.6	9.2
	Soil	Soil_4				4	7.9	9.0
	Sediment	Sed_ 5	5	43°26'14'' 4° 38' 19''	N/D	8.2	9.0
	Soil	Soil_5				4	8.8	8.7
	Sediment	Sed_12	12	43°28'38'' 4° 09' 32''	N/D	8.8	9.0
	Soil	Soil_12				2	8.8	9.1
	Sediment	Sed_14	14	43°33'48' 4° 08' 03''	N/D	8.9	9.2
	Soil	Soil_14				2	9.0	9.5
	Sediment	Sed_15	15	43°29'22'' 4° 26' 51''	N/D	8.1	9.0
	Soil	Soil_15				3	8.1	9.5
	Sediment	Sed_16	16	43°30'25'' 4° 27' 17''	N/D	8.1	8.9
	Soil	Soil_16				3	8.4	8.5
				Sediment sites
	Sediment	Sed_1	1	43°23'10'' 4° 47' 29''	N/D	7.6	8.8
	Sediment	Sed_2	2	43°23'37'' 4° 46' 79''	N/D	8.7	8.7
	Sediment	Sed_7	7	43°25'08'' 4° 39' 11''	N/D	8.6	9.2
	Sediment	Sed_10	10	43°27'22'' 4° 21' 07''	N/D	8.1	9.3
	Sediment	Sed_11	11	43°34'16'' 4° 18' 51''	N/D	8.9	8.9
	Sediment	Sed_13	13	43°33'57'' 4° 11' 05''	N/D	8.8	9.4
	Sediment Sed_17 c	17	43°33'25'' 4° 29' 25''	N/D	8.3	N/D
				Soil sites
	Soil	Soil_6	6	43°27'18'' 4° 36' 15''	4	7.5	8.2
	Soil	Soil_8	8	43°23'24'' 4° 34' 16''	2	9.5	9.9
	Soil	Soil_9	9	43°27'38' 4° 26' 02''	3	9.0	9.1
	a. Salinity (2=

Table 2

 2 Primer sets used for bacterial qPCR abundances

	Primer set	Sequence 5'-3'	Position 1	Tm 2	Reference
	338F-518R	ACTCCTACGGGAGGCAG	338-534	59°C	(Fierer et al.,
		CAGATTACCGCGGCTGCTGG			2005)
	917F-1046R	GAATTGACGGGGRCCCGC	917-1046 55°C	(Degnan et al.,
		CGACARCCATGCASCACCT			2012; Huse et al.,
					2008)
	1 Position based on the 16S rRNA gene sequence of E. coli
	2 Hybridization temperature

Table S . 2

 S2 Bacterial 16S rRNA genes copy

16S rRNA gene copy per gr of soil** or sediment*

	Sample		2013			2014
	name
		338F-518R 917F-1046R	Average	338F-518R 917F-1046R	Average
				Soil sites
	Soil_3	7.72 x 10 8	6.92 x 10 8	7.32 x 10 8	9.85 x 10 8	1.16 x 10 9	1.07 x 10 9
	Soil_4	1.38 x 10 8	1.94 x 10 8	1.66 x 10 8	4.39 x 10 8	6.48 x 10 8	5.43 x 10 8
	Soil_5	1.42 x 10 9	1.42 x 10 9	1.42 x 10 9	1.52 x 10 9	1.77 x 10 9	1.65 x 10 9
	Soil_6	2.04 x 10 9	1.24 x 10 9	1.64 x 10 9	4.94 x 10 8	7.08 x 10 8	6.01 x 10 8
	Soil_8	1.06 x 10 8	7.25 x 10 7	8.93 x 10 7	3.17 x 10 7	8.78 x 10 7	5.97 x 10 7
	Soil_9	1.36 x 10 9	1.13 x 10 9	1.24 x 10 9	1.17 x 10 9	2.09 x 10 9	1.63 x 10 9
	Soil_12	5.56 x 10 7	6.98 x 10 7	6.27 x 10 7	2.45 x 10 8	1.04 x 10 9	6.43 x 10 8
	Soil_14	4.00 x 10 8	5.48 x 10 8	4.74 x 10 8	9.56 x 10 8	2.75 x 10 9	1.85 x 10 9
	Soil_15	4.64 x 10 8	6.44 x 10 8	5.54 x 10 9	2.05 x 10 9	4.10 x 10 9	3.08 x 10 9
	Soil_16	1.08 x 10 9	1.13 x 10 9	1.11 x 10 9	4.66 x 10 9	6.79 x 10 9	5.72 x 10 9
				Sediment sites
	Sed_1	3.80 x 10 9	7.58 x 10 9	5.69 x 10 9	2.47 x 10 9	4.59 x 10 9	3.53 x 10 9
	Sed_2	3.60 x 10 9	6.70 x 10 9	5.15 x 10 9	3.45 x 10 9	6.14 x 10 9	4.80 x 10 9
	Sed_ 3	3.80 x 10 9	5.18 x 10 9	4.49 x 10 9	2.74 x 10 9	4.62 x 10 9	3.68 x 10 9
	Sed_4	8.55 x 10 8	7.66 x 10 8	8.11 x 10 8	5.43 x 10 8	7.08 x 10 8	6.25 x 10 8
	Sed_ 5	2.32 x 10 8	5.22 x 10 8	3.77 x 10 8	3.31 x 10 8	1.47 x 10 9	9.00 x 10 8
	Sed_7	4.12 x 10 7	7.56 x 10 7	5.84 x 10 7	2.60 x 10 8	4.30 x 10 8	3.45 x 10 8
	Sed_10	3.34 x 10 8	5.44 x 10 8	4.39 x 10 8	1.97 x 10 9	1.51 x 10 9	1.74 x 10 9
	Sed_11	3.20 x 10 8	4.46 x 10 8	3.83 x 10 8	4.05 x 10 7	1.04 x 10 8	7.23 x 10 7
	Sed_12	3.56 x 10 8	5.48 x 10 8	4.52 x 10 8	6.31 x 10 7	5.79 x 10 7	6.05 x 10 7
	Sed_13	3.28 x 10 9	3.88 x 10 9	3.58 x 10 9	3.27 x 10 8	3.43 x 10 8	3.35 x 10 8
	Sed_14	1.75 x 10 9	1.65 x 10 9	1.70 x 10 9	5.54 x 10 9	3.62 x 10 9	4.58 x 10 9
	Sed_15	1.26 x 10 9	1.57 x 10 9	1.42 x 10 9	3.64 x 10 9	2.33 x 10 9	2.99 x 10 9
	Sed_16	9.08 x 10 8	9.92 x 10 8	9.50 x 10 8	8.93 x 10 9	5.98 x 10 9	7.46 x 10 9
	Sed_17	5.58 x 10 9	5.12 x 10 9	5.35 x 10 9	N/A	N/A	N/A
	*wet weight
	** dry weight

