the response of soil microbial communities to variation in ... · in the quantity and quality of...

Submitted 8 May 2017Accepted 18 October 2017Published 9 November 2017

Corresponding authorFelipe Garciacutea-Olivafgarciaciecounammx

Academic editorJack Stanford

Additional Information andDeclarations can be found onpage 21

DOI 107717peerj4007

Copyright2017 Montiel-Gonzaacutelez et al

Distributed underCreative Commons CC-BY 40

OPEN ACCESS

The response of soil microbialcommunities to variation in annualprecipitation depends on soil nutritionalstatus in an oligotrophic desertCristina Montiel-Gonzaacutelez1 Yunuen Tapia-Torres2 Valeria Souza3 andFelipe Garciacutea-Oliva1

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

2 Escuela Nacional de Estudios Superiores Unidad Morelia Universidad Nacional Autoacutenoma de MeacutexicoMorelia Michoacaacuten Mexico

3 Instituto de Ecologiacutea Universidad Nacional Autoacutenoma de Meacutexico Mexico

ABSTRACTBackground Soil microbial communities (SMC) play a central role in the structureand function of desert ecosystems However the high variability of annual precipitationcould results in the alteration of SMC and related biological processes depending on soilwater potential The nature of the physiological adjustments made by SMC in order toobtain energy and nutrients remains unclear under different soil resource availabilitiesin desert ecosystems In order to examine this dynamic the present study examinedthe effects of variation in annual precipitation on physiological adjustments by theSMC across two vegetation-soil systems of different soil organic matter input in anoligotrophic desert ecosystemMethods We collected soil samples in the Cuatro Cieacutenegas Basin (Mexico) under twovegetation covers rosetophylous scrub (RS) and grassland (G) that differ in terms ofquantity and quality of organic matter Collections were conducted during the years2011 2012 2013 and 2014 over which a noticeable variation in the annual precipitationoccurred The ecoenzymatic activity involved in the decomposition of organic matterand the concentration of dissolved available and microbial biomass nutrients weredetermined and compared between sites and yearsResults In 2011 we observed differences in bacterial taxonomic composition betweenthe two vegetation covers The lowest values of dissolved available and microbialnutrients in both cover types were found in 2012 The G soil showed higher valuesof dissolved and available nutrients in the wet years Significant positive correlationswere detected between precipitation and the ratios CmicNmic and CmicPmic in theRS soil and CmicPmic and NmicPmic in the G soil The slopes of the regression withCmic and Nmic were higher in the G soil and lower in the RS soil Moreover the SMCunder each vegetation cover were co-limited by different nutrients and responded tothe sum of water stress and nutrient limitationDiscussion Soil community within both sites (RS and G) may be vulnerable todrought However the community of the site with lower resources (RS) is welladapted to acquire P resources by ecoenzyme upregulation during years with adequateprecipitation suggesting that this community is resilient after drought occurs Under

How to cite this article Montiel-Gonzaacutelez et al (2017) The response of soil microbial communities to variation in annual precipitationdepends on soil nutritional status in an oligotrophic desert PeerJ 5e4007 DOI 107717peerj4007

the Global Climate Change scenarios for desert ecosystems that predict reducedannual precipitation and an increased intensity and frequency of torrential rains anddrought events the soil microbial communities of both sites could be vulnerable todrought through C and P co-limitation and reallocation of resources to physiologicalacclimatization strategies in order to survive

Subjects Biodiversity Ecology Ecosystem Science Microbiology Soil ScienceKeywords Nutrients Enzymes Chihuahuan desert Microbial physiological adjustmentsStoichiometry ratio Threshold elemental ratio

INTRODUCTIONIn desert ecosystems precipitation is highly variable among years and this variability hasincreased in recent years due to the effect of Global Climate Change (GCC) (Bell et al2014 IPCC 2013) The scenarios derived from GCC models for desert ecosystems predictreduced annual precipitation as well as increases in the annual precipitation variability bythe end of the 21st century including an increase in the frequency and intensity of bothtorrential rain and drought events (Holmgren et al 2006 IPCC 2013) The high variabilityof annual precipitation projected for desert ecosystems could alter biological processesdependent on soil water potential as is the case with the processes related to soil organicmatter (SOM) decomposition (DrsquoOdorico amp Bhattachan 2012 Fay et al 2008 Thomey etal 2011) For example enzymatic activity stimulated by rainfall in desert ecosystems mayresult inmost of the total annualmineralization that occurs in desert soils (Manzoni Schimelamp Porporato 2012) However when soil water potential decreases the metabolic activity ofmost soil microbial species is reduced and thus a decline in nutrient mineralization canoccur Additionally soil drying reduces enzymatic activity and microbial mobility whichreduces substrate supply for the decomposers (Henry 2013Manzoni Schimel amp Porporato2012) Likewise studies in a semiarid region in NewMexico (Cregger et al 2012) and in theChihuahuan Desert (Bell et al 2009 Bell et al 2014) showed that the high precipitationvariability significantly altered the structure of the soil microbial community mainly dueto a change in the fungalbacterial ratio and consequently altered microbial communityfunctional dynamics

Microbial communities play a central role in the structure and functioning of desertecosystems since they represent an important pool of soil C N and P Indeed it has beensuggested that the amount of N and P contained within the soil microorganism biomassis comparable to the N and P content within the plant biomass in desert ecosystems(Coleman ampWhitman 2005) Moreover microbial communities can help accelerate thetransformation ofmolecules containing C N and P by producing soil extracellular enzymes(ecoenzymes) (Sinsabaugh amp Follstad Shah 2012 Sinsabaugh Hill amp Shah 2009) that leadto the fragmentation depolymerization and mineralization of organic matter (Singh etal 2014) Microorganisms can only assimilate soluble organic compounds of a molecularweight lower than 1 kDa and must therefore break down or depolymerize most of theorganic matter molecules (where between 72 and 87 of the DOC in grassland soils is

Montiel-Gonzaacutelez et al (2017) PeerJ DOI 107717peerj4007 228

larger than 1 kDa) in order to access the nutrients and energy contained within the organicmolecules (Cregger et al 2012 Farrell et al 2014 Jones et al 2012) The microorganismsproduce hydrolytic or oxidative ecoenzymes that degrade organic matter producingassimilable dissolved organic nutrients that are rapidly immobilized within their biomass(Conant et al 2011 Sinsabaugh amp Follstad Shah 2012) Additionally in desert ecosystemsthe natural distribution of different vegetation types can produce spatial heterogeneityin the quantity and quality of organic matter (Austin et al 2004 Housman et al 2007)In these ecosystems the depolymerization process will therefore require the productionof different ecoenzymes since the organic matter under each vegetation type containsa particular combination of structurally simple and complex molecules that promotedifferences in the soil nutrient dynamics mediated by the microbial community (Conant etal 2011) However the complete organic matter decomposition process requires a chainof enzymatic reactions where each ecoenzyme acts on a different substrate and is producedby different microbial groups (Ekschmitt et al 2005) Additionally the soil microbialcommunities can exhibit functional redundancy in the ecoenzyme production (Allison ampMartiny 2008)

Soil microorganisms have developed mechanisms of physiological acclimatization tocope with precipitation variability (Schimel amp Schaeffer 2012) These mechanisms generatephysiological costs for the microbial community that derive from the need for highinvestments of energy (C) and nutrients (N and P) in order to survive (Classen et al2015 Schimel Balser amp Wallenstein 2007 Schimel amp Schaeffer 2012) This high demandfor energy (C) and nutrients (N and P) can be offset by reallocation of these resourcesgenerating a trade-off in which the microbial community invests C N and P in eithergrowth or survival (Evans amp Wallenstein 2012 Schimel Balser amp Wallenstein 2007) Someconsequences of such resource redirection are (1) a limited production of ecoenzymesfor nutrient acquisition (ie for SOM decomposition) (Burns et al 2013 Henry 2013Steinweg et al 2013) and (2) reduced growth of the microbial community (ie decreasedprotein synthesis) (Schimel Balser amp Wallenstein 2007) Resource reallocation increases thevulnerability of some microbial groups that produce a change in the structure and functionof the soil microbial community also affecting the energy flow (C) and nutrient dynamicsof N and P at the ecosystem level (Esch Lipson amp Cleland 2017 Evans amp Wallenstein2012 Thibault amp Brown 2008) This variability strongly affects microbial communitydevelopment in resource-limited environments because the adaptation rates of microbialspecies are constrained by the resource cost of physiological adjustment (Wallenstein ampHall 2012) Wallenstein amp Hall (2012) proposed that sites limited by nutrients are morevulnerable to annual rainfall variability because the microbial community must investenergy in nutrient acquisition and consequently reducing its capacity for adaptationrequired by fluctuation in water availability Sites with low resource availability could betherefore more vulnerable to annual precipitation variability

The Chihuahuan desert has been classified as one of the most biologically outstandinghabitats globally by theWorldWildlife Fund (Archer amp Predick 2008) The Cuatro CieacutenegasBasin (CCB) which is the study site of the present investigation is part of the Chihuahuandesert and is considered the most important wetland of Mexico for its high levels of


endemism and biodiversity (Souza et al 2011) Moreover the CCB has been listedas an ultra-oligotrophic site due to low P concentrations in the water and soil whichcan constitute a strong potential for P limitation of microbial growth (Elser et al 2005Tapia-Torres et al 2015a) A study in the CCB desert reported that in the same soil typewith different vegetation cover (grassland and desert scrub) differences in OM contentpromotes variation in DOC concentration which represents the main energy source forsoil microorganisms (Tapia-Torres et al 2015b) The higher DOC concentration undergrassland soil compared to desert scrub soil favored a higher microbial N immobilizationand a higher C availability therefore significantly reducing soil N losses (Tapia-Torres etal 2015b) Another study in the CCB that compared two sites with different soil moisturecontent showed that the site with the highest moisture content and concentration of DOCalso exhibited higher NH+4 microbial C and N concentrations and also presented higherdiversity richness and evenness of soil bacterial community compared to the dry site(Loacutepez-Lozano et al 2012) Both studies suggest that differences in DOC concentration(energy availability) and microbial community composition promoted different nutrientdynamics In the sites with organic matter providing lower DOC concentrations themicrobial communities may be co-limited by energy and nutrients and yet they mustinvest more energy in order to obtain the most limiting nutrients An indicator that helpsus understand how resources are reallocated by the microbial community to cope with thenutrient limitation is the combination of (1) the stoichiometry ratios of CNP in the soiland microbial biomass (Cleveland amp Liptzin 2007) and (2) the Threshold Elemental Ratio(TER) (Sinsabaugh amp Follstad Shah 2012 Tapia-Torres et al 2015a) which defines theelement ratio at which growth is affected by nutrient limitation (represented by N and P athigh CN or CP) and by energy limitation (represented by C at low CN or CP) (Frost etal 2006 Sterner amp Elser 2002) The combination of stoichiometry ratios and TER indicatehow resources are reallocated towards enzyme activity depending on the availability ofenergy (C) and nutrients (N and P) in the soil This microbial co-limitation between energyand nutrient acquisition was also found in CCB by comparing the TERCNand TERCP fromtwo sites with the same vegetation cover (grassland) but different soil moisture and DOCavailability values (Tapia-Torres et al 2015a) The microbial communities were co-limitedby C and N in the site with higher water and C availability (Churince) and were co-limitedby C and P in the site with lower water and C availability (Pozas Azules) In addition theseauthors argue that this limitation favors an elevated allocation of N-acquisition enzymesrelative to energyC enzymes in Churince while for Pozas Azules an elevated investmentin ecoenzymes of P acquisition is found (Tapia-Torres et al 2015a) These results supportthe notion that soil microbial communities can adjust their metabolism by allocatingmore resources (ie energy and production of ecoenzymes) to the accumulation of scarcernutrients and fewer resources to the acquisition of abundant nutrients The ratios ofCNP in microbial biomass are therefore constrained relative to nutrient (Cleveland ampLiptzin 2007) and energy availability These studies suggest that both vegetation and soilmoisture content may determine differences in (1) soil nutrient dynamics (2) the diversityof the soil microbial community and (3) the CNP ratios of the microbial biomass in thisecosystem


