Biogeographical patterns of bacterial and archaeal communities from distant hypersaline environments

Accepted Manuscript Title: Biogeographical patterns of bacterial and archaeal communities from distant hypersaline environments Authors: M.R. Mora-Ruiz del, A. Cifuentes, F. Font-Verdera, C. P´erez-Fern´andez, M.E. Farias, B. Gonz´alez, A. Orfila, R. Rossell´o-M´ora PII: DOI: Reference:

S0723-2020(17)30178-9 https://doi.org/10.1016/j.syapm.2017.10.006 SYAPM 25883

To appear in: Received date: Revised date: Accepted date:

10-7-2017 22-10-2017 23-10-2017

Please cite this article as: M.R.Mora-Ruiz del, A.Cifuentes, F.Font-Verdera, C.P´erezFern´andez, M.E.Farias, B.Gonz´alez, A.Orfila, R.Rossell´o-M´ora, Biogeographical patterns of bacterial and archaeal communities from distant hypersaline environments, Systematic and Applied Microbiology https://doi.org/10.1016/j.syapm.2017.10.006 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Title Biogeographical patterns of bacterial and archaeal communities from distant hypersaline environments

Authors:

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Mora-Ruiz, M. del R.a, Cifuentes, A.a, Font-Verdera, F.a, Pérez-Fernández, C.b, Farias, M. E.c, González, B.d, Orfila, A.e, Rosselló-Móra, R.a

Department of Ecology and Marine Resources, Mediterranean Institute for Advanced Studies

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a

(IMEDEA, UIB-CSIC), Spain.

Environmental Microbiology Laboratory, Puerto Rico University, Rio Piedras campus.

c

Laboratorio de Investigaciones Microbiológicas de Lagunas Andinas (LIMLA), Planta Piloto

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b

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de Procesos Industriales Microbiológicos (PROIMI), CCT, CONICET, San Miguel de

Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez – Center of Applied Ecology

e

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and Sustainability, Santiago, Chile.

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d

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Tucumán, Tucumán, Argentina.

Marine Technology and Operational Oceanography Department, IMEDEA (CSIC-UIB),

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Esporles, Spain.

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Corresponding author: Mora-Ruiz, Merit del R. Marine Microbiology Group, Department of Ecology and Marine Resources, Mediterranean Institute for Advanced Studies, CSIC-UIB, C/Miquel Marqués 21, 07190 Esporles, Spain

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Tel: +34 971 611 827 ; Email: [email protected]

Abstract Microorganisms are globally distributed but new evidence shows that the microbial structure of their communities can vary due to geographical location and environmental parameters. In this 1

study, 50 samples including brines and sediments from Europe, Africa and South America were analysed by applying the operational phylogenetic unit (OPU) approach in order to understand whether microbial community structures in hypersaline environments exhibited biogeographical patterns. The fine-tuned identification of approximately 1,000 OPUs (almost equivalent to “species”) using multivariate analysis revealed regionally distinct taxa compositions. This

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segregation was more diffuse at the genus level and pointed to a phylogenetic and metabolic redundancy at the higher taxa level, where their different species acquired distinct advantages

related to the regional physicochemical idiosyncrasies. The presence of previously undescribed

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groups was also shown in these environments, such as Parcubacteria, or members of

Nanohaloarchaeota in anaerobic hypersaline sediments. Finally, an important OPU overlap was

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observed between anoxic sediments and their overlaying brines, indicating versatile metabolism

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for the pelagic organisms.

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Keywords: Archaea, Bacteria, brines, hypersaline sediments, operational phylogenetic units,

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salterns.

Introduction

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In recent decades, the paradigm of the global distribution of microorganisms ("everything is everywhere") has been constantly questioned [70,99]. Traditional explanations for cosmopolitan

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distributions are large microbial population sizes, high probability of dispersion and low probability of extinction [28]. The actual concept of microbial biogeography and the possibility of non-random distribution of microorganisms are currently considered to be hot topics

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[31,45,84]. The analysis of microbial communities sampled at different distant locations can facilitate the understanding of the underlying drivers causing the differentiation of populations/communities [38]. Some studies have been performed on the global distribution of microorganisms, but they have mostly focused on their possible pathogenic effects [24,30,52,102]. Nonetheless, attention has drifted away from clinical microbiology towards 2

global environmental studies, including oceanic sediments [47], ocean waters [13] and hypersaline environments such as salterns [8,36,97]. Extreme environments, due to their isolated nature, that are often scattered between different geographical points without direct connexions are excellent systems to evaluate biogeographical patterns and allopatric speciation [85,96]. Hypersaline environments are

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globally distributed in different climatic regions and include some of the most extreme environments, such as the Atacama desert, or the Arctic and Antarctic regions [27,29,69,98]. Therefore, they offer an excellent opportunity to compare complex halophilic communities in

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very distant yet similar hypersaline environments around the world. Depending on the origin of

the ionic composition of their brines, salterns can be divided into thalassohaline and

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athalassohaline. Briefly, thalassohaline salterns present a similar ionic composition to seawater and they ultimately occur due to evaporation of seawater or dissolution of evaporite rocks. On

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the other hand, athalassohaline hypersaline environments show different multiple ionic

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surrounding substrates [83].

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compositions, distinct from seawater, which depend directly on the composition of the

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In general, diversity in hypersaline environments is dominated by halophilic microorganisms belonging to the bacterial and archaeal taxa, such as Bacillus [49] and

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Salinibacter [5], and Haloquadratum, Halorubrum [22] and the candidate division Nanohaloarchaeota [3], respectively. Despite the local descriptions of hypersaline

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environments, a global vision of these environments is still necessary to understand microbial adaptation to different environmental conditions and its functioning [55]. In addition, the analysis of physicochemical characteristics is necessary for achieving a better understanding of

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habitats, as well as the response of the microbial communities to environmental variations [54,76,78]. Recently, Filker et al. [32], using some of the same samples also included in the present study, found a high degree of novel genetic diversity and an effect of geographical distance in protistan communities separated by more than 500 km. To complement the biogeographical 3

findings on protist global patterns, this current study characterized the structure of the bacterial and archaeal communities in a larger set from hypersaline sediments and brines in geographically distant salterns from Europe, Africa and South America. Coastal seawater-fed (thalassohaline) and inland endorheic (athalassohaline) systems were also compared.

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Materials and methods Sampling sites and sample collection

Between January and March 2011, a total of 17 brine and 33 sediment samples were collected

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from 27 sites in ten locations in Spain, Argentina and Chile (Table 1; Fig. 1). The Spanish samples included six located in the Mediterranean region, one located in the inland saltpan

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Peñahueca, and two insular salterns from the Canary Islands. Nine sampling sites were located in the Argentinean Altiplano. Finally, there was one sampling site on the Chilean Pacific Ocean

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coast from the Boyeruca salterns. In all cases, sediments were extracted using methacrylate

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cores, and sterilized bottles were filled with brines. All samples were stored at 4 °C until

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processing.

