Drivers of Plot-Scale Variability of CH4 Consumption in a Well-Aerated Pine Forest Soil

16 Pages·2017·3.24 MB·English

Article
Drivers of Plot-Scale Variability of CH Consumption
4
in a Well-Aerated Pine Forest Soil
MartinMaier1,*,SinikkaPaulus1,ClaraNicolai1,KentonP.Stutz1andPhilippA.Nauer2
1 ChairofSoilEcology,InstituteofForestScience,UniversityofFreiburg,Freiburg79098,Germany;
[email protected](S.P.);[email protected](C.N.);
[email protected](K.P.S.)
2 SchoolofEcosystemandForestScience,FacultyofScience,BurnleyCampus,TheUniversityofMelbourne,
Melbourne,VIC3121,Australia;[email protected]
* Correspondence:[email protected]
AcademicEditor:AlessandraLagomarsino
Received:7April2017;Accepted:31May2017;Published:3June2017
Abstract: Whiledifferencesingreenhousegas(GHG)fluxesbetweenecosystemscanbeexplainedto
acertaindegree,variabilityofthesameattheplotscaleisstillchallenging.Weinvestigatedthespatial
variabilityinsoil-atmospherefluxesofcarbondioxide(CO ),methane(CH )andnitrousoxide(N O)
2 4 2
to find out what drives spatial variability on the plot scale. Measurements were carried out in a
Scotspine(PinussylvestrisL.)forestinaformerfloodplainona250m2plot,dividedinhomogenous
strataofvegetationandsoiltexture. Soilgasfluxesweremeasuredconsecutivelyat60pointsalong
transects to cover the spatial variability. One permanent chamber was measured repeatedly to
monitortemporalchangestosoilgasfluxes. Theobservedpatternsatthiscontrolchamberwere
usedtostandardizethegasfluxestodisentangletemporalvariabilityfromthespatialvariabilityof
measuredGHGfluxes. Concurrentmeasurementsofsoilgasdiffusivityallowedderivinginsitu
methanotrophicactivityfromtheCH fluxmeasurements. ThesoilemittedCO andconsumedCH
4 2 4
andN O.SignificantlydifferentfluxesofCH andCO werefoundforthedifferentsoil-vegetation
2 4 2
strata,butnotforN O.SoilCH consumptionincreasedwithsoilgasdiffusivitywithinsimilarstrata
2 4
supportingthehypothesisthatCH consumptionbysoilsislimitedbythesupplywithatmospheric
4
CH . Methane consumption in the vegetation strata with dominant silty texture was higher at a
4
givensoilgasdiffusivitythaninthestratawithsandytexture. Thesamepatternwasobservedfor
methanotrophicactivity,indicatingbetterhabitatsformethantrophsinsilt. Methaneconsumption
increasedwithsoilrespirationinallstrata. Similarly,methanotrophicactivityincreasedwithsoil
respirationwhentheindividualmeasurementlocationswerecategorizedintosiltandsandbasedon
thedominantsoiltexture,irrespectiveofthevegetationstratum. Thus,wesuggesttherhizosphere
anddecomposingorganiclittermightrepresentorfacilitateapreferredhabitatformethanotrophic
microbes,sincerhizosphereanddecomposingorganicarethesourceofmostofthesoilrespiration.
Keywords: methane;carbondioxide;nitrousoxide;soilgases;methanotrophy;soiltexture
1. Introduction
Carbon dioxide (CO ), methane (CH ) and nitrous oxide (N O) are the most important
2 4 2
anthropogenicgreenhousegases(GHG)andco-responsibleforglobalclimatechange[1]. SoilCO
2
emissionsresultfromheterotrophicandautotrophicrespirationbymicrobes,faunaandrootsinsoil,
andamountsupto80%ofthecarbon(C)assimilatedbyplantphotosynthesis[2]. Soil-atmosphere
fluxesandatmosphericconcentrationsofN OandCH aremuchsmallerthanthoseofCO .Yet,dueto
2 4 2
thehigherradiativeforcingofN OandCH moleculesintheatmospherecomparedtoCO ,their
2 4 2
contributiontotheglobalwarmingissubstantial[3]. Soil-atmosphereGHGfluxesvarytemporally
Forests2017,8,193;doi:10.3390/f8060193 www.mdpi.com/journal/forests
Forests2017,8,193 2of16
onthedailyandseasonalscale,andtheyvaryspatially,betweenecosystemsandsites. Anaccurate
assessmentofsoil-atmospherefluxesisconsequentlychallenging.
WhilesoilgenerallyactsassourceofCO ,itcanbebothsourceandsinkforCH andN O[4,5].
2 4 2
Methanogenesisisstrictlyananaerobicprocess,butitcanoccurinaeratedsoilsifoxygen-depleted
zones exist such as within aggregates [6]. Oxidation of CH by methanotrophic bacteria in soils
4
canbedividedintotwoformsbasedonapparentenzymekinetics[7,8]. Thelow-affinityoxidation
occurswhenCH concentrationsareabove40ppm,specificallyinenvironmentswhereCH ishighly
4 4
enriched compared to the atmosphere (e.g., in landfills, peat soils, paddy soils). The high-affinity
oxidation occurs at and even below atmospheric CH concentrations and is ubiquitous in upland
4
soils [9]. Upland soils in forests are generally well-aerated and thus considered to be strong CH
4
sinksduetoadominanceofmethanotrophyovermethanogenesis. SimilartoCH ,N Ocanbeboth
4 2
producedandconsumedinsoildependingonoxygenavailability. ProductionofN Omainlyoccurs
2
insub-oranoxicmicrositeswithinsoilaggregates[6]asitisaby-productofaerobicnitrificationunder
oxygen-limitedconditionsandanaerobicdenitrification[5,10]. SoilscanalsoactasasinkforN O,
2
wherepresumablyN OisreducedtoN duringdenitrification[11],thoughnetsoiluptakeratesof
2 2
N Oareusuallylowcomparedtopotentialemissions. ConsumptionandproductionofCH andN O
2 4 2
oftenoccursimultaneouslyindifferentsoilcompartmentsduetoheterogeneousphysico-chemical
soilconditionsandmicrobialcompositionandactivity. Yet,commonchambertechniquesonlyallow
measuringthenetsoil-atmosphereexchange.