To date the physiological adjustments made by the soil microbial communities underdifferent soil resource availability in order to obtain energy and nutrients in desertecosystems with high precipitation variability remain unclear To elucidate this dynamicthe present study examined the effects of rainfall variation on the physiological adjustmentsmade in order to obtain energy and nutrients by the soil microbial community from twovegetation-soil systems with different soil organic matter inputs in an oligotrophic desertecosystem Our hypothesis is that in a site with high soil resources availability the soilmicrobial communities invest less energy in the acquisition of nutrients (ie ecoenzymaticproduction) favoring nutrient accumulations within the biomass (ie immobilization)Our predictions are (1) in a site that presents low soil nutrient availability (rosetophylousscrubmdashRS) the soil microbial community will invest more energy in the production ofecoenzymes in order to depolymerize and mineralize thus favoring nutrient availabilitywhile in a site with high soil nutrient availability (grasslandmdashG) the soil microbialcommunity will invest more energy in biomass growth and (2) in the site with greater soilresources availability (G) the microbial community will be less vulnerable to changes inprecipitation To test the hypothesis we collected soil samples in the CCB from sites undertwo vegetation covers (RS and G) that differ in terms of the quantity and quality of theorganic matter present Collections were conducted during years 2011 (February) 20122013 and 2014 (September) over which a noticeable variation in annual precipitation tookplace The ecoenzyme activity involved in the decomposition of organic matter as well asthe concentration of dissolved available and microbial biomass nutrient were determinedand compared between sites and years With the ecoenzymatic and biogeochemistry datawe calculated the TERCnutrient SEA the nutrient ratios and performed regressions betweenthe precipitation and the concentrations and ratios of C N and P in microbial biomass

MATERIAL AND METHODSStudy siteThe study was carried out in the Cuatro Cieacutenegas Basin (CCB 2645prime-2700primeN and 10148prime-10217primeW) in central northern Mexico within the Chihuahuan Desert The CCB has anarea of 150000 km2 with an elevation of 740 masl The climate is arid with an averageannual temperature of 21 C and 252 mm of annual rainfall which is concentrated duringthe summer months (httpsmncnagobmx) However in the last 30 years the annualprecipitation showed a high variability among years In this study the annual precipitationwas estimated as the amount of rain accumulated 9-months before the sampling monthThe precipitation data were obtained from meteorological station 5044 lsquolsquoCuatro Cienegasrsquorsquolocated at 2659prime0primeprimeN and 10104prime0primeprimeW (httpsmncnagobmx) Annual precipitation andthe average temperature of the sampling months varied strongly during the four studiedyears the year 2011 was the wettest year (348 mm and 25 C) 2012 was particularly dryand hot (89 mm and 28 C) and was followed by two wet years (217 mm and 230 mm for2013 and 2014 respectively) with lower temperatures (249 and 248 C for 2013 and 2014respectively)

Jurassic-era gypsum is the dominant parent material on the western side of the basin(McKee Jones amp Long 1990) According to theWRB classification (2007) the predominant


soil on the western side of the basin isGypsisol The main vegetation types are (1) grassland(G) dominated by Sporobolus airoides (Torr) Torr andAllenrolfea occidentalis (SWatson)Kuntze (2) microphyll scrub dominated by Jatropha dioica Cerv Larrea tridentate (DC)Cov and Fouqueria sp Kunth (Perroni Garciacutea-Oliva amp Souza 2014) and (3) rosetophylousscrub (RS) dominated by Dhasylirium cedrosanum Trel and Yucca treculeana Carrieacutere(Gonzaacutelez 2012)

SamplingMean air temperature for the sampling month (September) and annual rainfall data ineach studied year were obtained from the meteorological station lsquolsquoRancho Pozas AzulesrsquorsquoINIFAP Soil collection was carried out in Churince on the west side of the CCB whereGypsisol is the predominant soil type (Perroni et al 2014) The samples were taken fromtwo vegetation cover types rosetophylous scrub (RS) and grassland (G) during February(2011) and September (rainy of 2012 2013 and 2014) For each vegetation cover wesampled seven sites located at a distance of 140 m apart along a one km north-to-southtransect At each sampling site a 4 times 4 m plot was demarcated and five soil sampleswere taken from the first 15 cm of soil depth within the plot and mixed to produce onecompound sample per site A total of seven composite samples were therefore obtainedfrom each vegetation cover in each sampling year The soil samples were stored in blackplastic bags at 4 C until subsequent laboratory analysis

Moisture and pHSoil pH was measured in deionized water (soilsolution 12 wv) with a digital pH meter(CorningTM) A subsample of 100 g was oven-dried at 75 C to constant weight for soilmoisture determination using the gravimetric method

Biogeochemical analysesNutrient analysisAll Carbon (C) forms analyzed were determined with a Total Carbon Analyzer (UIC ModCM5012 Chicago USA) while nitrogen (N) and phosphorus (P) concentrations weredetermined by colorimetric analyses using a Bran Luebbe Auto Analyzer III (NorderstedtGermany) Microbial P and enzymatic activity were determined by colorimetric analysesusing a spectrophotometer Evolution 201 (Thermo Scientific Inc)

Total nutrientsPrior to analysis of total nutrient forms soil samples were dried and milled with a pestleand agate mortar Total C (TC) and inorganic C (IC) were determined by combustionand coulometric detection (Huffman 1977) Organic total C (OTC) was calculated asthe difference between TC and IC For total N (TN) and total P (TP) determination thesamples were digested in a mixture of concentrated H2SO4 H2O2 (30) and K2SO4 plusCuSO4 the latter acting as a catalyst at 360 C Nitrogen was determined by the macroKjeldahl method (Bremmer 1996) while P was determined by the molybdate colorimetricmethod following ascorbic acid reduction (Murphy amp Riley 1962)


Dissolved and available nutrients and those within the microbial biomassThe dissolved available and microbial nutrient forms were extracted from fresh fieldsoil samples Dissolved nutrients were extracted from 20 g of soil with deionized waterafter shaking for 45 min and then filtering through a Whatman No 42 and a 045 micromnitrocellulosemembrane (Jones amp Willett 2006) The filtratewas used to determine the totaldissolved C (TDC) as measured with an Auto Analyzer of carbon (TOC CM 5012) modulefor liquids (UIC-COULOMETRICS) Inorganic dissolved C (IDC) was determined in anacidificationmodule CM5130 One aliquot of the filtrate was used to determine ammonium(DNH+4 ) and dissolved inorganic P (DIP) in a deionized water extract Total dissolved Nand P (TDN and TDP respectively) were digested in a mixture of concentrated H2SO4H2O2 (30) at 250 C Nitrogen was determined by the macro Kjeldahl method (Bremmer1996) while P was determined by the molybdate colorimetric method following ascorbicacid reduction (Murphy amp Riley 1962) Dissolved organic C N and P (DOC DON andDOP respectively) values were calculated as the difference between the total dissolvedforms and the inorganic dissolved forms

Available inorganic nitrogen forms (NH+4 and NOminus3 ) were extracted from 10 g of soilwith 2M KCl followed by filtration through aWhatman No 1 paper filter and determinedcolorimetrically by the phenol-hypochlorite method (Technicon 1977) Available inorganicphosphorous (Pi) was extracted with 05 M NaHCO3 pH 85 (Tiessen amp Moir 2008)and determined colorimetrically using the molybdate-ascorbic acid method (Murphy ampRiley 1962)

Carbon (Cmic) and N (Nmic) concentrations within the microbial biomass weredetermined from 20 g of soil by the chloroform fumigation extraction method (VanceBrookes amp Jenkinson 1987) Fumigated and non-fumigated samples were incubated for24 h at 25 C and constant relative humidity Cmic and Nmic were extracted fromfumigated and non-fumigated samples with 05 MK2SO4 filtered through a 045 micromnitrocellulose membrane (Brookes Powlson amp Jenkinson 1984) Carbon concentrationwas measured from each extract as the total (TC) and inorganic (IC) carbon contentsusing the method described before The difference between TC and IC was used for Cmiccalculation To determine the Nmic concentration one aliquot of the filtrate extractedwas acid digested and determined as TN by Macro-Kjeldahl method (Brookes Powlson ampJenkinson 1984) Phosphorus within microbial biomass (Pmic) was extracted from 5 g ofsoil by the chloroform fumigation extraction and incubation method (Vance Brookes ampJenkinson 1987) Pmic was extracted using NaCO3 05M pH 85 and digested in a mixtureof H2SO4 11N and (NH4)2S2O8 at 50 with the latter acting as a catalyst at 120 C(Lajtha et al 1999) Pmic was determined colorimetrically by the molybdate-ascorbic acidmethod (Murphy amp Riley 1962) The values of Cmic Nmic and Pmic were calculated asthe difference between fumigated and non-fumigated samples using correction factors ofKEC 045 (Joergensen 1996) KEN 054 (Joergensen amp Mueller 1996) and KP 04 (Lajtha etal 1999) for Cmic Nmic and Pmic respectively Finally the values of Cmic Nmic andPmic were corrected to a dry soil basis


Molecular analysisBacterial composition analysis was performed on the samples from the wettest year (2011)We extractedDNA from each soil sample using themethodology described in Loacutepez-Lozanoet al (2013) and sent it to J Craig Venter Institute (JCVI) in order to construct a 16S libraryusing 454 ROCHE tag 50000 reads per site of 500 bp and primers 341F-926R Sequenceswere trimmed and chimeras eliminated using JCVI protocols Taxa were assigned usingBlast via JCVI pipeline these methods are detailed by Tanenbaum et al (2010)

Ecoenzyme activity analysesThe activities of six ecoenzymes (extracellular enzymes) involved in the cleavageof organic molecules with C N and P were measured β-14-glucosidase (BG)cellobiohydrolase (CBH) β-14-N-acetylglucosaminidase (NAG) polyphenol oxidase(PPO) phosphomonoesterase (PME) and phosphodiesterase (PDE) using assay techniquesreported by Tabatabai amp Bremner (1969) Eivazi amp Tabatabai (1977) Eivazi amp Tabatabai(1988) Verchot amp Borelli (2005) and Johannes amp Majcherczyk (2000)

For all ecoenzymes we used 2 g of fresh soil and 30 ml of modified universal buffer(MUB) at pH 9 for ecoenzyme extraction Three replicates and two control samples (soilextract with no substrate and pure MUB with substrate) were included per assay Allecoenzyme assays were incubated at 40 C the BG and CBH for 2 h NAG for 3 h PPO for25 h PME and PDE 125 h Following the incubation period the tubes were centrifugedat 10000 rpm for 2 min and 750 microl of supernatant was recovered

For all ecoenzymes with substrates containing p-nitrophenol (pNP) we diluted thesupernatant in 2ml of deionized water with 75microl of NaOH andmeasured the absorbance ofpNP liberated at 410 nm on an Evolution 201 spectrophotometer (Thermo Scientific Inc)For the PPOwe used 22prime-Azinobis [3-ethylbenzothiazoline-6-sulfonic acid]-diammoniumsalt (ABTS) as a substrate The resulting supernatant was measured directly at 410 nmEcoenzyme activities were expressed as nanomoles of pNP per gram of dry soil per hour(nmol pNP [g SDE]minus1 hminus1) for substrates containing p-nitrophenol (pNP) and O2 formedper gram of dry soil per hour (nmolO2 [g SDE]minus1 hminus1) for the PPO respectively Specificenzymatic activity was calculated using Eqs (1)ndash(3) (Chavez-Vergara et al 2014WaldropBalser amp Firestone 2000)

SEA micromol(mgCmich)=A(Cmictimes0001) (1)

SEA micromol(mgNmich)=B(Nmictimes0001) (2)

SEA micromol(mgPmich)=C(Pmictimes0001) (3)

where A is the enzymatic activity of BG or CBH or PPO B is the enzymatic activity ofNAG and C is the enzymatic activity of PME or PDE

