spatial heterogeneity of physicochemical properties ...1 instituto de ciencias del mar y...

Submitted 7 April 2016Accepted 17 August 2016Published 8 September 2016

Corresponding authorAna E Escalanteanaelenaescalantegmailcom

Academic editorBudiman Minasny

Additional Information andDeclarations can be found onpage 14

DOI 107717peerj2459

Copyright2016 Pajares et al

Distributed underCreative Commons CC-BY 40

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Spatial heterogeneity of physicochemicalproperties explains differences inmicrobial composition in arid soils fromCuatro Cienegas MexicoSilvia Pajares12 Ana E Escalante3 Ana M Noguez4 Felipe Garciacutea-Oliva5Celeste Martiacutenez-Piedragil5 Silke S Cram6 Luis Enrique Eguiarte4 andValeria Souza4

1 Instituto de Ciencias del Mar y Limnologiacutea Unidad Acadeacutemica de Ecologiacutea y Biodiversidad AcuaacuteticaUniversidad Nacional Autoacutenoma de Meacutexico Mexico City Mexico

2Departamento de Procesos y Tecnologiacutea Universidad Autoacutenoma Metropolitana Unidad CuajimalpaMexico City Mexico

3 Instituto de Ecologiacutea Laboratorio Nacional de Ciencias de la Sostenibilidad Universidad Nacional Autoacutenomade Meacutexico Mexico City Mexico

4 Instituto de Ecologiacutea Departamento de Ecologiacutea Evolutiva Universidad Nacional Autoacutenoma de MeacutexicoMexico City Mexico

5 Instituto de Investigaciones en Ecosistemas y Sustentabilidad Universidad Nacional Autoacutenoma de MeacutexicoMorelia Michoacaacuten Mexico

6 Instituto de Geografiacutea Universidad Nacional Autoacutenoma de Meacutexico Mexico City Mexico

ABSTRACTArid ecosystems are characterized by high spatial heterogeneity and the variationamong vegetation patches is a clear example Soil biotic and abiotic factors associatedwith these patches have also been well documented as highly heterogeneous inspace Given the low vegetation cover and little precipitation in arid ecosystemssoil microorganisms are the main drivers of nutrient cycling Nonetheless little isknown about the spatial distribution of microorganisms and the relationship that theirdiversity holds with nutrients and other physicochemical gradients in arid soils Inthis study we evaluated the spatial variability of soil microbial diversity and chemicalparameters (nutrients and ion content) at local scale (meters) occurring in a gypsum-based desert soil to gain knowledge on what soil abiotic factors control the distributionof microbes in arid ecosystems We analyzed 32 soil samples within a 64 m2 plot and(a) characterized microbial diversity using T-RFLPs of the bacterial 16S rRNA gene(b) determined soil chemical parameters and (c) identified relationships betweenmicrobial diversity and chemical properties Overall we found a strong correlationbetween microbial composition heterogeneity and spatial variation of cations (Ca2K+) and anions (HCOminus3 Cl

minus SO2minus4 ) content in this small plot Our results could be

attributable to spatial differences of soil saline content favoring the patchy emergenceof salt and soil microbial communities

Subjects Biodiversity Ecology Microbiology Soil ScienceKeywords Arid soils Physicochemical properties Spatial heterogeneity T-RFLPs Biological soilcrusts Cuatro Cienegas Basin

How to cite this article Pajares et al (2016) Spatial heterogeneity of physicochemical properties explains differences in microbial compo-sition in arid soils from Cuatro Cienegas Mexico PeerJ 4e2459 DOI 107717peerj2459

INTRODUCTIONSpatial heterogeneity is an inherent feature of soils and has significant functionalimplications including the fact that different soil patches and aggregates can presentvariation in nutrient transformation rates (eg respiration mineralization nitrogenfixation) (Noguez et al 2008 Strickland et al 2009 Zeglin et al 2009) particularly whenthe activities and distribution of microorganisms are considered The scale at whichenvironmental variation is considered in association with microbial diversity varies greatlyfrom tens to thousands of kilometers to meters and even at the microscale (Vos et al2013) Depending on the spatial scale at which microbial diversity is studied different envi-ronmental parameters and ecological processes may be associated to the observed diversitydistribution (Martiny et al 2011) At large spatial scales (tens to thousands of kilometers)soil microbial community structure is correlated to edaphic variables such as soil pH(Fierer amp Jackson 2006) temperature (Garcia-Pichel et al 2013) and moisture content(Angel et al 2010) At smaller scales (tens of meters) plant communities have been shownto have a strong influence on soil microbial diversity through interactions within therhizosphere (Berg amp Smalla 2009Hartmann et al 2009 Ben-David et al 2011) Howeverlittle is known about the effects of small-scale habitat variation on the spatial patterns ofmicrobial diversity and its interactions with soil abiotic properties (Maestre et al 2005)

Arid soils have a particularly heterogeneous spatial distribution of abiotic properties(Schlesinger et al 1996) particularly in nutrient content Vegetation patches deemedlsquolsquofertility or resource islandsrsquorsquo are also scarce and sparsely found in arid environments(Cross amp Schlesinger 1999 Hirobe et al 2001 Schade amp Hobbie 2005) At the same timethere are large areas deprived of vegetation and severely limited in nutrients and water(Evans et al 2001 Belnap et al 2005) in which microbial communities often referredto as lsquolsquobiological soil crustsrsquorsquo or lsquolsquobiocrustsrsquorsquo are the main drivers of energy input andbiogeochemical processes (Titus Nowak amp Smith 2002 Belnap 2003 Maestre et al 2005Housman et al 2007 Castillo-Monroy et al 2010 Bachar Soares amp Gillor 2012) Biocrustscontribute actively to natural small-scale soil heterogeneity not only in terms of biologicaldiversity but also in relation to soil function including nutrient cycling and physicochemicalproperties associated with their spatial structure (Maestre et al 2005)

Given the tight connection between microbial activity and nutrient cycling it is reason-able to think thatmicrobial distribution in soilsmight be somehow correlatedwith nutrientscontent across space (eg more nutrients more microbial biomass and diversity) Despitethe idea of resource island formation in arid soils studies have shown that spatial distribu-tion of microorganisms and nutrients is not correlated in these ecosystems (Belnap et al2005 Housman et al 2007 Geyer et al 2013) Some of these studies indicate that for aridoligotrophic ecosystems physicochemical parameters associated with water availabilityare better correlated with microbial distribution (Geyer et al 2013) Thus althoughthere are some studies in arid ecosystems (Barrett et al 2006 Zeglin et al 2009 Lee et al2012 Geyer et al 2013) it is of interest to gain better knowledge on the factors influencingmicrobial diversity having direct consequences in soil fertility and ecosystem processes(eg soil mineralization and respiration rates) (Maestre et al 2005 Ben-David et al 2011)