Soil-atmosphereGHGfluxeschangeovertimeandundergodiurnalandseasonalcycles[12,13].
ThemostimportantabioticfactorsinfluencingthefluxesofthementionedGHGaresoiltemperature
andsoilmoisture[5].Yet,theresponsetothesefactorsisdifferentforeachgasspecies.Thetemperature
responseisoftenexpressedasQ value,thefactorbywhichthefluxincreasesifthetemperatureincreases
10
by10◦C.Carbondioxidefluxesstronglyincreasewithsoiltemperature(Q 1.4–4.2)[5,14]whileCH
10 4
oxidationismuchlessdependentontemperature(Q around1.4)[5]. Incontrast,temperatureaffects
10
the production of CH and N O the strongest (Q > 10) [5,15]. Soil moisture can be limiting soil
4 2 10
respirationwhenitistoodryortoowet[5,13,16]. Methaneconsumptioncanalsobelimitedwhena
soilistoodry[17],butgenerallyCH consumptionincreasesinadryingsoil[16,18,19]. Lowersoil
4
moisture results in higher air-filled pore-space, and thus increasing soil gas diffusivity and better
supply of atmospheric CH . Fluxes of methane consumption are thus limited by the transport of
4
atmosphericCH intosoil(diffusion)andbythemethanotrophicactivity(acombinationofpopulation
4
densityandmethanotrophicactivitypercell)[20,21]. Nitrousoxidefluxesareoftendominatedby
peakemissionevents,whichfollowtemporaryanoxiainducedbysubstantialchangesinsoil-moisture
conditionssuchasfreeze-thaweventsorrewettingofdrysoil[22,23].
Soil-atmosphereGHGfluxesvaryspatiallybetweenecosystemsandsites,butalsoontheplot
scalethroughtemporal-dependentfactorssuchassoilmoistureandtemperature[12]. Forinstance,
methaneconsumptionatasiteisaffectedbysoilphysicalproperties[24,25],butalsobylitterquality
and the vegetation [25,26], and bedrock categories [27]. To study the effects of spatially variable
parameters such as soil properties or vegetation patterns at a plot scale, it is therefore necessary
to measure greenhouse gas fluxes at different locations at the same time. Since manual chamber
measurementsarelaboriousandtimeconsuming,thisisnotpossible,andmeasurementsareusually
performed consecutively. The measured fluxes include, thus, also a temporal component in their
variabilitythathastobeconsidered,e.g.,bymeasuringrepeatedlyacontrolchamber[14].
InvestigatingthespatialvariabilityofCO ,CH andN Ofluxesallowsforstudyingpossible
2 4 2
effects of vegetation and soil properties and finding links between the fluxes of the different gas
species[25,28]canpointtohowunderlyingmicrobialprocessesmightbeinterrelated. Understanding
patternsinthespatialvariabilityofsoilgasfluxes,e.g.,identifyingvegetationstrataorkeyspecies
ontheplotscalewouldalsoallowchoosingmeasurementlocationsformonitoringpurposesmore
efficiently. This would result in a better estimation of a representative flux of the area, and help
optimizingthepositionandnumberofsamplinglocations[14].
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Theobjectiveofourstudywastoidentifydriversofspatialvariabilityofmethaneconsumption
ontheplotscaleinawell-aerateduplandforest. Westratifiedtheplotaccordingtovegetationandsoil
texture,andhypothesizedthatsoil-gasfluxesofthesestrataweresignificantlydifferent. Toanalysethe
Forests 2017, 8, 193   3 of 16 
spatialvariabilityofGHGfluxesingeneral,andespeciallythelinksbetweenmethaneconsumption
and the fluxesThoef oCbjOec2tivaen odf oNu2r Ostuodny wthaes tpol iodtensctiafyle d,rwiveersd oefv seplaotipale vdaraiasbtialintyd oaf rmdeizthaatnioe ncopnsruomcepdtiuonre  that
allowsforodn itsheen ptalont gslcianleg itne am wpeolrl‐aaleraantedd suppalatinadl fvoarerisat.b Wiliet ystroaftiGfieHd Gthefl pulxoet sa.ccording to vegetation and 
soil texture, and hypothesized that soil‐gas fluxes of these strata were significantly different. To 
2. Materiaalnaalnydse Mthee tshpoatdiasl variability of GHG fluxes in general, and especially the links between methane 
consumption and the fluxes of CO2 and N2O on the plot scale, we developed a standardization 
2.1. ExperipmroecnetdaulrSei ttheaatn adlloSwoisl fCorh adrisaecntetarnisgtliicnsg temporal and spatial variability of GHG fluxes. 
Meas2u. rMemateerniatsl awnder Mecetahrordiesd  outinaplanted50yearoldScotspinestand(PinussylvestrisL.)next
totheHartheimforestmeteorologicalexperimentalsite. ThesiteislocatedintheUpperRhineValley
(South-We2s.1t. GExepremrimaennyta)l aSbiteo auntdo Snoiel Ckhilaoramcteertiestrices astoftheRhineRiver(47◦56(cid:48) N,7◦36(cid:48) E,201mabove
sea level). ThMeemaseuarnemaennntsu walertee mcarpreierda touurte inis a 1p0l.a3nt◦eCd 5a0n ydeatrh eoldm Secaonts apninneu satalnpdr e(Pciinpuist astyilovenstriiss 6L4.)2  mm
(Holstetanle.,xt2 t0o0 t8h)e. HTahrethseiitme wforaesstr megeuteloarrollyogflicoaol dexepderuimnetinltathl seitme. iTdheo sfitteh ies 1lo9ctahtecde innt tuhrey Uwppheer nRhthinee River
Valley (South‐West Germany) about one kilometer east of the Rhine River (47°56′ N, 7°36′ E, 201 m 
Rhinewascorrected. Thetopographyofthesiteisflatwithminorlineardepressions(lessthan1m
above sea level). The mean annual temperature is 10.3 °C and the mean annual precipitation is 642 
deep)resultingfromformercoursesofcreeksinthefloodplain. ThesoilisaHaplicRegosol(calcaric,
mm (Holst et al., 2008). The site was regularly flooded until the mid of the 19th century when the 
humic)[29R]i.veDr uRehitnoe twhaes hcoisrrteocrtyedo. fThaell tuovpioaglradpehpyo osfi ttihoen siotef iss uflbats twriatthe ms,inthoer lsinoeialrt edxepturersesicohnas n(lgesess thaabnr uptly
vertically1w mith dienepth) eressouilltipngro ffirolmea fnordmheor rciozuornsetsa lolyf cwreietkhsi nins thhoer ftlodoidstpalanicne. sT.hTeh seorile iiss aa HAahplhico Rriezgoonsool ffine
sand-silt-o(crallcoaarmic, yhusmiltico) f[259–].5 D0ucem to dtheep htihstoorny toof palloufvdiailf dfeerpeonsittiforna cotfi sounbsstoraftessa, nthde asonidl tegxrtauvree lchaatntghees most
abruptly vertically within the soil profile and horizontally within short distances. There is a Ah 
locations. Whilethepinestandwasplantedinregularrows,thegroundcoverandunderstoryallows
horizon of fine sand‐silt‐or loamy silt of 5–50 cm depth on top of different fractions of sand and 
separatingtheplotinseveralhomogenousvegetationstrata,furthercalledUnitsI–VI.