Measurement of the ionic composition

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Ionic concentration quantifications were carried out at the Research Technical Services of the University of Alicante by ion chromatography using a Metrohm, 850 ProfIC AnCat - MCS

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system. Sodium carbonate (3.6 mM) was used as eluent for anion detection, with a flow of 0.8 mL min-1 in a Metrosep A SUPP 7-250 (Metrohm) column plus a Metrosep ASSUP 4/6 as a pre-column, whereas nitric acid (3.5 mM) was the eluent for cation detection (flow of 1.9 mL

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min-1) in a Metrosep C3-250 (Metrohm) column plus a Metrosep C3 pre-column. The column temperature for both anion and cation determinations was 40 ºC. Total carbon and carbonate were measured using Bernard’s calcimeter method [51].

Retrieval of microbial biomass and DNA extraction 4

Sediment samples were processed as described by López-López et al. [54] with the difference of retrieving microbial biomass from 120 g of a homogenized sample (six subsamples, each one comprising 20 g of sediment from different horizons and cores down to 20 cm below the sea floor (bsf)). Sediment pellets were stored at -20 ºC for DNA extraction. In the case of brines, 250 mL of water were filtered through 0.22 µm pore size membrane filters (Durapore,

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Millipore), which were stored at -80 ºC until DNA extraction. Environmental DNA was extracted from sediment pellets and thawed cut filters from brine samples. Sediment pellets and cut filter pieces were separately vortexed in 2 mL

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extraction buffer (100 mM Tris-HCl, 100 mM EDTA) in 50 mL polypropylene centrifuge tubes.

The supernatant was then transferred to a new tube and 20 μL 10 mg mL-1 proteinase K

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(Roche), 24 μL 300 mg mL-1 lysozyme (Roche), and 20 μL 1,000 U mL-1 mutanolysin (Roche) were added, and the tubes were incubated for 1 h in an orbital shaker (Thermo Electron Corp.)

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at 15,700 xg at 37 ºC. After the incubation period, 10% sodium dodecyl sulphate (Panreac) was

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added to a final concentration of 1% and the tubes were incubated at 55 ºC for 30 min. Lysates

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were extracted with phenol-chloroform-isoamyl alcohol, as previously described [54]. Then, the

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DNA was precipitated overnight with 0.7 (v/v) isopropanol, centrifuged for 30 min at 15,700 xg and 4 ºC, rinsed with 70% ethanol (v/v) and centrifuged again for 15 min. After air-drying,

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ºC.

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nucleic acids were resuspended in 50 μL sterile nuclease-free water (Sigma), and stored at -20

PCR amplification and pyrosequencing of 16S rRNA 16S rRNA gene sequences of environmental samples were amplified using the primer pairs

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GM3 and S for Bacteria, and 21F and 1492R for Archaea [20,66,95] obtaining fragments of ~1,450 pb. The PCR reactions were performed as previously described by Mora-Ruiz et al. [63]. A secondary PCR reaction was performed to add barcodes and sequencing linkers to the previously obtained amplicons. This was undertaken using 1:10 µL of the original PCR product as a template, and the same PCR conditions but only for five cycles. Primers GM3-PS and 9075

PS were used for Bacteria [63] and 21F-PS for Archaea [64]. This PCR was carried out in triplicate for each sample in order to minimize PCR-bias, and the final products were mixed and purified using MSB® Spin PCRapace (INVITEK), following the manufacturer’s instructions. The samples were sequenced using the 454 GS-FLX+ Titanium technology. All sequences were submitted to the European Nucleotide Archive (ENA) under the accession numbers

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ERR2003672-ERR2003764.

Processing of pyrosequencing data

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Data were processed following the Mothur pipeline [87]. Briefly, low-quality sequences were removed (sequences <500 bp and quality score <25; no ambiguities were allowed and no

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mismatches in reads with primers and barcodes). The 10-bp barcodes were examined for the assignation of sequences to the samples. Chimeras were detected and removed with the

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application UCHIME implemented in Mothur [26]. Sequences were clustered into operational

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taxonomic units (OTUs) at the 98.7% level based on the minimum threshold discriminating

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species suggested by Stackebrandt and Ebers [88] using UCLUST [26] included in QIIME v.

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Phylogenetic affiliation

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1.9.0 [15] with the uclust method [25].

Representative sequences of the bacterial and archaeal OTUs were incorporated separately into

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the non-redundant SILVA REF 111 [81] database using the ARB package [56]. Sequence alignment was performed with the SINA tool (SILVA Incremental Aligner) [80] using the LTP 111 database as template and was manually improved following the reference alignment in

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ARB-editor [100]. The closest relative non-type strain SILVA REF 111 sequences of an acceptable quality according to the SILVA database [82] that affiliated with the OTU representatives were selected and merged with the LTP 111 type-strain sequence database. For the final tree reconstruction, the selected representative sequences with an additional set of approximately 750 supporting sequences (highest quality in the LTP) and covering a balanced 6

representation of all major phyla of both the Bacteria and Archaea domains were used for a neighbor-joining reconstruction [65]. All OTU representatives were inserted into this final topology using the parsimony tool, and they were clustered in OPUs (operational phylogenetic units; [35,63]) based on the visual inspection of the final tree. The thresholds used for taxonomic levels were those suggested previously where the threshold sequence identity for

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species was 98.7% [88], for genera 94.5% and for families 86.5% [89,101]. An OPU is the smallest monophyletic group of sequences containing OTU representatives together with the

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closest reference sequence, including the sequence of a type strain when possible [64].

Diversity and statistical analysis

The ionic composition of the samples was evaluated using a hierarchical cluster analysis using

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the Bray-Curtis distance and Ward’s linkage with previously normalized data. Rarefaction

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analyses for the two domains were conducted using PAST v 3.01 software [39]. For Jost q=0

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(richness) and q=1 (diversity) indices calculations, a re-sampling was conducted with the

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Monte-Carlo method using 1,000 simulations [16,64]. Pearson correlations between relative

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abundances for Bacteria and Archaea, and also between the relative abundances and environmental factors, were obtained with R Commander [34]. Beta diversity was calculated

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using the Whittaker index, and ordination analysis (e.g. non-metric multidimensional scaling (NMDS) was performed on the domain with previous normalization. The goodness of the

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NMDS was evaluated according to a stress value smaller than 0.366, which is considered acceptable for 50 samples, 0.337 for 33 samples and 0.228 for 17 samples [90]. Fitted vectors (environmental variables) were represented as arrows pointing in the direction of the highest

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strength change and the length of the arrow was proportional to the r2 obtained [21,73] using the vegan package [72] in R v 3.1.1. Additionally, a permutational multivariate analysis of variance (PERMANOVA; [2]) was used to test the statistical significance of the geographic location in the microbial communities. Simple and partial Mantel tests [57] running 5,000 randomizations were used to evaluate the significance and correlation coefficients between community, 7

geographic and environmental distance matrices. Bray-Curtis distances for paired locations were used to calculate the community distances, and Euclidean distances were used for geodesic distances based on longitude/latitude. Analyses were performed using the packages geosphere [42] and vegan [72] in R v 3.1.1. Finally, a pooled SIMPER analysis by Bray-Curtis dissimilarity was conducted to detect the OPUs, which generated the biogeographic patterns.

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SIMPER was performed with PAST v 3.01 software [39].

