Comparing how land use change impacts soil microbial catabolic respiration in Southwestern Amazon

ht t p : / / w w w . b j m i c r o b i o l . c o m . b r /

Environmental

Microbiology

Comparing

how

land

use

change

impacts

soil

microbial

catabolic

respiration

in

Southwestern

Amazon

Andre

Mancebo

Mazzetto

_,

_Brigitte

_Josefine

_Feigl

_,

Carlos

Eduardo

Pellegrino

Cerri

_,

_Carlos

_Clemente

_Cerri

a_{CentrodeEnergiaNuclearnaAgricultura,UniversidadedeSãoPaulo,Piracicaba,SP,Brazil} b_{EscolaSuperiordeAgricultura“LuizdeQueiroz”,UniversidadedeSãoPaulo,Piracicaba,SP,Brazil}

a

r

t

i

c

l

e

i

n

f

o

Received23January2015 Accepted17July2015

AssociateEditor:FernandoDini Andreote

Keywords: Amazonregion Landusechange Catabolicprofile Microbialcommunities

a

b

s

t

r

a

c

t

Land use changesstrongly impact soilfunctions, particularly microbialbiomass diver-sityandactivity.Wehypothesizedthatthecatabolicrespirationresponseofthemicrobial biomasswoulddifferdependingonlanduseandthatthesedifferenceswouldbeconsistent atthelandscapescale.Inthepresentstudy,weanalyzedthecatabolicresponseprofileofthe soilmicrobialbiomassthroughsubstrate-inducedrespirationindifferentlandusesovera widegeographicalrangeinMatoGrossoandRondôniastate(SouthwestAmazonregion).We analyzedthedifferencesamongnativeareas,pasturesandcropareasandwithineachland useandexaminedonlynativeareas(Forest,DenseCerradoandCerrado),pastures (Nom-inal,DegradedandImproved)andcropareas(Perennial,No-Tillage,ConventionalTillage). Themetabolicprofileofthemicrobialbiomasswasaccessedusingsubstrate-induced res-piration.Pasturesoilsshowedsignificantresponsestoaminoacidsandcarboxylicacids, whereasnativeareasshowedhigherresponsestomalonicacid,malicacidandsuccinic acid.Withineachlandusecategory,thecatabolicresponsesshowedsimilarpatternsinboth largegeneralcomparisons(nativearea,pastureandcropareas)andmorespecific compar-isons(biomes,pasturesandcroptypes).Theresultsshowedthatthecatabolicresponses ofthemicrobialbiomassarehighlycorrelatedwithlanduse,independentofsoiltypeor climate.The substrateinducedrespirationapproachisusefultodiscriminatemicrobial communities,evenonalargescale.

©2015SociedadeBrasileiradeMicrobiologia.PublishedbyElsevierEditoraLtda.Thisis anopenaccessarticleundertheCCBY-NC-NDlicense (http://creativecommons.org/licenses/by-nc-nd/4.0/).

∗ _{Correspondingauthorat:USP,CENA,LaboratóriodeBiogeoquímicaAmbiental,AvenidaCentenário,303,Piracicaba13400-970,Brazil.} E-mail:andremmazzetto@hotmail.com(A.M.Mazzetto).

http://dx.doi.org/10.1016/j.bjm.2015.11.025

1517-8382/©2015SociedadeBrasileiradeMicrobiologia.PublishedbyElsevierEditoraLtda.ThisisanopenaccessarticleundertheCC BY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Thegrowingdemandforfood,fiber andbiofuelshasledto manyenvironmentalproblemsandprimarilyreflectthe occu-pationoftheworld’sgreatestagriculturalfrontier:theborder betweenthe Amazon rainforestand the Cerrado (savanna) ofcentral Brazil.1 _{Generally, the flat topography eases the}

mechanizationandincentivizestheoccupationofthisregion. ThesouthwesternAmazon,particularlythestatesof Rondô-nia(RO) and MatoGrosso (MT), still practicedeforestation foragriculturallanduse.2 _{Theintensivelanduse invariably}

hasnegativeeffectsonboththeenvironmentandagricultural productivitywhenconservationpracticesarenotadopted.3,4

Interestinthe maintenanceofsoilquality, consistentwith Karlenetal.,5_{isessentialforthesustainabilityofthesenew}

agriculturalareas.

