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Perennials but not slope aspect affect the diversity of soil bacterial communities in the northern Negev Desert, Israel.

Introduction

Arid landscapes are typified by barren soil punctuated by areas of sparse vegetation consisting of hardy perennials whose rhizospheres are dominated by microorganisms. These areas are called resource or fertility islands (Noy-Meir 1973; Schlesinger et al. 1996) typified by nutrient increase and moderated temperatures (Carrillo-Garcia et al. 2000a, 2000b). One parameter that significantly affects the composition, structure, and density of desert plant communities is slope aspect (the compass direction that the slope faces) due to the differences in solar radiation. In the northern hemisphere, vegetation abundance and structure increase significantly in north- compared with south-facing slopes despite the short distance separating them (Sternberg and Shoshany 2001; Drezner and Lazarus 2008). Bacterial dispersion was also shown to be influenced by slope aspect in a Mediterranean ecosystem (known as an evolution canyon), demonstrated by different genomic and physiological features exhibited by Bacillus simplex on the northern compared with southern slopes (Sikorski and Nevo 2005). Likewise, studies in the Colorado Plateau and Chihuahuan, Sonoran, and Mohave Deserts showed that north-facing slopes support more biomass and higher species numbers of arid soil biocrust and lichen than comparable south-facing slopes (Iii et al. 1977; Bowker et al. 2006).

The distribution of arid soil microbial communities was correlated with the sparse shrub vegetation in numerous studies (reviewed in Bashan and de Bashan 2010). In the Negev (Saul-Tcherkas and Steinberger 2011; Yu and Steinberger 2011; Berg et al. 2015) and Chihuahuan (Herman et al. 1995) Deserts, bacterial diversities in barren and intra-canopy patches were significantly different. In the arid lands of Australia, plant-covered patches of soil exhibited an increase in the abundance and richness of protozoan communities (Robinson et al. 2002), Similarly, in the semiarid environments of Mexico (Camargo-Ricalde and Dhtllion 2003) and Chile (Aguilera et al. 2016), one particular population of fungi was closely associated with the presence of plants, whereas another was detected in barren soil (Steven et al. 2014a).

Bacterial diversity may be governed not only by resource islands, but also by shrub species and seasonality. In arid-soil studies that compared bacterial diversity in barren soil and under the canopy of shrubs Hammada scoparia (Berg et al. 2015) or Reaumuria negevensis (Saul-Tcherkas and Steinberger 2011), the detected communities differed in accordance with patch type even though samples were collected in close proximity. Likewise, markedly different bacteria were isolated from under the canopies of Atriplex halimus and Zygophyllum dumosum although both perennials grew side by side in the sampling sites (Kaplan et al. 2013). The difference in bacterial groupings corresponded to the plant type where the soil was sampled and not to the abiotic conditions. A similar result was found for the soil bacterial community under the shrub H. scoparia, which differed from communities found under artificial shrubs (Berg et al. 2015). The observed changes in microbial communities could be related to root exudates (Steven et al. 20146), but studies conducted thus far have been limited in scope. None has yet comprehensively analysed the diversity and community composition of bacteria in desert soil patches populated by various perennial species growing on different slope aspects.

In this study, we explored the impact of slope exposure and plant species on bacterial diversity of arid soils and the possible interactions between these two parameters. We hypothesised that the bacterial communities will change in accordance with slope as has been demonstrated for vegetation (Sternberg and Shoshany 2001). We also predicted that soil bacterial communities from the inter- and intra-shrub patches would significantly differ, yet the inter-shrub patches were predicted to have more in common with each other than with intra-shrub patches. To test our hypotheses, soil microbial diversity and abundance were studied at the end of a mild rainy season in the northern Negev Desert. Specifically, barren desert soils and soil under the canopies of two dominant perennials, Z. dumosum and Artemisia herba-alba (Asteraceae), were studied with the goal of assigning bacterial diversity and community composition in the different soil patches.

In the Negev Desert, typical of hot desert habitats, the slopes are dominated by communities of dwarf and semi-dwarf shrubs, among which sparse populations of annuals develop during the winter (Noy-Meir 1973). The perennials selected for this study, Z. dumosum and A. herba-alba, were both shown to dominate the rocky hills slopes of the northern and central Negev (Friedman 1971); although Z dumosum was shown to dominate the south-facing slopes, A. herba-alba dominates the northern slopes (Friedman et al. 1977). Z. dumosum (Zygophyllaceae) is a Saharo-Arabian shrub forming a compact multi-branched canopy either ascending or spreading low to the ground (Granot et al. 2009). It was shown to possess a remarkable survival capacity under extremely dry habitats, with a cumulated amount of 11 mm of annual rain sufficient for growth resumption and bud emergence (Granot et al. 2009). A. herba-alba, commonly known as white wormwood, is a dwarf perennial shrub growing in semiarid and arid deserts of the Middle East. This plant has been widely used in folk medicine for the treatment of diabetes and hyperglycemia (Harlev et al. 2013).