Pajares et al (2016) PeerJ DOI 107717peerj2459 220

In the present study we aim to determine the spatial heterogeneity of microbialdiversity and soil chemical parameters occurring in an arid soil of a hot desert ecosystemin order to contribute with information and gain understanding into the aspects of the soilenvironment that are more strongly associated with differences in microbial communitydistribution in these kind of soils We hypothesize that the spatial heterogeneity in chemicalproperties previously reported for desert soils (Schlesinger et al 1996) will be reflectedin microbial diversity distribution at a yet unexplored local scale (order of meters)Thereby in this study we (a) characterize microbial community structure (b) determinesoil physicochemical and biochemical parameters and (c) identify relationships amongmicrobial community structure and chemical soil properties at a local spatial scale

The study site Cuatro Cienegas Basin (CCB) is located in a desert ecosystem in themiddle of the Chihuahuan desert in Mexico This is a gypsum-based system and is oneof the most oligotrophic environments in the world In contrast the microbial diversityis very high in comparison to other arid soils (Loacutepez-Lozano et al 2012) providing theopportunity to investigate the spatial relationship between chemical distribution andmicrobial community structure as it has been done in other oligotrophic arid ecosystemsof Antarctica (Zeglin et al 2011 Geyer et al 2013)

MATERIALS AND METHODSStudy areaThe study site is locally known as lsquolsquoChurince systemrsquorsquo It is located in the western partof the CCB (2650primeN 10208primeW Fig 1) at 740 m asl The system consists of a springan intermediate lagoon and a dry desiccation lagoon connected by short shallow creeksThe annual precipitation in the area is less than 250 mm occurring mainly from Mayto October Temperatures fluctuate from 0 C in January to 45 C in July with a meanannual temperature of 214 C (CCB weather station) The vegetation is mainly halophileand gypsophile grasslands (Challenger 1998) The area is also dominated by physical andbiological soil crusts The soil is predominantly basic rich in calcium and sulfates but verypoor in nutrients and belongs to Gypsisol type (IUSS Working Group WRB 2015)

Sampling designThe sampling plot was approximately at 50 m from the dry desiccation lagoon Thegypsophile grass Distichlis spicata covered 10 of the plot (Fig 1) and it was the onlyspecies present in this area Physical and biological crusts occupied the open areas betweenplants In order to explore the spatial relationship between soil chemical distribution andmicrobial community structure at local scale we used a plot of 8 mtimes 8 m that consisted ofa nested system of four quadrats (AndashD quadrats) of 16m2 which were divided in eight 1 m2

lsquolsquoreplicatesrsquorsquo following a checkerboard pattern (Fig 1) Vegetation cover for each samplinglsquolsquoreplicatersquorsquo or site (1 m2) was registered qualitatively in order to have further ecologicalcontext for the results In August 2007 we collected soil samples (500 g) from the first10 cm at each site to a total of 32 samples (eight samples for each 4 m2 quadrat) under theSEMARNAT collection permits 0659006 and 0685507 Soil samples were homogenizedin the field and divided in two subsamples which were stored at minus20 C (for molecular


Figure 1 Sampling scheme An 8 times 8 m plot was selected in the Churince System within the CuatroCienegas Basin Meacutexico A checkerboard sampling scheme was followed (Noguez et al 2005) to a total of32 samples eight for each of the four quadrats (A B C and D) Soil parameters were determined for the32 samples Numbers with asterisks indicate samples that were also analyzed for microbial diversity Greencolored areas indicate presence of vegetation

analyses) and at 4 C (for chemical analyses) respectively Analyses were performed uponarrival to the laboratory

Physicochemical and biochemical analysesSoil samples were air dried and sieved through a 2 mmmesh prior to physicochemical andbiochemical determinations which were performed twice for each sample Total carbon(TC) was determined by dry combustion and coulometric detection (Huffman 1977)using a Total Carbon Analyzer (TOC UIC Mod CM5012 UIC Inc Chicago USA) Fortotal nitrogen (TN) and phosphorus (TP) the samples were acid digested and determinedcolorimetrically using a Bran-Luebbe Auto-analyzer III (Germany) according to Bremner(Bremmer amp Mulvaney 1982) and Murphy amp Riley (Murphy amp Riley 1962) respectivelyInorganic N forms (NH+4 and NOminus3 ) were extracted with 2 M KCl after shaking for 30 minfollowed by filtration through a Whatman 1 filter and measured colorimetrically by thephenolndashhypochlorite method Inorganic P (Pi) was extracted with sodium bicarbonateand determined colorimetrically by the molybdate-ascorbic acid method (Murphy amp Riley1962) Dissolved organic C (DOC) N (DON) and P (DOP) were extracted with deionizedwater after shaking for 1 h and then filtered through a Whatman 42 filter DOC wasdetermined with a TOC module for liquids (UIC-Coulometrics) while DON and DOPwere acid digested and measured colorimetrically

Electrical conductivity and pH were determined in soil with deionized water (soilsolution ratio 12) To quantify water-soluble cations (Ca2+ Mg2+ K+ Na+) and anions


(HCOminus3 Clminus SO2minus

4 ) soil samples were shaken with deionized water for 19 h centrifugedat 2500 rpm and filtered through a Whatman 42 filter Ca2+ and Mg2+ were analyzed byatomic absorption spectrophotometry with an airacetylene flame (Varian SpectrAA 110)while Na+ and K+ by flamometry (flame photometer Corning PFP7) (Bower Reitemeier ampFireman 1972) Anions were determined by liquid chromatography (Waters Mod 1525)with a mobile phase of borate sodium glucanate (Bower Reitemeier amp Fireman 1972)

Molecular analysesMicrobial community structure was characterized using terminal restriction fragmentlength polymorphisms (T-RFLPs) of bacterial 16S rRNA gene

GenomicDNAwas extracted from the soil samples using the SoilMaster DNAExtractionKit (Epicentre Biotechnology) with an additional previous step based on the fractionationcentrifugation technique in order to reduce the high salt concentration (Holben et al 1988)After extraction genomic DNA was cleaned with Microcon columns (Fisher Scientific)with the purpose of removing any substance that could inhibit PCR amplificationThese protocol modifications gave the best results from various methodologiestested however we were only able to amplify the 16S rRNA gene from 21 out of the32 soil sampling sites (amplicons obtained in each quadrat A= 3 B= 3 C = 7 D= 8)The low yield in the DNA amplification could be attributed to molecular applicationsinhibitors of unknown nature (Loacutepez-Lozano et al 2012)