Increasing evidence has shown that soil microbial attributesarepotentialearlyindicatorsofthechangesinsoil qualitybecausetheseparametersaremoresensitivethanare the chemical and physicalpropertiesof soil.6,7 _The

micro-bialbiomasshasbeencharacterizedasasensitiveindexfor changesinthesoilorganiccarbonthatresultfrom manage-ment and land use. Initially, microbial biomassundergoes fluctuationsuntilitreachesanewequilibrium.8_Onecommon

methodformeasuringthemetabolicfunctionofsoil microor-ganismsisthecatabolicresponseprofile.9,10_{AccordingtoSan}

Migueletal.,11_{analyzingthefunctionalandcatabolicdiversity}

isimportantbecauseitisdifficulttoinferwhethersomesoil functionshavebeenlostsolelybasedonchangesingenetic diversity.Stevensonetal.12_{showeddifferentpatternsinthe}

cataboliccapacityofthemicrobialcommunityunderforests andpasturesinNewZealand.

Inmostlandusesunderagriculturalpractices(plowing, fer-tilizing,liming, pesticideapplicationandother inputs),the availablesoilniches mightbeaffected.Eachchange repre-sents a renewal of selection pressure, which favors some componentsofthemicrobial communitywhileeliminating others,thereby relocatingthe equilibriumbetween popula-tions.Microbialdiversityreductionimpliesthelossofspecies thatmetabolizecertain functional groups,which resultsin adecreaseintheresilienceofthesystem.13_Theaimofthe

present study was toincrease the current knowledge con-cerningtheimpactoflanduseandlandusechangesonthe metaboliccapacityofthesoilmicrobialbiomass.Therefore, theobjectiveofthepresentstudywastocomparethe func-tionaldiversityofthesoilmicrobialbiomassinthenatural vegetationpriorto humaninterference and afterland use changes.Tothisend,wesamplednativeareas,pasturesand agriculturalareasintheSouthwestAmazonRegion.

Materials

and

methods

Studyarea

ThestudyareafocusesonthestatesofRondôniaandMato Grosso,whichformatransitionalregionbetweenthe Ama-zonBasinandthehighlandsoftheBrazilianCentralPlateau. The regional climate varies according to latitude and is

accordingtotheIntergovernmentalPanelforClimateChanges “GuidelinesforNationalGreenhouseGasInventories”.14_The

delimitationofthezoneswasperformedusingtheGeographic Information System ArcGis 9.0withcombined information onsoils,native vegetation,geology,climateand relief.This methodology generatedrelativelyhomogeneousareas,thus facilitatingtheextrapolationofthemicrobiological parame-tersfortheentireregion.Ineachofthe11zones,twosites wererandomlyselected,totaling22samplingpoints(Fig.1).In allsites,weidentifiedandsampledsoilfromonenativearea, one pasture and one agriculturalarea,totalizing 66 samp-lingpoints.Intheselocations,thesoilofnativesystemswas sampledtodeterminechemical,physicalandmicrobiological attributes.Thegeneralcharacteristicsofeachecoregionare providedinTable1(adaptedfromMaiaetal.15_)andinOnline Appendix1.

Soilsamplescollection

Soilcoreswerecollectedat0–10cmdepthsinfivereplicates fromeacharea,foratotalof330samples(110samplesineach managementsystem).Thesoilsampleswerebrokenapartand sievedthrougha2-mmmeshtoremoverocksandplant frag-ments.NativeareasweredenominatedForest(n=60);Cerrado (n=25),whichwasdefinedasthetropicalsavanna, species-richdensevegetationofshrubsandtrees,8–10mhigh,witha grassundergrowth16_{;andDenseCerradoor“Cerradão”(n=25),}

whichwassimilartoawoodlandsavannah,withtreesupto 20mhigh.ThepasturesweredescribedasImproved(n=25) whenatleastoneimprovement(fertilizerorlimeapplication, irrigation) was received; Nominal (n=25) when reasonable productivitywasmaintained,despitenoimprovements occur-ring;andDegraded(n=60)whenthetypicalmanagementwas receivedandareductioninproductivityduetoweed infesta-tion,baresoiland/orsoilerosion.15_{Theagriculturalsiteswere}

dividedintoConventional-Tillage(n=70)whenplantresidue wasincorporatedintothesoilandaggregateswereroutinely disruptedthroughtillage(physicalreleaseofprotectedorganic matter);No-tillage(n=20);andperennialcrops(n=20).