Materials and methods

Site description and sample collection To study plant-mediated changes in bacterial community composition, sampling was conducted in the northern Negev Desert (average annual precipitation, 90 mm). A hilly area (500-600 m above sea level) near the Sde Boker campus (30[degrees]51'26.3"N, 34[degrees]46'01.6"E) was chosen because it featured similarly positioned northern and southern slopes in three consecutive rocky hills populated by Z dumosum and A. herba-alba. Sampling was conducted at the end of the rainy season in April 2012 following a drought year (<65 mm annual rain).

For sampling, we placed lOmxlOm quadrants at approximately mid-hill for each of the six slopes (one quadrat per slope) and sampled three patch types: inter-shrub, intraZ. dumosum, and intra-/! herba-alba soil. Eight randomly selected subsamples were taken from each patch type in each of the six quadrats. The eight subsamples from each patch type were combined to represent an average for that slope, resulting in a total of three composite soil samples per slope.

Approximately 100-g subsamples were collected after removing the crust and litter from the top 5 cm, placed into sterile plastic bags (Whirl-Pack, Nasco, Fort Atkinson, WI, USA), and stored at 4[degrees]C. All soil samples were transported to the laboratory and homogenised within 24 h of sampling. The subsamples were combined according to slope and patch type amounting to 18 samples (six slopes x three patch types). A 50-g subsample was stored at -80[degrees]C for molecular analysis and the remainder of the soil was used for physicochemical analysis.

Soil physicochemical parameters

About 500 g of the collected samples were chemically analysed as previously described (Bachar et al. 2012) to assess changes in important soil parameters, such as pH, electrical conductivity (EC), soil total organic matter, and ammonium, nitrate, phosphorus, potassium, and chloride ions. Values of pH and EC were measured in a saturated soil paste extract. Phosphorus ions were extracted from the soil by the Olsen and Watanabe (1957) extraction method (with 0.5mol[L.sup.-1] NaHC[O.sub.3]) and analysed colourimetrically with an autoanalyser (ASX-520 series, Quickchem 8500 series 2, Lachat instruments, Milwaukee, WI, USA). Potassium, extracted by Ca[Cl.sub.2], was measured by flame atomic absorption spectrophotometry (Flame photometer M410; Sherwood Scientific, Cambridge, UK). Chloride was measured in saturated soil paste extract with a chloride meter (Chloride analyzer 926; Sherwood Scientific, Cambridge, UK). Soil organic matter content was estimated by the weight loss on ignition method.

DNA extraction

Total nucleic acids were extracted from the soil samples according to a previously described method (Angel and Conrad 2009) and the extract was purified using a DNA Purification Kit (Bioneer, Seoul, South Korea). The DNA samples were stored at -80[degrees]C for further analysis.

Quantitative PCR

In order to estimate the number of 16S rRNA units in the soil samples, a calibration curve of a known number of rRNA gene copies was constructed as follows. Environmental DNA was amplified using the S-D-Bact-0341-b-S-17/S-D-Bact0515-a-A-19 bacterial universal primer set (Klindworth et al. 2013). This primer set was evaluated in the Silva database (https://www.arb-silva.de/) and found to cover 91.2% of known sequences within total bacteria and 0% for archaea and fungi (Klindworth et al. 2013). Standards were generated from amplicons of Escherichia coli using the bacterial universal primer set cloned in a high copy-number plasmid. Each qPCR reaction contained the following mixture: 10pL of SYBR Absolute Blue qPCR Rox Mix (Thermo-Fisher, Waltham, MA, USA), 400 nmol[L.sup.-1] of each primer (Metabion, Munich, Germany), 5 [micro]L of template cDNA, and 3 (J.L of molecular grade water (HyLab, Rehovet, Israel). The qPCR assay was performed under the following conditions: 95[degrees]C for 15 min, followed by 35 cycles of 95[degrees]C for 10 s, 60[degrees]C for 15 s, and 72[degrees]C for 30 s of extension.

