Robust Summaries & Test Plan: Alkenes, C15-C18 Alpha, Reaction

17 Pages·2003·0.35 MB·English

PlantSoil
DOI10.1007/s11104-011-1113-3
REGULAR ARTICLE
Different plant traits affect two pathways of riparian
nitrogen removal in a restored freshwater wetland
A.E.Sutton-Grier&J.P.Wright&C.J.Richardson
Received:11October2011/Accepted:19December2011
#SpringerScience+BusinessMediaB.V.2012
Abstract importanceofplanttraitsandenvironmentalvariables
Background&aims Plantsmayhavedissimilareffects as predictors of each ecosystem function.
on ecosystem processes because they possess different Results We demonstrate that Water Use Efficiency
attributes. Given increasing biodiversity losses, it is (WUE) had a strong negative effect on biomass N.
importanttounderstandwhichplanttraitsarekeydrivers Root porosity and belowground biomass were nega-
of ecosystem functions. To address this question, we tively correlated with DNP. Trait ordination indicated
studied the response of two ecosystem functions that that WUE was largely orthogonal to traits that maxi-
removenitrogen(N)fromwetland soils,the accumula- mized DNP.
tion of N in plant biomass and denitrification potential Conclusions These results indicate that plant spe-
(DNP),tovariationinplanttraitcomposition. cies with different trait values are required to
Methods Our experiment manipulated plant composi- maintain multiple ecosystem functions, and provide
tion in a riparian wetland. We determined relative a more mechanistic, trait-based link between the
recent findings that higher biodiversity is necessary
for multi-functionality. While we selected plant
ResponsibleEditor:JeffR.Powell.
traits based on ecological theory, several of the
Electronicsupplementarymaterial Theonlineversionofthis plant traits were not good predictors of each eco-
article(doi:10.1007/s11104-011-1113-3)contains system function suggesting the ecological theory
supplementarymaterial,whichisavailabletoauthorizedusers.
linking traits to function is incomplete and requires
A.E.Sutton-Grier(*) strengthening.
NicholasSchooloftheEnvironmentandEarthSciences,
DukeUniversity,
Keywords Biodiversityandecosystemfunction.
Box90328,Durham,NC,USA
e-mail:[email protected] Denitrificationpotential(DNP).NorthCarolina.Plant
traits.Wetlandrestoration
J.P.Wright
BiologyDepartment,DukeUniversity,
Box90338,Durham,NC,USA
Abbreviations
e-mail:[email protected]
AGB aboveground biomass
C.J.Richardson AGCN aboveground biomasscarbon tonitrogen
NicholasSchooloftheEnvironmentandEarthSciences,
ratio
DukeUniversity,
BEF biodiversity and ecosystem function
Box90333,Durham,NC,USA
e-mail:[email protected] BGB belowground biomass
PlantSoil
BGCN belowground biomasscarbon to nitrogen ecological theories about the mechanistic links be-
ratio tween traits and ecosystem functioning can inform
BGR Below-ground Rooting Ratio hypotheses which can then be tested with field data
C carbon (see the sections on “Linking traits to biomass N or
CO carbon dioxide denitrification”). Although some plant traits vary
2
DEA Denitrification Enzyme Assay independently (e.g., Eviner 2004), many plant
DNP denitrification potential traits are correlated (e.g., Diaz et al. 2004; Reich
N nitrogen et al. 2006) which makes simple experimental tests
NH -N extractable ammonium more difficult. As a result, analytical methods that
4
NO -N extractable nitrate+nitrite account for these correlations between traits are
3
POR root porosity critical.
SEM structural equation modeling A secondchallenge is understanding how important
SLA specific leaf area planttraitsaretoregulatingecosystemprocessesrelative
SRL specific root length to environmental factors such as soil resource levels.