Results

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Description and ionic characterization of samples

The study comprised a total of 50 samples (Table 1), including coastal seawater-fed (20

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samples) and inland endorheic (30 samples) systems. The ionic composition, location and origin of the different samples are summarized in Table 1 (and Supplementary Table S1). Cl - was

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always the dominant anion, and Na+ was in most cases the dominant cation followed by Mg2+,

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with the exception of both Chilean brines, Peñahueca brine CM7 and Peñahueca sediment CM4,

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where the dominance of both cations was inverted (Supplementary Table S1). Moreover, all

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Argentinean samples, as well as some from Peñahueca, contained Li+, which was not detected in any of the coastal samples, and NO2-, NO3-, PO43- and NH4+ were higher in inland samples. The

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IB3 and IB4 sediments (from Campos) exhibited lower values of Br-, Cl-, K+, Mg2+ and Na+, and higher values for Ca2+ than other similar coastal samples, and were more similar to the inland

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samples measured, especially those from Peñahueca (Supplementary Table S1). In addition, brines from Peñahueca exhibited higher values of F- and Li+ that were more similar to the Argentinean samples, which was corroborated by the clustering analysis (Supplementary Fig.

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S1). The range of salinities for the samples was 25%-43%, with South American samples generally showing the highest salinities (Table 1).

Sequencing and OPU design

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After trimming, a chimera check and removal of low quality sequences, the approach used recovered 462,931 sequences for Bacteria and 692,411 for Archaea with a mean of 9,259 (±4,664) and 13,848 (±9,766) sequences per sample, respectively. The distribution of read lengths did not follow a normal distribution, but a Weibull distribution with a scale of 744 pb (scale is the equivalent parameter to a mean in a normal distribution; 500 - 898 bp range). The

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sequences were clustered in OTUs at a 98.7% identity, and gave a total of 103,616 OTUs for Bacteria and 77,839 for Archaea. The representative OTU sequences affiliated with a total of

844 OPUs (mean 138 ± 68 per sample) for Bacteria and 362 for Archaea (mean 84 ± 49 per

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sample) (Supplementary Table S2). In both cases, and for each individual sample, the diversity of OPUs reached saturation or was close to saturation based on the shape of the rarefaction

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curves (Supplementary Fig. S2).

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Bacterial diversities

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The bacterial richness for sediments was composed of a total of 788 OPUs. Of these, 471 (60%)

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affiliated with 326 known genera and 256 with known species. In addition, these OPUs

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affiliated with 44 bacterial phyla, including some Candidate Divisions, such as OP11 and TM6. Proteobacteria was the most represented phylum, followed by Firmicutes, Bacteroidetes and The

bacterial

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Cyanobacteria.

Halanaerobiaceae

(phylum

OPUs

detected

Firmicutes),

covered

159

Moraxellaceae,

known

families

with

Desulfohalobiaceae

and

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Commamonadaceae (the three within Proteobacteria) being the most important families in terms of abundance. In addition, the most representative genera were Halanaerobium, Acinetobacter, Desulfovermiculus and Halanaerobacter encompassing 42.1% of the total

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sequences. The genus Halanaerobium was also detected as the unique ubiquitous genus in all sediment samples (Fig. 2; Supplementary Fig. S3, Tables S3 and S4, and Supplementary Spreadsheet Tables SS1, SS2, SS3 and SS4). Despite the fact that some genera were ubiquitous (Halanaerobium, Rhodovibrio, Bacillus and Legionella) no OPUs representing putative single species of Bacteria were detected as ubiquitous in the sediment samples. However, certain 9

regionalisms could be observed. A total of 41 OPUs (1.2% of all the sequences from sediments) were detected exclusively from Spain (affiliating with Moraxellaceae, dominated by the genus Acinetobater, and families Rhodobacteraceae and Clostridiaceae), 83 OPUs (0.33% of the sediment sequences) from Argentina (within the Cytophagaceae, Hypomicrobiaceae, Rhodobacteraceae and Rhodothermaceae families), and 15 OPUs (0.13% of the sediment

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sequences) from Chile (affiliated with the Ectothiorhodospiraceae and Desulfobacteraceae families) (Supplementary Fig. S4 and Spreadsheet Tables SS4, SS5, SS6 and SS7).

In the brine samples, 662 bacterial OPUs were detected, of which 415 (63%) affiliated

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with 297 known genera and 218 known species (Fig. 2). With the exception of Bacteroidetes, which was more abundant in brines than in sediments, the abundance of the other phyla was

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similar to that observed in sediments (Fig. 3). The analyses detected 146 families, with the most important being Halanaerobiaceae (Firmicutes), Chitinophagaceae and Flavobacteraceae Rhodobacteraceae

and

Ectothirhodospiraceae

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(Bacteroidetes),

(Proteobacteria),

and

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Cyanobacteria Family I (Supplementary Fig. S3, Table S3 and Supplementary Spreadsheet

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Tables SS1 and SS3). A high number of genera detected in brines were putative anaerobes (81

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genera, e.g. Halanaerobium) or facultative anaerobic bacteria (94 genera, e.g. Gracilimonas), including those that showed the highest number of sequences (Halanaerobium and

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Halanaerobacter). In contrast, putative aerobic genera (e.g. Psychroflexus) were detected in lower abundances (mean 15.11% ± 14.4% sequences). Halanaerobium was the only

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cosmopolitan genus detected in all the brine samples. Additionally, a parallel increase was observed in the sequence amplicons of the genera Psychroflexus with Roseovarius (r2=0.89), as well as Rhodovibrio and family Hahellaceae (r2=0.88). Eleven OPUs (0.03% of the total

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number of brine sequences) were exclusively detected in Spain (including two OPUs in Bacillaceae), and 42 OPUs (0.3% of the brine sequences; Gallionellaceae, Cystobacteraceae, and Enterobacteriaceae) were detected exclusively in Argentina, and five OPUs (0.02% of the brine sequences) in Chile (including unclassified members of Clostridiales, Lactobacillales and Sphingobacteriales; Supplementary Fig. S4). Moreover, 211 OPUs were detected in a unique 10

location (Salar de Pocitos; ARG17), which was the single location without site-specific OPUs. Additionally, 22 OPUs (36.3% of the brine sequences) were found in all coastal brines, while no OPUs were detected as ubiquitous in the inland brines. Since both inland and coastal sample types were represented in Spanish locations, their shared groups were analysed. As a result, four and seven OPUs were detected in all Spanish coastal and inland sites, respectively, which in

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both cases affiliated principally to the genus Halanaerobium (three and four OPUs, respectively). More detailed information concerning the OPUs detected in Spain, Argentina, and Chile is given in the Supplementary Spreadsheet Tables SS5, SS6, and SS7, respectively.

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The members of the Salinibacter genus, identified as OPU318, OPU319, OPU573 and OPU747, were mainly observed in brines, since they were poorly detected in sediments.

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Salinibacter was present in brines of the three countries, but mainly (up to 17.3%) in the Spanish samples, which was in contrast with the Argentinian (up to 0.2%) and Chilean (up to

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0.02%) samples. From these four OPUs, OPU318 (uncultured Salinibacter) was the most

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abundant (up to 16.4% in SP-CN4), with OPU319 (S. ruber), OPU573 (S. iranicus/S. luteus)

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and OPU747 (uncultured Salinibacter) found in low abundances (< 0.23%) in all countries with

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the exception of the Pacific coast (0.85%).