Table S . 3

 S3 Bacterial genus composition according to the five bacterial groups of bacteria. A) halophilic bacterial groups and B) Sulphate Reducing Bacteria, C) Coliforms, D) Nitrate reducing bacteria and E) other bacterial groups. The values are represented as the average of the total samples in each group with standard deviation in each case. Only genera representing >1% of relative abundance in at least one sample were considered.

	A

Soil sites 2013 Soil sites 2014 Sediment sites 2013 Sediment sites 2014 Halophilic bacteria

	B Table S.4
	A PERMANOVA phylum level.
	Soil sites 2013 B PERMANOVA genus level.	Soil sites 2014	Sediment sites 2013	Sediment sites 2014
	Sulphate Reducing
	Bacteria A
	Desulfatiglans		<0.1			<0.1			0.44 ± 0.54	0.52 ± 0.55
	Desulfobulbus Variables	0.19 ± 0.23 Df	r	0.16 ± 0.27 p-value		0.74 ± 0.90	0.95 ± 1.22
	Desulfopila Salinity (only soil	0.20 ± 0.26			<0.1			0.48 ± 0.80	0.18 ± 0.32
	Desulfosarcina samples)	1	<0.1	0.06391		<0.1 0.0009		0.53 ± 0.78	0.44 ± 0.70
	Desulfotignum Desulfovermiculus Type of sample	0.21 ± 0.52 <0.1 1	0.2963		<0.1 <0.1 0.0009		1.76 ± 2.21 1.29 ± 2.12	2.07 ± 3.28 1.57 ± 2.80
	Desulfovibrio Year	1	<0.1	0.02165		<0.1 0.127872	0.34 ± 0.70	<0.1
	Altererythrobacter Desulfuromonas Geographical location Fusibacter Erythrobacter Sulfurovum Gracilimonas Illumatobacter Coliforms B Escherichia-Shigella Loktanella Nitrate Reducing Phaeodactylibacter Bacteria Ardenticatena Variables	0.37 ± 0.71 0.20 ± 0.34 0.36 ± 0.74 15 0.13 ± 0.16 <0.1 0.78 ± 0.86 0.77 ± 0.74 0.27 ± 0.12 <0.1 0.20 ± 0.39 0.17 ± 0.36 Df	0.44 ± 0.8 0.46 ± 0.70 0.13 ± 0.26 0.2815 0.0399 0.25 ± 0.29 <0.1 0.97 ± 1.67 0.46 ± 0.49 <0.1 <0.1 0.53 ± 0.59 0.72 ± 0.60 2.75 ± 6.03 <0.1 0.27 ± 0.35 <0.1 0.49 ± 0.88 0.99 ± 2.21 r p-value	<0.1 0.53 ± 0.83 1.14 ± 1.96 0.35 ± 0.54 0.42 ± 0.66 <0.1 <0.1 0.59 ± 0.69 0.30 ± 0.41 4.56 ± 4.05 <0.1 1.33 ± 1.37 0.39 ± 0.67 0.20 ± 0.25 <0.1 <0.1
	Psychrilyobacter Caldithrix Salinity (only soil	<0.1	<0.1	<0.1		<0.1	0.32 ± 1.18	0.74 ± 0.57	<0.1	0.58 ± 0.46
	Robiginitalea Nitriliruptor samples)	0.30 ± 0.49 1.84 ± 1.54 1	0.20 ± 0.56 1.41 ± 1.14 1.44 ± 1.34 0.07809 0.0009	1.05 ± 1.39 0.32 ± 0.30 0.58 ± 0.55
	Roseovarius Tropicimonas Other bacterial type Adhaeribacter Type of sample	0.17 ± 0.18 <0.1 0.38 ± 0.97 1	0.11 ± 0.16 <0.1 0.134	1.56 ± 1.77 0.46 ± 0.56 0.0009 <0.1	<0.1	2.81 ± 3.40 0.68 ± 0.76	<0.1