WUE wateruse efficiency Biodiversityandecosystemfunction(BEF)studieshave
shownthatchangesinplantcompositioncanaffectplant
productivity or nutrientcycling (Callaway et al. 2003a;
Introduction EngelhardtandRitchie2001;Hectoretal.1999;Tilman
etal.1997;Tilmanetal.2001;Zaketal.2003).Butthe
The planet is facing numerous anthropogenic stresses importanceofplanttraitsrelativetoenvironmentalfac-
including climate change, eutrophication, invasive torsisnotclear.Forexample,nutrientcyclinginecosys-
species introductions, and habitat loss (Houghton et temsisstronglycontrolledbyenvironmentalparameters,
al. 2001; Mack et al.2000 ; Millennium Ecosystem but can also be influenced by both plant (Fornara and
Assessment 2005; Vitousek 1997). These stresses are Tilman2008; Fornara et al. 2009; Templeretal. 2003)
changing the distribution and abundance of species and microbial communities (Cavigelli and Robertson
worldwide (Dukes 2000; Owensby et al. 1999; 2000; 2001; Wittebolle et al. 2009). Plants impact mi-
Sutherst2000)whichinturncanalterecosystemfunc- crobial processes largely by changing the soil environ-
tioning (Balvanera et al. 2006; Cardinale et al. 2006; mentinwhichthemicrobesliveandcompeteviainputs
Hooperetal.2005;NaeemandWright2003).Inorder of plant carbon (C), for example (Farrar et al. 2003;
to better manage our natural resources and mitigate Wardle 2002). While the influence of plant traits, such
undesirablefuturechanges,itiscriticalthatweunder- astheCtonitrogen(N)ratioofplanttissueinputs,onthe
stand the effects of different organisms on ecosystem processofdecompositionhasbeenrelativelywellstud-
processes (Chapin 2003). Because species vary in the ied(Cornwelletal.2008),theinfluenceofplanttraitson
trait values, or ecophysiological and morphological other microbial processes, including denitrification, are
attributes they possess, plant species differentially in- less well understood particularly as compared to the
fluence ecosystem functioning (Chapin et al. 2000; importanceofenvironmentaldriversontheseprocesses.
Wardle 2002). Given that differences in physiology, There is also increasing evidence from BEF
morphology, and phenology between species have studies that more diverse communities are better
important effects on ecosystem functioning, trait- able to maintain multiple ecosystem functions
based ecology has been proposed as a method for (Duffy 2009; Gamfeldt et al. 2008; Hector and
providing a mechanistic link between species and Bagchi 2007). This means that although there may
ecosystem functions (Chapin et al. 1996; Diaz and be redundancy in species trait values that support
Cabido 1997;Diazet al.2004; Wardle et al.1998). one particular ecosystem function, there is generally
Onechallengeintrait-basedecologyisdetermining much less multifunctional redundancy in the entire
which traits are related to particular ecosystem func- community (Gamfeldt et al. 2008). But this as-
tionsofinterest(LavorelandGarnier2002;McGillet sumption that greater trait diversity leads to multi-
al. 2006; Suding et al. 2008; Westoby and Wright functionality needs to be tested.