Amplification of 16S rRNA genes, and library generation

The genomic DNA was amplified using the 16S rRNA gene V4, V5 regions primer set (S-*-Univ-0515-a-S-19/S-D-Arch-0786a-A-20), adapted for MiSeq (Illumina, San Diego, CA, USA) sequencing by adding nine extra bases that included the Illumina flow cell adaptor sequences. The reverse amplification primer also contained a 12-base barcode sequence that supports pooling of up to 2167 different samples in each lane (Caporaso et al. 2011, 2012). The PCR amplification reaction contained 12 [micro]L of PCR Water (MoBio, Carlsbad, CA, USA), 10 [micro]L of HotMasterMix (5Prime, Hilden, Germany), 2 [micro]L primers (each at 200 pmol final concentration), and 1 [micro]L of template DNA. The conditions for PCR were as follows: 94[degrees]C for 3 min, 35 cycles at 94[degrees]C for 45s, 50[degrees]C for 60s, and 72[degrees]C for 90s, and a final extension of 10 min at 72[degrees]C. PCR amplifications were done in triplicate, pooled, and quantified using PicoGreen (Life Technologies, Carlsbad, CA, USA) according to the manufacturer's instructions. After quantification, different volumes of each amplicon were pooled together such that each amplicon was equally represented. The pool was then cleaned up using UltraClean[R] PCR Clean-Up Kit (MoBIO), quantified using the Qubit (Life Technologies) method, and diluted to 2 nM, denatured, and diluted to a final concentration of 4 pmol with a 30% PhiX spike for loading on the MiSeq sequencer. Amplicons were then sequenced in a 251 bp x 12bpx251bp MiSeq run using custom sequencing primers and procedures previously described (Caporaso et al. 2012).

Sequencing analysis

Of the total number of sequences, ~32% mismatched their barcode with the sample barcodes, 8% were less than 75 bp in length, and 0.05% consisted of ambiguous bases. Sequences that passed these filters were de-multiplexed based on exact matches to the barcode sequences. The QI1ME suite was used for the analysis of the samples. The sequences were clustered into 97% identity operational taxonomic units (OTUs) with the UCLUST reference-based method (Edgar 2010) and by comparison against the greengenes core set of aligned sequences (DeSantis et al. 2006). One representative from each OTU was selected for further analysis. Sequences were aligned using PyNAST (Caporaso et al. 2010) and chimeric sequences were removed with ChimeraSlayer (Haas et al. 2011). OTUs were classified taxonomically with the Mothur classifier (Schloss et al. 2009) using RDP v9 (http://rdp.cme.msu.edu/). All QI1ME scripts were from the 1.7.0 release (Kuczynski et al. 2012) and run using default parameters unless otherwise stated. Plots were produced in the R environment based on the Q11ME results. Diversity in the samples was estimated using the unweighted UniFrac method (Lozupone et al. 2011) on a subset of the sequences (containing 66 137 sequences each) to yield similar coverage (see Fig. SI available as Supplementary Material to this paper). Principal component analysis was generated based on the distances between the samples as determined by UniFrac (Lozupone et al. 2011). ANOSIM tests were performed with the QI1ME using_categories.py script. A heatmap was created in QIIME using make_otu_ heatmap.py. For technical reasons, we did not use all the barren soil samples.

Results

Physicochemical parameters

We found no significant differences between the soil physicochemical parameters of the northern compared with the southern slopes (P > 0.05), except for ammonium ([t.sub.(12)] = 2.17, P = 0.01) (Table 1). The physicochemical characteristics of the soil samples taken from under the Z. dumosum canopy at the end of the rainy season were distinct from the samples taken from under A. herba-alba and from barren soil in terms of organic matter, nitrate, potassium, and chloride concentrations, and EC and pH (Table 1). In contrast, ammonium, phosphorus, and water content showed no differences among the patches. Potassium concentrations exhibited a unique pattern in that its levels in all plant-covered soils were elevated compared with the barren soils (Table 1).

Bacteria 165 rRNA gene abundance

The Z dumosum supported a higher number of rRNA gene copies in the soil under its canopy, compared with the barren soil and the soil collected under A. herba-alba (Fig. 1 and Table 1). These differences in gene copies were significant ([F.sub.(2, 15)] = 6.84, P = 0.01). In contrast, slope aspect did not affect bacterial abundance supporting 4.4 x [10.sup.10] [+ or -] 3.3 x [10.sup.10] and 3.8 x [10.sup.10] [+ or -] 1.3 x [10.sup.10] rRNA gene copies per gram of soil taken from the southern and northern slopes, respectively ([t.sub.(16)] = 2.11, P=0.6).