	Fodinibius Bacillus	Year	6.05 ± 5.76 1.32 ± 1.90 1	2.79 ± 3.24 0.74 ± 1.06 0.49 ± 0.72 0.0183 0.1958	<0.1	0.85 ± 1.23	<0.1
	Halanaerobium Haliea Gemmatimonas Blastocatella Geographical location	1.27 ± 3.09 0.11 ± 0.13 <0.1 0.85 ± 2.10 15	0.11 ± 0.31 <0.1 0.26 ± 0.74 3.08 ± 5.69 0.46 ± 0.63 0.41 ± 0.95 0.38306 0.0089	<0.1 <0.1	4.42 ± 6.00 0.56 ± 0.85	<0.1 <0.1
	Halioglobus Geothermobacter	<0.1 0.44 ± 0.42	<0.1	0.25 ± 0.70 0.34 ± 0.41	0.23 ± 0.69 0.30 ± 0.45 0.37 ± 0.82
	Halomonas Geotoga	1.21 ± 1.70 <0.1	0.83 ± 0.99	<0.1	0.83 ± 1.09	0.81 ± 0.77 0.33 ± 1.24	<0.1
	Halothiobacillus Jhaorihella	0.19 ± 0.33 <0.1	<0.1		<0.1	0.52 ± 0.71	2.03 ± 4.35 0.29 ± 0.58 0.39 ± 0.67
	Marinicella Massilia	0.43 ± 0.42 0.42 ± 1.05	0.53 ± 0.53 0.10 ± 0.25 1.52 ± 0.81	<0.1	1.50 ± 0.85	<0.1
	Marinobacter Nibribacter	1.20 ± 1.45 0.22 ± 0.69	0.60 ± 0.87	<0.1	0.38 ± 0.41	<0.1	0.51 ± 0.71	<0.1
	Salegentibacter Nocardioides	0.32 ± 0.44 0.48 ± 1.17	0.35 ± 0.83 0.50 ± 1.38	<0.1	<0.1	0.21 ± 0.69	<0.1
	Salinibacter Pontibacter	0.47 ± 0.89 1.80 ± 4.01	0.23 ± 0.35 0.77 ± 1.64 0.52 ± 1.12	<0.1	0.75 ± 2.08	<0.1
	Salinimicrobium Rubellimicrobium	4.70 ± 6.06 <0.1	3.20 ± 5.96 0.23 ± 0.67	<0.1	<0.1	0.12 ± 0.21	<0.1
	Thiohalorhabdus Sphingomonas	0.47 ± 0.76 0.84 ± 1.56	<0.1	1.78 ± 4.22	<0.1	<0.1	0.31 ± 0.71	<0.1
	Spirochaeta 2	0.33 ± 0.39		0.13 ± 0.14		2.04 ± 1.12	1.81 ± 1.17
	Sva0081 sediment group	0.14 ± 0.16			<0.1			0.65 ± 0.84	0.57 ± 0.68
	Tangfeifania	0.50 ± 1.14		0.11 ± 0.20		0.27 ± 0.29	0.33± 0.72
	Thermovirga		<0.1			<0.1			0.21 ± 0.77	<0.1
	Truepera		0.30 ± 0.21		0.53 ± 0.60		0.85 ± 0.72	1.22± 1.01

Table 1

 1 Physical and chemical properties of the Dapani Padza samples

		Padza 1	Padza 2	Padza 3	Padza 4	Padza 5
	Clays (< 2μm) [g/kg]	458	687	601	666	673
	Silts (2/50 μm) [g/kg]	457	275	347	312	285
	Sands (50/2,000 μm) [g/kg]	85	38	52	22	42
	Organic carbon (C) [g/kg]	0.234	0.183	0.153	0.319	0.732
	Total nitrogen (N) [g/kg]	<0.02	<0.02	<0.02	<0.02	<0.02
	Organic material [g/kg]	0.405	0.316	0.264	0.551	1.27
	Aluminum (Al) [g/100g]	11.3	12	11.6	12	11.3
	Calcium (Ca) [g/100g]	<0.02	<0.02	<0.02	<0.02	<0.02
	Iron (Fe) [g/100g]	17.2	17.8	16.9	18.8	17
	Potassium (K) [g/100g]	0.121	0.043	0.0756	0.0311	0.0824
	Magnesium(Mg) [g/100g]	0.744	0.398	0.711	0.707	0.54
	Manganese(Mn) [mg/kg]	4,210	3,200	3,920	2,790	1,580
	Sodium (Na) [g/100g]	0.0843	0.0492	0.0779	<0.02	<0.02
	Phosphorus (P2O5) [g/100g]	0.601	0.508	0.473	0.451	0.808
	pH	5.89	5.53	6.06	4.77	4.74