gravel at the most locations. While the pine stand was planted in regular rows, the groundcover and 
understory allows separating the plot in several homogenous vegetation strata, further called Units 
2.2. ExperimentalDesign
I–VI. 
Themeasurementplotwaspositionedacrossahistoricalcreekbedthatformedaminordepression
2.2. Experimental Design 
(depth < 1 m) in the former floodplain (Figure 1a,b). The plot was homogenously covered by the
The measurement plot was positioned across a historical creek bed that formed a minor 
plantedpinetreesandcovereddifferenttypesofsoiltexture,groundcoverandunderstoryvegetation
depression (depth < 1 m) in the former floodplain (Figure 1a,b). The plot was homogenously covered 
(Figure1d–f). Measurementswereconductedalongthree32mlongparalleltransects8mapartthat
by the planted pine trees and covered different types of soil texture, ground cover and understory 
crossedthedepression. Measurementpointswereregularlydistributedontransectsaspairs0.5m
vegetation (Figure 1d–f). Measurements were conducted along three 32 m long parallel transects 8 m 
apartanda3pamrt btheatwt cereonssetdh ethpea diresp;reesascihont.r Manesaesuctreimncelnut dpeodint2s0 wmereea rseugruelmarleyn dtipstoriibnuttsedin otno ttaral.nsects as 
Measpuariresm 0.e5n mts apaanrdt asnadm 3 pml ibnegtwweeenr ethcea prariiresd; eaocuht trfarnosmect8 intcolu1d4edD 2e0c memeasbuerrem20en1t5 pioninstst ainb lteotawl.e ather
conditions. AtMaelalspuoreinmtesnwts eancdo nsadmupctliendg mweeraes cuarrerimede noutto ffrosomi l8g taos 1fl4 uDxeecsemanbedr t2o0o1k5 isno isltacboler ewseaamthperl esfor
conditions. At all points we conducted measurement of soil gas fluxes and took soil core samples for 
soil-physicalanalysis. Wesampledthesoilprofiledownto1mdepthatallpointsusingasoilprobe
soil‐physical analysis. We sampled the soil profile down to 1 m depth at all points using a soil probe 
andassessedthesoiltextureinthefieldwiththefingertest[30]. Atallsamplingpointstherewasa
and assessed the soil texture in the field with the finger test [30]. At all sampling points there was a 
subsurfacegravellayerthatatsomepointsreachedthesurface. Thetypicalsoundofthesoilprobe
subsurface gravel layer that at some points reached the surface. The typical sound of the soil probe 
goingthroguoginhg gthrraovueglha glrloavwele adlltohweedla tyheer latyoebr etoi bdee indteinfiteifdie,dy, eytets saammpplleess ooff ththisi slalyaeyr ewrewree mreosmtlyo lsotslyt. lost.

Figure1.Cont.
Forests2017,8,193 4of16
Forests 2017, 8, 193   4 of 16 

Figure 1F.igu(rae )1.T (oap) oTgopraogprhaipchailcaml maapp ooff tthhee HHartahretihme iemxpeerximpeenrtiaml esintet ablasseidte onb alasset‐dpuolsne llaassetr- pscualns e laser
data; (b) Position of the sampling transects on the topographical map; (c) Sampling plot divided into 
scan data; (b) Position of the sampling transects on the topographical map; (c) Sampling plot
understory vegetation strata; (d–f) Catenae along Transect A–C including the respective vegetation strata. 
dividedintounderstoryvegetationstrata;(d–f)CatenaealongTransectA–Cincludingtherespective
vegetatioTnhes tdroamtai.nating vegetation was used to stratify the plot into six sub‐units (Table 1) that showed 
mostly similar texture in their soil profiles (Table 1, Figure 1c–f). 
Thedominatingvegetationwasusedtostratifytheplotintosixsub-units(Table1)thatshowed
Table 1. Vegetation units used to stratify the sampling plot. 
mostlysimilartextureintheirsoilprofiles(Table1,Figure1c–f).
Unit  Topography  Understory & Ground Vegetation Soil Texture 
Unit I  Southern shoulder & plain  Solidago gigantea (L.) and moss species  Sand & gravel to loamy sand 
Table1.Vegetationunitsusedtostratifythesamplingplot.
Bushes, Ligustrum vulgare (L.); Crataegus 
Unit II  Southern slope  Sandy gravel to sandy loam 
monogyna (Jacq.) 
Unit Unit III  TopoBgortatpomhy‐slope  MUonsdseesr (set.og.r, ySc&lerGoprodoiuumnd puVruemge, Lta.)t ion Silt and siltSy oloialmTe. xture
10–20 year old broadleaves Juglans regia 
UnitI Southernshoulder&plain Solidagogigantea(L.)andmossspecies Sand&graveltoloamysand
Unit IV  Bottom  (L.), Tilia platyphyllos (L.), Carpinus betulus  Silt and silty loam. 
Bushes,L(Lig.)u, lsetsrsu gmrovuunldgcaorvee(rL .);Crataegus
UnitII Southernslope Sandygraveltosandyloam
Unit V  Norther slope & shoulder  Bushes, Ligusmtruonmo vguylngaare( J(aLc.)q; .C)rataegus  Sandy gravel to sandy loam 
monogyna (Jacq.) 
UnitIII Bottom-slope Mosses(e.g.,Scleropodiumpurum,L.) Siltandsiltyloam.