Amplification of the bacterial 16S rRNA genes was carried out in a final volume of 50 microLcontaining 02 microM of each fluorescently labeled domain-specific primers (VIC-27F andFAM-1492R) (Lane 1991) 02 mM of each dNTP 1 U of Taq Platinum DNA polymerase(Invitrogen) 25 microL DMSO 25 microL BSA 1 mM MgCl2 1 mM buffer and 20 ng of DNAFive independent PCR reactions were performed for each sample with the followingprogram 5 min at 94 C 30 cycles at 94 C for 1 min 52 C for 2 min 72 C for 3min and 72 C for 10 min PCR products were pooled and purified from 2 agarosegel (Gel extraction kit Qiagen Inc) The amplicons were restricted with AluI enzyme(Promega) at 37 C for 3 h and 65 C for 20 min Three independent readings of thesize and abundance of fluorescently labeled terminal restriction fragments (TRFs) wereperformed for each sample using an ABI 3100-Avant Prism Genetic Analyzer (AppliedBiosystems) as described previously (Coolen et al 2005) For each profile of TRFs weestablished a baseline and only those TRFs with peak heights ge50 fluorescent units wereused in subsequent analyses (Blackwood et al 2003) Each unique TRF was consideredto be an operational taxonomic unit (OTU) Estimations of diversity were derived frommatrices constructed based on the presence and abundance of TRFs using relative peakarea as an estimate of abundance calculated as

Ap= (niN )times100

in which ni represents the peak area of one distinct TRF and N is the sum of all peak areasin a given T-RFLP pattern (Lukow Dunfield amp Liesack 2000)


Statistical analysesStatistical and diversity analyses were performed in R (R Development Core Team 2011)mainly with vegan (Oksanen et al 2012) ggplots (Warnes 2012) and BiodiversityR (Kindtamp Coe 2005) packages

All soil properties data were expressed on a dry-weight basis Non-normal data werelog-transformed to normalize the distribution of the residuals We analyzed the data usingboth multivariate (MANOVA to detect patterns whether there were significant effects ofthe four quadrats on overall soil variables) and univariate (to detect significant differencesin individual variables) methods These analyses were followed by multiple pairwisetests using Tukeyrsquos honestly significant difference (HSD) at the 5 level of significanceto identify possible differences in the soil variables between quadrats The correlationsbetween each pair of variables were calculated using Pearsonrsquos correlation coefficient Soilproperties were then standardized and ordered by principal components analysis (PCA)and the sampling points from the four quadrats were visualized with the two first principalcomponents

Alpha diversity indices (Shannon Simpson and Berger-Parker) and richness estimateswere calculated for each quadrat using the T-RFLPs profiles Microbial diversity indiceswere also analyzed using ANOVA type III for unbalanced data and evaluated using Renyirsquosentropy profiles for eight scales (α= 0 025 05 1 2 4 8 infinite) (Reacutenyi 1961 Chao etal 2014) with the BiodiversityR package These profiles provide a comprehensive analysisof the diversity giving a parametric measure of the uncertainty of predicting the OTUsrichness as well as the relative abundance of OTUs at different scales between the fourquadrats To evaluate the sampling effort rarefaction curves were constructed for eachquadrat using EstimateS v910 (Colwell 2005) Microbial community structure betweenquadrats was examined through Venn diagrams (with the matrix of OTUs presence) andordination analyses (with the matrix of OTUs abundance) To visualize communitiesrsquostructure Bray-Curtis dissimilarity distances were calculated with the relative abundanceof T-RFLPs profiles Similar communities were then clustered using theWardrsquos hierarchicalclustering algorithm which tries to minimize variances in agglomeration A heatmap ofthe relative abundance of OTUs was constructed with dual hierarchical clustering

Community structure was also investigated for correlations with chemical parametersfollowing a multivariate analysis For this the relative abundance of T-RFLPs profiles wereordered by Detrended Correspondence Analysis (DCA) with Hellinger transformation(Blackwood et al 2003) and correlations between the ordination axes and soil propertieswere calculated This eigenvector-based ordination technique uses a chi-square distancemeasure and assumes that TRFs have a unimodal distribution along ecological gradients(Legendre amp Legendre 1998) which is a more appropriate assumption than linearity forecological analysis of T-RFLPs data (Culman et al 2008) Permutation tests under reducedmodel were used to identify significant explanatory soil variables (plt 005) which wereplotted onto the ordination map as vectors Only the soil variables corresponding to thesame sampling sites as the T-RFLPs data were used for this analysis

To further evaluate which soil abiotic properties could be used as predictors of thedistribution of OTUs diversity a multiple linear regression analysis was performed


Table 1 Physicochemical parameters Results from soil physicochemical analyses (meanplusmn standard deviation) of the four studied quadrats withinChurince System in the Cuatro Cienegas Basin (Mexico)

Variable Quadrat Overall mean

A B C D

Total C (mg gminus1) 24plusmn 08 24plusmn 04 26plusmn 04 28plusmn 06 26plusmn 06Total N (mg gminus1) 057plusmn 013 048plusmn 018 059plusmn 01 060plusmn 018 056plusmn 015Soil CN 46plusmn 23 61plusmn 4 46plusmn 07 50plusmn 13 51plusmn 24Total P (mg gminus1) 003plusmn 001 003plusmn 001 004plusmn 001 004plusmn 002 004plusmn 001NH+4 (microg gminus1) 40plusmn 06 42plusmn 08 40plusmn 06 36plusmn 1 40plusmn 07NOminus3 (microg gminus1) 18plusmn 19 15plusmn 15 16plusmn 17 23plusmn 15 17plusmn 16Dissolved organic C (microg gminus1) 973plusmn 278 758plusmn 30 830plusmn 33 124plusmn 213 951plusmn 332Dissolved organic N (microg gminus1) 146plusmn 34 181plusmn 123 179plusmn 87 196plusmn 92 176plusmn 87Dissolved organic CN 7plusmn 29 64plusmn 48 52plusmn 24 79plusmn 39 64plusmn 35Dissolved organic P (microg gminus1) 43plusmn 36 56plusmn 21 26plusmn 35 33plusmn 37 39plusmn 33pH 86plusmn 01 87plusmn 01 87plusmn 01 88plusmn 01 87plusmn 01Electrical conductivity (dSmminus1) 14plusmn 01 14plusmn 03 16plusmn 02 14plusmn 04 14plusmn 03Mg2+ (cmol kgminus1) 271plusmn 52 28plusmn 63 352plusmn 43 365plusmn 45 317plusmn 65Ca2+ (cmol kgminus1) 056plusmn 003a 055plusmn 002a 064plusmn 007ab 065plusmn 007b 059plusmn 007Na+ (cmol kgminus1) 140plusmn 159 127plusmn 161 166plusmn 381 157plusmn 284 147plusmn 291K+ (cmol kgminus1) 095plusmn 014a 082plusmn 019a 127plusmn 014b 133plusmn 025b 109plusmn 028HCOminus3 (cmol kgminus1) 28plusmn 02b 23plusmn 06b 12plusmn 02a 13plusmn 03a 19plusmn 08Clminus (cmol kgminus1) 28plusmn02b 25plusmn 03b 11plusmn 04a 13plusmn 03a 19plusmn 08SO2minus

4 (cmol kgminus1) 151plusmn 22b 164plusmn 25b 76plusmn 08a 72plusmn 09a 116plusmn 46

NotesVariable acronyms C carbon N nitrogen P phosphorousSignificant difference among quadrants (plt 005)Different letters indicate that means are significantly different among quadrats

Significant explanatory variables were chosen as the best predictors of OTU abundance bystepwise multiple regressions and by minimizing the Akaike Information Criterion (AIC)Collinearity between explaining variables was also tested using the Variance InflationFactor (VIF)