Data analysisBiogeochemistry and ecoenzymatic analysisSoil biogeochemistry and ecoenzymatic data were subjected to a repeated measures analysisof variance (RMANOVA) (Von Ende 2001) Vegetation cover types (RS and G) wereconsidered as a between-subject factor and year (2012 2013 and 2014) and their interaction


were considered as within-subject factors When RMANOVA indicated significant factoreffects mean comparisons were performed with Tukeyrsquos multiple comparisons test (VonEnde 2001) Ecoenzyme activities were normalized to units per microg of available organiccarbon (OC) using the DOC data corresponding to each sample (Tapia-Torres et al2015a) Data were loge-transformed prior to regression analysis in order to conform to theconventions of stoichiometric analyses and to normalize variance (Sinsabaugh amp FollstadShah 2012 Sterner amp Elser 2002) After that relationships between ecoenzyme activitieswere calculated with a type II regression using SMATR (R Development Core Team 2007)

To detect the relationship between precipitation and nutrients immobilized bymicrobialbiomass we applied two simple regression analyses using the annual accumulatedprecipitation prior to the sampling date with (1) nutrient concentration within themicrobial biomass (Cmic Nmic and Pmic) and (2) the microbial biomass nutrient ratios(CmicNmic CmicPmic and NmicPmic) The data used in the regression analysescorresponded to the years 2011 2012 2013 and 2014

Stoichiometric analyses and threshold elemental ratioWe calculated the degree of soil community-level microbial CN and CP homeostasis bycalculating the slope of loge CNR (resources) versus loge CNB (microbial biomass) orthe slope of loge CPR versus loge CPB scatterplot (Sterner amp Elser 2002) Moreover wefollowed Sinsabaugh Hill amp Shah (2009) in order to calculate the TER for CN and CP torelate the measured ecoenzyme activity with Ecological Stoichiometry Theory (EST) andthe Metabolic Theory of Ecology (MTE) using Eqs (4) and (5)

TERCN= ((BGNAG)BCN)n0 (4)

TERCP= ((BGPME)BCP)p0 (5)

where TERCN and TERCP are the threshold ratios (dimensionless) BGNAG is theecoenzymatic activity ratio for β-14-glucosidase and β-14-N-acetylglucosaminidaseBGPME is the ecoenzymatic ratio for β-14-glucosidase and phosphomonoesterase BCNand BCP are the CN or CP ratios of the microbial biomass (respectively) and n0 and p0 arethe dimensionless normalization constants for N and P respectively These normalizationconstants p0 and n0 are the intercepts in the SMA regressions for loge (BG) vs loge (NAG)and loge (BG) vs loge (PME) respectively (Tapia-Torres et al 2015a) For a more detailedanalysis of the derivation of the equations see Sinsabaugh Hill amp Shah (2009)

RESULTSSoil moisture and pHRegardless of vegetation cover soil moisture was higher in 2013 and 2014 than in 2012while the G soil had higher soil moisture than the RS soil regardless of year (Tables 1 and2) In the driest year (2012) soil pH was higher than in the wetter years (2013 and 2014)with an exception in the G soil in 2014 (Tables 1 and 2) Soil pH correlated with annualprecipitation in both sites (R2

=minus085 and R2=minus061 for RS and G respectively) as well

as soil moisture correlated with annual precipitation (R2= 076 and R2

= 088 for RS andG respectively)


Table 1 Means and (standard errors) of soil nutrients and ratios in the rosetophylous scrub (RS) andgrassland (G) soils over three consecutive years (2012 2013 and 2014) in the Cuatro Cieacutenegas BasinCoahuila MexicoDifferent uppercase letters (A and B) indicate significantly different means (P lt 005)between vegetation cover types (rosetophylous scrub and grassland) within the same sampling year (20122013 and 2014) whereas different lowercase letters (a b and c) indicate significantly different means (P lt005) among sampling dates within the same site

Year

2012 2013 2014

RS G RS G RS G

Moisture () 127 (11)Bc 246 (25)Ab 246 (3)Bab 435 (13)Aa 164 (10)Bb 371 (71)Aa

pH 85 (006)Aa 83 (004)Ba 81 (003)Ab 81 (002)Ab 81 (002)Ab 81 (01)Aab

Dissolved organic nutrient concentrationDOC (microg gminus1) 9 (1)Ab 19 (4)Ac 23 (4)Ba 52 (1)Ab 28 (2)Ba 67 (4)Aa

DON (microg gminus1) 41 (05)Bb 70 (06)Ab 55 (05)Bab 108 (08)Aa 69 (03)Ba 78 (04)Aab

DOP (microg gminus1) 12 (02)Ab 03 (03)Ab 28 (02)Ba 51 (02)Aa 28 (02)Ba 53 (05)Aa

DOCDON 23 (06) 31 (06) 42 (06) 49 (06) 40 (06) 68 (06)DOCDOP 79 (36) 153 (36) 82 (13) 188 (13) 103 (12) 130 (11)DONDOP 36 (04) 68 (33) 20 (01) 21 (01) 26 (04) 15 (01)

Available nutrient concentrationNH+4 (microg gminus1) 28 (02)Ba 63 (05)Ac 36 (02)Ba 118 (11)Aa 27 (04)Ba 8 9 (02)Ab

NOminus3 (microg gminus1) 104 (14)Aa 67 (14)Ba 17 (03)Ab 32 (04)Aab 17 (01)Ab 10 (01)Ab

Pi (microg gminus1) 19 (02) 25 (02) 29 (04) 45 (04) 39 (06) 53 (06)

NotesDOC dissolved organic carbon DON dissolved organic nitrogen DOP dissolved organic phosphorus NH+4 availableammonium NOminus3 available nitrate Pi Available inorganic phosphorus

Dissolved organic nutrients and available nutrientsFor the two vegetation covers the lowest values of DOCDON andDOPwere found in 2012(Table 1) In this year the RS and the G soils had similar DOC and DOP concentrationswhile the G soil had a higher DON concentration than the scrub soil moreover the G soilhad higher dissolved organic nutrient concentrations than in the RS soil in both 2013 and2014 (Tables 1 and 2) Consequently the DOCDON ratio was lower in 2012 than in theother two years (2013 and 2014) and the RS soil had lower values than the G soils (35 and49 respectively) the RS soil also had lower DOCDOP ratios than the G soil (9 and 16respectively)

The year trends of available NH+4 concentration differed between the two vegetationcover types Available NH+4 concentration was similar over the three years in the RSsoil while G soil samples from 2012 and 2013 had the lowest and the highest NH+4concentrations respectively (Tables 1 and 2) However the G soil had higher values thanthe RS soil in the three studied years In contrast the NOminus3 concentration was higher inthe samples collected in 2012 than those of the other two years while the RS soil hadhigher NOminus3 concentration than the G soil only in the 2012 samples (Tables 1 and 2) The2012 samples had lower available P concentration than in those collected in the other twoyears and the G samples had 40 higher available P concentration than the RS samplesregardless of the sampling year (41 and 29 microg P gminus1 respectively)


Table 2 F-ratios and significant levels of the repeated-measures ANOVA for soil variables quantifiedin the rosetophylous scrub and grassland soils over three consecutive years (2012 2013 and 2014) inCuatro Cieacutenegas Basin Coahuila Mexico

Parameters Source of variation

Between subjects Within subjects

Vegetation cover Year Vegetation cover X Year

Moisture 907 (lt00001) 491 (lt00001) 27 (008)pH 73 (002) 280 (lt00001) 54 (001)

Dissolved nutrientsDOC 1021 (lt00001) 792 (lt00001) 145 (lt00001)DON 385 (lt00001) 251 (lt00001) 38 (003)DOP 141 (0002) 550 (lt00001) 132 (00001)DOCDON 64 (002) 116 (00002) 20 (01)DOCDOP 91 (001) 05 (06) 18 (02)DONDOP 18 (02) 30 (007) 12 (03)

Available nutrientsNH+4 2368 (lt00001) 190 (lt0000) 105 (00005)NOminus3 18 (01) 47 (lt00001) 54 (001)Pi 142 (0003) 129 (0002) 11 (03)


Microbial nutrients and ecoenzymatic activitiesThe highest and the lowest values of Cmic and Nmic were found in 2014 and 2012respectively and Cmic values of the G soil samples were 39 higher than in the RS soilsamples regardless of sampling year (254 and 184 microg C gminus1 respectively) This was alsothe case with the Nmic and Pmic concentrations with an exception in the 2012 samples(Tables 3 and 4) In contrast Pmic concentrations presented no differences among yearswithin the RS samples while the 2012 samples had lower Pmic values than was the casein the other two years within the G samples (Tables 3 and 4) The 2014 samples hadlower CmicNmic than the other two years regardless of vegetation cover type (2012 and2013) while the lowest and the highest CmicPmic and NmicPmic ratios were found in2012 and 2014 respectively (Tables 3 and 4) The RS soil samples had higher CmicPmicand NmicPmic ratios than in the G soil samples with an exception in the 2012 samples(Tables 3 and 4)

Significant positive correlations were observed between precipitation and immobilizednutrients within the microbial biomass (Cmic Nmic Pmic) in both soils Moreoversignificant positive correlations were detected between precipitation and the CmicNmicand CmicPmic ratios in the RS soil and the CmicPmic and NmicPmic ratios in the Gsoil The slopes of the regression with Cmic and Nmic were higher in the G soil and lowerin the RS soil (Figs 1 2 and Table S1)

The specificenzymatic activity of BG under both vegetation cover types was lower inthe wet (2014) than in the dry year (2012 Fig 3A Table 4) while that of CBH in the dryyear was lower than in both wet years (2013 and 2014) in both vegetation covers (Fig 3B)


Table 3 Means and (standard errors) of microbial biomass nutrients andmicrobial nutrient ratios inthe rosetophylous scrub (RS) and the grassland (G) soils over three consecutive years (2012 2013 and2014) in the Cuatro Cieacutenegas Basin Coahuila MexicoDifferent uppercase letter (A and B) indicate thatmeans differ significantly (P lt 005) between vegetation cover types (RS and G) within the same samplingyear (2012 2013 and 2014) whereas different lowercase letters (a b and c) indicate significantly differentmeans (P lt 005) among sampling dates within the same site

Year

2012 2013 2014

RS G RS G RS G

Nutrients concentration within microbial biomassCmic (microg gminus1) 68 (12) 93 (12) 191 (20) 289 (20) 287 (16) 379 (1)Nmic (microg gminus1) 42 (06)Ab 64 (06)Ac 100 (10)Bb 220 (28)Ab 422 (17)Ba 598 (19)Aa

Pmic (microg gminus1) 23 (06)Aa 25 (13)Ab 24 (01)Ba 64 (06)Aa 22 (002)Ba 61 (04)Aa

CmicNmic 20 (4) 15 (4) 20 (2) 14 (2) 7 (03) 6 (03)CmicPmic 17 (5)Ac 9 (5)Ac 79 (6)Ab 48 (6)Bb 127 (02)Aa 63 (4)Ba

NmicPmic 09 (03)Ac 04 (02)Ac 42 (04)Ab 36 (06)Ab 187 (08)Aa 101 (07)Ba

NotesCmic microbial carbon Nmic microbial nitrogen Pmic microbial phosphorus

Table 4 F-ratios and significant levels of the repeated measures ANOVA for microbial nutrient con-centration microbial nutrient ratios and specific enzymatic activity quantified in the rosetophylousscrub (RS) and the grassland (G) soils over three consecutive years (2012 2013 and 2014) in CuatroCieacutenegas Basin Coahuila Mexico


Between subject Within subjects


Dissolved nutrientsCmic 621 (lt00001) 933 (lt00001) 23 (011)Nmic 487 (lt00001) 484 (lt00001) 129 (00001)Pmic 246 (00003) 58 (0008) 57 (0009)CmicNmic 40 (007) 123 (00002) 07 (05)CmicPmic 107 (lt00001) 92 (lt00001) 11 (00005)NmicPmic 42 (lt00001) 316 (lt00001) 34 (lt00001)