Substrate-inducedrespiration(SIR)

Estimatesofthecatabolicdiversityinthesoilmicrobial com-munity were obtainedbased onthe short-term respiratory responsesofsoilsamples,consistentwithDegensandHarris.9

Substrates(as2mLsolution)wereaddedtoa1-gequivalent dryweightofsoilinMacCartneybottlessealedwithvacutainer stoppers.Thefollowingsubstrateswereincludedinthis anal-ysis:2amines(glutamineandglucosamine),6aminoacids (arginine,glutamicacid,asparagine,histidine,lysineand ser-ine),2carbohydrates(glucoseandmannose)and12carboxylic acids (citricacid,ascorbicacid,gluconicacid,fumaricacid, malonicacid,malicacid,ketoglutaricacid,ketobutyricacid, pantothenicacid,quinicacid,succinicacidandtartaricacid). Ananalysiswithwaterwasalsoperformed.TheCO2fluxfrom eachsamplewasmeasuredusinganIRGA(LICOR-6262Model) afterincubationfor4hat25◦C.

Cities sampled 0 70 140 280 420 560 kilometers N ROC NRO NMT PP AX GD _CD PB PA AD NMT

Fig.1–Distributionoftheecoregionsinthestudyarea.AX,AltoXingu;PB,ParanaBasin;PP,ParecisPlateau;AD,Araguaia

Depression;CD,CuiabáDepression;DG,GuaporéDepression;NMT,NorthofMatoGrosso;NRO,NorthofRondônia;PA,

Pantanal;ROC,CentralRondônia.

Table1–Descriptionoftheecoregionsanalyzed.

Ecoregion Soil Vegetation Climate

AltoXingu Oxisols Seasonalsemi-deciduousforesttoopen Amazonforest

Climate:Ami–Rainfall:1750–2250mmyear−1 ParanaBasin Oxisolsand

Quartzipsamments

Cerradosensustricto Climate:AmandCwa–Rainfall: 1250–1750mmyear−1

ParecisPlateau Quartzipsammentsand Oxisols

CerradosensustrictoandSeasonal semi-deciduousforest

Climate:Ami–Rainfall:1500–2250mmyear−1 Araguaia

Depression

EntisolsandAquent Entisols

OpenCerrado(dominatedbygrasses)and Cerradosensustricto

Climate:Ami–Rainfall:1250–2000mmyear−1 CuaibaDepression InceptisolsandEntisols Cerradosensustricto Climate:Am–Rainfall:1500–1750mmyear−1

Guapore Depression

Oxisols,Ultisolsand Inceptisols

OpenAmazonforest(north)andSeasonal semi-deciduousforesttoCerrado(south)

Climate:Ami–Rainfall:1750–2250mmyear−1

NortheastofMato Grosso

Ultisols CerradotoaSeasonalsemi-deciduousforest Climate:Ami–Rainfall:2000–2250mmyear−1 NorthofRondônia Oxisols OpenAmazonForest Climate:Awi–Rainfall:2000–2500mmyear−1 NorthofMato

Grosso

Ultisols,Oxisolsand Inceptisols

OpenAmazonForesttoSeasonal semi-deciduousforest

Climate:AmiandAwi–Rainfall: 2000–2700mmyear−1

Pantanal EntisolsandAlfisols OpenCerradoandSeasonalsemi-deciduous forest

Climate:AmandCwa–Rainfall: 15,000–1750mmyear−1 CentralRondônia UltisolsandOxisols OpenAmazonForest Climate:AmiandAwi–Rainfall:

Car bohyd rat es Amin oaci ds C arboxi lic acids Asp ara gine Glut am icA c. Gluta mine Glucosamine Ascor bic Ac . Gluc om icA c. Fuma ric Ac. Maloni cAc. Ketog lutaric Ac. Ketobuti ric Ac. Pantot enic Ac. Quinic Ac . Suc cinic Ac . Tart aric Ac. 0 10 20 Amines W ate r Argin ine Hist idine_Lisine Gluc ose Mano se Citr icAc . Ser ine Mal icA c. O C g µ( RI S h li o s g₂ -1 -1 NAT CROP * * * * * * * * * * * * *P< .05 *P< .05 *P< .05 * * * * Carbohydr ates Ami no acids Carbo xilic aci ds Asp aragi ne Glu tam icAc . Gluta min e Glucosamine A scor bic Ac. Glu comic Ac. Fum aric Ac. Malon icA c. Ketog lutaric Ac. Ketobutiric Ac. Pantot enic Ac. Qui nic Ac. Succini cA c. Tart aric Ac. 0 10 20 30 Am ines Wat er Arg inine Histidine Lisin e Gluc ose Man ose Citric Ac. Ser ine Mal icA c. ) h li o s g O C g µ( RI S 2 1-FLO CER CERRA * * * * * * * * * Ca rbohy dra tes Am ino acids Carboxilic acids Asp aragi ne Glutamic Ac . Glu tamin e Gluc osa min e Asco rbic Ac. Glucomi cA c. Fumari cA c. Malonic Ac. Ketogl utaric Ac. Ketobutiric Ac. Pantotenic Ac. Qui nic Ac . Succini cA c. Tart aric Ac. 0 10 20 30 Am ine s Wat er Argini ne Hist idin e Lisine Glu cose ManoseCitric Ac. Se rine Malic Ac . ) h li o s g O C g µ( RI S 2 1-PERE NT CT * * * * * * * * * * * * * * * * * * * * * * Carb oh ydr ates Am inoa cids Carboxilic acids Aspa ragi ne Glutamic Ac. Glutam ine Gluco sam ine Ascorbic Ac. Glucomic Ac. Fumaric Ac. Malonic Ac. Ketoglutaric Ac. Ketobut iric Ac. Pantot enic Ac. Quin icAc . Succinic Ac. Tartaric Ac. 0 10 20 30 Ami nes Water Argi nin e Hist idin e Lisin e Glu cose Ma nose Citric Ac. Ser ine Malic Ac. ) h li o s g O C g µ( RI S 2 1-PASTD PAST PASTI * * * * * * * * * * * * * *

B

C

D

Fig.2–Communitylevelphysiologicalprofileofthelandusesanalyzed.(A)Differencesbetweenthelanduses:PAST,

pasture;NAT,nativeareas;CROP,areasundercrops.(B)DifferenceswithinNativeAreas:FLO,forestareas;CER,Cerradoareas;

CERRA,Dense-Cerradoareas.(C)Differenceswithinagriculturalareas:PERE,perennialcrops;CT,conventional-tillage;NT,

Chemicalanalysis

Totalcarbon wasmeasured throughdrycombustionon an LECOCNelementalanalyzer(furnaceat1350◦Cinpure oxy-gen).Thebulkdensity()wasmeasured fromeachpitand layerusingavolumetric(100cm3_{)steelring.Foreachsoillayer,} thecarbonstockswerecalculatedaftermultiplyingthe con-centrationoftheC(gg−1)by(kgm−3)andthelayerthickness (m).ThesoilpHwasassessedinboththewaterandtheKCl solution.17

Statisticalanalysis

One-wayanalysisofvariance(ANOVA)wasappliedto deter-minesignificantdifferencesbetweenthelandusesanalyzed. Tukey’stest was appliedat5%significance.The Canonical VariateAnalysis(CVA)wasappliedtodeterminewhetherthe SIRcandistinguishthelanduses.

Results

Comparisonbetweenlanduses

Pasturesoilsshowedhigherresponsestotheaminoacidand carboxylicacid groupsandtotheindividualsubstrates glu-cose,mannose,serine, fumaric acid,ketoglutaric acid,and ketobutyricacid(Fig.2).Nativeareas hadhigher responses tomalonicacid,malicacid andsuccinic acid.CVAshowed goodseparationoftheareasusingSIR(Fig.3).Thesubstrates thatcontributedtheobservedseparationinthefirst canoni-calaxis(CV1)wereglucosamineandglutamicacid(positive value), and asparagine (negative value) contributed to the observedseparationofthepastureareasfromthecropand nativeareas.Serine(negativevalue) washighlightedinthe

Cv 1 (67.5% ) -2 -1 0 2 1 3 -3 Cv2 (32.5%) -2 -1 0 1 2 3 -3 CROP PAST NAT 4 5 4 5

Fig.3–Canonicalvariateanalysisofthecatabolicprofileof

microorganismsinthestudiedareas.(CROP)cropareas;

×(NAT)nativeareas;(PAST)pastureareas.Thecircle

representsthe95%confidencearea.