RESULTSSpatial heterogeneity in physicochemical and biochemicalparametersThe total plant cover in the experimental plot was only 10 However quadrats C and Dwere more densely and homogeneously covered than A and B (Fig 1) Overall there was ahigh presence of soil crusts and biocrusts particularly in the A quadrat

Soil chemical properties varied significantly between the four quadrats (Wilksrsquo lambda= 0000 F = 7896 plt 005) Soil samples were alkaline (pH between 86ndash88) dueto the high presence of salt in this arid ecosystem (Table 1) Cations (Ca2+ K+) andanions (HCOminus3 Cl

minus SO2minus4 ) were the most variable parameters in this small plot showing

significant differences between quadrats which means that ions significantly contributeto soil heterogeneity in this system C and D quadrats had the greatest concentration ofcations (064 and 065 cmol Ca2+ kgminus1 127 and 133 cmol K+ kgminus1 respectively) except


for Mg2+ and Na+ while A and B quadrats had the highest concentration of anions (28and 23 cmol HCOminus3 kgminus1 28 and 25 cmol Clminus kgminus1 156 and 164 cmol SO2minus

4 kgminus1respectively) The high concentration of Na+ found in these soils (mean value of 147 cmolkgminus1) indicates salinity stress Total forms and nutrients content were very low in thisarid soil (TC 24ndash28 mg gminus1 TN 048ndash06 mg gminus1 TP 003ndash004 mg gminus1 NH+4 36ndash42microg gminus1 NOminus3 15ndash23 microg gminus1) as expected and they did not show significant differencesamong quadrats The total CN ratio (a quality index for soil organic matter) was also verylow (from 46 to 61) and did not show significant differences between the four quadratsOn the other hand C availability (DOC) was higher in D quadrat (124 microg gminus1) whichmeans greater substrate availability for microbial metabolism in this quadrat As expectedthe pHwas positively correlated withMg2+ andNa+ (Table S1) The TCwas only positivelycorrelated with Ca2+ while TP was positively correlated with pH and cations as well asnegatively with DOP The TN was positively correlated with DON and negatively with CNNH+4 NO

minus

3 and HCOminus3 Finally N inorganic forms were also positively correlated betweenthem and the CN ratio

The complex chemical spatial heterogeneity among these four quadrats was exploredusing a PCA (Fig 2) The first component (PC1) explained 543 while the secondcomponent (PC2) explained 343 of the total variation in the soil parameters amongquadrats (Table S2) The variables associated with the PC1 were cations and anions aswell as pH TP DOC and DOP The PC2 was mainly related to soil nutrients (TN CNNH+4 NO

minus

3 DON DOCDON) A clear separation between quadrats was observed alongthe PC1 axis mainly explained by the spatial heterogeneity distribution of ions

Spatial heterogeneity of microbial diversityA total of 184 different OTUs were obtained in the four quadrats of which 121 OTUshad less than 1 of the total maximum relative abundance Unfortunately the number ofavailable samples was unbalanced for the microbial diversity study in this plot (ampliconsin each quadrat A= 3 B= 3 C = 7 D= 8) potentially due to the presence of inhibitorsof unknown nature which hampered the 16S rRNA amplification from all samples sitesDespite this constraint rarefaction curves showed a good community sampling for quadratsA C and D with evident subsampling for quadrat B which is one of the two quadrats forwhich only three out of eight samples could be analyzed in terms of microbial diversity(Fig S1) Significant variation in alpha diversity indices among quadrats was detectedAlthough the A quadrat was also limited in the number of analyzed samples (3) it was themost diverse (H 331) and with the highest evenness (1D 0944 BP 0153) followedby C D and B (Table 2) It was also evident the high variability in microbial diversityamong replicates (with the exception of A) which reflects the spatial heterogeneity at smalllocal scale of this arid soil A summary of diversity indices was obtained by calculatingRenyirsquos community profiles Ranking based on these profiles is preferred to ranking basedon single indices because rank order may change when different indices are used (Kindt ampCoe 2005) These profiles showed the same pattern of diversity both in terms of richnessand evenness where the highest diversity was found for quadrat A and the lowest forquadrat B while C and D presented intermediate diversity (Fig 3)


Figure 2 Biplot generated from Principal Component Analysis (PCA) of the standardized soil vari-ables for the four quadrats Symbols represent the different quadrats Each vector points to the directionof increase for a given variable and its length indicates the strength of the correlation between the vari-able and the ordination scores Ellipses show confidence intervals of 95 for each sample type The firstcomponent of the PCA analysis accounted for 543 of the total variation and the second component ac-counted for 343 of the variation

Table 2 Alpha diversity estimatesOTUs diversity indices (meanplusmn standard deviation) from the T-RFLPs data of the four quadrats (A 3 samples B 3 samples C 7 samples D 8 samples)

Quadrat Richness (S) Shannon (H) Simpson (1D) BergerndashParker

A 48plusmn 9 331plusmn 008a 0944plusmn 0004a 0153plusmn 0004b

B 36plusmn 15 198plusmn 062b 0704plusmn 0145b 0497plusmn 0144a

C 45plusmn 13 256plusmn 044ab 08plusmn 0105ab 0393plusmn 0138a

D 47plusmn 19 23plusmn 077ab 0738plusmn 0189b 0426plusmn 0203a

NotesSignificant difference among quadrats (plt 005)Different letters indicate that means are significantly different among quadrats

Despite the high heterogeneity in microbial diversity in such a small plot Venn diagramrevealed a considerable overlap of OTUs among the four quadrats 18 of OTUs wereshared by all quadrats (Fig 4) which represent 442 of the total abundance of themicrobial community recovered from this plot with the T-RFLP technique The C quadrathad the most lsquolsquouniquersquorsquo OTUs (12 representing 05 of total abundance) followed byquadrat D (114 representing 17 of total abundance) B (103 representing 39 oftotal abundance) and A (54 representing 03 of total abundance) in decreasing orderInterestingly C and D quadrats shared 84 of the 184 recovered OTUs (46) suggestingthat both quadrats had more similar community composition than A and B quadratsMoreover the heatmap (Fig S2) and the cluster dendrogram (Fig S3) of OTUs abundanceshowed the same pattern of grouping as the PCA analysis for soil chemical properties


Figure 3 Renyirsquos entropy profiles for the studied quadrats (A 3 samples B 3 samples C 7 samplesD 8 samples) Profiles were calculated with the OTUs abundance matrix The alpha scale shows the dif-ferent ways of measuring diversity in a community Alpha= 0 is richness alpha= 1 shows Shannon di-versity alpha= 2 is Simpson index (only abundant species are weighted) and alpha= Infinite only dom-inant species are considered (BergerndashParker index) The height of H -alpha values show diversity (for moreinformation see Kindt amp Coe 2005)

Figure 4 Venn diagramsDisplaying the degree of overlap of OTUs composition among the four studiedquadrats (A 3 samples B 3 samples C 7 samples D 8 samples)

separating quadrats in two groups AndashB and CndashD As in the PCA analysis there was asample in the C quadrat that clearly deviated from the other samples