2006). As we continue to develop our understanding Inthisstudy,weexamine twoecosystemfunctions
of how plant traits influence ecosystem functioning, in a restored riparian wetland. One of the primary
PlantSoil
goalsofmanyriparianrestorationeffortsistheremov- assume this relatively tight relationship between pro-
alofNfromthesystemtominimizeexporttoadjacent ductivity and biomass N, our hypotheses are mainly
aquaticsystems.Therearetwoprimarypathways(i.e. focused on predicting standing crop production. We
processes) by which N can be removed from ground- hypothesize that traits associated with high growth
water;plantuptakeofsoilNandsubsequentincorpo- rates will lead to increases in biomass N (Cornelissen
ration into biomass and microbial denitrification, the et al. 2003). More specifically, we hypothesize that
process bywhich facultative anaerobic microbes con- plants could affect biomass N through two categories
vert nitrate to N gas using organic C as an energy of traits: 1) resource capture and use (Water Use
2
source.Thelatterpathwayismicrobially-mediatedbut Efficiency—WUE; Specific Root Length—SRL; and
alsoindirectlyaffectedbytheplantcommunityinputs Specific Leaf Area—SLA), or 2) through different
of carbon and oxygen to the soil. Given the potential plantarchitecturalgrowthstrategiesaboveandbelow-
links between these two functions, it is a particularly ground (Plant Height and Below-ground Rooting
interestingquestionwhethermultipleplantspeciesare Ratio—BGR). See Table 1 for the definitions and
necessarytomaximizethesetwoN-removingecosys- justification for each trait we selected. Although
tem functions and what plant traits might affect these WUE may seem a counterintuitive trait to measure
processes. on wetland plants, the hydrology of riparian wetlands
We examine the relationship between functional is largely driven by precipitation inputs which can
plant traits, environmental variables, and two ecosys- mean that soils have no standing water and are not
temfunctions:(1)biomassN,definedasthegramsof saturated for significant parts of the growing season
N in aboveground biomass, and (2) potential denitri- (Mitsch and Gosselink 2000). At our study site, we
fication (DNP). We ask first, ‘Do plant traits signifi- observed relatively dry soil conditions (on average
cantly affect biomass N and DNP independent of the between 17% and 35% moisture by weight) with no
effectsofenvironmentalvariables?’Iftraitsdoimpact standing water in any of our plots for the majority of
ecosystem function, we ask, ‘Which specific traits the growing season. Thus, WUE of the plants may
controlsthesefunction?’Wealsoask,‘Areplanttraits likely play a more important role in driving plant
or environmental variables more important predictors impactson ecosystem processes.
of each ecosystem function?’ Based on the results of
our plant traitanalysis,we examine whetherthere are Linkingtraitstodenitrificationpotential
certaincombinationsoftraitsand/orspeciesthatmax-
imize biomass N, DNP, or both functions. In other Soil conditions, such as soil N and soil C, have been
words, from an ecosystem management perspective, shown to be important drivers of denitrification rates
we ask, ‘Are the traits controlling different functions (Druryetal.1991;Ettemaetal.1999;Groffman1994;
tightly correlated across species or are species with GroffmanandCrawford2003;LowranceandHubbard
different sets of traits necessary to maximize multiple 2001), and can potentially be affected by plant traits.
ecosystem functions?’ Otherstudieshavefoundthatspecifictypesofplants,
such as annuals, and specific species, such as Typha
latifolia, influence denitrification enzyme activity
Materials and methods (Crush 1998; Hernandez and Mitsch 2007; Hume et
al.2002;Linetal.2002;Patraetal.2006;Pinayetal.
Conceptual model 2007). We hypothesize that plants are likely to influ-
ence denitrification mainly via their C inputs both in
LinkingtraitstobiomassN terms of C quantity and quality, both of which have
been shown to limit denitrification (Groffman et al.