Table2–Canonicalvariatevectorsandmeansforthe comparisonbetweenlanduses.

Nativeareas %Variation CV1 67.50 CV2 32.50 Total 100 Vectors CV1 CV2 Water −2.46 −0.24 Arginine 0.37 −0.16 Asparagine −3.29 0.59 GlutamicAc. 3.08 −0.83 Glutamine −0.88 0.83 Glucosamine 4.12 −0.88 Histidine −2.30 −0.16 Lysine 0.64 −0.12 Glucose 0.23 0.81 Mannose 1.27 0.79 CitricAc 0.10 −0.36 AscorbicAc −0.09 0.33 Serine 1.66 −1.61 GluconicAc −0.78 0.24 FumaricAc 0.98 −0.50 MalonicAc 0.33 −0.17 MalicAc 0.32 −0.06 KetoglutaricAc −0.02 0.05 KetobutyricAc −0.19 0.52 PantothenicAc 0.83 0.88 QuinicAc −2.07 −1.73 SuccinicAc −0.55 0.37 TartaricAc −0.98 1.75 C.M. CV1 CV2 CROP −1.38 −1.02 NAT −0.58 1.34 PAST 1.96 −0.32

C.M.,canonicalmean;CROP,cropareas;NAT,nativeareas;PAST, pastureareas.

secondaxis(CV2),whichseparatesnativeareasfrompasture

andcropareas(Table2).ThesoilpHinnativeareaswas

dif-ferent from thatinother land uses.SoilpHwaspositively correlated (r2_{=0.72)withhistidine, whereasother chemical} attributes didnotshowasignificant correlationinall land uses.PastureshowedthehighestdensityandCstock(Table3).

Comparisonwithinlanduses

Forestareashadhigherresponsestoaminesandaminoacid groupsandtheindividualsubstratesarginine,glucosamine, histidine,ascorbicacidandketoglutaricacid(Fig.2).SIRwas used to show separation among the native areas (Fig. 4). Thesubstrates thatcontributed tothe observedseparation inCV1 were glucosamineand glucose(positivevalue), and glutamine(negativevalue)contributedtotheobserved sep-aration ofthe forestsfromthe Cerradoand Dense Cerrado areas.Glucosamine(positivevalue)washighlightedintheCV2 asasparagine,whichseparatestheDenseCerradofromthe forestand Cerrado(Table4).Thesoilchemical characteris-ticsweresimilarwithinlanduses.Significantdifferenceswere

Average SD CV Average SD CV Average SD CV Average SD CV Average SD CV Landuses Native areas 2.11a 0.99 46.91 11.00b 4.07 36.97 1.09c 0.19 17.81 5.26b 0.95 18.01 4.47b 1.02 22.92 Pasture 1.95a 0.79 40.81 12.54a 4.77 38.05 1.34a 0.19 14.55 5.97a 0.50 8.46 5.24a 0.72 13.66 Agricul-tural areas 1.94a 0.67 34.90 10.86b 3.1 28.62 1.17b 0.16 13.71 5.96a 0.47 7.86 5.27a 0.55 10.50 Nativeareas Forest 2.18a 1.06 48.6 11.65a 4.31 37.00 1.12a 0.19 16.8 5.48b 1.11 20.2 4.78a 1.19 24.9 Cerrado 1.95a 0.59 30.25 9.82a 2.98 30.34 1.06a 0.22 21.1 4.95a 0.52 10.5 3.91b 0.19 4.86 Cerradão 2.09a 0.99 47.36 10.53a 4.16 39.50 1.07a 0.19 18.00 5.04a 0.73 14.4 4.26ab 0.7 16.3 Pasture Nominal pasture 1.88b 0.61 32.4 12.10ab 3.5 29.00 1.30a 0.10 7.60 6.21a 0.36 5.87 5.59a 0.61 11.00 Degraded pasture 1.82b 1.13 62.2 10.67b 4.4 41.20 1.31a 0.26 20.00 5.80b 0.35 6.00 5.09b 0.53 10.30 Improved pasture 2.01a 0.77 38.4 13.16a 5.18 39.30 1.37a 0.20 14.80 5.93b 0.56 9.40 5.16b 0.77 14.80 Agriculturalareas No-tillage 2.17a 0.78 36.00 11.70a 2.95 25.20 1.12a 0.16 14.1 6.11a 0.40 6.53 5.33a 0.35 6.59 Tillage 1.95a 0.74 37.90 10.86b 3.48 32.00 1.18a 0.18 15.4 6.01a 0.44 7.26 5.26a 0.54 10.30 Perennial 1.75a 0.27 15.40 10.45b 1.38 13.20 1.20a 0.08 6.74 5.74b 0.57 9.97 5.25a 0.70 13.30 - 3 - 2 - 1 0 1 2 3 - 3 - 2 - 1 0 2 1 3 Cv1 (72.79%) Cv2 (27.21%) CER CERRA