Specific enzymatic activityBG 12 (028) 228 (lt00001) 11 (033)CBH 3 (01) 99 (lt00001) 02 (07)NAG 81 (001) 52 (lt00001) 108 (lt00001)PPO 88 (0011) 34 (lt00001) 4 (003)PME 137 (lt00001) 444 (lt00001) 80 (lt00001)PDE 67 (lt00001) 232 (lt00001) 19 (lt00001)

NotesCmic microbial carbon Nmic microbial nitrogen Pmic microbial phosphorus BG β-14-glucosidase CBH cellobio-hydrolase NAG β-14-N-acetylglucosaminidase PPO polyphenol oxidase PME phosphomonoesterase PDE phospho-diesterase


60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 3600

10

20

30

40

50

60

70

G N

mic

microg

g

y = 01476x + 05771

Rsup2 = 0429

p= 00001

D)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

Precipitation (mm)

-1

0

1

2

3

4

5

6

7

8

9

10

RS

Pm

ic micro

g

g

y = 00077x + 11766

Rsup2 = 02933

p=00029

E)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

Precipitation (mm)

-1

0

1

2

3

4

5

6

7

8

9

10G

Pm

ic micro

g

g

y = 00125x + 24839

Rsup2 = 02339

p= 00091

F)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-200

0

200

400

600

800

1000

1200

RS

Cm

ic micro

g

g

y = 29606x - 29377

Rsup2 = 07664

plt0005

A)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 3600

200

400

600

800

1000

1200

G C

mic

microg

g

y = 34031x -3015226

R2=0831532

plt 005

B)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 3600

10

20

30

40

50

60

70

RS

Nm

ic micro

g

g

y = 0074x + 33776

Rsup2 = 01995

p=00172

C)

Figure 1 Simple linear regressions between annual accumulated precipitation before the samplingdate for four years and nutrients immobilized by microbial biomass for RS soil and G soil The dottedline represents the standard deviation at 095

Full-size DOI 107717peerj4007fig-1

The specific enzymatic activity of the PPO in the scrub soil did not differ among yearswhile the dry year (2012) had lower values than the wet years (2013 and 2014) in the G soil(Fig 3C and Table 4) Furthermore the G soil had higher specific PPO enzimatic activitythan the RS soil in the wet year (2014) In contrast the wet year (2014) had the lowestNAG specific enzymatic activity under both vegetation cover types and the RS soil hadlower values only in the dry year (2012 Fig 3D) The specific enzymatic activity of


60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

Precipitation (mm)

-2

0

2

4

6

8

10

12

14G

Nm

icP

mic

y = 0028x - 07109

Rsup2 = 04646

plt 0005

F)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

Precipitation (mm)

-2

0

2

4

6

8

10

12

14

16

18

20

22

24

RS

Nm

icP

mic

y = 00219x +25719

R2= 0083543

p= 0135

E)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-20

0

20

40

60

80

100

120

RS

Cm

icN

mic

y = 01009x + 16994

Rsup2 = 02216

P=00115

A)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 3604

6

8

10

12

14

16

18

20

22

24

26

28

30

32

34

G C

mic

Nm

ic

y = 00295x + 84326

Rsup2 = 01305

p= 0058

B)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-50

0

50

100

150

200

250

300

350

RS

Cm

icP

mic

y = 07078x - 47985

Rsup2 = 08131

plt0005

C)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-20

0

20

40

60

80

100

120

140

160

180

200

220

240

260

G C

mic

Pm

ic

y = 06341x - 63665

Rsup2 = 08144

plt0005

D)

Figure 2 Simple linear regressions between the annual accumulated precipitation before the samplingdate for four years and ratios of nutrients immobilized by microbial biomass for RS soil and G soil


PME and PDE was similar and the lowest values of specific enzymatic activity were in thedriest year (2012) In the two wet years the RS soil presented higher specific activities thanthe G soil (Figs 3E and 3F)

Soil bacterial compositionEven at 97 similarity a very high diversity was found encompassing all the knownphyla of bacteria but a very low diversity and abundance of Archaea A total of 46898sequences were obtained for the RS soil and 9979 for the G soil comprising 24 phyla We


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Figure 3 Specific enzymatic activity (A) β-14-glucosidase (BG) (B) cellobiohydrolase (CBH) (C)polyphenol oxidase (PPO) (D) β-14-N-acetylglucosaminidase (NAG) (E) phosphomonoesterase (PME)and (F) phosphodiesterase (PDE) in the rosetophylous scrub (RS) and grassland (G) soils over three con-secutive years (2012 2013 and 2014) in the Cuatro Cieacutenegas Basin Coahuila Mexico Different uppercaseletters (A and B) indicate significantly different means (P lt 005) between vegetation cover types (RS andG) within the same sampling year (2012 2013 and 2014) whereas different lowercase letters (a b and c)vertically indicate significantly different means (P lt 005) among sampling dates within the same site



0

10

20

30

40

50

60

70

80

90

100

Rosetophylous scrub Grassland

S

eq

uen

ces

Unclassified

WS3

Verrucomicrobia

TM7

Synergistetes

Spirochaetes

Proteobacteria

Planctomycetes

OP11

OP10

OD1

Nitrospira

Gemmatimonadetes

Fusobacteria

Firmicutes

Deinococcus-Thermus

Deferribacteres

Cyanobacteria

Chloroflexi

Chlorob

Chlamydiae

Bacteroidetes

BRC1

Actinobacterias

Acidobacteria

Figure 4 Taxonomic distribution of sequences obtained from Pyrosequencing of 16S rRNA tags ofrosetophylous scrub and grassland soils during a wet year (2010)


observed a high number of unclassified bacteria 26 for the RS soil and 20 for the Gsoil (Fig 4) In the two vegetation cover types the Proteobacteria was the most abundantbacterial phylum accounting for 20 in the RS soil and 30 in the G soil SimilarlyActinobacteria was the second most dominant phylum in the RS soil and in the G soilwith an abundance of 14 in both soils Interestingly the Cyanobacteria was the thirdmost dominant phylum with 13 of abundance both soils suggesting the importance ofthe desert crust in both sites Other important phyla observed were Chloroflexi (10)Bacteroidetes (5) Plantomycetes (4) Firmicutes (4) Nitrospira (1 in the RS and05 in the G soils) and Acidobacteria (6 in RS and 08 in G Fig 4)

Ecoenzymatic stoichiometry homeostasis and threshold elementalratiosIn all of the model II regressions analyzed there were no differences found in slopesbetween soils of the two vegetation cover types within sampling years (Figs S1 and S2)To test the strength of stoichiometric homeostasis we analyzed for associations betweenmicrobial biomass elemental ratios and those in the soil resources (Tapia-Torres et al2015a) In both soil vegetation cover types the relationships between log CNR and logCNB and between log CPR and log CPB did not differ from zero (pgt 005) regardlessof year (Figs S1 and S2) indicating strong community-level elemental homeostasis in thesoil of both sites


0

20

40

60

80

100

120

140

2012 2013 2014

TE

R C

P

Ab

Aab

Aa

Bb

Aa

Ba

B)

0

5

10

15

20

25

30

35

2012 2013 2014

TE

R C

N


Aa

Aa

AaAa

AbAb

A)

Figure 5 Threshold Elemental Ratio CN and CP (A and B respectively) of the soil microbial commu-nity over three consecutive years (2012 2013 and 2014)


Moreover we used the parameters generated from the type II regressions using enzymaticdata and microbial CNP stoichiometric values to estimate TERCN and TERCP values Thelowest TERCN values were observed in 2014 (wet year) but no differences were observedbetween 2012 and 2013 or even between vegetation cover types (RS and G) among studyyears (Fig 5) The opposite was found for TERCP where we obtained the lowest value inthe dry year (2012) but only in the RS soil For the dry year (2012) no differences wereobserved between vegetation cover types while we observed lower TERCP values in the Gsoil than in the RS soil for the wet years (2013 and 2014 Fig 5)


DISCUSSIONReallocation of resources by the soil microbial communityOur first prediction that the soil microbial community invests more energy in theproduction of ecoenzymes to acquire nutrients in sites of low resource availability suchas the RS soil was confirmed We observed that the RS soil showed a lower concentrationof available P than the G soil in the three years studied and consequently the RS soilmicrobial community invested more energy in the acquisition of P (increased enzymaticactivity of phosphomonoesterase and phosphodiesterase) than the G soil microbialcommunity only during the two wet years In contrast the G soil had higher CmicNmic and Pmic concentrations and lower enzymatic activity of phosphomonoesteraseand phosphodiesterase than the RS soil during both wet years which also supports ourprediction (Fig 1 and Table 3) These results suggest that the microbial communityin the RS soil with lower resource availability must reduce growth as a result of (1)the physiological cost associated with a low reallocation to P-rich ribosomal RNA assuggested by the growth rate hypothesis (GRH) (Sinsabaugh amp Follstad Shah 2012 Sterneramp Elser 2002 Zechmeister-Boltenstern et al 2015) and (2) the required investment ofenergy towards the acquisition of P in order to produce ecoenzymes (Evans amp Wallenstein2012 Schimel Balser amp Wallenstein 2007 Wallenstein amp Hall 2012) The microbial CNPratio was greater in the RS soil (127191) than in the G soil (63101) suggesting that themicrobial community in the former site is more P-constrained (Cleveland amp Liptzin 2007)The studied soils are characterized by low P availability and a high capacity for P occlusionwithin inorganic molecules mainly by Ca-bound (Perroni et al 2014) Therefore themain source of available P is mineralization of organic P mediated by phosphatase activity(Waring Weintraub amp Sinsabaugh 2014) Among organic P molecules phosphodiesterforms are the preferred substrate in P-limited ecosystems (Karl 2014 Tapia-Torres et al2016) although phosphomonoester forms may also be an important source of availableP in most soils (Turner Mahieu amp Condron 2003) In our study sites phosphodiesteraseactivity was almost ten times higher than that of phosphomonoesterase mainly in the scrubsoil suggesting mineralization of phosphodiesters as the main source of soil available PSeveral bacteria isolates from CCB soils prefer to grow in DNA as a P source associatedwith phosphodiesterase activity (Tapia-Torres et al 2016) We suggest that the main Psource in sites with low nutrient availability such as the RS soil is recycling of the organicmolecules that are the product of cellular lysis

However the G soil had higher enzymatic activity of polyphenol oxidase in the wet year2014 thanwas the case in the RS soil This result is consistent with other studies (Sinsabaugh2010 Sinsabaugh amp Follstad Shah 2011) which have reported that polyphenol oxidaseactivity does not present the same behavior as the β-14-glucosidase and other hydrolasesthat degrade labile C Microbial community size begins to be limited by the availabilityof labile C which produces a change in the microbial community composition towardsmicrobial guilds with lower growth rates (low concentration of Cmic) but with the capacityto produce polyphenol oxidase to break down structurally complex molecules and obtainC (Moorhead amp Sinsabaugh 2006) This situation is comparable to the conditions of the G


soil in the wet year 2014 where the microbial community was required to cleave lignin inorder to maintain its growth rate

Furthermore the differences in soil nutrient dynamics between both sites can be stronglyaffected by soil microbial composition While analyses of soil bacterial composition wereonly determined for 2011 this year presented the highest soil water availability andalso showed higher concentrations of Cmic Nmic and Pmic than in the other studiedyears Several studies (Nemergut et al 2010 Philippot et al 2009) have reported thatheterotrophic decomposition depends on the relative abundance of specific taxa becausedifferent species process organicmatter at different rates even under similar soil conditionsThe G soil had a higher proportion of Proteobacterias Actinobacterias and Bacteroidetesthan the RS soil and some species of these taxa have the capacity to produce β-glucosidase(BG) (Moreno et al 2013) cellobiohydrolase (CBH) poliphenoloxidase (PPO) (Kirk ampFarrell 1987) glucanases and glycosidases (Xie et al 2007) which act to cleaveCmolecules