Bacterial diversity

We extracted the DNA from the soil samples and estimated the number of 16S rRNA gene copies under the different shrub canopies and barren soil. The sequencing effort yielded 16 samples (two samples taken from barren soil failed) and 1 600 729 quality sequences were obtained (see Materials and methods). When grouped at 97% similarity level, these clustered into 84470 OTUs. Of these OTUs, 10244 (or 12.1%) were earmarked as chimeric and the remaining OTU sequences were divided unevenly between the samples, ranging from 66137 to 229480 sequences per sample (see Table SI available as Supplementary Material to this paper). Of the 74226 OTUs obtained, 889 were unclassified, 296 were classified as archaea, and 73 041 as bacteria. Of the 73 337 OTUs classified as archaea or bacteria, 14551 could not be classified to known phyla. Fifty percent of the OTUs were represented by a single sequence in the entire dataset and 64% of the OTUs were found in a single soil sample. At the sampling depth used in this study, the coverage did not quite reach saturation (Fig. SI).

Bacterial community composition

Our results suggest that the bacterial community in soil samples taken from under Z dumosum plants clustered separately from soil taken from under A. herba-alba and from barren soil (r=0.77, P=0.001) (Fig. 2a), but was independent of the slope aspect (r=0.44, P=0.62) (Fig. 2b). To elucidate the contribution of the perennials to the soil bacterial taxa, we clustered the samples in a heatmap (Fig. 3), which showed that the relative abundance of Acidobacteria, Betaproteobacteria, Cyanobacteria, Deltaproteobacteria, and Verrucomicrobia increased in soils collected under A. herba-alba and in barren soil, whereas Alphaproteobacteria, Bacteroidetes, Gammaproteobacteria, and Firmicutes were more prevalent in soils under the Z. dumosum canopy (Fig. 3). We further determined which group had the highest influence on the uniqueness of the Z. dumosum sample communities. To that end, we performed non-metric multidimensional scaling (NMDS) on the OTU table and assigned taxa (normalised to the sum of sequences per sample, so that the total count per sample equalled 1) (Fig. 3). The pattern whereby soil communities under the canopy of Z. dumosum clustered separately was constant, and could be attributed to the changes in the abundance of Firmicutes and Alpha-proteobacteria (Fig. 3). Other groups, such as Gamma-protcobacteria and Bacteroidetes, were also more abundant under the Z. dumosum canopy (Table S2).

Discussion

The results indicated that the slope aspect did not determine the subterranean bacterial community (Fig. 2b) in contrast to studies suggesting that radiation and increased evaporation in south-facing slopes yielded decreased in the microbial communities compared with northern slopes (Iii et al. 1977;

Sikorski and Nevo 2005; Bowker et al. 2006). We hypothesise that microorganisms residing in the soil surfaces would be directly exposed to the differences in solar radiation, temperature, and precipitation resulting from the slope aspect (Noy-Meir 1974). Reports have shown that biocrust and lichen increase in diversity and abundance in the north-facing slopes that are less exposed to radiation (Iii et al. 1977; Bowker et al. 2006). In contrast, the top soil (collected after the removal of the crust and litter) under the canopy of Z. dumosum and A. herba-alba did not differ in physicochemical parameters, bacterial abundance, diversity, and community composition (Figs 1-3).

Soil was sampled at the end of a drought winter season with 30% less rain than the annual average, which has been shown to result in a decrease in annuals' biomass and limited growth resumption and bud emergence for annuals (Reynolds et al. 1999). These stressful conditions are predicted to lessen the biotic and abiotic effects of resource islands (Bachar et al. 2012). The results suggest that the soil bacterial community composition detected under A. herba-alba and barren soil overlapped (Figs 2, 3) similar to the diversity patterns depicted at the end of the dry season (Martirosyan et al. 2016). The results suggest that this annual no longer served as a resource island to its soil-associated communities. In contrast, the soil diversity under Z. dumosum differed from the other patches. The canopy of Z. dumosum is larger than that of A. herba-alba and the foliage more pronounced (Friedman 1971); moreover, this annual was shown to be highly resistant to drought conditions (Granot et al. 2009), indicating that despite the drought, Z. dumosum was able to maintain its ecosystem function.