A total of 192 OPUs (2.8% of the total sequences) were exclusive to sediments,

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principally affiliating with Rhodobacteraceae, Caulobacteraceae, and Thermotogaceae. In contrast, 66 OPUs (0.51% of the total sequences) were present exclusively in brines. Five OPUs

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affiliated with Bacillaceae, and three with each of Enterobacteraceae and Family I (Cyanobacteria) (Supplementary Fig. S4 and Spreadsheet Tables SS8 and SS9). On the other hand, 530 common OPUs were detected between sediments and brines, for example, OPUs 194,

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195 and 196 (affiliated with Candidatus “Parcubacteria”). However, OPU188 (uncultured Rickettsiales) was the only one present in all sediments and brines of Argentina. Eleven OPUs were common in brines and sediments from Chile (dominated by Halanaerobiaceae with two OPUs), and ten OPUs were present in all sediments and brines from Spain (principally Halanaerobiaceae with two OPUs and Comamonadaceae with one OPU). Additionally, 134 11

OPUs (0.56% of the total sequences), principally distributed in Alpha-, Beta- and Gammaproteobacteria, were site-specific (Fig. 2, Supplementary Figs. S3, S4 and Spreadsheet Table SS10). The highest richness value was found for site SP-CM5 (312 OPUs) and the lowest for site SP-IB4 (30 OPUs). In general, the richness in the South American samples was higher for

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sediments than their respective brines. However, this pattern was not observed clearly in the Spanish samples. Additionally, none of the Jost indices revealed a pattern for Bacteria by

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sample type, country or inland/coastal region.

Archaeal diversities

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The total archaeal diversity in sediments was represented by 360 OPUs (Fig. 2) from 13 different phyla. From the 360 OPUs, 31 (5.1% of the total sequences) were assigned as

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uncultured or candidate archaeal lineages, such as the Deep Sea Euryarchaeotal Group (DSEG;

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seven OPUs), the Miscellaneous Crenarchaeotic Group (MCG; ten OPUs) and MSBL1 (two

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OPUs). All of them generally showed low abundances (<11%), with the exception of DSEG,

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which had abundances of up to 55% (in SP-IB4; Supplementary Spreadsheet Table SS12). The highest dominances (Fig. 3) were for Euryarchaeota and Nanohaloarchaeota that had a

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negative correlation between their abundances (r2=-0.72). Thaumarchaeota was also abundant in Chilean sediments and in two Balearic sediments (IB4 and IB6). Additionally, Mediterranean

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Sea Brine Lakes 1 (MSBL1), Deep Sea Hydrothermal Vent Group 6 (DHVEG 6) and MCG were principally distributed in Spanish locations. Interestingly, some archaeal populations, such as the Miscellaneous Euryarchaeotic Group (MEG) and KTK 28A, only appeared in Spanish

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samples (Supplementary Fig. S5, Tables S5 and S6). For additional information concerning archaeal OPUs see Supplementary Spreadsheet Tables S11 and S12. A total of 16 archaeal families (Fig. 3) were identified that had a marked dominance of Halobacteriaceae and four families of Nanohaloarchaeota (based on the thresholds suggested by Yarza et al. [101] where the minimal identity threshold for a family was 86.65%), with a 12

notable dominance of Nanohaloarchaoeta-1 (12.9% of the sequences) in sediments and brines. An exceptional case was ARG18 where Methanosarcinaceae was the most representative taxon. From the 105 OPUs (64.2% of the total sequences) affiliating with known genera (94 OPUs only within the family Halobacteriaceae), only 26 OPUs were identified as known species. The most relevant genera were Halorhabdus (20 OPUs; 7.2% of the sequences), Halobacterium (9

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OPUs; 7.8%), Halorubrum (6 OPUs; 9.4%) and Natronomonas (3 OPUs; 5.1%), but none of these, or any other genera, were ubiquitous in all sediments. Regionally, Halomicroarcula (four OPUs) was higher in Spanish samples (up to 22.4% of the sequences in SP-CM4), while

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Haloferax (one OPU) was detected principally in Argentinean sediments, with low proportions

in some samples from Spain (Supplementary Spreadsheet Table SS14). In addition, an increase of abundances correlated (co-occurrence) between certain genera, such as Halobonum ~

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Halosimplex (r2=0.82) and Halorubellus ~ Halomicroarcula (r2=0.84). Other correlations were

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also observed in the abundances for specific genera and ions, such as F- ~ Natronomonas, PO43-

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~ Halobonum, Mg2+ ~ Halorubellus (r2=0.89) and Ca2+ ~ Halomicrobium (r2=0.88) (for more

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information see Supplementary Spreadsheet Table SS13).

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The archaeal sediment richness showed OPU106 (Uncultured Natronomonas), OPU053 and OPU138 (Uncultured Halorubrum) as the most abundant, but none of them were

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ubiquitous. Seventy OPUs (5.9% of the sediment sequences) were exclusively found in Spain (Nanohaloarchaeota and Methanomicrobia Group C; Supplementary Fig. S6 and Spreadsheet

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Table SS15), 20 OPUs (0.01% of the sediment sequences), mainly putative methanogens (Methanobacteriaceae and Methanothermaceae), were detected exclusively in Argentina (Supplementary Fig. S6 and Spreadsheet Table SS16), and three OPUs were from Chile (0.08%

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of the sediment sequences, mainly included in Candidates TMG III and MBGB; Supplementary Fig. S6 and Spreadsheet Table SS17). Richness was lower in brines than in sediments, with 179 OPUs included in seven phyla, also with an important dominance of Euryarchaeota (114 OPUs; 84.2% of the total brine sequences), followed by Nanohaloarchaeota (59 OPUs; 15.7% of the brine sequences; Fig. 3) 13

and, as in sediments, their abundances were highly correlated (r2=-0.99). The remaining phyla (six OPUs) contributed to the communities with less than 0.1% of sequences (Supplementary Table S6 and Supplementary Spreadsheet Table SS12). Halobacteriaceae (108 OPUs) was notably more abundant followed by Nanohaloarchaeota 1 (51 OPUs) (Fig. 3 and Supplementary Spreadsheet Table SS13). The most important genera were Halorubrum (five

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OPUs), Natronomonas (three OPUs), and Halonotius (four OPUs). However, it is important to note that Halorubrum, Halorhabdus, Halobacterium, Halovenus, and Natronomonas were present in all samples (Supplementary Spreadsheet Table SS14), but similar to the sediments,

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their respective species occurred distinctly in different regions. In relation to the environmental

variables, the results also showed an increase of abundance of Halonotius when Mg2+ increased

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(r2=0.86). As in the sediments, the most abundant OPUs were OPU053 (Uncultured Halorubrum) and OPU106 (Natronomonas).

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Although most of the genera were detected in both brines and sediments, they did not

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coincide at the OPU level (Fig. 2 and Supplementary Fig. S5). For example, in the case of the

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most abundant genus Halorubrum, four of the six OPUs were exclusively detected in sediments.