Multivariate analyses of microbial community structureTo explore the association between community structure and soil parameters we performeda DCA analysis The original 19 soil parameters were reduced to seven non-redundant


Figure 5 Detrended Correspondence Analysis (DCA) of the T-RFLPs profiles with respect to the soilproperties Sample sites for the four quadrats are represented by symbols and OTUs are represented bygrey crosses Ellipses show confidence intervals of 95 for each sample type Vectors stand for significantsoil variables (p lt 01) Each vector points to the direction of increase for a given variable and its lengthindicates the strength of the correlation with the axes

explanatory variables (TN DON Ca2+ K+ HCOminus3 Clminus and SO2minus

4 Table S3) which werethe factors that contributed significantly to differences in community composition amongthe four quadrats in the ordination analysis The DCA showed a clear separation of thequadrats in two groups mainly explained by soil salinity anions (HCOminus3 Cl

minus and SO2minus4 )

significantly correlated with OTUs from the A and B quadrats while TN DON Ca2+

and K+ significantly correlated with OTUs from the C and D quadrats (Fig 5) Thus thegrouping pattern of these microbial communities showed in the above analyses was alsoconfirmed by the DCA analysis

To further investigate the relation among OTUs diversity distribution and soil abioticproperties a full linear model was then tested and reduced for a model where all thecoefficients were statistically significant and the best AIC achieved The best regressionmodel indicated that DON Ca2+ K+ and SO2minus

4 were statistically significant to explain thedistribution of OTUs diversity in this soil (Table S4 R2

= 059 plt 001)

DISCUSSIONChemical heterogeneity at local spatial scale is mainly due to ionsconcentration variabilityThe values of TC TN and TP in the experimental plot were lower than the values reportedfor other deserts (Thompson et al 2006 Strauss Day amp Garcia-Pichel 2012) as well as forsoils in the CCB (Loacutepez-Lozano et al 2012 Tapia-Torres et al 2015) The very low CNratio also suggests deficiency of soil organic C therefore a low nutrient availability to soilmicrobes and vegetation limiting the N cycle due to the lack of C availability The Redfield


ratio in this soil was 71171 which suggests that in these quadrats the C is the limitingnutrient in comparison with a general lsquolsquoaveragersquorsquo soil CNP of 186131 (Cleveland ampLiptzin 2007) On the other hand this result also differs from the Redfield ratio of 10451reported for the same soil system (Loacutepez-Lozano et al 2012) These differences could beattributable to the great heterogeneity of this arid environment and the different time of soilsampling in both studies February 2007 (dry cold season with low evapotranspiration) inLoacutepez-Lozano et al (2012) andAugust 2007 (rainy hot seasonwith high evapotranspiration)in this study

All soil samples in this study had high alkalinity produced by the elevated concentrationsof ions which is a general pattern in desert soils (Titus Nowak amp Smith 2002) The highpH decreases P availability which is very scarce in these soils and it is bonded to Ca2+

and Mg2+ (Cross amp Schlesinger 2001 Perroni et al 2014) It is worth to mention that ionsvaried spatially in identity in this small plot quadrats A and B were significantly high inanions while quadrats C and D were significantly high in cations The huge concentrationof Na+ in the four quadrats is an indicator of the extremely high salinity in these soils whichnegatively affects the soil aggregates stability as well as nutrients and water availability forplants favoring the development of soil crusts which are typical in arid and semiarid soils(Belnap 2003 Zhang et al 2007) In particular salt crusts are abundant in the CCB areaThey consist of layers at the soil surface mainly formed by soluble salt crystallizing soilparticles at shallow saline groundwater level regions (Zhang et al 2013)

The high concentration of ions can be attributed to the gypsum-rich nature of the CCBsoils where groundwater rises to the surface by soil capillarity action and water evaporationpromotes salt accumulation This situation results in rivers with a steep salinity gradient(Cerritos et al 2011) and pools surrounded by saline soils rich in sulfates and extremelypoor in nutrients (Loacutepez-Lozano et al 2012) Therefore it is not surprising to find thatthe soil properties variation in this small plot was mainly explained by ions concentrationgrouping the four quadrats in two broad clusters AndashB and CndashD These clusters had aqualitative pattern associated with the vegetation cover being quadrats C and D moredensely and homogeneously covered by vegetation than A and B Although the presentresearch analyzes soil communities a previous study of microbial communities of the watersystem associated with the studied plot showed a correlation of microbial compositionand water conductivity gradients (Cerritos et al 2011) Thus the spatial variation in thesephysicochemical properties among the four quadrats may be a consequence of differencesin moisture content due to the proximity to a subterranean water flow indirectly evidencedby the marked patchy distribution of the vegetation cover and the lsquolsquoopenrsquorsquo areas occupiedby soil crusts (Loacutepez-Lozano et al 2012)

Heterogeneity in microbial diversity at local spatial scale is explainedby physicochemical factors not by vegetation cover nor nutrientcontentDespite recent important advances in our knowledge of the structure composition andphysiology of biotic components in arid soils (Belnap et al 2005 Caruso et al 2011Maestre et al 2015 Makhalanyane et al 2015) little is known about the spatial variability


of microbial diversity at local scales and its interactions with chemical heterogeneity inthese ecosystems (Housman et al 2007 Castillo-Monroy et al 2011 Andrew et al 2012)T-RFLPs fingerprinting was used in this study to assess the relationship between microbialstructure and the small-spatial heterogeneity of soil chemical properties We are awarethat this technique cannot recognize taxonomic groups and accounts mainly for relativelyabundant microbial groups Nevertheless given the aims of this study of characterizingmicrobial communities structure and its relationship with abiotic or physichochemicalparameters a fingerprint approach such as T-RFLPs is adequate to provide replicablevalid and sufficient data (Angel et al 2013) as it has done for many other studieslooking at patterns of correlation between microbial diversitycomposition andenvironmental factors (Fierer amp Jackson 2006)

The heterogeneity in OTUs diversity among these quadrats is evident being the Aquadrat the most different with respect to the other quadrats Despite the fact that theA quadrat had scarce plant cover and similar nutrients and ions concentrations to theB quadrat it showed the greatest microbial diversity which could be related to the highpresence of biocrusts that may incorporate more resources to the soil potentially increasingorganic C and biomass and in turn diversity (Geyer et al 2013) On the other hand the Bquadrat had the lowest microbial diversity which could be related to the lowest values ofDOC found in this quadrat Labile organic matter fractions such as DOC are the primaryenergy source for soil microorganisms and are characterized by rapid turnover (Bolanet al 2011) It has been reported that even in disturbed sites DOC is the main sourceof C influencing the composition of the microbial community (Churchland Graystonamp Bengtson 2013) Then changes of soil microbial community could be regulated by Cavailability through labile soil organic matter pools as have recently been shown to happenin McMurdo Dry Valleys arid soils in Antarctica (Geyer et al 2013)