Unit VI  Northern plain  (few) Solidago gigantea (L.), grass species  Sand and gravel 
10–20yearoldbroadleavesJuglansregia
UnitIV2.3. MeasuremenBtso ottfo Smoil Gas Fluxes  (L.),Tiliaplatyphyllos(L.),Carpinusbetulus Siltandsiltyloam.
(L.),lessgroundcover
Soil‐atmosphere gas fluxes were measured using non‐steady‐state flow‐through chambers. The 
Bushes,Ligustrumvulgare(L.);Crataegus
UnitVchambeNrso rctohnersissltoepde o&f sah PouVlCde crollar and a mobile lid with a vent [31]. The collSaarnsd (ydigarmaveetletro 0s.a1n7d my,l oam
monogyna(Jacq.)
height 0.25 m) were installed the day before the measurement and inserted approximately 3 cm into 
UnitVI Northernplain (few)Solidagogigantea(L.),grassspecies Sandandgravel
the soil. The air in the chamber was circulated via tubes to a Greenhouse Gas Analyzer (GGA; 
version Ultraportable, Los Gatos Research, CA, USA) for CH4 and CO2 measurements and to a Gas 
2.3. MeaMsuorneimtoern t1s41o6f S(oLiulmGaasseFnslue,x eBsallerup,  Denmark)  for  N2O  and  CF4  (see  Section  2.4)  analysis. 
Measurement frequency was 0.5 Hz for the GGA, and every 60 s for the Gas Monitor. Water vapour 
Soil-atmosphere gas fluxes were measured using non-steady-state flow-through chambers.
was measured by both devices and stabilized using a dew point controller set to 4 °C. This greatly 
The chaimmbpreorvsedco anccsuisratecyd aonfd aprPecVisCionc oofl ltahre aNn2Od maemasuorbeimleenlitds, aws citohnfairmveedn tby[ 3te1s]t.s wTihthe ac loalbloarrasto(rdyi ameter
0.17m,GheCi.g  ht0.25m)wereinstalledthedaybeforethemeasurementandinsertedapproximately
3 cm into thCehsaomilb.erTs hweeraei rclionsetdh eforc h15a–m20b emrinw faors tchier cfululxa tmedeasvuiraemtuebnet.s Ftluoxaes Gofr eCeHn4h aonuds CeOG2 awseAren  alyzer
calculated linearly based on the concentration changes over time of the first five and three minutes, 
(GGA;versionUltraportable,LosGatosResearch,CA,USA)forCH andCO measurementsand
4 2
respectively (R2 > 0.95). For N2O flux estimation a linear approach over the whole time span of 15–20 
toaGasMonitor1416(Lumasense,Ballerup,Denmark)forN OandCF (seeSection2.4)analysis.
2 4

Measurementfrequencywas0.5HzfortheGGA,andevery60sfortheGasMonitor.Watervapourwas
measuredbybothdevicesandstabilizedusingadewpointcontrollersetto4◦C.Thisgreatlyimproved
accuracyandprecisionoftheN Omeasurements,asconfirmedbytestswithalaboratoryGC.
2
Chamberswereclosedfor15–20minforthefluxmeasurement. FluxesofCH andCO were
4 2
calculatedlinearlybasedontheconcentrationchangesovertimeofthefirstfiveandthreeminutes,
respectively (R2 > 0.95). For N O flux estimation a linear approach over the whole time span of
2
Forests2017,8,193 5of16
15–20minwasusedsincesoil-atmospherefluxesofN Owerenegativeandlow. Whennolineartrend
2
wasobserved(p>0.05)N Ofluxesweresettozero[32]. FluxmeasurementsofN Owithvariable
2 2
watervapour(coefficientofvariation>0.01)wereexcluded. Linearregressionswerecalculatedwith
PROCREGinSAS(SAS9.2,SASInstituteInc.,Cary,NC,USA)
2.4. MeasurementsofSoil-GasDiffusionCoefficients
InadditiontoourmeasurementsofCO ,CH andN Ofluxes,weusedCF asatracergasto
2 4 2 4
measuresoilgasdiffusivityinsituusingamodifiedMcIntyreandPhilipapproach[31]. Atotalof
1mLofdilutedCF wasinjectedintothechamberafterclosuresothattheinitialCF concentration
4 4
reached15–20µmolmol−1. ThedecreasingCF concentrationwasmeasuredovertimebytheGas
4
Monitor. Thefunctionalrelationshipbetweensoilgasdiffusivityandair-filledpore-spaceallowed
thesoilgasdiffusivityofthetopsoiltobederived. WeusedatransferfunctionfromMaieretal.[33]
thatwasbasedontopsoilsamplesfromthesamesite. Anerrorfunctionwasfittothedecreasing
CF concentrationstocalculatethesoilgasdiffusioncoefficientforCF (D CF4)(PROCNLIN,SAS9.4,
4 4 S
SASInstituteInc.,Cary,NC,USA)[31]. D CF4 wasdividedbythediffusioncoefficientofCF inair
S 4
(D CF4)atthegiventemperature[34]toobtaintherelativesoilgasdiffusioncoefficientD D −1 ,
0 S 0 insitu
whichisindependentofthegasspeciesandausefulparametertoassesssoilaeration. Gastransport
insoilisdominatedbymoleculardiffusion. Yet,windandwind-inducedpressurefluctuationscan
affectsoilgastransportaswell[33,35,36]. Toavoidwind-inducedartefactsitisthereforeimportantto
measuresoilgasdiffusivityinsituundercalmconditions. Itisalsoimportanttoensurepropermixing
ofairinmeasurementchambersforbothinsituandlabmeasurements,andtorestrictthemovement
ofairinthechamberstotaminimum.
Aftercompletingchambermeasurements,two200cm3 coresamples(5cmheight)weretaken
at each measurement point that was covered by the chamber; and the cores included the humus
layer and topsoil. In the lab, the field-fresh soil cores were analysed for soil-physical parameters.
Air-filledpore-spacewasdeterminedbyvacuumpycnometry. Soilgasdiffusivity(D D −1 )was
S 0 lab
measured using a nonstationary one-chamber method [37] with neon as a tracer gas at starting
concentrationsof100–800µmolmol−1,andweremeasuredusingamicro-gaschromatograph(CP2002P,
Chrompack,Middelburg,TheNetherlands,withaCP-Molsieve-5AcolumnandHeascarriergas).