BiomassNisafunctionofbothplantproductivityand 1991; Groffman and Crawford 2003; Hernandez and
theC:Nratiooftheplantbiomass.Inecosystemssuch Mitsch 2007; Hill 1996; Hill and Cardaci2004;
as riparian wetlands, with high nutrient fluxes and Schipper etal.1994).Plantsmay also affect denitrifi-
high productivity (Mitsch and Gosselink 2000), even cationthroughmodificationoftheredoxconditionsin
plants with high C:N can have high relative growth the soil via root delivery of oxygen through radial
rates and sequester large amounts of N. Because we oxygen loss. Therefore, we selected five traits to
PlantSoil
strategy,eachtraitisdescribedaswellasthejustificationforthetraitchoiceincludinganyproposed JustificationforTraitChoice IncreasedWUE,ameasureofCassimilatedperunitwatertranspired(Diaz2001),couldincreasegrowthandtherefore,biomassN. SRL,theamountoflengthofrootpergramofroot,isanindexofplantwaterandnutrientuptakestrategyrelatedtotheamountofrootincontactwithsoil.HigherSRLisrelatedtohighergrowthrates(Cornelissenetal.2003)andhence,potentiallygreaterbiomassN. SLA,ameasureoftheareaofleafpergramofleaf,shouldreflectrelativegrowthrates(Cornelissenetal.2003;DiazandCabido1997)andnutrientacquisition(Chapinetal.1996),withhigherSLAattributedtogreatergrowthratesandhence,potentiallygreaterbiomassN. Averageplantheightisameasureofplantgrowthstrategiesthatisrelatedtoplantvigorandiscorrelatedwithabovegroundbiomassandleafspread(Cornelissenetal.2003)andthereforemaybepositivelyrelatedtobiomassN. BGRrepresentsdifferentbelowgroundresourceacquisitionorexploitationstrategies.Thedistributionofrootsbelowgroundprovidesinformationaboutbelowgroundcarbonstorageaswellaswhereaspeciesacquiresnutrientsandwaterinthesoilprofile(Cornelissenetal.2003).DifferencesinBGRarelikelytorelatetodifferentsourcesofnutrientsandthereforedifferencesinbiomassN. AAtdftntmC(we2mrsrtt1cciaihirhniooBanaavuo09ooeasheeaeomsttaegset09GlnnnQiiasifidtuirootrsc31sansplsltgCy(,etuaombpnnahoc))uelGry(ab.abweiitosNacrAtpl,opbotdl.doreiirF.erietltomo)ouriedtG.tisiiMSof,seynreoptffttewsfmFaeeyrdfBesianraahmedtsncleeteoprweClhsa)nis(aauittisrmaaGltfahoiTlssdvneelaaeaf(ysntequtrfsaeeneBanrdbuliolouroeb2armtcrt1cetormeGueebiesafrltoe)oc9lumeofngtesneideBrttwnth8mnteaerchehrAhtae2tee7eipe)asdentrieresatnartdbasru;uosyt.fufhntacchp‘etooasorrMtlsrIeqhhiiosenehowmmxretv1ntsuddmdosonam9ehidoetroaeesosrfwdtogo8aoeoflateurtn1fcoiisarot7eifdfsnntntin)tamooustodaytut;hemtfeutirolurst,toeernlBepilGa’mssfosoiiifenntcssiiaclyonxietfcersedouiaaowecrhrtgsrglottleulibniastoorrtedi.tfflea(egiertdvCothxwdifsEfin1smecrdrcrmeeieourym:eovats9cgoaenanNntbglotwaatit8rretbeaiseoinheeeoeonntt8oaitosiisrfcnaeesctiurons;inuvivuraecraafonuvengeePtcceueithnttfhadreccapeialhgasdsaepoecaaocgddonearrteldeahotthoCgsoo.eetdmaairreo(tdocnnnussrs1e:reActpnonioNetnooiiiinCsn9ehattmztlnussutGondrraist9naiheoaiiiinln.t,neein9rffatddCalibntraiittatlFlgp)iptchciwidNtnnd.ahbfeooooitiiaaaCiiloonngdoeees----)-r-ft.
h
ac the role of biodiversity on ecosystem function (see
e
n reviews in Balvanera et al. 2006; Cardinale et al.