In contrast the scrub soil had higher proportion of Acidobaterias and Firmicutesincluding species with the capacity for producing enzymes for P mineralization (Koch etal 2008 Tan et al 2013) Moreover the acidobacterias of the RS soil could contribute tothe release of unavailable P through organic acid release (Tan et al 2013) and togetherwith Firmicutes can mineralize P via the production of phosphatases as has been observedin isolates of acidobacterias from substrates with low C concentrations (Koch et al 2008Tan et al 2013) Chloroflexi was present in a higher proportion in the RS than in the Gsoil but both soils had a similar proportion of Cyanobacteria suggesting that the amountof microbial desert crust is similar in both sites Both phyla are facultative autotrophicbacteria (Smith 1983) and therefore have the capacity to fix atmospheric C and to produceecoenzymes for depolymerization and mineralization of C (Berg et al 2010 Mitsui et al1986 Smith 1983) The Cyanobacteria also have the capacity to fix atmospheric N Fixationin the microbial biomass of C and N by these taxa could represent an important inputof both nutrients to the soil (Mitsui et al 1986 Smith 1983) Wallenstein amp Hall (2012)proposed that sites limited by nutrients are more vulnerable to rainfall variability becausethe microbial community must invest energy in nutrient acquisition thus reducing itscapacity for adaptation required by fluctuation in water availability We proposed thatsites with low resources availability such as the RS soil could be thus more vulnerable toannual precipitation variability

Resilience in the face of precipitation changesOur second prediction that the microbial community will be more vulnerable to variabilityin precipitation in the site with lower soil resources (RS) was not confirmed because thesoil community was resilient to soil P coinstrains by ecoenzyme upregulation during timesof adequate moisture In both vegetation cover types nutrient availability increased withincreased precipitation The correlation between precipitation and the Cmic Nmic andPmic indicate that a higher amount of rainfall favored the microbial immobilizationof these nutrients under both vegetation cover types Nevertheless compared to the RSsoil the G soil showed steeper slopes in regressions between the precipitation and theconcentrations of Nmic and ratios of CmicPmic and NmicPmic (Table S1) suggesting


that the microbial community of the grassland soil has the ability to immobilize moreN within its microbial biomass and more rapidly than the microbial community of theRS soil Positive correlations between Cmic and rainfall have been reported for an oakforest (Baldrian et al 2010) and a semiarid grassland (Zhou et al 2013) but a correlationbetween precipitation with Nmic and Pmic concentrations has not hitherto been reportedfor natural ecosystems

Furthermore in the soil community homeostasis analyses the relationships betweenlog CNR and log CNB and between log CPR and log CPB in the G and the RS soilshad slopes that did not differ significantly from zero (Figs S1 and S2) suggesting that thesoil microbial communities adjust physiologically (Sinsabaugh amp Follstad Shah 2012) toprocessing low N and P resources in order to cope with the nutrient limitation particularlyin dry years Our data also suggest that these physiological adjustments occurred differentlyin the soil microbial communities of the two vegetation covers and was related to bothprecipitation quantity and nutrient availability

Our results show how values of TERCN and TERCP may shift with respect to variationin annual rainfall and different vegetation cover The estimated TERCN was lower in thewet year for both sites indicating greater sensitivity to N limitation due to the rapid growthof the microbial community produced by the water availability For TERCP we observedsite-specific differences The TERCP was higher in the RS soil than in the G soil for 2013and 2014 indicating a greater sensitivity of the microbial community to P limitation inthe G soil However in order to determine the nutritional limitations of the microbialcommunity we also compared the estimated TER values and the CN or CP ratios of theorganic matter If the CN or CP ratio of the organic matter being consumed is greaterthan the TER for that element this would suggest nutrient limitation (Sterner amp Elser2002) We observed P limitation in both soils regardless of year (CP gt TERCP plt 005)and N limitation in the G soil in the wet year (CN= 113 and TERCN= 6 p= 0002) Ourresults for the dry year (2012) showed that the ecoenzymatic activities associated with Cand P acquisition were lowest in the RS and G soils Values for TERCN and TERCP weresimilar between the RS and G soils suggesting that both sites may be vulnerable to droughtHowever with the increase of the annual precipitation (years 2013 and 2014) the G soilmicrobial community requires more P and N to meet its metabolic demands and it makesmetabolic adjustments in order to maintain its growth which makes it more susceptible orsensitive to resource limitation Similarly increased ecoenzyme activities associated withP acquisition and elevated TERCP values when the water is not limiting (2013 and 2014)suggest that the RS soil microbial community is well adapted to acquire P resources viaecoenzyme upregulation post drought

We suggested that under the scenario proposed by Global Climate Change modelsfor desert ecosystems that predict reduced annual precipitation and increased rainfallvariability the microbial community from both sites could be vulnerable to droughtevents but the RS soil microbial communities can make adjustments in order to obtainnutrients in wet years suggesting that this community is resilient post drought


CONCLUSIONSoil communities of both sites (RS and G) may be vulnerable to drought However thecommunity at the site with lower resources (RS) may have evolved adaptations such asrapid ecoenzymatic upregulation under chronic P limitation This adaptation confersgreater resilience within the community to respond to precipitation events post droughtUnder the Global Climate Change scenarios for desert ecosystems that predict reducedannual precipitation and an increased intensity and frequency of torrential rains anddrought events soil microbial communities within both sites could be vulnerable todrought through the combination of C and P co-limitation and reallocation of energy andnutrient resources to physiological acclimatization strategies in order to survive

ACKNOWLEDGEMENTSThis paper is presented by Cristina Montiel-Gonzaacutelez as partial fulfillment of a doctoraldegree at the lsquolsquoPrograma de Posgrado en Ciencias Bioloacutegicas UNAMrsquorsquo We thank RodrigoVelaacutezquez-Duraacuten for his assistance during chemical analyses and Angel Bravo-Monzoacuten forhis helpful comments on earlier versions of this manuscript We thank two anonymousreviewers for comments on a draft of the manuscript

ADDITIONAL INFORMATION AND DECLARATIONS

FundingThis work was financed by the Universidad Nacional Autoacutenoma de Meacutexico (PAPIITDGAPA-UNAM grant Anaacutelisis de la vulnerabilidad de la dinaacutemica de nutrientes enun ecosistema aacuterido de Meacutexico IN204013) and an Alianza WWF-FCS grant to ValeriaSouza The lsquolsquoPosgrado en Ciencias Bioloacutegicasrsquorsquo and the lsquolsquoConsejo Nacional de Cienciay Tecnologiacutearsquorsquo provided C Montiel-Gonzaacutelez a scholarship during her doctoral studies(CONACyT 332733) The funders had no role in study design data collection and analysisdecision to publish or preparation of the manuscript

Grant DisclosuresThe following grant information was disclosed by the authorsUniversidad Nacional Autoacutenoma de Meacutexico IN204013Alianza WWF-FCSPosgrado en Ciencias BioloacutegicasConsejo Nacional de Ciencia y Tecnologiacutea 332733

Competing InterestsValeria Souza is an Academic Editor for PeerJ

Author Contributionsbull Cristina Montiel-Gonzaacutelez conceived and designed the experiments performed theexperiments analyzed the data wrote the paper prepared figures andor tablesbull Yunuen Tapia-Torres analyzed the data wrote the paper


bull Valeria Souza contributed reagentsmaterialsanalysis tools reviewed drafts of the paperbull FelipeGarciacutea-Oliva conceived anddesigned the experiments performed the experimentsanalyzed the data contributed reagentsmaterialsanalysis tools wrote the paper

Supplemental InformationSupplemental information for this article can be found online at httpdxdoiorg107717peerj4007supplemental-information

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Archer SR Predick KI 2008 Climate change and ecosystems of the Southwestern UnitedStates Rangelands 3023ndash28

Austin AT Yahdjian L Stark JM Belnap J Porporato A Norton U Ravetta DASchaeffer SM 2004Water pulses and biogeochemical cycles in arid and semiaridecosystems Oecologia 141221ndash235 DOI 101007s00442-004-1519-1

Baldrian P Merhautovaacute V PetraacutenkovaacuteM Cajthaml T Šnajdr J 2010 Distri-bution of microbial biomass and activity of extracellular enzymes in a hard-wood forest soil reflect soil moisture content Applied Soil Ecology 46177ndash182DOI 101016japsoil201008013

Bell CW Acosta-Martinez V McIntyre NE Cox S Tissue DT Zak JC 2009 Linkingmicrobial community structure and function to seasonal differences in soil moistureand temperature in a Chihuahuan Desert grasslandMicrobial Ecology 58827ndash842DOI 101007s00248-009-9529-5

Bell CW Tissue DT LoikMEWallensteinMD Acosta-Martinez V Erickson RA ZakJC 2014 Soil microbial and nutrient responses to 7 years of seasonally altered pre-cipitation in a Chihuahuan Desert grassland Global Change Biology 201657ndash1673DOI 101111gcb12418

Berg IA Kockelkorn D Ramos-VeraWH Say RF Zarzycki J Huumlgler M Alber BEFuchs G 2010 Autotrophic carbon fixation in archaea Nature Reviews Microbiology8447ndash460 DOI 101038nrmicro2365

Bremmer JM 1996 Nitrogen-total In Sparks D Page AL Helmke PA LoerppertRGH Soltanpour PN Tabatabai MA Jhonson CT Sumner ME edsMethods ofsoil analysis part 3 chemical analysis Madison Soil Sicence Society of American andAmerican Society of Agronomy 1085ndash1121

Brookes PC Powlson DS Jenkinson DS 1984 Phosphorus in the soil microbialbiomass Soil Biology and Biochemistry 16169ndash175DOI 1010160038-0717(84)90108-1

Burns RG De Forest JL Marxsen J Sinsabaugh RL Stromberger MEWallensteinMDWeintraubMN Zoppini A 2013 Soil enzymes in a changing environment currentknowledge and future directions Soil Biology and Biochemistry 58216ndash234


Chavez-Vergara B Merino A Vaacutezquez-Marrufo G Garciacutea-Oliva F 2014 Organicmatter dynamics and microbial activity during decomposition of forest floor undertwo native neotropical oak species in a temperate deciduous forest in MexicoGeoderma 235ndash236133ndash145 DOI 101016jgeoderma201407005

Classen AT Sundqvist MK Henning JA Newman GS Moore JAM Cregger MAMoorhead LC Patterson CM 2015 Direct and indirect effects of climate changeon soil microbial and soil microbial-plant interactions what lies ahead Ecosphere61ndash21 DOI 101890ES15-002171

Cleveland CC Liptzin D 2007 CNP stoichiometry in soil is there a lsquolsquoredfield ratiorsquorsquo forthe microbial biomass Biogeochemistry 85235ndash252

Coleman DCWhitmanWB 2005 Linking species richness biodiversity and ecosystemfunction in soil systems Pedobiologia 49479ndash497

Conant RT RyanMG Aringgren GI Birge HE Davidson EA Eliasson PE Evans SEFrey SD Giardina CP Hopkins FM Hyvoumlnen R KirschbaumMUF Lavallee JMLeifeld J PartonWJ Megan Steinweg J WallensteinMDMartinWetterstedtJAring BradfordMA 2011 Temperature and soil organic matter decompositionratesndashsynthesis of current knowledge and a way forward Global Change Biology173392ndash3404 DOI 101111j1365-2486201102496x

Cregger MA Schadt CWMcDowell NG PockmanWT Classen AT 2012 Response ofthe soil microbial community to changes in precipitation in a semiarid ecosystemApplied and Environmental Microbiology 788587ndash8594 DOI 101128AEM02050-12

DrsquoOdorico P Bhattachan A 2012Hydrologic variability in dryland regions impacts onecosystem dynamics and food security Philosophical Transactions of the Royal SocietyB Biological Sciences 3673145ndash3157 DOI 101098rstb20120016

Eivazi F Tabatabai MA 1977 Phosphatases in soils Soil Biology and Biochemistry9167ndash172 DOI 1010160038-0717(77)90070-0

Eivazi F Tabatabai MA 1988 Glucosidases and galactosidases in soils Soil Biology andBiochemistry 20601ndash606 DOI 1010160038-0717(88)90141-1