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In the same way, Uncultured Halobacterium OPU004 was only found in sediments, and Nanohaloarcheota OPU399 was only found in brines. However, a large number of the archaeal

OPUs

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OPUs were present in both habitats (178 OPUs; 84.4% of the total sequences). A total of 183 (21.1%)

were

found

exclusively

in

sediments,

principally

affiliated

to

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Nanohaloarchaeota, Methanomicrobia group C, Marine Benthic Group D, and DHVEG-1 (Supplementary Spreadsheet Table SS11). Interestingly, Haloquadratum (two OPUs) in brines occurred at relatively low abundance

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(0.39% of the reads) in comparison with other genera, such as Halorubrum and Halonotius (with 34.4% and 7.1% of the reads, respectively). No ubiquitous OPUs specific for coastal or inland habitats were detected. On the other hand, Santa María exhibited the lowest richness and diversity value (Supplementary Table S2). In general, Spanish samples showed higher diversity, with Lanzarote (SP-CN4) having the highest diversity (q1=56, Supplementary Table S2). 14

Beta diversity analyses and environmental influences As mentioned earlier, some location-specific OPUs were detected and, based on multivariate analysis, the samples showed segregation by country (Spain, Argentina and Chile; Fig. 4). On the other hand, these biogeographic patterns based on genera (Supplementary Fig. S7) had

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lower resolution, since some ubiquitous genera were represented regionally by distinct species. For example, Halanaerobium showed distinct occurrence of its species, with OPU413 (Uncultured Halanaerobium) being more abundant in Spain (both sediments and brines),

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OPU416 (Uncultured Halanaerobium) in the Argentinean sediments, and OPU432

(Halanaerobium lacunarum/H. salinarus) in the Chilean brines (Figs. 2 and 6; Supplementary

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Table S7). The main bacterial genera explaining the distribution by country were Halanaerobium, Acinetobacter, and Desulfovermiculus that were more abundant in Spain and,

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for brines, Halanaerobacter and Idiomarina that were notably more important in Chile

A

(Supplementary Fig. S4 and Table S9). In contrast, for Archaea, Halorubrum was the most

M

important genus explaining the dissimilarity by region, since it was more abundant both in

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sediments and brines in Argentina, followed by Natronomonas (Supplementary Fig. S6 and Table S10).

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The SIMPER analysis for Bacteria showed that > 70% of the biogeographic segregation could be defined by the contribution of 44 OPUs and their relative abundances (Fig. 6;

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Supplementary Table S7). For Archaea, the dissimilarity presented by country was explained at >70% by 45 OPUs (Supplementary Table S8). From these, OPU053 (Uncultured Halorubrum) was the most represented in brines independently of the location. However, other OPUs were

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highly represented in one specific country, such as OPU033 (Uncultured Halorhabdus) and OPU106 (Uncultured Natronomonas), which were more abundant in Argentinean sediments, and OPU111 (Uncultured Haloarcula) in Chilean brines (Fig. 4, Supplementary Table S8). As three different regions represented Spain, an ordination analysis was performed for Spanish sediments and it confirmed that a biogeographic pattern could also be detected at the 15

regional level (Fig. 2, Supplementary Figs. S5 and S8). Some of the OPUs responsible for this segregation included OPU013 and OPU014 (both Uncultured Halovenus), which were more abundant in samples from the Canary Islands; OPU031 (Uncultured Halomicroarcula), OPU032 (Halorhabdus utahensis), OPU077 (Uncultured Halobellus), and OPU097 (Halosimplex carlsbadense) with a higher representation in the inland samples; and OPUs

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OPU125 (Uncultured Halobacteriaceae), OPU131 (Uncultured Halococcus), OPU148 (DSEG), and OPU253 (Marine Benthic Group D and DHVEG-1) that were more abundant in the Balearic samples.

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Based on the bacterial diversity indices, the Spanish sediment samples SP-CN2 and SPIB6 (Supplementary Table S11) were the most similar (the lowest Whittaker value of 0.34),

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whereas the maximum dissimilarity was observed between SP-CN1 and ARG20 (0.95). For brines, the highest similarity was found between CHL3-CHL4 (0.21) and the lowest between

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ARG4-ARG23 (0.77). The dissimilarity among the last samples was related to the higher

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concentrations of NO2-, PO43-, NH4+ and K+, and lower Ca2+ in ARG4. For archaeal sediments,

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the highest Whittaker value (0.92) occurred in ARG3-SP-CM2 (Supplementary Table S12). On

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the contrary, SP-IB4 and SP-IB6 shared all OPUs (Whittaker value = 0) and they had a similar ionic composition, with the exception of Ca2+ that was higher in SP-IB4.

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For both domains, the beta diversity showed that the sample type (brines or sediments) had less influence than the location (Figs. 2 and 4; Supplementary Figs. S4, S6, S9 and S10),

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which was most pronounced for Bacteria where the Argentinian samples seemed closer to the Spanish than to the Chilean samples, and the Balearic samples showed the strongest similarity. On the other hand, the Spanish samples remained grouped and closely related to the Chilean

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samples when Archaea were analysed. The simple and partial Mantel tests exhibited significant positive correlation with the geographic distance (Table 2). The PERMANOVA analysis showed significant variations depending on the country. This behaviour was shown for both domains, Bacteria (F-statistic=2.43, p<0.001; Fig. 4) and Archaea (F-statistic=4.19, p=0.001; Supplementary Fig. S7). The relationships between the community structure and environmental 16

parameters indicated that for Bacteria, NO2- (r2=0.26; p=0.001), Br- (r2=0.17; p=0.01) and salinity (r2=0.14; p=0.03) were the environmental variables with higher effect, whereas for Archaea, the significant explanatory variables for the distribution of the samples were salinity (r2=0.28; p=0.001), F- (r2=0.18; p=0.009), PO43- (r2=0.17; p=0.009), Mg2+ (r2=0.16; p=0.02)

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and NO2- (r2=0.11; p=0.04).

DISCUSSION

In this study, the microbial composition of hypersaline sediments and brines was analysed and

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compared for 22 different locations in Spain, including the Atlantic Ocean and Mediterranean

Sea, the South American Altiplano in Argentina, and the South Pacific Ocean coast of Chile.

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The OPU approach was applied for partial 16S rRNA gene sequence analyses, where the supervised affiliation renders a much more fine-tuned picture of the identity of the amplicons

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[18,61,94] than the use of conventional OTU assignment. With this analysis, a total of 1,026

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OPUs were detected that could be understood as putative species, which varied in abundance

M

and presence/absence in the different communities, and showed a geographical segregation.