Regarding similarity in microbial composition among the four quadrats clusterdendrogram and multivariate analyses showed two clear groups which were AndashB andCndashD corresponding to the same clustering of quadrats based on soil chemical parametersA common explanation for the soilmicrobial composition patterns is related to the presenceof plants controlling levels of microbial diversity and driving community assembly (Singh etal 2007 Berg amp Smalla 2009 Ben-David et al 2011) However in our study the observedspatial pattern of microbial diversity distribution at such local scale does not seem tobe associated with vegetation cover For example the A quadrat is the most diverse inmicrobial community and the less vegetated suggesting that microbial diversity in thisarid soil could be more related to the presence of lsquolsquoopenrsquorsquo areas occupied by biocrusts Onthe other hand abiotic factors such as ionic content are statistically explanatory variablesin the spatial ordering (DCA) of the microbial communities analyzed In addition themultiple regression model selected shows the importance of DON Ca2+ K+ and SO2minus

4 inexplaining the distribution of OTUs abundance in this arid soil Abiotic drivers of microbialdiversity in arid soils has been also reported for the Sonoran desert (Andrew et al 2012)where location pH cation exchange capacity and soil organic C were highly correlatedwith microbial composition Therefore we showed that microbial community diversity


and distribution responds to andor influences local soil physicochemical characteristics ata small spatial scale in this arid ecosystem

CONCLUSIONSIn desert areas such as CCB soil moisture is one of main limiting factors affectingvegetation growth and distribution as well as soil microbiology The gypsum-based watersystem controls the soil physicochemical factors and ultimately the microbial communitydistribution in this arid ecosystem Thus the high heterogeneity in the soil propertiesand microbial community among these small four quadrats seems to be a consequence ofdifferences in the soil saline content In addition the high concentration of Na+ favors theemergence of both salt and biological crusts and the irregular plant cover distribution inthis system Local spatial variability of physicochemical properties and microbial diversityobserved in this arid ecosystem is likely to exist in most soils ecosystems and needs to beconsidered when making ecological inferences and when developing strategies to samplethe soil environment A better understanding of the role of spatial heterogeneity in bioticand abiotic factors will help to determine the relevance of small-scale studies for large-scalepatterns and processes

ACKNOWLEDGEMENTSWe want to thank Dr Laura Espinosa-Asuar and Rodrigo Gonzaacutelez Chauvet for technicalsupport during the development of this study

ADDITIONAL INFORMATION AND DECLARATIONS

FundingThis work was supported by the Universidad Nacional Autoacutenoma de Meacutexico (UNAM-PAPIIT grant IA200814 to AEE) and SEMARNAT-CONACyT (grant 23459 to VS) SPreceived financial support from a CONACyT research visiting fellowship (186372) Thefunders had no role in study design data collection and analysis decision to publish orpreparation of the manuscript

Grant DisclosuresThe following grant information was disclosed by the authorsUniversidad Nacional Autoacutenoma de Meacutexico IA200814SEMARNAT-CONACyT 23459CONACyT research visiting fellowship 186372

Competing InterestsValeria Souza and Luis E Eguiarte are Academic Editors for PeerJ

Author Contributionsbull Silvia Pajares conceived and designed the experiments analyzed the data wrote thepaper prepared figures andor tables reviewed drafts of the paper


bull Ana E Escalante conceived and designed the experiments analyzed the data wrote thepaper reviewed drafts of the paper contributed reagentsmaterialsanalysis toolsbull Ana M Noguez conceived and designed the experiments performed the experimentsanalyzed the data wrote the paper reviewed drafts of the paperbull Felipe Garciacutea-Oliva Luis Enrique Eguiarte and Valeria Souza conceived and designedthe experiments contributed reagentsmaterialsanalysis toolsbull Celeste Martiacutenez-Piedragil performed the experimentsbull Silke S Cram performed the experiments contributed reagentsmaterialsanalysis tools

Field Study PermissionsThe following information was supplied relating to field study approvals (ie approvingbody and any reference numbers)

The field permit for biological sample collection was granted by the EnvironmentalCouncil in Mexico (SEMARNAT) to Valeria Souza Permit numbers 0659006 and0685507

Data AvailabilityThe following information was supplied regarding data availability

The raw data has been supplied as Data S1

Supplemental InformationSupplemental information for this article can be found online at httpdxdoiorg107717peerj2459supplemental-information

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Caruso T Chan Y Lacap DC LauMCY McKay CP Pointing SB 2011 Stochastic anddeterministic processes interact in the assembly of desert microbial communities ona global scale The ISME Journal 51406ndash1413 DOI 101038ismej201121

Castillo-Monroy AP Bowker MA Maestre FT Rodriacuteguez-Echeverriacutea S Martinez IBarraza-Zepeda CE Escolar C 2011 Relationships between biological soil crustsbacterial diversity and abundance and ecosystem functioning insights from asemi-arid Mediterranean environment Journal of Vegetation Science 22165ndash174DOI 101111j1654-1103201001236x

Castillo-Monroy AP Maestre FT Delgado-BaquerizoM Gallardo A 2010 Biologicalsoil crusts modulate nitrogen availability in semi-arid ecosystems insights from aMediterranean grassland Plant Soil 333(1ndash2)21ndash34DOI 101007s11104-009-0276-7

Cerritos R Eguiarte LE Avitia M Siefert J TravisanoM Rodriacuteguez-Verdugo ASouza V 2011 Diversity of culturable thermo-resistant aquatic bacteria alongan environmental gradient in Cuatro Cieacutenegas Coahuila Meacutexico Antonie vanLeeuwenhoek 99303ndash318 DOI 101007s10482-010-9490-9

Challenger A 1998Utilizacioacuten y conservacioacuten de los ecosistemas terrestres de MeacutexicoPasado presente y futuro Mexico CONABIO-Instituto de Biologiacutea-AgrupacioacutenSierra Madre

Chao A Gotelli NJ Hsieh TC Sander EL Ma KH Colwell RK Ellison AM 2014Rarefaction and extrapolation with Hill numbers a framework for sampling and


estimation in species diversity studies Ecological Monographs 8445ndash67DOI 10189013-01331

Churchland C Grayston SJ Bengtson P 2013 Spatial variability of soil fungal andbacterial abundance consequences for carbon turnover along a transition from aforested to clear-cut site Soil Biology and Biochemistry 635ndash13DOI 101016jsoilbio201303015

Cleveland C Liptzin D 2007 C N P stoichiometry in soil is there a lsquolsquoRedfield ratiorsquorsquo forthe microbial biomass Biogeochemistry 85235ndash252DOI 101007s10533-007-9132-0

Colwell RK 2005 EstimateS statistical estimation of species richness and shared speciesfrom samples Available at http viceroyeebuconnedu estimates

CoolenMJL Post E Davis CC Forney LJ 2005 Characterization of microbial com-munities found in the human vagina by analysis of terminal restriction fragmentlength polymorphisms of 16S rRNA genes Applied and Environmental Microbiology718729ndash8737 DOI 101128AEM71128729-87372005