Ekschmitt K LiuM Vetter S Fox OWolters V 2005 Strategies used by soil biota toovercome soil organic matter stabilitymdashwhy is dead organic matter left over in thesoil Geoderma 128167ndash176 DOI 101016jgeoderma200412024

Elser JJ Schampel JH Garcia-Pichel F Wade BD Souza V Eguiarte L EscalanteANA Farmer JD 2005 Effects of phosphorus enrichment and grazing snails onmodern stromatolitic microbial communities Freshwater Biology 501808ndash1825DOI 101111j1365-2427200501451x

Esch EH Lipson D Cleland EE 2017 Direct and indirect effects of shifting rainfall onsoil microbial respiration and enzyme activity in a semi-arid system Plant and Soil411333ndash346

Evans SEWallensteinMD 2012 Soil microbial community response to drying andrewetting stress does historical precipitation regime matter Biogeochemistry109101ndash116 DOI 101007s10533-011-9638-3


Farrell M Prendergast-Miller M Jones DL Hill PW Condron LM 2014 Soil microbialorganic nitrogen uptake is regulated by carbon availability Soil Biology and Biochem-istry 77261ndash267 DOI 101016jsoilbio201407003

Fay PA Kaufman DM Nippert JB Carlisle JD Harper CW 2008 Changes in grasslandecosystem function due to extreme rainfall events implications for responses toclimate change Global Change Biology 141600ndash1608DOI 101111j1365-2486200801605x

Frost PC Benstead JP CrossWF Hillebrand H Larson JH Xenopoulos MA YoshidaT 2006 Threshold elemental ratios of carbon and phosphorus in aquatic consumersEcology Letters 9774ndash779 DOI 101111j1461-0248200600919x

Gonzaacutelez MF 2012 Las zonas aacuteridas y semiaacuteridas de Meacutexico y su vegetacioacuten Meacutexico CityInstituto Nacional de Ecologiacutea Secretariacutea de Medio Ambiente y Recursos Naturales

Henry HAL 2013 Reprint of lsquolsquoSoil extracellular enzyme dynamics in a changingclimatersquorsquo Soil Biology and Biochemistry 5653ndash59 DOI 101016jsoilbio201210022

HolmgrenM Stapp P Dickman CR Gracia C Graham S Gutieacuterrez JR Hice C JaksicF Kelt DA Letnic M LimaM Loacutepez BC Meserve PL MilsteadWB Polis GAPrevitali MA Richter M Sabateacute S Squeo FA 2006 Extreme climatic events shapearid and semiarid ecosystems Frontiers in Ecology and the Environment 487ndash95DOI 1018901540-9295(2006)004[0087ECESAA]20CO2

Housman DC Yeager CM Darby BJ Sanford Jr RL Kuske CR Neher DA Belnap J2007Heterogeneity of soil nutrients and subsurface biota in a dryland ecosystemSoil Biology and Biochemistry 392138ndash2149 DOI 101016jsoilbio200703015

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

IPCC 2013 Stocker TF Qin D Plattner G-K Tignor M Allen SK Boschung J NauelsA Xia Y Bex V Midgley PM eds Climate change 2013 the physical science basisContribution of working group I to the fifth assessment report of the intergovernmentalpanel on climate change Cambridge New York Cambridge University Press

Joergensen RG 1996 The fumigation-extraction method to estimate soil microbialbiomass calibration of the kEC value Soil Biology and Biochemistry 2825ndash31DOI 1010160038-0717(95)00102-6

Joergensen RG Mueller T 1996 The fumigation-extraction method to estimate soilmicrobial biomass calibration of the kEN value Soil Biology and Biochemistry2833ndash37 DOI 1010160038-0717(95)00101-8

Johannes C Majcherczyk A 2000 Laccase activity tests and laccase inhibitors Journal ofBiotechnology 78193ndash199 DOI 101016S0168-1656(00)00208-X

Jones DLWillett VB 2006 Experimental evaluation of methods to quantify dissolvedorganic nitrogen (DON) and dissolved organic carbon (DOC) in soil Soil Biologyand Biochemistry 38991ndash999 DOI 101016jsoilbio200508012

Jones DLWillett VB Stockdale EA Macdonald AJ Murphy DV 2012Molecularweight of dissolved organic carbon nitrogen and phenolics in grassland soils SoilScience Society of America Journal 76142ndash150 DOI 102136sssaj20110252


Karl DM 2014Microbially mediated transformations of phosphorus in the sea newviews of an old cycle Annual Review of Marine Science 6279ndash337DOI 101146annurev-marine-010213-135046

Kirk TK Farrell RL 1987 Enzymatic lsquolsquocombustionrsquorsquo the microbial degradation of ligninAnnual Review of Microbiology 41465ndash501DOI 101146annurevmi41100187002341

Koch IH Gich F Dunfield PF Overmann J 2008 Edaphobacter modestus gen novsp nov and Edaphobacter aggregans sp nov acidobacteria isolated from alpineand forest soils International Journal of Systematic and Evolutionary Microbiology581114ndash1122 DOI 101099ijs065303-0

Lajtha K Driscoll TC Jarrell MW Edward TE 1999 Soil phosphorus characterizationand total element analysis In Robertson GP Coleman CD Bledsoe SC SollinsP eds Soil methods for long-term ecological research New York LTER OxfordUniversity Press 115ndash142

Loacutepez-Lozano NE Eguiarte LE Bonilla-Rosso G Garciacutea-Oliva F Martiacutenez-PiedragilC 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

Loacutepez-Lozano NE Heidelberg KB NelsonWC Garciacutea-Oliva F Eguiarte LE SouzaV 2013Microbial secondary succession in soil microcosms of a desert oasis in theCuatro Cienegas Basin Mexico PeerJ 1e47 DOI 107717peerj47

Manzoni S Schimel JP Porporato A 2012 Responses of soil microbial com-munities to water stress results from a meta-analysis Ecology 93930ndash938DOI 10189011-00261

McKee JW Jones NW Long LE 1990 Stratigraphy and provenance of strata along theSan Marcos fault central Coahuila Mexico Geological Society of America Bulletin102593ndash614 DOI 1011300016-7606(1990)102lt0593SAPOSAgt23CO2

Mitsui A Kumazawa S Takahashi A Ikemoto H Cao S Arai T 1986 Strategy bywhich nitrogen-fixing unicellular cyanobacteria grow photoautotrophically Nature323720ndash722 DOI 101038323720a0

Moorhead DL Sinsabaugh RL 2006 A theoretical model of litter decay and microbialinteraction Ecological Monographs 76151ndash174DOI 1018900012-9615(2006)076[0151ATMOLD]20CO2

Moreno B Cantildeizares R Nuntildeez R Benitez E 2013 Genetic diversity of bacterial β-glucosidase-encoding genes as a function of soil management Biology and Fertilityof Soils 49735ndash745 DOI 101007s00374-012-0765-3

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

Nemergut DR Cleveland CCWiederWRWashenberger CL Townsend AR 2010Plot-scale manipulations of organic matter inputs to soils correlate with shifts inmicrobial community composition in a lowland tropical rain forest Soil Biology andBiochemistry 422153ndash2160 DOI 101016jsoilbio201008011


Perroni Y Garciacutea-Oliva F Souza V 2014 Plant species identity and soil P forms in anoligotrophic grasslandndashdesert scrub system Journal of Arid Environments 10829ndash37DOI 101016jjaridenv201404009

Perroni Y Garciacutea-Oliva F Tapia-Torres Y Souza V 2014 Relationship between soil Pfractions and microbial biomass in an oligotrophic grassland-desert scrub systemEcological Research 29463ndash472 DOI 101007s11284-014-1138-1

Philippot L Bru D Saby NPA Čuhel J Arrouays D ŠimekM Hallin S 2009 Spa-tial patterns of bacterial taxa in nature reflect ecological traits of deep branchesof the 16S rRNA bacterial tree Environmental Microbiology 113096ndash3104DOI 101111j1462-2920200902014x

RDevelopment Core Team 2007 R a language and environment for statistical com-puting Vienna R Foundation for Statistical Computing Available at httpwwwr-projectorg

Schimel J Balser TCWallensteinM 2007Microbial stress-response physiology and itsimplications for ecosystem function Ecology 881386ndash1394 DOI 10189006-0219

Schimel J Schaeffer SM 2012Microbial control over carbon cycling in soil Frontiers inMicrobiology 31ndash11

Singh BK Quince C Macdonald CA Khachane A Thomas N Al-SoudWA SoslashrensenSJ He ZWhite D Sinclair A Crooks B Zhou J Campbell CD 2014 Loss of mi-crobial diversity in soils is coincident with reductions in some specialized functionsEnvironmental Microbiology 162408ndash2420

Sinsabaugh RL 2010 Phenol oxidase peroxidase and organic matter dynamics of soilSoil Biology and Biochemistry 42391ndash404 DOI 101016jsoilbio200910014

Sinsabaugh RL Follstad Shah JJ 2011 Ecoenzymatic stoichiometry of recalcitrantorganic matter decomposition the growth rate hypothesis in reverse Biogeochemistry10231ndash43 DOI 101007s10533-010-9482-x

Sinsabaugh RL Follstad Shah JJ 2012 Ecoenzymatic stoichiometry and ecolog-ical theory Annual Review of Ecology Evolution and Systematics 43313ndash343DOI 101146annurev-ecolsys-071112-124414

Sinsabaugh RL Hill BH Follstad Shah JJ 2009 Ecoenzymatic stoichiometry ofmicrobial organic nutrient acquisition in soil and sediment Nature 462795ndash798DOI 101038nature08632

Smith AJ 1983Modes of cyanobacterial carbon metabolism Annales de lrsquoInstitutPasteurMicrobiologie 13493ndash113

Souza V Siefert JL Escalante AE Elser JJ Eguiarte LE 2011 The Cuatro Cieacutenegas Basinin Coahuila Mexico an astrobiological precambrian park Astrobiology 12641ndash647

Steinweg J Dukes J Eldor PWallensteinM 2013Microbial responses to multi-factorclimate change effects on soil enzymes Frontiers in Microbiology 4146

Sterner RW Elser JJ 2002 Ecological stoichiometry the biology of elements from moleculesto the biosphere Princeton Princeton University Press

Tabatabai MA Bremner JM 1969 Use of p-nitrophenyl phosphate for assay of soilphosphatase activity Soil Biology and Biochemistry 1301ndash307DOI 1010160038-0717(69)90012-1


Tan H Barret M Mooij MJ Rice O Morrissey JP Dobson A Griffiths B OrsquoGara F2013 Long-term phosphorus fertilisation increased the diversity of the total bacterialcommunity and the phoD phosphorus mineraliser group in pasture soils Biology andFertility of Soils 49661ndash672 DOI 101007s00374-012-0755-5

TanenbaumDM Goll J Murphy S Kumar P Zafar N ThiagarajanMMadupu RDavidsen T Kagan L Kravitz S Rusch DB Yooseph S 2010 The JCVI standardoperating procedure for annotating prokaryotic metagenomic shotgun sequencingdata Standards in Genomic Sciences 2229ndash237 DOI 104056sigs651139

Tapia-Torres Y Elser JJ Souza V Garciacutea-Oliva F 2015a Ecoenzymatic stoichiometry atthe extremes how microbes cope in an ultra-oligotrophic desert soil Soil Biology andBiochemistry 8734ndash42 DOI 101016jsoilbio201504007

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

Tapia-Torres Y Rodriacuteguez-Torres MD Elser JJ Islas A Souza V Garciacutea-Oliva FOlmedo-Aacutelvarez G 2016How to live with phosphorus scarcity in soil and sedi-ment lessons from bacteria Applied and Environmental Microbiology 824652ndash4662

Technicon 1977 Technicon Industrial System Method No 329-74 WB Individu-alsimultaneous determinations of nitrogen andor phosphorus in BD acid giestAnalytical Chemistry 49427Andash427A DOI 101021ac50012a750