FOR

Fig.4–Canonicalvariateanalysisofthecatabolicprofileof themicroorganismsinnativeareas.(CER)Cerrado;× (CERRA)Cerradão;(FOR)forest.Thecirclerepresentsthe 95%confidencearea.

onlyobservedinsoilpHH2OandKCl(Table3).InCerrado,pH

washighlycorrelatedwitharginine,whereastheC%was cor-relatedwithasparagine.Insiteswherethenativeareawas theCerradão,pHwashighlycorrelated(r2_>0.70)with16 sub-strates,including allgroups(amines, carbohydrates,amino acidsandcarboxylicacids).

Nominal pastures showed higher responses to amines and carbohydrategroupsandtheindividual substrates glu-cosamine,lysineandsuccinicacid.Improvedpasturesshowed significantly responses to amino acid and carboxylic acid groups,whereasdegradedpasturesshowedhigherresponses to the individual substrates citricacid, ascorbic acid, mal-onic acid, malic acid, ketoglutaric acid, ketobutyric acid, and tartaric acid (Fig. 2).SIR wasused toshowedthe sep-aration of the pasture areas (Fig. 5). The substrates that contributed tothe observedseparation inCV1 were quinic acid and mannose (positive value) and ascorbic acid, glu-coseandglutamicacid(negativevalue),thusseparatingthe improved pastures from the degraded and nominal pas-tures. Glucosamine (positive value) was as highlighted in CV2,asasparagine,whichseparatesdegradedpasturesfrom improvedandnominalpastures(Table4).Improvedpastures showed higher C% and C stock, whereas nominal pasture showedlessacidicpH(Table3).ThesoilpHandsoilbulk den-sityofnominalpastureswerehighlycorrelated(r2_>0.7)with ketoglutaric acid,whereas theC%and Cstockwere highly correlatedwith9substrates,particularlycarbohydrates.The soilpHinimprovedpastureswashighlycorrelatedwith car-boxylic acid and the C stock was correlated with tartaric acid.

Perennial crops showed a higher response to amines, aminoacidandcarboxylicacidgroupsandallofthe individ-ualsubstrates,exceptlysine,glucose,quinicacidandsuccinic acid (Fig.2).Amongthe cropareas,CVAshowed good sep-arationusingSIR(Fig.6).Thesubstratesthatcontributedto theobservedseparationinCV1wereserine,glucosamineand mannose,(positivevalue)andglucose,pantothenicacidand quinic acid (negative value), thus separating conventional-tillage areas from no-tillage and perennial areas. Histidine

Table4–Canonicalvariatevectorsandmeansforthecomparisonwithinlanduses.