Cross AF SchlesingerWH 1999 Plant regulation of soil nutrient distribution in thenorthern Chihuahuan Desert Plant Ecology 14511ndash25DOI 101023A1009865020145

Cross AF SchlesingerWH 2001 Biological and geochemical controls on phosphorusfractions in semiarid soils Biogeochemistry 52155ndash172DOI 101023A1006437504494

Culman SW Gauch HG Blackwood CB Thies JE 2008 Analysis of T-RFLP datausing analysis of variance and ordination methods a comparative study Journal ofMicrobiological Methods 7555ndash63 DOI 101016jmimet200804011

Evans RD Rimer R Sperry L Belnap J 2001 Exotic plant invasion alters nitrogendynamics in an arid grassland Ecological Applications 111301ndash1310DOI 1018901051-0761(2001)011[1301EPIAND]20CO2

Fierer N Jackson RB 2006 The diversity and biogeography of soil bacterial communi-ties Proceedings of the National Academy of Sciences of the United States of America103626ndash631 DOI 101073pnas0507535103

Garcia-Pichel F Loza V Marusenko Y Mateo P Potrafka RM 2013 Temperaturedrives the continental-scale distribution of key microbes in topsoil communitiesScience 3401574ndash1577 DOI 101126science1236404

Geyer KM Altrichter AE Van Horn DJ Takacs-Vesbach CD Gooseff MN BarrettJE 2013 Environmental controls over bacterial communities in polar desert soilsEcosphere 4(10)Article 127

Hartmann A Schmidt M Van Tuinen D Berg G 2009 Plant-driven selection ofmicrobes Plant Soil 321235ndash257 DOI 101007s11104-008-9814-y

HirobeM Ohte N Karasawa N Zhang GSWang LH Yoshikawa K 2001 Plant specieseffect on spatial patterns of soil properties in the Mu-us desert ecosystem InnerMongolia China Plant and Soil 234195ndash205 DOI 101023A1017943030924


HolbenWE Jansson JK Chelm BK Tiedje JM 1988 DNA probe method for thedetection of specific microorganisms in the soil bacterial community Applied andEnvironmental Microbiology 54703ndash711

Housman DC Yeager CM Darby BJ Sanford RL Kuske CR Neher DA Belnap J 2007Heterogeneity of soil nutrients and subsurface biota in a dryland ecosystem SoilBiology and Biochemistry 392138ndash2149 DOI 101016jsoilbio200703015

Huffman EWD 1977 Performance of a new carbon dioxide coulometerMicrochemicalJournal 22567ndash573 DOI 1010160026-265X(77)90128-X

IUSSWorking GroupWRB 2015World reference base for soil resources 2014 update2015 International soil classification system for naming soils and creating legendsfor soil maps World Soil Resources Reports No 106 FAO Rome

Kindt R Coe R 2005 Tree diversity analysis a manual and software for common statisticalmethods for ecological and biodiversity studies Nairobi World Agroforestry Centre(ICRAF) 207 p

Lane DJ 1991 16S23S rDNA sequencing Nucleic acid techniques In Stackebrandt EGoodfellow M eds Bacterial systematics New York John Wiley amp Sons 115ndash175

Lee CK Barbier BA Bottos EMMcDonald IR Cary SC 2012 The inter-valley soilcomparative survey the ecology of dry valley edaphic microbial communities TheISME Journal 61046ndash1057 DOI 101038ismej2011170

Legendre P Legendre L 1998Numerical ecology Amsterdam ElsevierLoacutepez-Lozano NE Eguiarte LE Bonilla-Rosso G Garciacutea-Oliva F Martiacutenez-Piedragil

C Rooks C Souza V 2012 Bacterial communities and the nitrogen cycle in thegypsum soils of Cuatro Cieacutenegas Basin Coahuila a Mars analogue Astrobiology12699ndash709 DOI 101089ast20120840

Lukow T Dunfield PF LiesackW 2000 Use of the T-RFLP technique to assess spatialand temporal changes in the bacterial community structure within an agriculturalsoil planted with transgenic and non-transgenic potato plants FEMS MicrobiologyEcology 32241ndash247 DOI 101016S0168-6496(00)00033-7

Maestre FT Delgado-BaquerizoM Jeffries TC Eldridge DJ Ochoa V Gozalo B QueroJL Garciacutea-GoacutemezM Gallardo A UlrichW Bowker MA Arredondo T Barraza-Zepeda C Bran D Florentino A Gaitaacuten J Gutieacuterrez JR Huber-Sannwald E JankjuMMau RL Miriti M Naseri K Ospina A Stavi I Wang DWoods NN Yuan XZaady E Singh BK 2015 Increasing aridity reduces soil microbial diversity andabundance in global drylands Proceedings of the National Academy of Sciences of theUnited States of America 11215684ndash15689 DOI 101073pnas1516684112

Maestre FT Escudero A Martiacutenez I Guerrero C Rubio A 2005 Does spatial patternmatter to ecosystem functioning Insights from biological soil crusts FunctionalEcology 19566ndash573 DOI 101111j1365-2435200501000x

Makhalanyane TP Valverde A Gunnigle E Frossard A Ramond JB Cowan DA2015Microbial ecology of hot desert edaphic systems FEMS Microbiology Reviews39203ndash221 DOI 101093femsrefuu011


Martiny J Eisen JA Penn K Allison SD Horner-devine MC 2011 Drivers of bacterialbeta-diversity depend on spatial scale Proceedings of the National Academy of Sciencesof the United States of America 1087850ndash7854 DOI 101073pnas1016308108

Murphy J Riley JP 1962 A modified single solution method for the determination ofphosphate in natural waters Analytica Chimica Acta 2731ndash36DOI 101016S0003-2670(00)88444-5

Noguez AM Arita HT Escalante AE Forney LJ Garciacutea-oliva F Souza V 2005Microbial macroecology highly structured prokaryotic soil assemblages in a tropicaldeciduous forest Global Ecology and Biogeography 14241ndash248DOI 101111j1466-822X200500156x

Noguez AM Escalante AE Forney LJ Nava-MendozaM Rosas I Souza V Garcia-Oliva F 2008 Soil aggregates in a tropical deciduous forest effects on C and Ndynamics and microbial communities as determined by t-RFLPs Biogeochemistry89209ndash220 DOI 101007s10533-008-9214-7

Oksanen J Blanchet FG Kindt R Legendre P OrsquoHara RB Simpson GL Solymos PStevens MHHWagner H 2012 vegan Community Ecology Package R packageversion 1 R package version 20ndash4

Perroni Y Garciacutea-Oliva F Tapia Y Souza V 2014 Relationship between soil P fractionsand microbial biomass in an oligotrophic grassland-desert scrub system EcologicalResearch 29(3)463ndash472 DOI 101007s11284-014-1138-1

RDevelopment Core Team 2011 R a language and environment for statistical comput-ing Vienna R Foundation for Statistical Computing Available at httpwwwR-projectorg DOI 101007978-3-540-74686-7