Thesoilcoreswereplacedontopofachamberwithgasexchangerestrictedtodiffusionthroughthe
sample. Afterinjectingneonintothechamber,thedecreasingneonconcentrationovertimeallowedto
determinethesoilgasdiffusivity. Airpermeabilitywasmeasuredwithanapparatusasdescribedby
Iversenetal.[38]byrelatingairflowthroughthesampletothepressuregradientacrossthesample.
Thevolumetricsoilmoisturecontentwasdeterminedbythermogravimetry.
2.5. MethanotrophicActivity
AssumingnegligiblesoilCH production,fluxesofatmosphericCH intothesoilarelimited
4 4
by physical transport and methanotrophic activity. Historically, methanotrophic activity has been
measuredinthelabthroughincubationexperimentwithsievedsoil[20,39],i.e.,indisturbedsystems.
Methanotrophicactivityundernaturalconditionscanbeestimatedusinggaspush-pulltests[40]ora
combinationofchambermeasurementsofCH consumptionanddiffusivityinthefield[21]. Weused
4
theapproachofvonFischeretal.[21]tocalculatemethanotrophicactivityµ(s−1).
µ= (cid:18)FCH4(cid:19)2 1 µ (1)
C0 DCH4·ε
S
whereF isthesoil-atmospheremethaneflux(molm−2s−1),C theCH concentrationatthestart
CH4 0 4
of the measurement (mol m−3), DCH4 the soil gas diffusion coefficient of CH (m2 s−1), and ε the
S 4
air filled porosity (m3 m−3). Methanotrophic activity was calculated twice: (1) Using the soil gas
diffusivitymeasuredinsitutocalculatethediffusioncoefficientofCH inthesoilbymultiplying
4
Forests2017,8,193 6of16
D D −1 by D of CH in free air (0.21 cm2 s−1), and (2) using the mean D D −1 of the two
S 0 insitu 0 4 S 0 lab
soilcores. Thesetwoapproacheswereusedsincelaboratoryandinsitudeterminationofsoilgas
diffusivitycanyielddifferentvaluesanditisunclearwhichresultsaremorerealistic. D ofCH in
0 4
freeairforbothapproacheswascorrectedforbarometricpressureandairtemperature[41].
2.6. StandardizationofGHGFluxes
Sincesoilgasfluxescanshowcleardiurnalpatternatthissite[13]weincludedacontrolchamber
thatwasmeasuredatthebeginning,middayandlateafternooneachdaythroughoutthecampaign.
The observed diurnal flux pattern of the control chamber was used to calculate correction factors
andstandardizethemeasuredfluxestothetimeofthestartofthecampaign. Standardizedfluxes
(CO : Fstand.)werecalculatedbydividingtheobservedflux(Fmeas)bytherespectivecorrectionfactor
2 CO2 CO2
(CF ):
CO2
Fmeas.
Fstand. = CO2 (2)
CO2 CF
CO2
TheCO fluxofthecontrolchambershowedastrongerdependencyonairtemperaturethan
2
onsoiltemperature. WethususedairtemperaturetocalculateCF forallCO fluxmeasurement
CO2 2
similartotheroutinedescribedbyDarenovaetal.[14]:
CF = T × a+b (3)
CO2 air
whereT istheairtemperature(◦C),aistheslope,andbtheinterceptofthelinearregressionbetween
air
CO fluxandairtemperatureatthecontrolchamber.
2
ThecorrelationbetweentheCH fluxesofthecontrolchamberandsoiltemperaturewasweak,
4
butacleardiurnalpatternwasstillobserved. CorrectionfactorsforCH fluxeswerethuscalculated
4
based on the observed diurnal cycle and correction factors were interpolated. For N O some flux
2
measurementsofthecontrolchambershowednolineartrendandhadtobesettozeroordiscarded
due to measurement quality issues. Since no clear pattern was observed for the control N O flux,
2
standardizationwasomitted.
2.7. StatisticalAnalysis
Forthestatisticalanalysiswefollowedthreeapproaches. (1)Totestwhetherstratificationofthe
plotbasedonvegetationpatternscouldbeusedtooptimizethesamplingdesign,wefocusedonflux
differencesbetweenUnitI–VI.(2)Tofocusonthesoil-vegetationeffect,wemergedUnitI–VIintothe
categoryShoulder(UnitI+VI),Bottom(UnitIII+IV),andTransition(UnitII+V)accordingtotheir
similaritiesinsoiltexture,vegetationandtopography. (3)Tofocusonthetextureeffectseparately,
alllocationswereclassifiedindividuallyaccordingtothedominanttextureintoSandorSilt. Locations
withhighgravelcontentorcoarsewoodydebriswereseparatelyclassifiedasDisturbed.
TotestdifferencesinmeanGHGfluxesbetweenthevegetationstrata(UnitI–VI)weusedat-test
fornormallydistributeddataandaMann-Whitney-Utestfornon-normallydistributeddata. Weused
thecoefficientofvariation(CV)asameasureofvariabilityanduncertaintyofthemeanestimated
soil-atmospheregasfluxes. Toevaluatetheun-stratifiedandthestratifiedapproachesinasimpleway
wecalculatedtheCVforbothapproaches[14]. Forthestratifiedapproachtheweightedmeanofthe
CVofthestratawastakenintoaccountaccordingtothesamplesizeofeachstratum. Correlations
betweenvariableswasevaluatedusingPearson’scorrelationcoefficientrfornormallydistributed
dataandSpearman’srankcorrelationcoefficientr fornon-normallydistributedvariablesusingPROC
s
CORR(SAS9.2,SASInstituteInc.,Cary,NC,USA).Significancewassettop<0.05forallprocedures.
To identify the effect of both continuous (e.g., D D −1 ) and categorical variables (texture
S 0 lab
categories,vegetationunits)onresponsevariables(Fstand.,Fstand.F. )weusedRandomForest[42].