The observations presented here are in agreement with previous studies (Saul-Tcherkas and Steinberger 2011; Yu and Steinberger 2011; Bachar et al. 2012; Berg et al. 2015) showing that in hot deserts the perennial canopy creates a 'resource island' whereby plant spatial heterogeneity is mirrored by subterranean microbial diversity, abundance, and productivity. We propose that resource islands are plant-dependent: the microbial communities under plants that are less tolerant to desiccation, such as A. herba-alba (Friedman et al. 1977), are unable to support their unique community and end up sharing the majority of their communities with the barren soil. In contrast, desiccation resistant annuals, such as Z. dumosum (Granot et al. 2009), maintain their community even under severe drought. Finally, plant-associated resource islands are the major contributors to arid soil bacterial composition whereas slope exposure, although significant to plant dispersal and diversity (Sternberg and Shoshany 2001; Sikorski and Nevo 2005), could not be linked to the bacterial diversity or community composition.

https://doi.org/10.1071/SR17010

Conflicts of interest

The authors declare no conflicts of interest. Acknowledgements

The authors gratefully acknowledge the support of the Sol Leshin Program for BGU-UCLA Academic Cooperation. We thank Matan Jaffe for his help during sampling and Lusine Ghazaryan for her help in the sample processing.

References

Aguilera LE, Armas C, Cea AP, Gutierrez JR, Meserve PL, Kelt DA (2016) Rainfall, microhabitat, and small mammals influence the abundance and distribution of soil microorganisms in a Chilean semi-arid shrubland. Journal of Arid Environments 126, 37-46. doi:10.1016/ j.jaridenv.2015.11.013

Angel R, Conrad R (2009) In situ measurement of methane fluxes and analysis of transcribed particulate methane monooxygenase in desert soils. Environmental Microbiology 11, 2598-2610. doi:10.111 l/j.14622920.2009.01984.x Bachar A, Soares MI, Gillor O (2012) The effect of resource islands on abundance and diversity of bacteria in arid soils. Microbial Ecology 63, 694-700. doi:10.1007/s00248-011-9957-x

Bashan Y, de-Bashan LE (2010) Microbial populations of arid lands and their potential for restoration of deserts. In 'Soil biology and agriculture in the tropics'. (Ed. P Dion), pp. 109-137. (Springer: Berlin, Heidelberg, Germany)

Berg N, Unc A, Steinberger Y (2015) Examination of biotic and abiotic controls of soil bacterial diversity under perennial shrubs in xeric soils. Catena 127, 124-128. doi:10.1016/j.catena.2014.12.029

Bowker MA, Belnap J, Davidson DW, Goldstein H (2006) Correlates of biological soil crust abundance across a continuum of spatial scales: support for a hierarchical conceptual model: Scale-dependent soil crust distribution. Journal of Applied Ecology 43, 152-163. doi:10.1111/ j. 1365-2664.2006.01122.x

Camargo-Ricalde SL, Dhillion SS (2003) Endemic Mimosa species can serve as mycorrhizal 'resource islands' within semiarid communities of the Tehuacan-Cuicatlan Valley, Mexico. Mycorrhiza 13, 129-136. doi:10.1007/s00572-002-0206-5

Caporaso JG, Bittinger K, Bushman FD, DeSantis TZ, Andersen GL, Knight R (2010) PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266-267. doi:10.1093/bioinformatics/ btp636

Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, Fierer N, Knight R (2011) Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proceedings of the National Academy of Sciences of the United States of America 108, 4516-4522. doi:10.1073/pnas.1000080107

Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M, Gormley N, Gilbert JA, Smith G, Knight R (2012) Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. The ISME Journal 6, 1621-1624. doi:10.1038/ismej.2012.8

Carrillo-Garcia A, Bashan Y, Rivera ED, Bethlenfalvay GJ (2000a) Effects of resource-island soils, competition, and inoculation with Azospirillum on survival and growth of Pachycereus pringlei, the giant cactus of the Sonoran Desert. Restoration Ecology 8, 65-73. doi:10.1046/j.1526100x.2000.80009.x

Carrillo-Garcia A, Bashan Y, Bethlenfalvay GJ (2000ft) Resource-island soils and the survival of the giant cactus, cardon, of Baja California Sur. Plant and Soil 218, 207-214. doi:10.1023/A:1014953028163

DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL (2006) Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Applied and Environmental Microbiology 72, 5069-5072. doi:10.1128/AEM. 03006-05

Drezner TD, Lazarus BL (2008) The population dynamics of columnar and other cacti: a review. Geography Compass 2, 1-29. doi:10.1111/j.17498198.2007.00083.x

Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460-2461. doi:10.1093/bioinformatics/ btq461

Friedman J (1971) The effect of competition by adult Zygophyllum dumosum Boiss. on seedlings of Artemisia herba-alba Asso in the Negev Desert of Israel. Journal of Ecology 59, 775. doi:10.2307/2258139