Reacutenyi A 1961 On measures of entropy and information Entropy 547547ndash561DOI 101021jp106846b

Schade JD Hobbie SE 2005 Spatial and temporal variation in islands of fertility in theSonoran Desert Biogeochemistry 73541ndash553 DOI 101007s10533-004-1718-1

SchlesingerWH Raikes JA Hartley AE Cross AF 1996 On the spatial pattern of soilnutrients in desert ecosystems Ecology 77364ndash374

Singh BK Munro S Potts JM Millard P 2007 Influence of grass species and soil type onrhizosphere microbial community structure in grassland soils Applied Soil Ecology36147ndash155 DOI 101016japsoil200701004

Strauss SL Day TA Garcia-Pichel F 2012 Nitrogen cycling in desert biological soilcrusts across biogeographic regions in the Southwestern United States Biogeochem-istry 108171ndash182 DOI 101007s10533-011-9587-x

StricklandMS Lauber C Fierer N BradfordMA 2009 Testing the functional signifi-cance of microbial community composition Ecology 90441ndash451

Tapia-Torres Y Loacutepez-Lozano NE Souza V Garciacutea-Oliva F 2015 Vegetation-soilsystem controls soil mechanisms for nitrogen transformations in an oligotrophicMexican desert Journal of Arid Environments 11462ndash69DOI 101016jjaridenv201411007


Thompson TL Zaady E Huancheng PWilson TB Martens DA 2006 Soil C and Npools in patchy shrublands of the Negev and Chihuahuan Deserts Soil Biology andBiochemistry 381943ndash1955 DOI 101016jsoilbio200601002

Titus JH Nowak RS Smith SD 2002 Soil resource heterogeneity in the Mojave DesertJournal of Arid Environments 52269ndash292 DOI 101006jare20021010

VosMWolf AB Jennings SJ Kowalchuk GA 2013Micro-scale determinants ofbacterial diversity in soil FEMS Microbiology Reviews 37936ndash954DOI 1011111574-697612023

Warnes GR 2012 gplots various R programming tools for plotting data Journal ofPhycology 33569ndash575 DOI 101111j0022-3646199700569x

Zeglin LH DahmCN Barrett JE Gooseff MN Fitpatrick SK Takacs-Vesbach CD2011 Bacterial community structure along moisture gradients in the parafluvialsediments of two ephemeral desert streamsMicrobial Ecology 61543ndash556DOI 101007s00248-010-9782-7

Zeglin LH Sinsabaugh RL Barrett JE Gooseff MN Takacs-Vesbach CD 2009Landscape distribution of microbial activity in the McMurdo Dry Valleys linkedbiotic processes hydrology and geochemistry in a cold desert ecosystem Ecosystems12562ndash573 DOI 101007s10021-009-9242-8

Zhang YM Chen J Wang LWang XQ Gu ZH 2007 The spatial distribution patternsof biological soil crusts in the Gurbantunggut Desert Northern Xinjiang ChinaJournal of Arid Environments 68599ndash610 DOI 101016jjaridenv200606012

Zhang J Xu X Lei J Li S 2013 Research on chemical characteristics of soil salt crustswith saline groundwater drip-irrigation in the Tarim Desert Highway ShelterbeltSpringerplus 2(Suppl 1)S5 DOI 1011862193-1801-2-S1-S5


INTRODUCTIONSpatial heterogeneity is an inherent feature of soils and has significant functionalimplications including the fact that different soil patches and aggregates can presentvariation in nutrient transformation rates (eg respiration mineralization nitrogenfixation) (Noguez et al 2008 Strickland et al 2009 Zeglin et al 2009) particularly whenthe activities and distribution of microorganisms are considered The scale at whichenvironmental variation is considered in association with microbial diversity varies greatlyfrom tens to thousands of kilometers to meters and even at the microscale (Vos et al2013) Depending on the spatial scale at which microbial diversity is studied different envi-ronmental parameters and ecological processes may be associated to the observed diversitydistribution (Martiny et al 2011) At large spatial scales (tens to thousands of kilometers)soil microbial community structure is correlated to edaphic variables such as soil pH(Fierer amp Jackson 2006) temperature (Garcia-Pichel et al 2013) and moisture content(Angel et al 2010) At smaller scales (tens of meters) plant communities have been shownto have a strong influence on soil microbial diversity through interactions within therhizosphere (Berg amp Smalla 2009Hartmann et al 2009 Ben-David et al 2011) Howeverlittle is known about the effects of small-scale habitat variation on the spatial patterns ofmicrobial diversity and its interactions with soil abiotic properties (Maestre et al 2005)

Arid soils have a particularly heterogeneous spatial distribution of abiotic properties(Schlesinger et al 1996) particularly in nutrient content Vegetation patches deemedlsquolsquofertility or resource islandsrsquorsquo are also scarce and sparsely found in arid environments(Cross amp Schlesinger 1999 Hirobe et al 2001 Schade amp Hobbie 2005) At the same timethere are large areas deprived of vegetation and severely limited in nutrients and water(Evans et al 2001 Belnap et al 2005) in which microbial communities often referredto as lsquolsquobiological soil crustsrsquorsquo or lsquolsquobiocrustsrsquorsquo are the main drivers of energy input andbiogeochemical processes (Titus Nowak amp Smith 2002 Belnap 2003 Maestre et al 2005Housman et al 2007 Castillo-Monroy et al 2010 Bachar Soares amp Gillor 2012) Biocrustscontribute actively to natural small-scale soil heterogeneity not only in terms of biologicaldiversity but also in relation to soil function including nutrient cycling and physicochemicalproperties associated with their spatial structure (Maestre et al 2005)

Given the tight connection between microbial activity and nutrient cycling it is reason-able to think thatmicrobial distribution in soilsmight be somehow correlatedwith nutrientscontent across space (eg more nutrients more microbial biomass and diversity) Despitethe idea of resource island formation in arid soils studies have shown that spatial distribu-tion of microorganisms and nutrients is not correlated in these ecosystems (Belnap et al2005 Housman et al 2007 Geyer et al 2013) Some of these studies indicate that for aridoligotrophic ecosystems physicochemical parameters associated with water availabilityare better correlated with microbial distribution (Geyer et al 2013) Thus althoughthere are some studies in arid ecosystems (Barrett et al 2006 Zeglin et al 2009 Lee et al2012 Geyer et al 2013) it is of interest to gain better knowledge on the factors influencingmicrobial diversity having direct consequences in soil fertility and ecosystem processes(eg soil mineralization and respiration rates) (Maestre et al 2005 Ben-David et al 2011)


















Ap= (niN )times100











A B C D












minus



minus



























= 059 plt 001)
























































































































Ap= (niN )times100











A B C D












minus



minus



























= 059 plt 001)
















































































































A B C D












minus



minus



























= 059 plt 001)










































































































minus



minus



























= 059 plt 001)































































































































= 059 plt 001)








































































































spatial heterogeneity of physicochemical properties ...1 instituto de ciencias del mar y...

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