Nativeareas Pasture Agriculturalareas

%Variation CV1 72.79 63.43 95.55 CV2 27.21 36.57 4.45 Total 100 100 100 Vectors CV1 CV2 CV1 CV2 CV1 CV2 Water −0.37 0.73 CV1 CV2 −14.22 15.82 Arginine −0.17 0.07 0.04 1.25 −0.01 0.04 Asparagine 0.52 1.10 0.12 0.25 0.32 −15.55 GlutamicAc. −0.41 −0.02 −0.94 −0.67 0.02 0.41 Glutamine −2.47 −0.03 −10.65 −0.82 0.23 −0.54 Glucosamine 1.73 1.19 0.85 0.09 15.90 −10.53 Histidine −0.86 −1.31 0.98 −0.68 −0.29 30.47 Lysine −0.33 0.25 −0.06 0.73 0.34 −0.92 Glucose 2.00 0.57 −0.68 0.33 −22.98 0.84 Mannose −0.84 −0.58 −10.66 −0.82 13.68 0.19 CitricAc −0.07 0.27 10.61 0.47 −0.01 0.01 AscorbicAc −0.28 0.29 0.08 −0.03 0.06 −0.54 Serine −0.51 0.13 −12.51 −0.21 18.42 −14.14 GluconicAc 0.19 0.40 0.02 −0.83 −0.29 0.02 FumaricAc 0.21 −0.12 0.02 0.65 0.22 0.48 MalonicAc 0.06 0.35 −0.01 −0.39 −0.73 0.91 MalicAc 0.16 0.27 0.23 0.30 −0.73 −0.35 KetoglutaricAc −0.07 0.02 −0.11 0.33 0.61 −0.12 KetobutyricAc 0.06 0.36 −0.10 0.01 0.43 0.12 PantothenicAc −0.20 0.41 −0.01 0.36 −12.94 16.18 QuinicAc 0.93 0.04 0.20 0.03 −10.45 −0.82 SuccinicAc −0.02 0.10 19.32 0.99 0.60 −0.84 TartaricAc 0.09 0.61 0.62 0.28 0.21 0.10 C.M. CV1 CV2 C.M. CV1 CV2 C.M. CV1 CV2

CER 1.76 1.11 PASTN 2.93 0.98 PERE 8.51 0.14

CERRA 1.07 −1.17 PASTD −0.90 0.42 NT −1.88 −0.41

FOR −1.12 0.13 PASTI 0.51 −2.00 T −2.96 2.60

C.M.,canonicalmean;CER,Cerrado;CERRA,Cerradão;FOR,forest;PASTN,nominalpasture;PASTD,degradedpasture;PASTI,improved pasture;PERE,perennialcrop;NT,no-tillage;T,conventionaltillage.

andpantothenicacid(positivevalue)wereashighlightedin

theCV2asserine,glucosamineandasparagine,which

sepa-rateperennialareasfromconventional-tillageandno-tillage

(Table4).Thechemicalattributes,exceptCstockandpH,were similarwithinagriculturalareas(Table4).ThesoilpHandsoil bulkdensityintillageareaswerehighlycorrelated(r2_>0.70) withaminoacidsandcarboxylicacids.

Discussion

Several authors18–22 _{have demonstrated that plants}

signif-icantly influence soilmicrobial biomass diversity. Microor-ganismsdependonexternalcarbonsources.Therefore,litter quantity/qualityandfloristicdiversitycanbeimportant fac-torsindeterminingmicrobialbiomassdiversity.Zhangetal.23

showedthatlitterquantityinfluencedsoilmicrobial commu-nitystructureandfunctionalstability.Accordingtotheresults ofthepresentstudy,themostimportantsubstratesusedto differentiatethelandusesand/ormanagements(asparagine,

serine,glucose)wereassociatedwithrootexudates24_or

com-ponents ofplanttissue(quinicacid).25 _{When the catabolic}

diversity of microorganisms partially depends on the var-ied quality and production of litter, it is expected that different land uses would result in different microbial communities.

Forthousandsofyears,naturalfire,duringthewetseason, andanthropogenicfire,duringthedryseason,coexistedinthe Cerradoregion,particularlyinthemoreopenphysiognomic form.AccordingtoArocenaandOpio,26firemajorlyimpacts soil-aggregatestabilityandsoilpH,significantlyinfluencing themicrobialbiomass.AccordingtoZimmermanandFrey,27

the increaseinsoilpHinresponsetoashes contributesto thedevelopmentofthemicrobialcommunity.Roscoeetal.28

demonstrated in Cerradosoilsthat the soilorganic matter origin changeswhenfiresare frequentinthe system,with 34.6% ofthe original C derivedfrom C3plants substituted withCderivedfromC4plants.AccordingtoHart,29_firecan

be considered a selection factor of areas exposed to peri-odicfireevents,astheseeventschangethestructureofthe

Cv1 (63 .43 %) -2 -1 0 2 1 3 -3 Cv2 (36.57%) -2 -1 0 1 2 3 -3

PAST

D

PAST

PAST

I

Fig.5–Canonicalvariateanalysisofthecatabolicprofileof

themicroorganismsinpastureareas.(PASTD)degraded

pasture;×(PAST)nominalpasture;(PASTI)improved

pasture.Thecirclerepresentsthe95%confidencearea.