Friedman J, Orshan G, Ziger-Cfir Y (1977) Suppression of annuals by Artemisia herba-alba in the Negev Desert of Israel. Journal of Ecology 65, 413. doi:10.2307/2259491

Granot G, Sikron-Persi N, Gaspan O, Florentin A, Talwara S, Paul LK, Morgenstern Y, Granot Y, Grafi G (2009) Histone modifications associated with drought tolerance in the desert plant Zygophyllum dumosum Boiss. Planta 231, 27 34. doi:10.1007/s00425-009-1026-z

Haas BJ, Gevers D, Earl AM, Feldgarden M, Ward DV, Giannoukos G, Ciulla D, Tabbaa D, Highlander SK, Sodergren E, Methe B, DeSantis TZ, The Human Microbiome ConsortiumPetrosino JF, Knight R, Birren BW (2011) Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Research 21, 494-504. doi:10.1101/gr.112730.110

Harlev E, Nevo E, Mirsky N, Ofir R (2013) Antidiabetic attributes of desert and steppic plants: a review. Planta Medica 79, 425-436. doi:10.1055/s0032-1328331

Herman RP, Provencio KR, Herrera-Matos J, Torrez RJ (1995) Resource islands predict the distribution of heterotrophic bacteria in Chihuahuan Desert soils. Applied and Environmental Microbiology 61, 1816-1821.

Iii THN, White SL, Marsh JE (1977) Lichen and moss distribution and biomass in hot desert ecosystems. The Bryologist 80, 470. doi:10.2307/ 3242022

Kaplan D, Maymon M, Agapakis CM, Lee A, Wang A, Prigge BA, Volkogon M, Hirsch AM (2013) A survey of the microbial community in the rhizosphere of two dominant shrubs of the Negev Desert highlands, Zygophyllum dumosum (Zygophyllaceae) and Atriplex halimus (Amaranthaceae), using cultivation-dependent and cultivation-independent methods. American Journal of Botany 100, 1713-1725. doi:10.3732/ajb.1200615

Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, Glockner FO (2013) Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Research 41, el. doi:10.1093/nar/gks808

Kuczynski J, Stombaugh J, Walters WA, Gonzalez A, Caporaso JG, Knight R (2012) Using QIIME to analyze 16S rRNA gene sequences from microbial communities. In 'Current Protocols in Microbiology' Chapter 1, Unit 1E.5. doi:10.1002/9780471729259.mc01 e05s27

Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R (2011) UniFrac: an effective distance metric for microbial community comparison. The ISME Journals, 169-172. doi:10.1038/ismej.2010.133

Martirosyan V, Unc A, Miller G, Doniger T, Wachtel C, Steinberger Y (2016) Desert perennial shrubs shape the microbial-community miscellany in laimosphere and phyllosphere space. Microbial Ecology 72, 659-668. doi:10.1007/s00248-016-0822-9

Noy-Meir I (1973) Desert ecosystems: environment and producers. Annual Review of Ecology and Systematics 4, 25-51. doi:10.1146/ annurev.es.04.110173.000325

Noy-Meir I (1974) Desert ecosystems: higher trophic levels. Annual Review of Ecology and Systematics 5, 195-214. doi:10.1146/annurev. es.05.110174.001211

Olsen SR. Watanabe FS (1957) A Method to Determine a Phosphorus Adsorption Maximum of Soils as Measured by the Langmuir Isotherm 1. Soil Science Society of America Journal 21, 144. doi:10.2136/ sssaj1957.03615995002100020004x

Reynolds JF, Virginia RA, Kemp PR, de Soyza AG, Tremmel DC (1999) Impact of drought on desert shrubs: effects of seasonality and degree of resource island development. Ecological Monographs 69, 69-106. doi:10.1890/0012-9615(1999)069[0069:10DODS]2,O.CO;2

Robinson BS, Bamforth SS, Dobson PJ (2002) Density and diversity of protozoa in some arid Australian soils. The Journal of Eukaryotic Microbiology 49, 449 453. doi:10.1111/j.1550-7408.2002.tb00227.x

Saul-Tcherkas V, Steinberger Y (2011) Soil microbial diversity in the vicinity of a Negev Desert shrub--Reaumuria negevensis. Microbial Ecology 61, 64-81. doi:10.1007/s00248-010-9763-x

Schlesinger WH, Raikes JA, Hartley AE, Cross AF (1996) On the spatial pattern of soil nutrients in desert ecosystems. Ecology 77, 364. doi:10.2307/2265615

Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger GG, Van Horn DJ, Weber CF (2009) Introducing MOTHER: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology 75, 7537-7541. doi:10.1128/AEM. 01541-09