CO2 CH4 N2O
RandomForestisatreebasedensemblelearningmethodthatisnowwidelyusedinecologyandalso
insoilsciencestoanalysecomplexdatasetandtoderivepedo-transferfunctions[43,44]. Weusedthe
Forests2017,8,193 7of16
RpackagerandomForesttobuildthemodel[45]. Priortomodelbuilding,allcontinuousvariableswere
standardisedandthestrengthofcollinearitybetweenthepredictorswasassessed. Frompredictor
pairswithaPearson’scorrelationcoefficient>0.5,thepredictorvariablecontributinglesstothefinal
modelwasexcluded[46]. Toavoidoversaturationofthemodel,thenumberoftreestogrowinthe
forestwassetto2000,thenumberofrandomlyselectedpredictorvariablesateachnodewassetto
3, andtheminimalnumberofobservationsattheterminalnodesofthetreesweresetto5[43,44].
Five-foldcross-validationwasperformedtoassessthepredictionqualityofthemodel. Thecoefficient
ofdetermination(R2)andtherootmeansquarederror(RMSE)werecalculatedaccordingly.
BasedontheRandomForestwebuiltGeneralizedMixedModels,withvegetationcategoryor
textureclassasnominalcategories(PROCGLM,SAS9.2,SASInstituteInc.,Cary,NC,USA).Vegetation
unitandtextureclasswerepositivelycorrelatedandconsequentlyoneortheotherwasusedtobuilda
model. ThefinalmodelwasbuiltupbystepwisereducingAIC(Akaikeinformationcriterion)that
waschosentoaccountforsmallsamplingsize.
3. ResultsandDiscussion
3.1. StandardizationofTemporalVariabilityofGreenhouse-GasFluxes
ThesoilattheHartheimsitewasasourceofCO andsinkforCH andN Oatallmeasurement
2 4 2
timesandpointsdespitetopsoilmoistureconditionsbeingnearsaturationduetoseveralrainevents
beforethecampaign. Duringthecampaigntherewasfoganddew,meaningsoilmoisturewasstable
at12%vol. at0.3mdepth(permanentlyinstalledmonitoringprobe)and25%inthetopsoil(mean
of120soilcoresamples). Meansoiltemperatureat1cmdepthwas6.0◦Cduringthecampaignand
rangedfrom5.3to7.1◦C.Meanairtemperaturewas5.0◦C,andrangedfrom1.0◦Cto10.3◦C.Wind
conditionswerecalm.
Repeated measurements of the CO flux at the control chamber showed a strong correlation
2
(R2=0.83)withairtemperature(Figure2a)thatwasreflectedinthemeasurementthroughoutthe
spatialreplicates(Figure2a,greyx-symbols). Theestimatedslope(Equation(3),a=0.68µmolm−2
s−1 ◦C−1)andintercept(b=0.15µmolm−2 s−1)ofthislinearregressionwereusedtostandardize
measured CO fluxes to temperature at the starting time of the campaign. Measured CO fluxes
2 2
were0.52–2.51µmolm−2s−1andtemperature-derivedcorrectionfactorsforCO fluxeswere0.8–2.1
2
(Figure2b,solidline). StandardizingofCO fluxes(Equation(2))resultedinaslightlysmallerrange
2
offluxvalues(Fstand.0.52–2.42µmolm−2s−1).
CO2
RepeatedmeasurementsoftheCH fluxatthecontrolchamberwerenotsignificantlycorrelated
4
withairtemperature,butslowchangesovertimewereobserved(Figure2b). Wecalculatedcorrection
factors by dividing observed fluxes at the control chamber by the CH flux at the starting time of
4
thecampaign. TheCH fluxcorrectionfactorwastheninterpolatedbetweenmeasurementsofthe
4
controlchamber. ThecorrectionfactorforCH fluxeswere0.7–1.1(Figure2e,solidline),meaningthe
4
standardizationeffectforCH fluxeswasmuchweakercomparedtoCO fluxes.
4 2
Outofthetotal62N Ofluxmeasurements, 6measurementsfailedduetoproblemswiththe
2
watervapourconditioningand18measurementsweresettozeroflux. Nitrousoxidefluxwasalways
negative, i.e., N O was consumed by the soil, and N O fluxes were much lower than CH fluxes
2 2 4
(Figure 2c,f). Consequently, N O flux measurements were not standardized due to an insufficient
2
numberofhigh-quality,non-zerofluxmeasurementsatthecontrolchamber(Figure2c).
TheobservedCO fluxesagreewellwiththefluxesknownfrommonitoringcampaignsatthis
2
site [13,37]. The observed mean CH flux of −2.39 nmol s−1 m−2 is at the higher end of uptake
4
measurementsaccordingtoSmithetal.[18],whichwouldbeexpectedforthewellaeratedforestsoil
atHartheim. TheobservedmeanN Ofluxof−0.05nmols−1m−2iswithintherangeofvalueslisted
2
byChapuis-Lardyetal.[11],whereN Ofluxesasnegativeas−3.1nmolm−2swerereported. Since
2
soiltemperatureabove5◦CareconsideredtofavourN Oconsumptionbysoils,evenhigherN O
2 2
uptakecanbeexpectedatoursiteinwarmerperiodsoftheyear[47,48].
Forests2017,8,193 8of16
Forests 2017, 8, 193   8 of 16 

Figure2.GreFiegnuhreo 2u. sGeregeanshofluusex egass mfluexaessu mreeadsuirnedt hine tchoe nctornotrlocl hchaammbbeerrss (b(blalcakc) ka)nda nspdatsiapl arteipalilcarteeps licates(grey)
(grey) for (a) CO2, (b) CH4, and (c) N2O, and in the spatial replicates for (d) CO2, (e) CH4, and (f) N2O. 
for(a)CO , (b)CH , and(c)N O,andinthespatialreplicatesfor(d)CO , (e)CH , and(f)N O.