Cv1 (95.55%) -2 -0 2 6 4 8 -4 Cv2 (4.45%) -2 -0 2 4 6 8 -4 T NT PERE 10 12 10 12

Fig.6–Canonicalvariateanalysisofthecatabolicprofileof

themicroorganismsincropareas.(PERE)perennialcrop;

×(T)tillage;(NT)no-tillage.Thecirclerepresentsthe95%

confidencearea.

microbialbiomass.Theresultsofthepresentstudyshowed thatpHwascorrelatedwithallsubstrategroupsinDense Cer-radoareas.Campbelletal.30_{demonstratedthattheutilization}

of carbonate substrates decreases with burning, suggest-ing the decreased resistance/resilience of the microbial community.

Theseparationobservedinpastureareasmightbe associ-atedwiththetotalCstockinsoil.Landusesormanagements that induce the accumulation oforganic matter increased

pastures (Table3).Innominal pastureareas,soilC%and C stockwerehighlycorrelatedwithcarbohydrates,whereasthe soilpHwassignificantlycorrelatedwithcarboxylicacidsin improvedpastures.

Lupwayietal.34_{alsoobserveddifferencesinthemicrobial}

diversityinareasunderconventionalandno-tillage manage-ment.Conventional-tillageinduceschangesintheavailability ofsubstrates,35 _{which modifies the pattern ofuse and the}

microbialcommunityintheseareas.36_{Thedifferentpatterns}

suggest thepresenceofdifferentmicrobial communitiesin each land use. No-tilled surface soils (0–5cm) have more lightweightmaterial.37_{Theseconditionsstimulatethegrowth}

andactivityofsoilmicroorganisms.Furthermore,these char-acteristics indicate that SOM has chemical and structural differencesinthetwosystems.37,38_{Anotherfactorthatmight}

contributetotheobserveddifferenceistheincreasedorganic carbonstorageinthesoilduetotheuseofno-tillagesystems. Maiaetal.39_{reportedthepotentialofareasunderno-tillage}

toincreasetheorganiccarboncontentinthesamesoils.The applicationofherbicides,whichisacommonpracticein con-ventional tillage,alsodecreases soilmicrobial biomassand activity.40

According to Brady and Weil,41 _{pH affects the physical,}

chemical andbiologicalpropertiesofsoil.Zimmermanand Frey27 _{demonstratedthatpHplaysakey roleinthe}

micro-bial community dynamics of forest soils. Other studies at the continentalscale alsoidentifiedpH asthe mainfactor determiningthemicrobialcommunitystructure.42,43

Camp-bell et al.30 _{reported the same conclusion from studies of}

Australianforests.

Many studies have supported the idea that free-living microorganisms exhibit biogeographic patterns.44 _Theidea

that “every thing is in everywhere, but the environment selects”45_{isconsistentwiththeresultsofthepresentstudy.}

The separation observed in the CRP analysis (Figs. 3–6) indicated the importance of the specific characteristics of each landuseinmicrobial biomassdiversity,eveninareas with different climates and soils, such as the evaluated eco-regions. Differences in the C content and the nature of substrates induce changes inthe utilization patterns of organic substrates.31,46 _{The different patterns suggest the}

presenceofdifferentmicrobialcommunitiesineach vegeta-tioncover.Despitethelargeextensionoftheareaevaluated, the microbial community was stronglycorrelated with the soil-plant componentinthepresentstudy.Consistentwith Stevensonetal.,12_{wealsodemonstratedthatthefunctional}

microbialcommunitychangedinresponsetodifferentland uses.

Conclusion

Thedifferent responsestotheaddition ofthese substrates suggest thepresenceofdifferentmicrobial communitiesin each land use or management. Despite the different soil type, climate and topography observed in each ecoregion, themicrobialcommunitiesshowedsimilaritiesineachland

use.Plantcomposition(andconsequentlyrootexudates)and soilpHwere the mainfactors determining thesimilarities betweenandwithinlanduses.Inthepresent study, differ-enceswereobservedinlargecomparisons(Landuses–3.1) tobiomesandmorespecificmanagements(3.2),regardlessof climateorsoiltype.

Conflicts

of

interest

Theauthorsdeclarenoconflictsofinterest.

Appendix

A.

Supplementary

data

Supplementarydataassociatedwiththisarticlecanbefound, intheonlineversion,atdoi:10.1016/j.bjm.2015.11.025.

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