Table 2

 2 Primer sets used in qPCR abundance quantification 1 Position based on the 16S rRNA gene sequence of E. coli and T. gammatolerans, respectively 2 Hybridization temperature

	Primer set	Targeted	Sequence 5'-3'	Position 1	Tm 2	Reference
		domain
	338F-518R	Bacteria	ACTCCTACGGGAGGCAG		338-	59°C	(Fierer et al.
			CAGATTACCGCGGCTGCTGG	534		2005)
	917F-1046R	Bacteria	GAATTGACGGGGRCCCGC		917-	55°C	(Degnan and
			CGACARCCATGCASCACCT	1046		Ochman 2012;
							Huse et al. 2008)
	A917F-A1060R Archaea	AATTGGCGGGGGAGCAC		915-	60ºC	(Caldillo-Quiroz
			GGCCATGCACCWCCTCTC		1048		et al. 2006)
	AB789F-AR9R Archaea	TAGATACCCSSGTAGTCC		789-	50ºC	(Baker et al.
			CCCGCCAATTCCTTTAAGTTTC	927		2003; Kvist et al.
							2007)

Table 3

 3 Relative 16S rRNA gene abundance calculated by quantitative qPCR of the

	Dapani Padza samples
		Quantification 1 with	Quantification 1 with		Estimated
	Samples	Bacterial primers 338F-	Bacterial primers 917F-	Average 1	number of
		518R	1046R		cells/ g soil 2
	Padza 1	1.7 x 10 5	2 x 10 4	9.5 x 10 4	2.26 x 10 4
	Padza 2	1 x 10 5	3.8 x 10 4	7 x 10 4	1.7 x 10 4
	Padza 3	1.6 x 10 5	1.15 x 10 5	1.4 x 10 5	3.3 x 10 4
	Padza 4	4.5 x 10 4	3.5 x 10 4	4 x 10 4	9.52 x 10 3
	Padza 5	6 x 10 4	7.5 x 10 4	7 x 10 4	1.7 x 10 4
	1 number of 16S rRNA genes per gram of soil
	2 value obtained divided by 4.2 (average number of 16S rRNA gene copies per bacterial
	genome) (Větrovský and Baldrian 2013)

Table 5

 5 Significant correlation coefficients of the Dapani Padza samples richness and diversity indexes and the soil physical and chemical properties. (Only p-value* <0.05 revealing a significative correlation are shown).

		OTUs	Chao1	Shannon
	Calcium	r=0.9804;p-value=1 x 10 -4	r=0.9944;p-value=0	r=0.9209;p-value=0.0032
	Aluminum	r=-0.9806;p-value=1 x 10 -4	r=-0.9934;p-value=0	r=-0.9347;p-value=0.002
	Iron	r=-0.9745;p-value=2 x 10 -4	r=-0.991;p-value=0	r=-0.9188;p-value=0.0035
	Potassium	r=0.9799;p-value=1 x 10 -4	r=0.9944;p-value=0	r=0.9236;p-value=0.003
	Sodium	r=0.9768;p-value=2 x 10 -4	r=0.9932;p-value=0	r=0.9146;p-value=0.0039
	Phosphorus	r=-0.8651;p-value=0.0119 r=-0.8895;p-value=0.0073 r=-0.7463;p-value=0.054
	*p-value adjusted for multiple tests, false discovery rate (FDR) correction.
	**n (number of tests) = 18

Table 2 .

 2 Classification levels of the GreenGenes and SILVA databases

	Deepest classification GreenGenes	SILVA
	Kingdom	234	0
	Phylum	2166	25
	Class	10006	13081
	Order	34147	17520
	Family	63436	26317
	Genus	72718	40003
	Species	20745	5000661
	Total	203452	597607
	RDP (18), UCLUST (19) and the online version of SILVA NGS (16) classifier
	programs were also examined.

Laboratoire de Génomique et Biodiversité Microbienne des Biofilms, Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Saclay, Univ Paris-Sud, Bâtiment 409, 91405 Orsay, France

Laboratoire de Génomique et Biodiversité Microbienne des Biofilms, Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Saclay, Univ Paris-Sud, Bâtiment 409, 91405 Orsay, France

2 Laboratoire de Microbiologie Fondamentale et Pathogénicité, UMR-CNRS 5234, Université de Bordeaux, 146 rue Léo

Saignat, 33076 Bordeaux, France

Acknowledgements

I would like to take this opportunity to express my thanks to the people who have helped, guided, and supported me throughout the course of my thesis. First, I would like to express my sincere recognitions to Pr. Michael DuBow for his mentorship, as well for the opportunity and confidence that gave me during these four years of research.