Withi ble) 2006). In terms of maintaining ecosystem function,
a
bywhichplantscouldaffectbiomassN.aitwillimpactbiomassN MeasuredTraits 1)WaterUseEfficiency(WUE),calculatedas(Photosynthesis/Transpiration),unitless−12)SpecificRootLength(SRL),cmg −21g3)SpecificLeafArea(SLA),cm 1)Averageplantheight(Height),cm 1)Belowgroundbiomassdistributionratio(BGR)Calculatedas:(Biomassabovewatert(Biomassbelowwatertable),unitless hfb1petttmsirhupxoe9ooiiinbetw9snnsaeccue8setnetidttd;ielitvhioeosHseeantalnoroasosl,(‘irrtGotmkgoywtoapheefrsahelielttisymyihrhinscmfaeta-ehtubetropnnarytod1peoetcbisr9lctttoVaeattiho9a)o’nleisn8cnottytpolt)hafcos.slualeahtipsssenoTnaeteemrdrttrlchkahyyaiiebivecet1rppiwtissedode9rrfhroimue9(aovitidpfa7shcearftlie)eoehrsic.rscs,scsttpGisa.itest”ieebuynreosctlgiIrceofmitgaetowfhietsfefsthtriiossyralsoe(tallsrs1mltiteoshd9tbetstwalo9mheuiit(tm8aksdGsdt)aeyfifreflucnp“ryiftoonamtwreehonntmccroeee----tt
traitsthetr epxlaanmtitnraeittshoeninefclouseynscteemoffubnicotmioansss-twhaetigrhemteodvaevNer.age
Table1Categoriesofmechanismsbywhich Category ResourceAcquisition PlantArchitecture(Aboveground) (Belowground) TA2200hlt00eh44oc;)ou,Rngmshetiarscunohcymteepmtlpoaalndla.tenl2tts0rtar0ai6tist;saWvraerriygchiontrdreeetlpaeatenl.dde2(n0Dt0liy4a)z(Ewevthiniacelhr.
PlantSoil
Table2 Categoriesoftraitsthroughwhichplantscouldaffectdenitrification.Categoriesincludethemeasuredtraitandthejustification
forthetraitchoiceincludinganyproposedmechanismsbywhichthetraitwillimpactdenitrification
Category MeasuredTraits JustificationforTraitChoice
CarbonQuantity 1)Abovegroundbiomass Greaterplantproductivityabovegroundwillincreasethequantityofplantlitter
(AGB),g inputstosoilandhencesoilcarbonavailabilityfordenitrifiers(Diaz2001).
2)Belowgroundbiomass Greaterplantproductivitybelowgroundwillincreasebelowgroundnutrientflux
(BGB),g (Chapinetal.1996)aswellasthequantityofplantrootexudates(Farraretal.
2003)androotturnovertosoiland,hence,soilcarbonavailablefordenitrifiers.
CarbonQuality 1)C:Nratioofroots LowerC:Nratiosmakeplantinputsofhigherquality(Burkeetal.1998)soroots
(BGCN),unitless aremoreeasilydecomposedbymicrobes,providingneededcarbontodenitrifiers.
2)C:Nofaboveground AsdescribedunderC:Nofroots,leaflitterinputsofhigherquality(i.e.thatare
biomass moreeasilydecomposedbymicrobes)canprovideneededcarbontostimulate
(AGCN),unitless denitrifiers(Chapinetal.1996;Diaz2001;Melilloetal.1982).
Redox 1)Rootporosity IncreasedPORcouldbringO totherootingzonewhichmightinhibitdenitrification,
2
(POR),unitless oritcouldpromoteNO -productionintherootzone(CronkandFennessy2001;
3
Ehrenfeldetal.2005;Reddyetal.1989)whichcouldthendiffusetotheadjacent
anaerobiczoneandstimulatedenitrification.Inanaerobicwetlandsoils,PORislikely
tostimulatenitrificationandresultingreaterdenitrification.
makes simple experimental tests more difficult. Soils wetland and stream were extensively restored to ad-
variables also tend to be correlated, therefore, analyt- dresswaterqualityissues.Intheprocessofrestoration,
ical methods such as structural equation modeling all riparian vegetation was removed and the site was
(SEM), that account for these correlations, are useful gradedtoaconstantelevation.Intheprocessofresto-
(McCune and Grace 2002). In this study, we devel- ration, soils were homogenized as much as possible,
oped two proposed (construct) models, one for bio- however, soils are naturally heterogeneous and some
mass N and one for denitrification potential (Fig. 1a degree of variation in soil resources remained even
andb).Theconstructmodelsincludebothplant traits aftersoilsweregradedandhomogenized.