Sikorski J, Nevo E (2005) Adaptation and incipient sympatric speciation of Bacillus simplex under microclimatic contrast at 'Evolution Canyons' I and II, Israel. Proceedings of the National Academy of Sciences of the United States of America 102, 15924-15929. doi:10.1073/pnas.05 07944102

Sternberg M, Shoshany M (2001) Influence of slope aspect on Mediterranean woody formations: Comparison of a semiarid and an arid site in Israel. Ecological Research 16, 335-345. doi:10.1046/j.1440-1703.2001.00393.x

Steven B, Gallegos-Graves LV, Yeager C, Belnap J, Kuske CR (2014a) Common and distinguishing features of the bacterial and fungal communities in biological soil crusts and shrub root zone soils. Soil Biology & Biochemistry 69, 302-312. doi:10.1016/j.soilbio.2013.11.008

Steven B, Gallegos-Graves LV, Yeager C, Belnap J, Kuske CR (20146) Common and distinguishing features of the bacterial and fungal communities in biological soil crusts and shrub root zone soils. Soil Biology & Biochemistry 69, 302-312. doi:10.1016/j.soilbio.2013.11.008

Yu J, Steinberger Y (2011) Vertical distribution of microbial community functionality under the canopies of Zygophyllum dumosum and Hammada scoparia in the Negev Desert, Israel. Microbial Ecology 62, 218-227. doi:10.1007/s00248-011-9846-3

Ahuva Vonshak (A), Menachem Y. Sklarz (B), Ann M. Hirsch (C), and Osnat Gillor (A,D)

(A) Zuckerberg Institute for Water Research, Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Israel.

(B) Bioinformatics Core Facility, Ben-Gurion University of the Negev, Israel.

(C) Department of Molecular, Cell and Developmental Biology and Molecular Biology Institute, University of California Los Angeles, Los Angeles, CA, USA.

(D) Corresponding author. Email: gilloro@bgu.ac.il

Received 9 January 2017, accepted 25 July 2017, published online 18 December 2017

Caption: Fig. 1. Bacterial copy numbers in arid soil samples. Spatial distribution of the abundance of total bacteria 16S rRN A copy numbers under the canopies of Zygophyllum dumosum and Artemisia herba-alba and in barren soil. The bars are average of duplicate measurements of triplicate samples at each time point [+ or -] standard deviation.

Caption: Fig. 2. Cluster analysis of the soil bacterial community based on UniFrac. Differences are depicted by patch type including barren soil (circles), Zygophyllum dumosum (triangles), and Artemisia herba-alba (diamond), as well as slope direction including northern (open symbols) and southern slopes (closed symbols). Of the variance, 53.6% was explained by principal component 1 (PCI) and 18.4% by PC2.

Caption: Fig. 3. Heatmap linking bacterial groups with patch type. Rows refer to the sample origin and columns to the affiliated bacterial phylum or class. The clustering of the samples (rows) is based on the Manhattan distance measure, but was stable when using other distance measures. The colour depicts the relative abundance of OTUs per phylum or class normalised between samples. The colour scale range between rare (green) and abundant (red) though the numbers used are bacterial group dependent and thus arbitrary. The ordering of the bacterial phylum/class is based on the NMDS analysis.
Table 1. Physicochemical analysis of soil samples collected under
the canopics of Zygophyllum dumosum and Artemisia herba-atba and in
barren soil. Values represent average [+ or -] standard deviation.
EC = Electric conductivity; OM = organic matter; WC = water
content; * Statistieal analysis of the differences between patches
was performed with one-way ANOVA; ** Statistical analysis between
slopes was performed using two-tailed t-test assuming unequal
variance

Soil                          Plant type
parameters
                              Zygophyllum dumosum

                 Units               Northern
                                     (n = 3)

Ammonium-N   mg[kg.sup.-1]        17 [+ or -] 1.5
Nitrate-N    mg[kg.sup.-1]      75.5 [+ or -] 4.5
Phosphorus   mg[kg.sup.-1]      15.2 [+ or -] 1.0
Potassium    mg[kg.sup.-1]     289.8 [+ or -] 52.2
Chloride     mg[kg.sup.-1]    1333.3 [+ or -] 292.8
EC           dS [m.sup.-1]       4.4 [+ or -] 0.8
PH                               7.7 [+ or -] 0.1
OM                 %             4.4 [+ or -] 0.5
WC                 %             2.6 [+ or -] 0.6

Soil                          Plant type
parameters
                              Zygophyllum dumosum

                 Units               Southern
                                     (n = 3)