Beside my supervisor, I would like to thanks all the jury members to accept be part of this thesis.

I also want to thanks Christophe Regeard and Micheline Terrier for his help and interesting discussions.

Furthermore, my colleagues Gustavo, Yang, Nga and Catherine.

My Ph.D. in France was supported by the Chilean Government "Becas Chile", CONICYT, from which I am very grateful.

Finally I would like to thanks all my family, my parents, sister, brother, uncles, cousins and friends for the encouragement.

Acknowledgements

We thank all members of the Laboratoire de Genomique et Biodiversite Microbienne des Biofilms (LGBMB) for interesting discussions. We also thank Cyrille Thomas from the Centre Français du Riz (CFR), who allowed us the permission for sampling. This work was supported by the Centre National de la Recherche Scientifique (CNRS), France.

Jorge Osman is a doctoral scholarship recipient of the CONICYT -BECAS CHILE program from the Chilean Government.

Acknowledgements

We thank all members of the Laboratoire de Genomique et Biodiversite Microbienne des Biofilms (LGBMB) for interesting discussions. We also thank Eric Coulet, Director of the Camargue National Reserve, who provided us permission for sampling. This work was supported by the Centre National de la Recherche Scientifique (CNRS), France.

Jorge Osman is a doctoral scholarship recipient of the CONICYT -BECAS CHILE program from the Chilean Government.

Acknowledgements

We thank all members of the Laboratoire de Génomique et Biodiversité Microbienne des Biofilms (LGBMB) for interesting discussions. This work was supported by the Centre National de la Recherche Scientifique (CNRS), France. Jorge Osman is a doctoral scholarship recipient of the CONICYT -BECAS CHILE program from the Chilean Government.

Abstract

Bacterial communities present in the rhizosphere of different plants, including rice (Oryza sativa), play an essential key role in biogeochemical cycles, plant nutrition and disease biocontrol. The Camargue area of France, part of the Rhône river delta flowing into the Soil sites Soil_3 38,823 40,813 36,753 34,398 33,694 31,295 32,656 31,228 Soil_4 52,917 35,076 50,024 27,474 47,553 25,595 47,434 25,472 Soil_5 39,147 44,490 36,660 35,825 32,306 31,617 30,257 31,585 Soil_6 54,352 44,502 50,916 37,166 45,107 32,832 44,720 32,666 Soil_8 87,026 43,109 81,095 36,323 75,841 33,602 75,793 33,588 Soil_9 58,024 103,34 54,730 87,152 46,577 76,358 45,870 76,175 Soil_12 32,928 54,151 31,053 45,353 28,983 40,486 28,969 40,451 Soil_14 65,781 59,629 62,709 51,189 55,958 41,555 55,602 41,456 Soil_15 48,898 64,917 45,151 53,821 39,338 43,596 38,685 43,560 Soil_16 65,884 27,580 62,515 22,904 57,190 17,486 57,087 17,451 Sediment sites Sed_1 59,715 59,637 55,977 49,471 45,288 45,744 45,270 45,653 Sed_2 63,507 20,806 58,509 16,807 50,271 14,199 50,121 14,040 Sed_ 3 31,899 24,232 30,342 19,779 27,734 18,189 27,479 18,173 Sed_4 52,917 35,076 50,024 27,474 47,553 25,595 47,434 25,472 Sed_ 5 46,046 23,605 42,619 18,743 39,72 17,809 39,589 17,782 Sed_7 23,434 45,81 21,429 36,043 20,486 33,931 20,392 33,655 Sed_10 41,092 31,531 37,987 23,656 34,890 20,747 34,811 20,729 Sed_11 53,973 51,433 50,704 42,051 48,857 38,252 47,519 37,206 Sed_12 58,769 52,716 55,418 39,725 51,272 37,396 50,954 37,308

List of contents

Bacterial communities from the Camargue rice fields (France) (E = 10 1-s -1, with s = slope of the standard curve) between 85% and 105% were analyzed.

Each sample was quantified at two different dilutions (Bustin et al., 2009). If the differences between the two were not proportional to the respective dilutions, further dilutions and qPCR reactions were performed.

16S rDNA library preparation and pyrosequencing

The 16S rRNA genes of bacteria were amplified from 10 ng of total extracted DNA. PCR reactions (25 μl each) were performed using the universal 16S rDNA bacterial primers S .3 B). Members belonging to this genus were observed in Asian deserts (An et al., 2013) and some species may pose a risk of infection for humans (An et al., 2015).