Thibault KM Brown JH 2008 Impact of an extreme climatic event on communityassembly Proceedings of the National Academy of Sciences of the United States ofAmerica 1053410ndash3415 DOI 101073pnas0712282105

ThomeyML Collins SL Vargas R Johnson JE Brown RF Natvig DO FriggensMT 2011 Effect of precipitation variability on net primary production and soilrespiration in a Chihuahuan Desert grassland Global Change Biology 171505ndash1515DOI 101111j1365-2486201002363x

Tiessen H Moir JO 2008 Characterization of available P by sequential extraction InCarter MR Gregorich EG eds Soil sampling and methods of analysis Second EditionBoca Raton CRC Press 293ndash306

Turner BL Mahieu N Condron LM 2003 Phosphorus-31 nuclear magnetic resonancespectral assignments of phosphorus compounds in soil NaOHndashEDTA extracts SoilScience Society of America Journal 67497ndash510 DOI 102136sssaj20034970

Vance ED Brookes PC Jenkinson DS 1987 An extraction method for mea-suring soil microbial biomass C Soil Biology and Biochemistry 19703ndash707DOI 1010160038-0717(87)90052-6

Verchot LV Borelli T 2005 Application of para-nitrophenol (pNP) enzymeassays in degraded tropical soils Soil Biology and Biochemistry 37625ndash633DOI 101016jsoilbio200409005

Von Ende NC 2001 Repeated-measures analysis In Scheiner MS Gurevitch J edsDesing and analysis of ecological experiments Second edition New York OxfordUniversity Press 134ndash157


WaldropMP Balser TC FirestoneMK 2000 Linking microbial community compo-sition to function in a tropical soil Soil Biology and Biochemistry 321837ndash1846DOI 101016S0038-0717(00)00157-7

WallensteinMD Hall EK 2012 A trait-based framework for predicting when andwhere microbial adaptation to climate change will affect ecosystem functioningBiogeochemistry 10935ndash47 DOI 101007s10533-011-9641-8

Waring BGWeintraub SR Sinsabaugh RL 2014 Ecoenzymatic stoichiometry ofmicrobial nutrient acquisition in tropical soils Biogeochemistry 117101ndash113DOI 101007s10533-013-9849-x

Xie G Bruce DC Challacombe JF Chertkov O Detter JC Gilna P Han CS Lucas SMisra M Myers GL Richardson P Tapia R Thayer N Thompson LS Brettin TSHenrissat B Wilson DB McBride MJ 2007 Genome sequence of the cellulolyticgliding bacterium Cytophaga hutchinsonii Applied and Environmental Microbiology733536ndash3546 DOI 101128AEM00225-07

Zechmeister-Boltenstern S Keiblinger KMMooshammerM Pentildeuelas J Richter ASardans J WanekW 2015 The application of ecological stoichiometry to plantndashmicrobialndashsoil organic matter transformations Ecological Monographs 85133ndash155DOI 10189014-07771

Zhou X Chen CWang Y Xu Z Duan J Hao Y Smaill S 2013 Soil extractable carbonand nitrogen microbial biomass and microbial metabolic activity in response towarming and increased precipitation in a semiarid Inner Mongolian grasslandGeoderma 20624ndash31 DOI 101016jgeoderma201304020


the Global Climate Change scenarios for desert ecosystems that predict reducedannual precipitation and an increased intensity and frequency of torrential rains anddrought events the soil microbial communities of both sites could be vulnerable todrought through C and P co-limitation and reallocation of resources to physiologicalacclimatization strategies in order to survive

Subjects Biodiversity Ecology Ecosystem Science Microbiology Soil ScienceKeywords Nutrients Enzymes Chihuahuan desert Microbial physiological adjustmentsStoichiometry ratio Threshold elemental ratio

INTRODUCTIONIn desert ecosystems precipitation is highly variable among years and this variability hasincreased in recent years due to the effect of Global Climate Change (GCC) (Bell et al2014 IPCC 2013) The scenarios derived from GCC models for desert ecosystems predictreduced annual precipitation as well as increases in the annual precipitation variability bythe end of the 21st century including an increase in the frequency and intensity of bothtorrential rain and drought events (Holmgren et al 2006 IPCC 2013) The high variabilityof annual precipitation projected for desert ecosystems could alter biological processesdependent on soil water potential as is the case with the processes related to soil organicmatter (SOM) decomposition (DrsquoOdorico amp Bhattachan 2012 Fay et al 2008 Thomey etal 2011) For example enzymatic activity stimulated by rainfall in desert ecosystems mayresult inmost of the total annualmineralization that occurs in desert soils (Manzoni Schimelamp Porporato 2012) However when soil water potential decreases the metabolic activity ofmost soil microbial species is reduced and thus a decline in nutrient mineralization canoccur Additionally soil drying reduces enzymatic activity and microbial mobility whichreduces substrate supply for the decomposers (Henry 2013Manzoni Schimel amp Porporato2012) Likewise studies in a semiarid region in NewMexico (Cregger et al 2012) and in theChihuahuan Desert (Bell et al 2009 Bell et al 2014) showed that the high precipitationvariability significantly altered the structure of the soil microbial community mainly dueto a change in the fungalbacterial ratio and consequently altered microbial communityfunctional dynamics

Microbial communities play a central role in the structure and functioning of desertecosystems since they represent an important pool of soil C N and P Indeed it has beensuggested that the amount of N and P contained within the soil microorganism biomassis comparable to the N and P content within the plant biomass in desert ecosystems(Coleman ampWhitman 2005) Moreover microbial communities can help accelerate thetransformation ofmolecules containing C N and P by producing soil extracellular enzymes(ecoenzymes) (Sinsabaugh amp Follstad Shah 2012 Sinsabaugh Hill amp Shah 2009) that leadto the fragmentation depolymerization and mineralization of organic matter (Singh etal 2014) Microorganisms can only assimilate soluble organic compounds of a molecularweight lower than 1 kDa and must therefore break down or depolymerize most of theorganic matter molecules (where between 72 and 87 of the DOC in grassland soils is












































Year

2012 2013 2014

RS G RS G RS G









Pi (microg gminus1) 19 (02) 25 (02) 29 (04) 45 (04) 39 (06) 53 (06)


















Year

2012 2013 2014

RS G RS G RS G














60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 3600

10

20

30

40

50

60

70

G N

mic

microg

g

y = 01476x + 05771

Rsup2 = 0429

p= 00001

D)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

Precipitation (mm)

-1

0

1

2

3

4

5

6

7

8

9

10

RS

Pm

ic micro

g

g

y = 00077x + 11766

Rsup2 = 02933

p=00029

E)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

Precipitation (mm)

-1

0

1

2

3

4

5

6

7

8

9

10G

Pm

ic micro

g

g

y = 00125x + 24839

Rsup2 = 02339

p= 00091

F)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-200

0

200

400

600

800

1000

1200

RS

Cm

ic micro

g

g

y = 29606x - 29377

Rsup2 = 07664

plt0005

A)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 3600

200

400

600

800

1000

1200

G C

mic

microg

g

y = 34031x -3015226

R2=0831532

plt 005

B)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 3600

10

20

30

40

50

60

70

RS

Nm

ic micro

g

g

y = 0074x + 33776

Rsup2 = 01995

p=00172

C)





60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

Precipitation (mm)

-2

0

2

4

6

8

10

12

14G

Nm

icP

mic

y = 0028x - 07109

Rsup2 = 04646

plt 0005

F)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

Precipitation (mm)

-2

0

2

4

6

8

10

12

14

16

18

20

22

24

RS

Nm

icP

mic

y = 00219x +25719

R2= 0083543

p= 0135

E)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-20

0

20

40

60

80

100

120

RS

Cm

icN

mic

y = 01009x + 16994

Rsup2 = 02216

P=00115

A)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 3604

6

8

10

12

14

16

18

20

22

24

26

28

30

32

34

G C

mic

Nm

ic

y = 00295x + 84326

Rsup2 = 01305

p= 0058

B)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-50

0

50

100

150

200

250

300

350

RS

Cm

icP

mic

y = 07078x - 47985

Rsup2 = 08131

plt0005

C)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-20

0

20

40

60

80

100

120

140

160

180

200

220

240

260

G C

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Pm

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y = 06341x - 63665

Rsup2 = 08144

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0

10

20

30

40

50

60

70

80

90

100


S

eq

uen

ces

Unclassified

WS3

Verrucomicrobia

TM7

Synergistetes

Spirochaetes

Proteobacteria

Planctomycetes

OP11

OP10

OD1

Nitrospira

Gemmatimonadetes

Fusobacteria

Firmicutes

Deinococcus-Thermus

Deferribacteres

Cyanobacteria

Chloroflexi

Chlorob

Chlamydiae

Bacteroidetes

BRC1

Actinobacterias

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0

20

40

60

80

100

120

140

2012 2013 2014

TE

R C

P

Ab

Aab

Aa

Bb

Aa

Ba

B)

0

5

10

15

20

25

30

35

2012 2013 2014

TE

R C

N


Aa

Aa

AaAa

AbAb

A)



































































































































































Year

2012 2013 2014

RS G RS G RS G









Pi (microg gminus1) 19 (02) 25 (02) 29 (04) 45 (04) 39 (06) 53 (06)


















Year

2012 2013 2014

RS G RS G RS G














60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 3600

10

20

30

40

50

60

70

G N

mic

microg

g

y = 01476x + 05771

Rsup2 = 0429

p= 00001

D)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

Precipitation (mm)

-1

0

1

2

3

4

5

6

7

8

9

10

RS

Pm

ic micro

g

g

y = 00077x + 11766

Rsup2 = 02933

p=00029

E)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

Precipitation (mm)

-1

0

1

2

3

4

5

6

7

8

9

10G

Pm

ic micro

g

g

y = 00125x + 24839

Rsup2 = 02339

p= 00091

F)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-200

0

200

400

600

800

1000

1200

RS

Cm

ic micro

g

g

y = 29606x - 29377

Rsup2 = 07664

plt0005

A)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 3600

200

400

600

800

1000

1200

G C

mic

microg

g

y = 34031x -3015226

R2=0831532

plt 005

B)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 3600

10

20

30

40

50

60

70

RS

Nm

ic micro

g

g

y = 0074x + 33776

Rsup2 = 01995

p=00172

C)





60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

Precipitation (mm)

-2

0

2

4

6

8

10

12

14G

Nm

icP

mic

y = 0028x - 07109

Rsup2 = 04646

plt 0005

F)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

Precipitation (mm)

-2

0

2

4

6

8

10

12

14

16

18

20

22

24

RS

Nm

icP

mic

y = 00219x +25719

R2= 0083543

p= 0135

E)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-20

0

20

40

60

80

100

120

RS

Cm

icN

mic

y = 01009x + 16994

Rsup2 = 02216

P=00115

A)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 3604

6

8

10

12

14

16

18

20

22

24

26

28

30

32

34

G C

mic

Nm

ic

y = 00295x + 84326

Rsup2 = 01305

p= 0058

B)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-50

0

50

100

150

200

250

300

350

RS

Cm

icP

mic

y = 07078x - 47985

Rsup2 = 08131

plt0005

C)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-20

0

20

40

60

80

100

120

140

160

180

200

220

240

260

G C

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Pm

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y = 06341x - 63665

Rsup2 = 08144

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10

20

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60

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100


S

eq

uen

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Unclassified

WS3

Verrucomicrobia

TM7

Synergistetes

Spirochaetes

Proteobacteria

Planctomycetes

OP11

OP10

OD1

Nitrospira

Gemmatimonadetes

Fusobacteria

Firmicutes

Deinococcus-Thermus

Deferribacteres

Cyanobacteria

Chloroflexi

Chlorob

Chlamydiae

Bacteroidetes

BRC1

Actinobacterias

Acidobacteria






0

20

40

60

80

100

120

140

2012 2013 2014

TE

R C

P

Ab

Aab

Aa

Bb

Aa

Ba

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0

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2012 2013 2014

TE

R C

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Aa

AaAa

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Year

2012 2013 2014

RS G RS G RS G














60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 3600

10

20

30

40

50

60

70

G N

mic

microg

g

y = 01476x + 05771

Rsup2 = 0429

p= 00001

D)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

Precipitation (mm)