2 For CO2, th4e correlation be2tween air temperature and CO2 fluxes in the control chamb2er (dashed l4ine  2
ForCO2, thein c(oa)r) rwelaas tuiosend btoe tcwalceuelante aciorrrteecmtiopn efarcatotursr e(soalindd linCesO i2n fl(bu))x aensd isntanthdaerdciozendt rCoOl2 cfhluaxmes ber(dashed
linein(a))w(cairscluess) efrdomto mceaaslucruedla CtOe2c folurxrees c(ttriioannglfeas)c. tFoorrs C(Hs4o, cliodrrelliantieons winith( bai)r) teamnpdersattuarne dwaars dwiezaekd;  CO fluxes
2
interpolated CH4 fluxes in the control chamber (dashed lines in (c)) were used directly to calculate 
(circles)frommeasuredCO fluxes(triangles).ForCH ,correlationwithairtemperaturewasweak;
correction factors (so2lid lines in (d)) and standardized CH44 fluxes (circles) from measured CH4 fluxes 
interpolated(tCriaHng4lefls)u foxre tshei nrestpheectcivoen dtartoe lancdh atimmeb. Setran(ddaardsihzaetdionl ionf eNs2Oin fl(ucx)e)s wwaesr neout psoesdsibdlei rdeucet tloy tocalculate
correctionfaicntsourffsic(iesnot lindumlibneer sofi nqu(adli)t)y amnedassutraemndenatsr diniz tehde cConHtr4olfl cuhaxmebse(rc (ier)c. lHese)ncfer,o mmeamsuereads uNr2eOd  CH4fluxes
fluxes were used for downstream analysis. 
(triangles)fortherespectivedateandtime. StandardizationofN Ofluxeswasnotpossibledueto
2
insufficie3n.2t. Snpuamtiabl Veraroiafbiqliutya olfi tSyoiml Geaass Fulurxeems entsinthecontrolchamber(e).Hence,measuredN2Ofluxes
wereusedfordownstreamanalysis.
CH4 fluxes between Unit I–VI differed substantially (Table 2, Figure 3b). Lower but significant 
differences were observed in CO2 fluxes (Figure 3a). N2O fluxes of the different strata were not 
3.2. SpatialVsaigrniaifbicialinttylyo dfiSffeorielnGt, awshFiclhu ixse psrobably due to the high number of zero‐flux measurements in all 
strata (Figure 3c). The highest CH4 consumption was observed in the two silt strata (Unit III & IV) 
CH fluaxneds thbee stowutheeernn sUhonuiltdeI–r oVf Ithde idfefperreesdsiosnu (Ubnsitta In, stainadl/lgyra(vTeal;b Fligeu2re, 3Fbi)g. ure3b). Lowerbutsignificant
4
differences were observed in CO fluxes (Figure 3a). N O fluxes of the different strata were not
Table 2. Mean flux and coe2fficient of variation (CV) of GHG fo2r all measurement or as weighted sum 
significantlydifoffe arlel sntrta,taw.  hichisprobablyduetothehighnumberofzero-fluxmeasurementsinall
strata(Figure3c). ThehighesFtluCx H4consumApllt MioenasuwreamsenotsbserveWdeiinghttehde Stuwm oof sSitlrtatsa trata(UnitIII&IV)and
thesouthernshoulderofthedepression(UMenaint I,sandC/V gravel;MFeiagn ure3b).CV 
CO2 (μmol s−1 m−2)  1.13  36%  1.13  33% 
CH4 (nmol s−1 m−2)  −2.39  38%  −2.39  24% 
Table2.MeanfluxaNnd2O c(nomefofil sc−i1 emn−t2) ofvari−a0t.0io5 n(CV)8o8f%G  HGfo−r0a.0l5l measure8m7%e ntorasweightedsum
ofallstrata. 

AllMeasurements WeightedSumofStrata
Flux
Mean CV Mean CV
CO (µmols−1m−2) 1.13 36% 1.13 33%
2
CH (nmols−1m−2) −2.39 38% −2.39 24%
4
N O(nmols−1m−2) −0.05 88% −0.05 87%
2
Consideringtheentireun-stratifiedplot,spatialvariability(evaluatedascoefficientofvariation
CV)ofthestandardizedCO andCH fluxesandnon-standardizedN OfluxwashighestforN Oand
2 4 2 2
similarforCO andCH (Table2)andagreeswellwithdatafromliterature[14,49].Sincethevegetation
2 4
stratacouldexplainsomeofthespatialvariabilityofthesoilgasfluxesatoursite,ourstratification
Forests 2017, 8, 193   9 of 16 
Considering the entire un‐stratified plot, spatial variability (evaluated as coefficient of variation 
Forests2017,8,193 9of16
CV) of the standardized CO2 and CH4 fluxes and non‐standardized N2O flux was highest for N2O 
and similar for CO2 and CH4 (Tab. 1) and agrees well with data from literature [14,49]. Since the 
vegetation strata could explain some of the spatial variability of the soil gas fluxes at our site, our 
approach reduced the CV for CH substantially, from 38% to 24% (Table 2); however, it was less
4
stratification approach reduced the CV for CH4 substantially, from 38% to 24% (Tab. 1); however, it 
effectiveforCO andhadalmostnoeffectforN O.
was less ef2fective for CO2 and had almost no effe2ct for N2O 

Figure 3. Boxplots of standardized soil-atmosphere gas fluxes of (a) CO and (b) CH ,
2 4
Figure  3. Boxplots of standardized soil‐atmosphere gas fluxes of (a) CO2 and (b) CH4, (c) 
(c) non-standardized N O fluxes, (d) soil water content, (e) soil gas diffusivity measured in situ
(D D −n1on‐stan)d,aardnidze(df N)s22Oo ifllugxaess, d(di)f fsuosili wviattyerm coenatesnutr, e(ed) sionilt ghaes dlaifbfuosriavtitoyr mye(aDsurDed −in1 situ) (oDfSDth0−e1 ind ifferent
S 0 situ)i,n asnitdu (f) soil gas diffusivity measured in the laboratory (DSD0−1lab) of the diffSere0nt sulabbplot units. 
subplotTuhne imtse.aTnh vealmuee aisn invdailcuaetedis biny dthicea rtheodmbby atnhde trhhe ohmorbizaonntdalt lhineehs oinrdizicoanteta tlhlei n2e5tshi n50dtihc aatned t7h5eth2 5th50th
and75tpherpceenrctielen.t Siligen.fSiciganntfi dciaffnertedncifefse irne tnhcee mseinant hvaelume eaaren invdailcuaeteadr beyi an ddiifcfaerteendt bleyttear dabifofveer.e ntletterabove.