Ammonium-N   mg[kg.sup.-1]      14.8 [+ or -] 1.6
Nitrate-N    mg[kg.sup.-1]      74.2 [+ or -] 25.0
Phosphorus   mg[kg.sup.-1]      17.5 [+ or -] 4.5
Potassium    mg[kg.sup.-1]     230.4 [+ or -] 32.2
Chloride     mg[kg.sup.-1]    1133.3 [+ or -] 344.9
EC           dS [m.sup.-1]       3.3 [+ or -] 0.6
PH                               7.8 [+ or -] 0.1
OM                 %             3.6 [+ or -] 1.5
WC                 %               2 [+ or -] 0.4

Soil                          Plant type
parameters
                              Artemisia herba-alba

                 Units              Northern
                                    (n = 3)

Ammonium-N   mg[kg.sup.-1]     15.7 [+ or -] 1.1
Nitrate-N    mg[kg.sup.-1]     40.8 [+ or -] 13.2
Phosphorus   mg[kg.sup.-1]     12.6 [+ or -] 3.5
Potassium    mg[kg.sup.-1]    257.9 [+ or -] 14.3
Chloride     mg[kg.sup.-1]    377.3 [+ or -] 470
EC           dS [m.sup.-1]      1.8 [+ or -] 1.3
PH                                8 [+ or -] 0.1
OM                 %            2.8 [+ or -] 0.7
WC                 %            2.3 [+ or -] 0.2

Soil                          Plant type
parameters
                              Artemisia herba-alba

                 Units              Southern
                                    (n = 3)

Ammonium-N   mg[kg.sup.-1]     15.2 [+ or -] 2.7
Nitrate-N    mg[kg.sup.-1]     29.5 [+ or -] 29.6
Phosphorus   mg[kg.sup.-1]     12.8 [+ or -] 2.9
Potassium    mg[kg.sup.-1]    206.1 [+ or -] 26.2
Chloride     mg[kg.sup.-1]    123.3 [+ or -] 8.0
EC           dS [m.sup.-1]      1.1 [+ or -] 0.6
PH                                8 [+ or -] 0.1
OM                 %            2.3 [+ or -] 0.6
WC                 %              2 [+ or -] 0.5

Soil
parameters

                 Units              Northern
                                    (n = 3)

Ammonium-N   mg[kg.sup.-1]     16.7 [+ or -] 1.7
Nitrate-N    mg[kg.sup.-1]     11.9 [+ or -] 19.4
Phosphorus   mg[kg.sup.-1]     14.5 [+ or -] 3.2
Potassium    mg[kg.sup.-1]    138.4 [+ or -] 34.2
Chloride     mg[kg.sup.-1]       57 [+ or -] 78.0
EC           dS [m.sup.-1]      0.6 [+ or -] 0.7
PH                                8 [+ or -] 0.1
OM                 %            2.1 [+ or -] 0.8
WC                 %            2.5 [+ or -] 1.2

Soil                                                Among patches *
parameters

                 Units             Southern           F        P
                                    (n = 3)

Ammonium-N   mg[kg.sup.-1]    12.1 [+ or -] 1.9      0.825    0.45
Nitrate-N    mg[kg.sup.-1]    23.9 [+ or -] 16.2     8.37     0.005
Phosphorus   mg[kg.sup.-1]     6.9 [+ or -] 1.9      4.54     0.02
Potassium    mg[kg.sup.-1]    91.9 [+ or -] 28.3    25.02    <0.001
Chloride     mg[kg.sup.-1]     129 [+ or -] 96.0    31.25    <0.001
EC           dS [m.sup.-1]       1 [+ or -] 0.9     11.14     0.001
PH                             7.9 [+ or -] 0.1     11.14     0.001
OM                 %           2.3 [+ or -] 1.1     10.76     0.001
WC                 %           3.3 [+ or -] 0.6      1.98     0.17

Soil                          Between
parameters                    slopes **

                 Units            P

Ammonium-N   mg[kg.sup.-1]      0.01
Nitrate-N    mg[kg.sup.-1]      0.88
Phosphorus   mg[kg.sup.-1]      0.56
Potassium    mg[kg.sup.-1]      0.14
Chloride     mg[kg.sup.-1]      0.68
EC           dS [m.sup.-1]      0.58
PH                              0.72
OM                 %            0.51
WC                 %            0.92
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Author:Vonshak, Ahuva; Sklarz, Menachem Y.; Hirsch, Ann M.; Gillor, Osnat
Publication:Soil Research
Article Type:Report
Geographic Code:7ISRA
Date:Mar 1, 2018
Words:5611
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