Members belonging to the Firmicutes phylum in the genus Halanaerobium, contain the highest proportions of halophilic bacteria found in this study, especially in sediment samples from 2014, while also present in soil samples from 2013 (Table S.3A). Members from this genus were also detected by DGGE profiles in the Camargue region, but restricted to a mat sample in a southern saltern [START_REF] Villanueva | Analysis of diurnal and vertical microbial diversity of a hypersaline microbial mat[END_REF] and also isolated from the Great Salt Lake sediments in Utah (USA) [START_REF] Ventosa | Halophilic and Halotolerant Micro-Organisms from soils[END_REF]. Members belonging to the coliform group (Escherichia-Shigella genera) were detected in some sediments samples. We presume that these genera are reflection of the presence of feces from the diverse fauna present in the Camargue wetlands, in particular birds in migration (Tourenq et al., 2001;Fasola et al., 2006). Members belonging to the Bacteoidetes phylum were also found to be abundant in sediment and soil samples. Abundant genera belonging to this phylum include halophilic bacteria (Salinomicrobium and Fodinibius genus) (Table S.3A) and other bacterial types (Pontibacter genus) (Table S.3B). Members belonging to the Fodinibius genus were recently described and isolated from a salt mine in China [START_REF] Wang | Fodinibius salinus gen. nov., sp. nov., a moderately halophilic bacterium isolated from a salt mine[END_REF], while members belonging to the Salinomicrobium genus are known to be observed both in desert ecosystems (An et al., 2013) and high salt soils [START_REF] Ma | A meta-analysis of the publicly available bacterial and archaeal sequence diversity in saline soils[END_REF].

Nitrate reducing bacteria (NRB) present higher proportions in soil and sediment samples from 2013 and 2014 (genus Nitriliruptor) and soil sites from 2014 (Ardenticatena genus).

Members belonging to the Nitriliruptor genus (Actinobacteria phylum) were also detected in saline ecosystems [START_REF] Ma | A meta-analysis of the publicly available bacterial and archaeal sequence diversity in saline soils[END_REF], while members belonging the

Abstract

The composition and functions of indigenous microbial populations is now an essential parameter for understanding life on Earth, even when comparing seemingly similar ecosystems in different locations. To better understand microbial populations present in atypical soil environments, and to discern any relations between these environments and their bacterial communities, we examined the "Padza de Dapani" on the island of Mayotte off the east coast of Africa. This area is not a true (hot) desert, but resembles one in many places due to extensive soil erosion. We collected surface soil samples from five different sites of the Padza de Dapani in Mayotte. We examined bacterial biodiversity using pyrosequencing of PCR-amplified 16S V1-V3 rDNA sequences from total extracted DNA. Our results show that in the acidic (pH 4.6-6), oligotrophic (organic carbon: 0.1-0.7 g/kg of soil) and mineralized (Fe: 18g/100g; Al: 12g/100g) Dapani Padza soil samples, members of the Actinobacteria and Proteobacteria phyla dominated the bacterial communities. Interestingly, members belonging to the genera Acinetobacter, Arthrobacter and Bacillus were found to be very abundant in our samples, as is also observed in hot Asian deserts such as the Gobi (Mongolia) and Taklamaken (China) deserts.

We also compared our overall results with those published from other soil studies to better understand microbial populations in oligotrophic soils, and found that the bacterial communities of the Dapani area were closely related with those present in hot desert soils, thus possibly aiding an understanding of desertification and potential recovery strategies.

Key words: bacteria; biodiversity; 16S rRNA; pyrosequencing; soil erosion

erosion, plus the formation of rills and gullies which are the products of the rain runoff [START_REF] Audru | Major natural hazards in a tropical volcanic island: A review for Mayotte Island, Comoros archipelago, Indian Ocean[END_REF]. Rain is a major problem, as the intense erosion of clays can play a role in the destruction of agricultural soils, along with the accumulation of clays in the littoral and the ultimate siltation of the lagoon, altering the natural biogeochemical cycles and, as a consequence, the mineralization of surface soils [START_REF] Safford | Important Bird Areas in Africa and associated islands -Mayotte[END_REF][START_REF] Nehlig | Notice explicative, Carte géol[END_REF].

The aim of this study was to examine the bacterial community of the surface of eroded (UPGMA) were based on relative abundances and on Bray-Curtis distances, respectively, using the "ade4" R package (Chessel et al. 2004).

Sample comparison with other environments

In order to compare our samples with others, we analyzed several available pyrosequencing results from different environments. The classification of the sequences for the thirteen selected samples was performed in a manner identical to those for the Dapani Padza samples. The samples correspond to a mineralized soil dominated by Scots pine trees (Pine-MS) and a pristine peat soil (Pine-PP2) from a Boreal forest in Finland (Sun et al. 2014). Another three samples, called 2728 (Atacama 1), 2547 (Atacama 2) and 2514 (Atacama 3), are from the Atacama desert of Chile [START_REF] Neilson | Life at the hyperarid margin: novel bacterial diversity in arid soils of the Atacama Desert, Chile[END_REF]. Four soil samples from Saudi Arabia, from different elevations, using two samples from the Tabalah location (Swh1 and Swh2) and another two from the Al Samalah location (Swh7 and Swh 8) [START_REF] Yasir | Composition of soil microbiome along elevation gradients in southwestern highlands of Saudi Arabia[END_REF] were used. Four samples from the Jug Handle State Natural

Reserve located in Mendocino County, California (USA), were also selected from three terraces, with different levels of weathering, and assigned as T1, T2 and T3 [START_REF] Thomas | Metagenomics -a guide from sampling to data analysis[END_REF]. We used the three samples from the T1 terrace, denoted as John 13, John 14 and John 15, and one sample from the T2 terrace, denoted as John 6.