The RandomForest model showed that the vegetation units contributed substantially to the 
The RandomForest model showed that the vegetation units contributed substantially to the
explanation of the spatial variability of the CO2 flux and especially of the CH4 flux. Total porosity, air 
explanafitliloedn poofret‐hsepascpe,a rteialaltivvae rsioaibl iglaitsy doifffutshiveitCy Oan2dfl auirx paernmdeaebsiplietyc iwalelrye ohifgthhlye cCorHre4laflteudx w. iTtho toanlep orosity,
airfilledanpotohreer-. sTphaecreef,orreel, aotnivlye resloaitlivgea ssoidl igfafus dsiivffiutsyivaitnyd waaisr upseedrm ine tahbei fliintyal wmeordeelh. Vigehgelytatcioonrr uenlaitt ewdasw  ithone
anothera.lsToh heirgehfloy rceo,rroenlaltyedre wlaitthiv seoisl oteixltguares cdlaisffsu, ssoi vthitayt twhea lsatutesre wdaisn etxhcleufidenda flrmomo dtheel .fiVnaelg metoadteilo. nunitwas
alsohig hlycorrelatedwithsoiltextureclass,sothatthelatterwasexcludedfromthefinalmodel.
FortheCH flux,therankingofVariableImportanceintheRFmodelrevealedvegetationunit,
4
CO Flux and soil water content to be the most relevant explanatory variables. Mean decrease of
2
accuracyinpredictionontheOutofbagsamples(%IncMSE)whenexcludedfromthemodelwere
75%,32%,and13%. ThedecreaseinNodeimpuritywas25,11,and5. Thecoefficientofdetermination
wasfoundtobe0.95ontheoriginaldataset. R2wasfoundtobe0.57–0.81with5-foldcross-validation.
RMSEvariedbetween19%to34%ofthemeanflux. Thus,theselectedvariablesgenerallyareusefulto
explaintheCH flux,butpredictiveaccuracyislimited.
4
Forests2017,8,193 10of16
TheRandomForestmodelshowedthatCO fluxcouldbebestexplainedbyCH flux,relativesoil
2 4
gasdiffusivityandvegetationunit. Relativeimportancewasmuchlowerandindependentvariables
withintherankingwerelessunequivocalfortheCO fluxwith%IncMSEvaluesof26%,20%and19%.
2
DecreaseinNodeimpuritywas14,13,and10. Althoughthecoefficientofdeterminationoftheoriginal
datawas0.91,cross-validationresultsshowedmuchlesspredictiveaccuracywith0.06–0.66R2. RMSE
wasalsopoorwith53%to95%ofmeanflux.
Stratificationapproacheshavebeensuccessfullyappliedusingtopographicalelementslikeditches
andfields[50],hill-slopeposition[51],andplantcommunities[52]. Fewstudiesthoughcouldstratify
orexplainspatialvariabilityofGHGfluxesontheplotscale. Shvalevaetal.[49]showedthattree
coverhadaneffectonnetN OfluxesandthatCH fluxeswereaffectedbysoilorganiccarboncontent.
2 4
Darenovaetal.[14]identifiedlitterthicknessandlocalsoilmoistureasimportantfactorsaffecting
CO fluxesontheplotsscalewithinaforestandgrassland. Wedidnotobservesucheffects,yetwe
2
identifiedthevegetationstrataasimportantfactoraffectingCO andCH fluxes. Sincesoiltexture
2 4
andvegetationwerestronglycorrelatedatoursite,itisnotpossibledisentanglingthetwofactors.
Nevertheless,stratifyingtheplotbydominantunderstoryvegetationhelpedtoreducetheuncertainty
intheestimatedmeanCO andCH fluxstemmingfromspatialvariability. Thus,samplingdesigns
2 4
formonitoringGHGatthissitecouldbeoptimizedbystratifyingthesiteintodifferentvegetation
units,anddistributingequallysamplinglocationswithineachunit.
Studies comparing plots of different vegetation could show that tree species can affect CH
4
consumptionsbysoils[25,26,49,53,54]. Menyailoetal.[26]observedthattreespecieshadaneffecton
methanotrophicactivityinanafforestation,butthatthecompositionofhighaffinitymethantrophs
was not altered. Borken & Beese [25] attributed the observed differences in CH uptake between
4
forestsitestothedifferentlitterqualityandsoilmoisture. Similarastooursite,Borken&Beese[25]
observednoeffectofvegetationonN Ofluxes. Niklausetal.[55]observedthatCH uptakeandsoil
2 4
N Oemissionsdecreasedwithplantspeciesrichnessinagrasslandexperiment. Theyattributedthe
2
decreaseinCH uptaketoanincreaseinsoilwatercontent. Incontrast,UnitIwithmosscoverhad
4
significantlyhigherCH uptakethanUnitVIwithgrasscover,whilebothhavesimilarwatercontent,
4
soiltextureanddiffusivity(Figure3d–f). ThiscouldindicateadirecteffectofthevegetationonCH
4
uptake,witheitherspeciesofhighersuccession(grasses)inhibitingCH uptake,orspeciesoflower
4
succession(mosses)enhancingCH uptake,asknownfrompeatlands[56,57]. Understandingwhich
4
dynamicisoccurringmeritsfurtherinvestigation.
3.3. InteractionofSoilPhysicalParameters,SoilGasFluxes,andSoil-VegetationUnits
Air-filledpores-space,soilgasdiffusivityandairpermeabilityofthesoilcoresamplescorrelated
wellwitheachother(Pearson’sr>0.7).Labmeasurementsofsoilgasdiffusivity(D D −1 )correlated
S 0 lab
well with in situ measurements (D D −1 ) (r: 0.59), while D D −1 yielded larger values
S 0 insitu S 0 insitu
(0.12–0.74)thanD D −1 (0.11–0.49)(Figure3e,f). Thiswasalsoobservedinpreviousstudies[21,31]
S 0 lab
and can be attributed to uncertainties in the methods and different reference volumes between
laboratory and field chambers. Soil gas diffusivity in Unit I was significantly higher than in the
otherunitsaccordingtobothmethods,andbothvegetationunitswithsilt(UnitsIII&IV)hadlow
diffusivityvaluesandlessvariability.
ForfurtheranalysisweusedmergedtopographicalcategoriesShoulder(UnitI+VI),Bottom(Unit
III+IV),andTransition(UnitII+V).Soiltextureandaggregationwassubstantiallydifferentbetween
the topographical categories. We observed similar relationships between fluxes and soil-physical
parameters(Figure4),albeitshiftedbetweencategories. Correlationsweregenerallylesspronounced
fortheTransitioncategory,sincedifferenttexturesandconditionsweremerged. AsN Ofluxescould
2
notbestandardized,nofurtheranalysisofthedatawasperformed.