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This geographical discrimination was more diffuse (i.e. of lower resolution) when units with higher taxonomic rank (i.e. genera) were used. The results showed that distinct OPUs (or

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species) of the same genus might distinctly occur in geographically distant locations. On the other hand, it could not be discarded that the individuals of coincident species (or OPUs) from

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different regions differed in their geno- and phenotypes, since the partial 16S rRNA gene sequences lacked sufficient resolution to guarantee the detection of genetic drifts [40]. In addition, even within a single site, distinct populations of the same species with identical 16S

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rRNA gene sequences may coexist, expressing different metabolic profiles and exhibiting distinct genomic structures [4]. The regionalism observed was probably related to the environmental conditions (i.e. ionic composition or other site-dependent environmental parameters, such as altitude, rain and insolation regimes, origin of the solutes, etc.) rather than only geographical distance [40], since each OPU may have adapted to the specific 17

environmental conditions of each site, as demonstrated for different S. ruber strains [85]. However, beyond the clear large-scale regionalism (South America - Europe - Africa), this geographic differentiation was also observed for the three Spanish zones sampled (Canary Islands, Mediterranean coast and inland). In fact, the results exhibited a relevant proportion of the variability in the structure of the community produced by the geographical distance (up to

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36% in the Mantel test results), in accordance with a recent study performed in different salterns that showed the distance effect exhibited an influence on protistan communities more than 500

km apart [32]. Some OPUs were located in a unique specific location (13% of the total), and an

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important number together represented 40.6% of all OPUs detected in a specific country (13.5% for Spain, 24.8% for Argentina, and 2.3% for Chile). These seemed to be regionally exclusive,

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but since none of them were present in a large number of the samples from a respective region they could not be treated as regional endemisms, which is the most evident demonstration of

N

biogeography [40].

A

Some community structure differences correlated with distinct ion composition, such as

M

Mg2+ for some Archaea, or Li+ between inland and coastal sites. In this regard, salinity and

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substrate type (sediments vs. brines) are considered to be the two most important factors that structure diversity [55] and, for this reason, marked dissimilarities were expected between

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sediments and brines, as previously reported in marine environments [102]. However, in this current study, OPUs from brines were highly represented in the corresponding sediments,

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suggesting a strong connection between both habitats, in a similar way to that observed for protists [32]. However, protists were only studied in the oxic upper layers of the sediments, whereas, in our study, the first 20 cm of the sediments were pooled and they were undoubtedly

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anoxic, as shown by the grey to black coloration found in the first millimetres that indicated high sulphide concentrations and anaerobic conditions [54]. Despite the fact that brines are aerobic, eventual episodes of anoxia can happen when respiration exceeds primary productivity [44] and high summer temperatures reduce oxygen solubility [91], making the presence of facultative anaerobes feasible as part of their communities. On the other hand, aerobes were 18

mainly detected as important proportions in sediments, suggesting an active presence rather than only precipitating from the water column. Some of them, such as Bacillus sp. [93] or Haloferax sp. [14,77], could potentially have been denitrifiers, which would appear to be in accordance with NO3- influencing the community structures (both in Bacteria and Archaea) together with the low concentration of NO2-. The higher presence of Firmicutes in inland samples had also

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been reported in some studies of athalassohaline lagoons, lakes [46,62] and sediments [43]. Despite the fact that the family Halanaerobiaceae (moderately halophilic bacteria) was

expected to be the most abundant in sediments [1,74], other families (Moraxellaceae and

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Desulfohalobiaceae) also occurred in high abundance at the Spanish locations. For example, Acinetobacter (Moraxellaceae family) was one of the most important bacterial genera found in

U

this study and it had already been reported in other brines [17,86], but is poorly studied in hypersaline sediments [33]. Also relevant was the candidate bacterial phylum Parcubacteria,

N

which has been observed in a wide range of anoxic environments [41], and was present in this

A

current study in some brines and sediments. Its presence in brines was in accordance with the

M

codification of genes related to aerobic metabolism [68].

ED

On the other hand, the high abundance of Euryarchaeota is well known in brines [22,30,79,92] and sediments [43,54]. Curiously, Nanohaloarcheota was detected in sediments

PT

when these have been reported as aerobes occurring exclusively in brines [67,79]. To our knowledge, this is the first report of Nanohaloarchaeota with a notable abundance in

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hypersaline sediments. In addition, the MCG group was detected especially in the Spanish locations and was poorly represented in the South American sites. This group was previously hypothesized to be numerically and ecologically important in anoxic marine sediments

A

[9,50,60,75], and its abundance has been related to SO42- [50]. In our case, although there was no correlation with SO42-, there was correlation with other ions, such as Mg2+, which also correlated with the community trends of total archaea, especially Halonotius and Halorubellus. The former showed a positive correlation with Mg2+, which was expected given the study of Burns et al. [12]. However, the correlation of Halorubellus with Mg2+ was unexpected, since it 19

had been reported not to require this ion for growth [19]. Representative sequences were also detected for MSBL1, a putative archaeal lineage responsible for methanogenic processes in hypersaline anaerobic environments of the Mediterranean [10,53,54] and the Red Sea [7]. MSBL1 was also detected in South American samples (e.g. CHL1) pointing to a possible global distribution of this group, together with DHVEG-1 and MBGB that have recently been found

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with high abundances in the Brava and Tebenquiche lakes of Atacama (Chile) (Fernández et al., unpublished data). However, the most abundant known archaeal genera in sediments affiliated

with Halorubrum, Halobacterium, Halorhabdus and Natronomonas, mostly known as aerobes,

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as well as some carrying facultative anaerobiosis [6,37,48,59]. The presence of Haloferax (facultative anaerobe) [14,23] was remarkably high in Argentinean sediments. It was also

U

notable that Halorubrum dominated over Haloquadratum, which is often reported as the major component of brines [11,36,58,71,78]. However, the results were similar to a few other studies

N

that detected low abundance of Haloquadratum and high abundance of Halorubum [62,98].

A

In summary, the brines and sediments studied here exhibited chemical and biological

M

differences related to their geographical distribution. Although some environmental parameters

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did not seem to affect the community structures considerably, others, such as salinity, NO3-, Br-, F-, PO43- and Mg2+, might play an important role in the selection of some specific groups. It was

PT

remarkable that the geographical patterns were less clear at the genus than at the OPU (or species) level. This can be understood as phylogenetic and metabolic redundancy in high taxa

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where their different species have acquired distinct advantages related to the regional physicochemical characteristics. In addition, the important OPU overlap between anoxic sediments and their overlaying brines, even for unexpected lineages such as Nanohaloarchaeota

A

and Parcubacteria, pointed to a versatile metabolism of pelagic organisms rather than just accumulation due to particle sink.

Acknowledgements

20

The authors would like to thank all those responsible for the Salines d’es Trenc, Salines de s’Avall, Salinas de Formentera, Salinas de Ibiza, Salines de la Trinitat, Salinas de Fuerteventura, Salinas de Janubio, Salinas de Santa Pola and Salinas de Lo Valdivia, for access to their installations and samples. In addition, Josefa Antón and Víctor Parro are acknowledged for their help in sampling the Peñahueca saltpan, and Carlos Díaz Gil for his help with the

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development of scripts used in this study. This study was funded by the Spanish Ministry of Economy projects CGL2012-39627-C03-03 and CLG2015_66686-C3-1-P, which were also supported with European Regional Development Fund (FEDER) funds, and the preparatory

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phase of the Microbial Resource Research Infrastructure (MIRRI) funded by the EU (grant

number 312251). PICT 3825-730. MMR’s PhD was supported by fellowship CVU 265934 from

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M

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the National Council of Science and Technology (CONACyT), Mexico.

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List of figures

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Fig. 1. Sampling locations in Chile, Argentina and Spain.