Sequence accession numbers (SRA)

The DNA sequences from the five Dapani Padza samples were deposited in the Sequence Read Archive (SRA), under the accession number SRR316973

Results

Soil physical and chemical properties

Characterization of the soil samples (Table 1) shows that they were composed principally of clays (458-687 g/kg) and silts (275-457 g/kg) and less so by sands (22-85 g/kg). The pH values of the samples were acidic and ranged from 4.74 (Padza 4) to 6.06 (Padza 3).

Selected chemical analyses found results ranging from 0.153 to 0.732 g/kg (C), 0.264 to 1.27 g/kg (Organic Material), 0.451 to 0.808 g/100g (P205) and <0.02 g/kg (N) for the five samples. Inorganic metal averages were 11.6 g/100g for Al and 17.5 g/100 g for Fe.

Principal Component analyses (PCA) were performed for the physical-chemical variables (Figure 2). The first two axes of the PCA explained 87% of the total variance. Observed

Quantitative qPCR

The copy numbers of 16S rRNA genes in 1 gr of soil varied by almost fourfold, from an average of 4 x10 4 copies of the 16S rRNA gene (9.52 x 10 3 bacterial cells) (Padza 4) to 1.4 x10 5 copies of the 16S rRNA gene (3.3 x 10 4 bacterial cells) (Padza 3) for the two primer pairs for bacterial quantification (Table 3). The results for Archaea are not shown due to the fact that they were below the detection limit of 9,000 copies of the 16S rRNA genes per gr of soil.

Pyrosequencing data

We obtained 35,209 raw sequences after pyrosequencing of PCR amplified 16S rDNA genes from total extracted DNA from our set of five samples. After the trimming process, 97% of the sequences remained. A total of 1,299 sequences (3.7%) were detected as chimeras and removed from the remaining sequences. The Padza 3 sample contained the highest number of sequences after cleaning (11,265 sequences) while the Padza 5 sample has the lowest number (2,195 sequences). A total of 30,026 sequences in the set of five samples were considered as clean and used for further analyses (Table 4).

Bacterial richness and diversity in the Dapani Padza samples

The number of Operational Taxonomic Units (OTU´s) differing at <97% varied from 167 (Padza 2) to 414 (Padza 3) in the five samples (Table 4). The Chao1 estimator, representing an estimate of the absolute number of species in an assemblage, ranged from

Bacterial genera composition of the Dapani Padza samples

We found that between 10-60% of the sequences belong to currently unknown bacterial genera. The most abundant genera, based on their relative abundance (Figure 4A) are represented by members belonging to the Arthrobacter (1-27%), followed by the Bacillus (0.05-25%) and Acinetobacter (1-12%) genera. We found that the abundant genera can significantly vary in proportion among our set of samples. We also found abundant genera represented by members belonging to the Burkholderia (0.05-8%), Paracoccus (0.05-7%), Propionibacterium (1-8%) and Tepidomonas (0.005-6%) genera.

Discussion

Site characteristics

In order to reveal the potential bacterial diversity of an eroded tropical soil ecosystem, we examined five surface soils sites in the Padza de Dapani area, situated in the southern portion of the Large Island of Mayotte, surrounded by the known invasive shrub Acacia mangium Willd. introduced from Australia and disseminated throughout Mayotte in the 1980s for the purpose of reforestation agroforestry to control the erosion of areas such as the Padza badlands [START_REF] Vos | Case Studies on the Status of invasive Woody Plant Species in the Western Indian Ocean: 2. The Comoros Archipelago (Union of the Comoros and Mayotte)[END_REF][START_REF] Kull | Invasive Australian acacias on western Indian Ocean: a historical and ecological perspective[END_REF][START_REF] Baret | Threats to paradise? Plant invasions in protected areas of the western Indian Ocean Islands[END_REF]). The Padza de Dapani zone is characterized by surface erosion caused principally by deforestation, runoff and tropical cyclones and, as a consequence, producing mineralogical weathering of the basaltic rock. This erosion can also be linked to human activities and climate change [START_REF] Audru | Major natural hazards in a tropical volcanic island: A review for Mayotte Island, Comoros archipelago, Indian Ocean[END_REF][START_REF] Bastida | Deforestation fosters bacterial diversity and the cyanobacterial community responsible for carbon fixation processes under semiarid climate: a metaproteomics study[END_REF]. Soil minerals can constitute a major reservoir of nutrients for forest ecosystems. The weathering process of eroded soils can modify the original chemistry and mineralogy of the soil, causing changes in the availability of soil inorganic nutrients (Uroz et al. 2015;Uroz et al. 2012). Due to their low quantity of nutrients (organic carbon < 0.75g/kg, nitrogen < 0.02 g/kg, phosphorus < 0.09 g/kg) and high mineral concentrations (Al >11 g/100g, Fe >18g/100g), the sampled sites from the Padza de Dapani represent an oligotrophic, high mineral soil ecosystem. The term "oligotrophy" refers to "poor food" and describes a wide range of environments, including soils and other terrestrial ecosystems (Raven et al. 2005). Microbial populations have been surveyed in low nutrient availability environments, classed as oligotrophic and/or hostile, such as hot deserts (Andrew et al. 2012;An et al. 2013;Campbell et al. 2013). As pH values are considered an important determinant of soil bacterial community composition (Lauber et al. 2009), we measured the pH values of our samples, which were found to be acidic (4.6< pH <6.1), as was also observed in surface soils from a Boreal forest in Finland (Sun et al. 2014) and a forest steppe in western Siberia (Schnecker et al. Abbreviation: OTU, Operational Taxonomic Unit 1 The sequences numbers of each sample were normalized to 2195 using sub.sample in Mothur (Schloss et al. 2009). In all cases, I used pyrosequencing of PCR-amplified 16S rDNA sequences from total extracted DNA. I also used bioinformatic tools for 16S rDNA sequence analyses.