and environmental factors because we hypothesize ThestudysitewaslocatedadjacenttotheSWAMP
that both are important predictors of ecosystem restored stream in the riparian wetland. Fifty-one 2×
function. In these construct models we hypothesized 2mplotswereplantedinMay2005withatotalof100
that soil properties (soil N and soil OM) were seedlings (added as plugs) with different mixtures of
correlated to each other and that plant traits were speciesselectedfromapoolof10species.Thespecies
correlated to each other. Although traits can vary in the study (see list in Table 3) were selected from a
with environmental conditions (Callaway et al. listofrecommendedspeciesforNorthCarolinastream
2003b), for simplicity we followed the typical con- restoration(Hall2003)basedoncommercialavailabil-
vention of assuming a single trait value for each ityandtomaximizetraitdiversity.Formoredetailsof
species (Diaz and Cabido 1997; Diaz et al. 2004; thestudydesignandgeneralsoilpropertiesatthesite
Wright et al. 2004). see Sutton-Grier etal. (2011).
SWAMP study area and experimental design Soil sampling and laboratory analysis
TheDukeUniversityStreamandWetlandAssessment Soil samples were collected in the second growing
Management Park (SWAMP) is located along Sandy season of the experiment, 2006, in May, July, and late
Creek in the Duke Forest in Durham, NC (36° 00′ N, August.Twosoilsamples(2.5cmdiameter)fromeach
78°54′W).SoilsareprimarilyCartecaysiltloamsand plot were collected in plastic sleeves from the upper
Mayodan sandy loams (Kirby 1971). The site is a 15cmofeachplotusingapistoncorer.Duetomultiple
riparian wetland formerly dominated by Acer rubrum samplingdatesandotherusesoftheexperimentalplots,
(redmaple),Liriodendrontulipifera(tulippoplar),and moreextensivesamplingwasnotpossible.Thesesam-
Liquidambar styraciflua L. (sweet gum). In 2004, the pleswerestoredoniceandtransportedbacktothelab
PlantSoil
0(n3) 0.02) 0.03) 0.03) 0.04) 0.01) 0.04) 0.02) 0.01) 0.03) 0.03) 0.02) dplot
OR 10( 17( 32( 06( 16( 24( 02( 03( 20( 30( 09( hte
P 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. eig
w
a
nd2 0BGCN(n3) 57.29(3.26) 44.45(3.75) 48.06(6.19) 54.97(3.37) 34.74(3.74) 24.98(0.68) 22.40(2.40) 53.97(12.23) 27.60(9.45) 32.87(2.99) 55.45(12.94) estocalculate
a ci
1 e
Tables BGB* 0.184 0.293 0.152 0.467 0.098 0.018 0.072 0.481 0.053 0.101 0.135 eachsp
in or
d f
efoun AGB* 80 290 332 140 239 405 124 37 127 474 301 Nratio
nb C:
unitsca 0(n3) 1.68) 0.49) 0.09) 0.21) 0.07) 2.40) 8.81) 12.5) 12.1) 0.33) 0.19) average
nsand BGR 3.60( 2.66( 0.54( 4.09( 2.28( 4.74( 13.7( 197( 22.7( 1.45( 1.24( usean
o