A

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M

Fig. 2. Venn diagram with the percentage taxa detected in sediment (A, C) and brine (B, D) samples at the OPU (A, B) and genus (C, D) levels. Distribution of OPUs (E) and genera (F) in each country by type of sample (sediments and brines) for the Bacteria and Archaea domains.

Fig. 3. Taxonomic distribution of sediments and brines at the phylum and family levels for the Bacteria and Archaea domains. DSEG=Deep Sea Eukaryotic Group, Family I= Family of Cyanobacteria.

35

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A

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Fig. 4. Two dimensional non-metric multidimensional scaling (NMDS) of sediments (A-B) and brines (C-D) for Bacteria (A-C) and Archaea (B-D) based on the operational phylogenetic unit (OPU) distributions. Stress value: A (0.18), B (0.18), C (0.12) and D (0.10). Brown lines show the gradient for the significant parameters in the distribution of the community.

36

A

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Fig. 5. Relative abundances of the main operational phylogenetic units detected by similarity percentage (SIMPER) analysis for type of sample (brines and sediments). Bacteria: B413 (Uncultured Halanaerobium), B432 (Halanaerobium lacunarum/H. salinarus), B313 (Uncultured Sphingobacteriales), B127 (Uncultured Rhodobacteraceae), B026 (Uncultured Acinetobacter) and B416 (Halanaerobium sp.). Archaea: A053 (Halorubrum sp.), A106 (Natronomonas sp.), A111 (Uncultured Haloarcula), A070 (Uncultured Halonotius), A033 (Uncultured Halorhabdus) and A138 (Uncultured Halorubrum).

37

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Tables List:

Table 1. Overview of sampling location and environmental parameters analysed for samples from Spain, Argentina and Chile.

0 0

ED

Campos 1 Campos 2 Campos 2

Santa Pola 2

Salines Trinidad 1 Salines Trinidad 2

SPCM1

Peña Hueca 1

SPCM2

Peña Hueca 2

S S B S S S

0

Spain

SPAR1 SPAR2

S

S

S 0

Coastal

SPVC2

S

0

Mediterranean

Santa Pola 1

A

SPVC1

0

0

0

S S

529 S

Inland

Ibiza

0

0

Central Spain

Formentera 2

0

Sample source

A

0

S´Avall 2

Formentera 1

Altitude (masl)

M

S´Avall 1

CC E

SPIB1 SPIB2 SPIB3 SPIB4 SPIB5 SPIB6 SPIB7 SPIB8

Coun Regi Ty try on pe

Location

PT

ID

529 S

Coordin ates 39. 32 39. 32 39. 35 39. 35 39. 35 38. 73 38. 73 38. 85 38. 19 38. 19 40. 53 40. 58 39. 52 39. 52

2.9 9 2.9 9 3.0 1 3.0 1 3.0 1 1.4 2 1.4 2 1.4 0 0.5 9 0.5 9 0.6 9 0.6 9 3.3 4 3.3 4

Salini ty Cl[%] 17569 27 8.8 18493 28 1.3 34421. 28 3 27323. 27 8 19550 31 3.8 16401 24 8.8 17017 25 6.3 18504 27 6.3

2624 8.8 2316 0 3494 7.5 2114 8.8 2654 1.3 7402 6.3 1803 3.8 3059 6.3

633. 8

31

16672 5

4053 3.8

1013 168. 0 .8 8

25

0

71036. 30328. 8456. 121.3 0 3 8 3

0.00 0

32

19163 5

6006 3.8

1340

137. 0 5

30

0

76691. 39893. 1448 197.5 3 8 3.8

0

0.00 0

17944 5 17928 7.5

1700 3.8 2163 1.3

571. 3 687. 5

135

30

0

96600

112. 0 5

28.8 0

91645

3958. 0 8 19183. 5177. 192.5 0 8 5

0.00 0 0.00 0

35

42980

1898 0

53.8

167. 10 5

32.5

36

40030

1598 1.3

57.5

170

47.5 6535 23130

29 29

SO42- Br-

775 152. 5 146. 3 1096 .3 1112 .5 517. 5 1206 .3

NO

NO

-

-

3

2

202. 0 5 112. 0 5 136. 0 3 150

0

PO43

F-

-

Na+

176. 3

95903. 8 97333. 8 18387. 5 13776. 3

31.3 0 27.5 0 32.5 0 35

137. 0 5

21.3 22.5

96.3 0

30

131. 0 3 171. 0 3

0

0

0

33.8 0 26.3 0

Mg2+

Ca2+

3732. 5 1257. 17770 5 1503 3033.8 8.8 8931. 3376.3 3 20203. 1476. 95475 8 3 29996. 86150 66.3 3 94937. 12626. 1357. 5 3 5 90132. 22675 101.3 5

6211 25080 .3

14640

NH

K+ 4667. 5 5816. 3 1046. 3 1112. 5 6706. 3 7856. 3 3653. 8 9086. 3

+

4

0 0 0 0 0 0 0 0

15395

265

5580

5843. 1705 8

5303.8

5448. 1838. 115 8 8

Li + 0.00 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 0

108. 0.00 8 0 0.00 0

38

I Peña Hueca 3

SPCM4

Laguna Azul

SPCM5

Laguna Azul

SPCM6

Laguna Amarilla

SPCM7

Laguna Amarilla

SPCN1

Fuerteventura 1

SPCN2

Fuerteventura 2

SPCN3

Lanzarote 1

SPCN4

Lanzarote 2

ARG 1

Salar de COIPA

ARG 2

Salar de COIPA

ARG 3

Laguna Santa María

ARG 4

Laguna Santa María

529

S 529

529

A

S

M

B

PT

ED

529

CC E

S

529 B 0 S 0 S 0

Coastal

Atlantic

S 0 S 3650 S 3650 B

S

Inland

Altiplano

3508

Argentina

A

N U SC R

SPCM3

3508 B

39. 52 39. 51 39. 51 39. 50 39. 50 28. 36 28. 36 28. 94 28. 94 24. 52 24. 52 24. 09 24. 09

3.3 4 3.3 4 3.3 4 3.3 5 3.3 5 13. 86 13. 86 13. 82 13. 82 68. 21 68. 21 67. 36 67. 36