My principal challenge was to discern the bacteria groups present in these atypical soils, sediments and rhizosphere environments and try to discern any relationships between particular environments and their bacterial populations.

2-Techniques and methods used 2.1 Soil sampling

Soils are a discontinuous and heterogeneous medium that can contain large numbers of diverse microorganisms and these can vary depending on the depth and soil type studied.

Populations can also vary among sites and even within seemingly close sites because of natural micro site variations that can allow very different microorganisms to exist (1). For each soil site sampled, we collected three different soil samples, separated by 3-5 meters, in order to create an image of the potential soil bacterial diversity of the site. Pepper et al.,(2) suggest that because of the great variability in local bacterial soil populations, it is often necessary to take more than one sample to obtain a representative microbial analysis of a site. The most accurate approach to date is to take many samples within a given site and perform a separate DNA sequence and bioinformatic analysis for each sample. However, this is still costly and our approach was designed to obtain an overview of these three unique regions to determine their potential interest for further study.

DNA extraction

Total DNA extractions were performed according to a modified protocol of Zhou et al.

(3), using 2-5 gr of soil from each sand soil sample and 2 gr of soil for rhizosphere soils and sediments, due to their higher DNA content. This method uses lysis with a high-salt extraction buffer (1.5 M NaCl) and extended incubation (2 to 3 h) of the soil suspension in the presence of sodium dodecyl sulfate (SDS), hexadecyltrimethylammonium bromide (CTAB), RNAse A and self digested pronase after a 1 hour incubation in a plant based (tryptic soy) dilute broth to allow spores to germinate and cells to rehydrate to increase soil DNA extraction and recovery. Several studies have attempted to increase DNA recovery using physical treatments such as bad beating and/or sonication to lyse bacterial cells. However, the recovered DNA is often composed of broken fragments that may not

To estimate certain statistics of bacterial biodiversity present in each soil sample, we calculated the number of OTUs, to reflect the number of species present. In our samples, we used RDP for the Jizan study and the CD-HIT "greedy heuristic algorithm" for the Mayotte and Camargue studies, which estimates the similarity between two sequences without performing multiple alignments of all pairs of sequences (20).

Based on this calculation, diversity indices of bacteria are used to estimate the absolute number of species present in a sample (Chao1) and characterize the relative abundance and evenness of species present in a sample (Shannon) by the following equations:

In conclusion, we examined the bacterial populations and diversity present in three different biomes using pyrosequencing of 16S rDNA amplicons. In the future, these studies should include metagenomic technologies to reveal the Archaeal and Fungal diversity present in these environments in order to create a more complete image of the microbial populations living there and discern functional relations of their communities with their specific environments. The surface and rhizosphere soil from pioneer plants in the Jizan desert in Saudi Arabia.

A. Saline areas are widely distributed on Earth's and are represented by both saline lakes and saline soils. We examined the bacterial composition of estuary sediments, brackish and sandy soil samples from the Camargue region (Rhône delta in southern France) sampled in two consecutive years. Members belonging to the Proteobacteria, Bacteroidetes, Chloroflexi, Firmicutes, Acidobacteria and Actinobacteria phyla were found principally in saline sediment and soil samples. We found that members from these phyla were associated principally to halophilic bacteria, sulphate reducing bacteria (SRB), nitrate reducing bacteria and coliforms, and that their varying proportions were likely affected by salinity and geographical location.

B. Bacterial populations associated with the rhizosphere of plants are known to play essential roles in biogeochemical cycles, plant nutrition and disease biocontrol.

We examined the bacterial populations of the rhizosphere of rice (Oryza sativa) growing in the Camargue region in 2013 and 2014. The most abundant bacterial populations were found to be members belonging to the Proteobacteria, Acidobacteria, Chloroflexi and Gemmatimonadetes phyla. The genera members belong these phyla were found to participate in soil biogeochemical processes such as nitrification, denitrification,