m.Traitdefiniti 0Height(n5) 111.4(11.4) 49.6(7.0) 58.0(5.4) 54.4(7.7) 51.2(9.9) 22.5(2.8) 37.6(9.9) 14.3(6.0) 45.5(6.8) 40.6(3.6) 64.4(3.9) ore,wedidnot
u ef
ne er
ostigiumvimi 0SLA(n10) 312.5(30.0) 318.9(16.2) 257.3(19.7) 400.2(24.7) 317.8(13.2) 141.6(10.9) 506.7(36.9) 643.5(78.7) 263.4(15.6) 214.6(11.5) 362.8(52.5) use eachplot.Th
wae4prs6mocFtpieoanaoo.a0vih7mgatcinasn°ehlU.se5hslCntsopsy1serpmotve,aazndphwotrmeeCtaerelmrn,tdodininoohpetsattdhunoisdebl(anaesigtilwyryldniyett,a.rhdrhvipeutbCv,dvweeercitr1siornaacehtsiteypr0calereuMrrawr5fhblesaeialilslgo°uoyoylaetipChdliretsiikilwterltgntvotohiloeclfohsnnseiiteodorntp.essfaheraenedeoaAlnidstsac2vrdrnoaetbte4(wgoiliasaaoryconeut)sheurnsewtibalrbDihos4lvtst-iwetoenoo°.esIaiermdmaCsrAestdky,morawaeal,redsolnuottpraeseetrcfedonslhrorNeomhecttmbeoseilsoeloloudatiietltslennfvswlhoctlvedeiteheatwenhcoedeet(aetsatddbghysicowttco)roehwifwotonedlcrh,crmhuo)resoorea.eidnnomoluusoleiriducetlliilrdddfuseageibrfwtidslahoniruaiciebcoalmoratacyheeaes--ttt Table3Traitvalues(withSEinparentheses)forplantedspeciesandMicr 00SpeciesAbbrev.WUE(n4)SRL(n15) AsclepiasincarnataAscinc5.19(0.74)1.19(0.15) CarexcrinitaCarcri2.62(0.35)0.83(0.06) CarexluridaCarlur2.70(0.28)0.66(0.05) EupatoriumfistulosumEupfis3.24(1.32)1.44(0.12) ChasmanthiumlatifoliumChalat6.11(0.51)0.51(0.06) JuncuseffususJuneff3.06(0.35)8.88(0.59) LobeliacardinalisLobcar3.45(0.17)5.66(0.53) MicrostegiumvimineumMicvim4.17(0.48)7.45(0.57) PanicumvirgatumPanvir9.20(0.43)0.58(0.06) ScirpuscyperinusScicyp3.34(0.28)0.49(0.05) VernonianoveboracensisVernov4.28(0.84)1.32(0.15) 0*measuredinthefieldplots;allothertraitsweremeasuredinthegreenho AGCNwascalculatedforeachplotusingtheC:Nratioforeachspeciesinaverage,soAGCNvaluesarenotshownhereforeachspecies.
PlantSoil
bulkdensityofeachcore(Culley1993).Asub-sample stiltgrass), an exotic grass that is common in riparian
ofthisdriedsoilwasthenusedtodeterminepercentage zones(Barden1987)andistheonlyfrequentinvaderof
soilorganicmatter(OM)bylossonignition(Patraetal.) our experimental plots. All samples were dried at 60°C
at450°Cforfourhours(Storer1984).Tworeplicate3g foraweekandthenweighedtodeterminetotalbiomass
field-moist sub-samples were analyzed for 2 M KCl foreachspeciesineachplot.Arepresentativesub-sample
extractable nitrate+nitrite (NO -N) and ammonium ofeachspecies,basedonaweightedallocationofstems,
3
(NH -N) (Maynard and Kalra 1993) on a Lachat leaves,andreproductivestructures,wasthengroundwith
4
QuikChem 8000 (Lachat Instruments, Loveland, CO, a Wiley Mill or liquid N and then analyzed for total N
USA.). using dry combustion on a FlashEA 1112 Elemental
DenitrificationEnzymeAssay(DEA)(Groffmanetal. Analyzer (Thermo Scientific, Waltham, MA, USA.).