37

35595

1541 6.3

55

170

23. 8

39

44885 5

8526 8.8

305

123. 13. 8 8

33.8 0

29

64495

1750 6.3

91.3

233. 12. 8 5

25

4632 34803. 6796.3 6940 .5 8

30

89196. 4354 3 1.3

16.3

236. 11. 3 3

25

91.3

43

24404 6.3

6935 0

100

318. 11. 8 3

35

16498 8.8

1020 95

911. 3

36

18332 3.8

2481 3.8

665

32

18345 5

31

19419 8.8

39

82696. 6951 3 1.3

108. 8

31

13643 5

461.3

1622 151. 0 .5 3

45

11696 0

1157 0

225

43

24782. 3673. 153. 5 8 8

43.8

4052 20856. 5355 .5 3 13146. 16592 3 2.5

4727. 1467. 0.00 83.8 5 5 0 1205

1028 5

47.5

0.00 4

1438. 0 8

0.00 0

48657. 12171. 9246. 572.5 0 5 3 3

0.00 4

21.3 72.5

23258. 87410 8

0

0.00 5

132. 0 5

30

72770

0

0.00 0

153. 0 8

27.5 0

94801. 19527. 57.5 3 5

5483. 0 8

0.00 0

4188 7.5

1318 116. 0 .8 3

32.5 0

76897. 33230 5

192.5

1002 2.5

0

0.00 0

5971 2.5

1787 115 .5

31.3 0

68753. 45125 8

75

1500 0

0

0.00 0

75

73306. 1179 6151.3 3 5

0

192. 11. 5 3

0

57.5

46.3 97.5

37341. 66.3 3

86382. 826.3 5

143. 0 8

212. 86503. 2490 156.3 5 8

252. 10 5

75

95

556.3 3830

18198. 586.3 8

3220 5

9511. 201. 0.06 3 3 2

1671. 2311. 0 3 3 278.8

3280 0

4208. 3060 8

0.00 7

38.8

0.05 5

0

0.07 1

39

I Ojo Naranja Antofalla

ARG 6

Ojo Naranja Antofalla

ARG 7

Ojo Naranja Antofalla

ARG 8

Ojo Blanco de Antofalla

ARG 9

Ojo Blanco de Antofalla

ARG 10

Ojo Seco de Antofalla

ARG 11

Ojo Seco de Antofalla

ARG 12

Laguna Diamante

ARG 13

Laguna Diamante

ARG 14

Laguna Cabe

ARG 15

Lagua Cabe

ARG 16

Salar de Pocitos

ARG 17

Salar de Pocitos

3338

S 3338

3338

A

B

M

B

ED

3338

PT

CC E

A

N U SC R

ARG 5

S

3338 B 3338 S 3338 B 4560 S 4560 B 4255 S 4255 B 3673 S 3673 B

25. 57 25. 57 25. 58 25. 56 25. 56 25. 55 25. 55 26. 03 26. 03 26. 25 26. 25 24. 37 24. 37

67. 60 67. 60 67. 59 67. 59 67. 59 67. 57 67. 57 67. 04 67. 04 67. 06 67. 06 66. 98 66. 98

29

17707 0

1876 2.5

2710

271. 0 3

103. 172. 8 5

12020 1.3

210

196.3

1216 2.5

0

0.08 2

28

81519 3.8

2266 8.8

622. 5

118. 0 8

31.3 43.8

54145 0

262.5

3440

5395

0

0.13 2

29

19515 2.5

2131 2.5

107. 5

232. 0 5

18.8 96.3

13242 3.8

825

197.5 5555

0

0.06 2

34

17712 1.3

1361 6.3

0

113. 0 8

12.5 0

11555 5

597.5

1766. 2592. 0 3 5

0.07 9

35

91585 1.3

6323 5

0

196. 0 3

33.8

141. 3

60027 7.5

1406.3

2630 1.3

1927. 0 5

0.00 7

34

49442. 1149 5 15

0

140

0

40

0

29250

486.3

4878 6.3

317.5 0

0.00 9

32

30570

4838. 12.5 8

190

10

17.5 23.8

16497. 1553.8 1985 5

1220

50

0.08 7

33

12130 0

8856 3.8

202. 5

136. 0 3

12.5 23.8

95535

8875

801.3

1132 3.8

38.8

0.04 4

32

17034 8.8

2577 8.8

102. 5

212. 11. 5 3

25

10208 1.3

3630

7451. 1017 3 8.8

35

19363 2.5

2336 2.5

36.3

606. 0 3

13.8 0

11134 7.5

6913.8 1230

31

16025 1.3

2059 7.5

51.3

186. 12. 3 5

30

0

81285

3337.5

31

38366 7.5

3498. 173. 8 8

595

125

0

19595 0

11973. 2177 8 6.3

7606. 0 3

0.02 7

43

14672 1.3

1844 7.5

218. 0 8

293.8

3699 6.3

0.00 8

226. 3

0

36.3

263. 13660 3345 8 1.3

2644 6.3

845

1426 7.5

281. 0.08 3 2 0

0.08 2

2517. 0.11 36.3 5 6

0

40

I Tolar Grande

ARG 19

Tolar Grande

ARG 20

Salar de Llullialliaco

ARG 21

Salar de Llullialiaco

ARG 22

Laguna Negra, Tinogasta-Catamarca

ARG 23

Laguna Negra, Tinogasta-Catamarca

CHL 1

Boyeruca 1

CHL 3

Boyeruca 1

CHL 2

Boyeruca 2

CHL 4

Boyeruca 2

3508

S 3508

3677

A

B

M

S

ED

3677

PT

B

S

B 0 S 0

0 S

Coastal

Pacific coast

B

Chile

CC E

A

N U SC R

ARG 18

0 B

24. 55 24. 55 24. 8 24. 8 27. 63 27. 63 34. 70 34. 70 34. 69 34. 69

67. 49 67. 49 68. 29 68. 29 68. 55 68. 55 72. 01 72. 01 72. 00 72. 00

40

14283 7.5

1467 7.5

37

16689 5

36

276. 3

12. 5

328. 3583 10382 8 .8 5

1386. 2033 165 3 .8

0

67.5

18911 1.3

1962. 1387 190 5 .5

0

16.3 0

36

19022 7.5

1855

13.8

187. 0 5

15

35

10640 6.3

1193. 42.5 8

118. 0 8

48.8 43.8

43

91375 0

6250 0

171. 3

195

35

44

13820 2.5

2482 5

868. 8

111. 0 3

44

18828 0

5931 3.8

1603 130 .8

44

17125 6.3

3015 6.3

956. 3

44

19360 6.3

6744 2.5

1977 155 .5

390

0

0

106. 0 3 0

103. 8

198. 8

137. 5

418.8

116.3

4125 8.8

43.8

0.85 9

10193 6.3

1836.3 3795

8047. 356. 0.64 5 3 4

11338 6.3

1375

915

7513. 0 8

0.01 6

11390 1.3

1250

1213. 7721. 0 8 3

0.06 2

62748. 2236. 6536. 1266.3 0 8 3 3

0.02 1

60000 0

1405

2625 0

1925

0

0.00 3

26.3 0

64036. 20525 3

480

6643. 0 8

0.00 0

35

62703. 47750 8

125

8897. 0 5

0.00 0

28.8 0

78196. 25953. 120 3 8

4966. 0 3

0.00 0

32.5 0

57317. 54163. 67.5 5 8

1071 8.8

0.00 0

0

0

S: Sediment; B: Brine. masl: metres above sea level. The coordinates are given using the decimal degrees system. Ion concentration values are shown in ppm.

41

Table 2. Pearson correlation and p-values for simple and partial Mantel tests in the Bacteria and Archaea domains. ENV=environmental factor, Geo=geographical distance

Partial Env 0.13 0.001

Geo 0.18 0.006

Archaea Simple Env Geo 0.13 0.36 0.008 0.0001

Partial Env Geo 0.08 0.36 0.05 0.0001

A

CC E

PT

ED

M

A

N

U

SC R

IP T

Bacteria Simple Env Geo r 0.21 0.13 p 0.007 0.0001

42