-1

0

1

2

3

4

5

6

7

8

9

10

RS

Pm

ic micro

g

g

y = 00077x + 11766

Rsup2 = 02933

p=00029

E)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

Precipitation (mm)

-1

0

1

2

3

4

5

6

7

8

9

10G

Pm

ic micro

g

g

y = 00125x + 24839

Rsup2 = 02339

p= 00091

F)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-200

0

200

400

600

800

1000

1200

RS

Cm

ic micro

g

g

y = 29606x - 29377

Rsup2 = 07664

plt0005

A)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 3600

200

400

600

800

1000

1200

G C

mic

microg

g

y = 34031x -3015226

R2=0831532

plt 005

B)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 3600

10

20

30

40

50

60

70

RS

Nm

ic micro

g

g

y = 0074x + 33776

Rsup2 = 01995

p=00172

C)





60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

Precipitation (mm)

-2

0

2

4

6

8

10

12

14G

Nm

icP

mic

y = 0028x - 07109

Rsup2 = 04646

plt 0005

F)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

Precipitation (mm)

-2

0

2

4

6

8

10

12

14

16

18

20

22

24

RS

Nm

icP

mic

y = 00219x +25719

R2= 0083543

p= 0135

E)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-20

0

20

40

60

80

100

120

RS

Cm

icN

mic

y = 01009x + 16994

Rsup2 = 02216

P=00115

A)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 3604

6

8

10

12

14

16

18

20

22

24

26

28

30

32

34

G C

mic

Nm

ic

y = 00295x + 84326

Rsup2 = 01305

p= 0058

B)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-50

0

50

100

150

200

250

300

350

RS

Cm

icP

mic

y = 07078x - 47985

Rsup2 = 08131

plt0005

C)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-20

0

20

40

60

80

100

120

140

160

180

200

220

240

260

G C

mic

Pm

ic

y = 06341x - 63665

Rsup2 = 08144

plt0005

D)






$

$

amp

(

amp()+)-0)12345)657

8

4

9(7

$ amp

(

)

$

amp

(

)

amp()2)-0)12345)657

7

8

9

9

$

$

$

amp

amp

amp()lt)$amp()$+)7

(9

9

(9

27

(8(9

(9

$

$

$

amp

amp

(

amp()=(+)-0)1=345)657

(9

(8(4

(8

(9

gt7

9

$

(

$

$

amp

amp

amp()A)-0)1345)657

7

(9

(8(8

9

(9

9

$

(

$

$

amp()gt)-0)1345)657

(9B7

(8 (8

(9

9 9

$




0

10

20

30

40

50

60

70

80

90

100


S

eq

uen

ces

Unclassified

WS3

Verrucomicrobia

TM7

Synergistetes

Spirochaetes

Proteobacteria

Planctomycetes

OP11

OP10

OD1

Nitrospira

Gemmatimonadetes

Fusobacteria

Firmicutes

Deinococcus-Thermus

Deferribacteres

Cyanobacteria

Chloroflexi

Chlorob

Chlamydiae

Bacteroidetes

BRC1

Actinobacterias

Acidobacteria






0

20

40

60

80

100

120

140

2012 2013 2014

TE

R C

P

Ab

Aab

Aa

Bb

Aa

Ba

B)

0

5

10

15

20

25

30

35

2012 2013 2014

TE

R C

N


Aa

Aa

AaAa

AbAb

A)

























































































































60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 3600

10

20

30

40

50

60

70

G N

mic

microg

g

y = 01476x + 05771

Rsup2 = 0429

p= 00001

D)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

Precipitation (mm)

-1

0

1

2

3

4

5

6

7

8

9

10

RS

Pm

ic micro

g

g

y = 00077x + 11766

Rsup2 = 02933

p=00029

E)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

Precipitation (mm)

-1

0

1

2

3

4

5

6

7

8

9

10G

Pm

ic micro

g

g

y = 00125x + 24839

Rsup2 = 02339

p= 00091

F)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-200

0

200

400

600

800

1000

1200

RS

Cm

ic micro

g

g

y = 29606x - 29377

Rsup2 = 07664

plt0005

A)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 3600

200

400

600

800

1000

1200

G C

mic

microg

g

y = 34031x -3015226

R2=0831532

plt 005

B)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 3600

10

20

30

40

50

60

70

RS

Nm

ic micro

g

g

y = 0074x + 33776

Rsup2 = 01995

p=00172

C)





60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

Precipitation (mm)

-2

0

2

4

6

8

10

12

14G

Nm

icP

mic

y = 0028x - 07109

Rsup2 = 04646

plt 0005

F)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

Precipitation (mm)

-2

0

2

4

6

8

10

12

14

16

18

20

22

24

RS

Nm

icP

mic

y = 00219x +25719

R2= 0083543

p= 0135

E)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-20

0

20

40

60

80

100

120

RS

Cm

icN

mic

y = 01009x + 16994

Rsup2 = 02216

P=00115

A)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 3604

6

8

10

12

14

16

18

20

22

24

26

28

30

32

34

G C

mic

Nm

ic

y = 00295x + 84326

Rsup2 = 01305

p= 0058

B)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-50

0

50

100

150

200

250

300

350

RS

Cm

icP

mic

y = 07078x - 47985

Rsup2 = 08131

plt0005

C)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-20

0

20

40

60

80

100

120

140

160

180

200

220

240

260

G C

mic

Pm

ic

y = 06341x - 63665

Rsup2 = 08144

plt0005

D)






$

$

amp

(

amp()+)-0)12345)657

8

4

9(7

$ amp

(

)

$

amp

(

)

amp()2)-0)12345)657

7

8

9

9

$

$

$

amp

amp

amp()lt)$amp()$+)7

(9

9

(9

27

(8(9

(9

$

$

$

amp

amp

(

amp()=(+)-0)1=345)657

(9

(8(4

(8

(9

gt7

9

$

(

$

$

amp

amp

amp()A)-0)1345)657

7

(9

(8(8

9

(9

9

$

(

$

$

amp()gt)-0)1345)657

(9B7

(8 (8

(9

9 9

$




0

10

20

30

40

50

60

70

80

90

100


S

eq

uen

ces

Unclassified

WS3

Verrucomicrobia

TM7

Synergistetes

Spirochaetes

Proteobacteria

Planctomycetes

OP11

OP10

OD1

Nitrospira

Gemmatimonadetes

Fusobacteria

Firmicutes

Deinococcus-Thermus

Deferribacteres

Cyanobacteria

Chloroflexi

Chlorob

Chlamydiae

Bacteroidetes

BRC1

Actinobacterias

Acidobacteria






0

20

40

60

80

100

120

140

2012 2013 2014

TE

R C

P

Ab

Aab

Aa

Bb

Aa

Ba

B)

0

5

10

15

20

25

30

35

2012 2013 2014

TE

R C

N


Aa

Aa

AaAa

AbAb

A)

























































































































60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

Precipitation (mm)

-2

0

2

4

6

8

10

12

14G

Nm

icP

mic

y = 0028x - 07109

Rsup2 = 04646

plt 0005

F)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

Precipitation (mm)

-2

0

2

4

6

8

10

12

14

16

18

20

22

24

RS

Nm

icP

mic

y = 00219x +25719

R2= 0083543

p= 0135

E)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-20

0

20

40

60

80

100

120

RS

Cm

icN

mic

y = 01009x + 16994

Rsup2 = 02216

P=00115

A)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 3604

6

8

10

12

14

16

18

20

22

24

26

28

30

32

34

G C

mic

Nm

ic

y = 00295x + 84326

Rsup2 = 01305

p= 0058

B)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-50

0

50

100

150

200

250

300

350

RS

Cm

icP

mic

y = 07078x - 47985

Rsup2 = 08131

plt0005

C)

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360-20

0

20

40

60

80

100

120

140

160

180

200

220

240

260

G C

mic

Pm

ic

y = 06341x - 63665

Rsup2 = 08144

plt0005

D)






$

$

amp

(

amp()+)-0)12345)657

8

4

9(7

$ amp

(

)

$

amp

(

)

amp()2)-0)12345)657

7

8

9

9

$

$

$

amp

amp

amp()lt)$amp()$+)7

(9

9

(9

27

(8(9

(9

$

$

$

amp

amp

(

amp()=(+)-0)1=345)657

(9

(8(4

(8

(9

gt7

9

$

(

$

$

amp

amp

amp()A)-0)1345)657

7

(9

(8(8

9

(9

9

$

(

$

$

amp()gt)-0)1345)657

(9B7

(8 (8

(9

9 9

$




0

10

20

30

40

50

60

70

80

90

100


S

eq

uen

ces

Unclassified

WS3

Verrucomicrobia

TM7

Synergistetes

Spirochaetes

Proteobacteria

Planctomycetes

OP11

OP10

OD1

Nitrospira

Gemmatimonadetes

Fusobacteria

Firmicutes

Deinococcus-Thermus

Deferribacteres

Cyanobacteria

Chloroflexi

Chlorob

Chlamydiae

Bacteroidetes

BRC1

Actinobacterias

Acidobacteria






0

20

40

60

80

100

120

140

2012 2013 2014

TE

R C

P

Ab

Aab

Aa

Bb

Aa

Ba

B)

0

5

10

15

20

25

30

35

2012 2013 2014

TE

R C

N


Aa

Aa

AaAa

AbAb

A)

























































































































$

$

amp

(

amp()+)-0)12345)657

8

4

9(7

$ amp

(

)

$

amp

(

)

amp()2)-0)12345)657

7

8

9

9

$

$

$

amp

amp

amp()lt)$amp()$+)7

(9

9

(9

27

(8(9

(9

$

$

$

amp

amp

(

amp()=(+)-0)1=345)657

(9

(8(4

(8

(9

gt7

9

$

(

$

$

amp

amp

amp()A)-0)1345)657

7

(9

(8(8

9

(9

9

$

(

$

$

amp()gt)-0)1345)657

(9B7

(8 (8

(9

9 9

$




0

10

20

30

40

50

60

70

80

90

100


S

eq

uen

ces

Unclassified

WS3

Verrucomicrobia

TM7

Synergistetes

Spirochaetes

Proteobacteria

Planctomycetes

OP11

OP10

OD1

Nitrospira

Gemmatimonadetes

Fusobacteria

Firmicutes

Deinococcus-Thermus

Deferribacteres

Cyanobacteria

Chloroflexi

Chlorob

Chlamydiae

Bacteroidetes

BRC1

Actinobacterias

Acidobacteria






0

20

40

60

80

100

120

140

2012 2013 2014

TE

R C

P

Ab

Aab

Aa

Bb

Aa

Ba

B)

0

5

10

15

20

25

30

35

2012 2013 2014

TE

R C

N


Aa

Aa

AaAa

AbAb

A)

























































































































0

10

20

30

40

50

60

70

80

90

100


S

eq

uen

ces

Unclassified

WS3

Verrucomicrobia

TM7

Synergistetes

Spirochaetes

Proteobacteria

Planctomycetes

OP11

OP10

OD1

Nitrospira

Gemmatimonadetes

Fusobacteria

Firmicutes

Deinococcus-Thermus

Deferribacteres

Cyanobacteria

Chloroflexi

Chlorob

Chlamydiae

Bacteroidetes

BRC1

Actinobacterias

Acidobacteria






0

20

40

60

80

100

120

140

2012 2013 2014

TE

R C

P

Ab

Aab

Aa

Bb

Aa

Ba

B)

0

5

10

15

20

25

30

35

2012 2013 2014

TE

R C

N


Aa

Aa

AaAa

AbAb

A)

























































































































0

20

40

60

80

100

120

140

2012 2013 2014

TE

R C

P

Ab

Aab

Aa

Bb

Aa

Ba

B)

0

5

10

15

20

25

30

35

2012 2013 2014

TE

R C

N


Aa

Aa

AaAa

AbAb

A)

























































































































the response of soil microbial communities to variation in ... · in the quantity and quality of...

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