1999; Smith and Tiedje 1979) was used as an index of The percent N of each species was then multiplied by
denitrificationpotential(DNP).DEAmeasurespotential themassofthatspeciesineachplottogetgofNforthat
denitrification because C and N are supplied in excess species; total biomass N was the sum of the g of N of
and the incubation is carried out under anaerobic con- eachspeciesinaplot.
ditionssuchthatN Ogasproducedisonlyafunctionof
2
thelevelofenzymeinthesample.Inthisway,DEAisa Trait measurements
usefultechniquetostudymaximumpotentialdenitrifica-
tioninecosystemsbecauseenzymeconcentrationiscor- Ideally,wewouldhavepreferredtomeasurealltraitson
related with long-term patterns of environmental plantsgrowinginthefield,however,duetothedestruc-
conditionsthatcontroldenitrification,suchassoilmois- tivenatureofsomeofthemeasurements,wewereunable
tureandCavailability(Groffmanetal.1999).Inthelab, to measure all traits in the field without destroying our
duplicatesamplesof5gofhomogenized,field-moistsoil fieldplots,butforthosetraitswhereiswaspossible,we
wereamendedwithasolutionofdextrose(0.5gperL) measured them in the field. Three traits, C:N ratio of
andKNO (0.72gperL)toensurenon-limitingsubstrate aboveground biomass (AGCN), aboveground biomass
3
conditionsandchloramphenicol(0.125gperL)toinhibit (AGB),andbelowgroundbiomass(BGB),fromourfield
proteinsynthesis.Theslurriesweremadeanaerobicwith plots (See above for details of the harvest). AGB was
repeated flushing with N gas. Flasks (125 mL) were calculated for each species as the average aboveground
2
ventedwithaneedlefollowedbyaninjectionof10mL biomassharvestedinSeptember2006fromthetwofield
of acetylene into each flask to inhibit N production monoculturesofeachspecies.Theonlyexceptiontothis
2
(Groffman et al. 1999; Smith and Tiedje 1979). Flasks was Lobelia cardinalis because monocultures of this
wereplacedonanorbitalshakerandshakenat125rpm species were severely impacted by deer herbivory in
for90min.Gassamplesfromtheheadspaceoftheflasks 2006.Instead,wesubstitutedsimilarmeasurementstaken
werecollectedat30,60and90minwithasyringe.Gas in2005onadifferentsetofplotsatthesamesitethatalso
sampleswerestoredinevacuatedglassvialsuntilanaly- included L. cardinalis monocultures but that were less
sis(maxholdingtime48h)onaShimadzuGC-17A63Ni heavily deer-browsed. BGB was similarly calculated as
electroncapturedetectorgaschromatograph(Shimadzu, theaveragetotalrootbiomassfromtheAugust2006soil
Inc., Columbia, MD, USA.). N O dissolved in sample from the two monocultures of each species with two
2
water was corrected with the Bunsen equation exceptions. We used root biomass from the same plots
(MoraghanandBuresh1977).LinearratesofDNPwere of L. cardinalis from 2005 that we used for biomass
calculated based on the maximum activity measured estimates. Because we did not have any monocultures
duringtheincubationperiod. of M. vimineum, we used the root biomass from an
adjacent unplanted control plot with naturally recruited
Aboveground plant biomass harvesting M. vimineum accounting for 63% of the plot above-
and labanalysis groundbiomass.
Wemeasuredtheremainingplant traitsonindivid-
InSeptember2006abovegroundbiomassintwo0.25m2 uals of the same species grown in the greenhouse
quadrats was harvested and bulked from each plot. under greenhouse conditions designed to replicate
Species were sorted in the field into the 10 planted temperature, humidity, and photoperiod at the field
species, ‘other,’ or Microstegium vimineum (Japanese site.Individualsofeachspecieswerestartedfromseed
PlantSoil
andthentransplantedtoindividualpotsasseedlingsand throughoutthegrowingseasonineachplot.Theseanal-
growninanormalgreenhousepottingmix(Metromix). yseswereperformedusingthesoftwarepackageR2.4.1
Specieswerethengrowninpotsfortwomonthsandkept (RDevelopmentCoreTeam2008).
ataconstantwaterlevelwithsaturationat15cmbelow Weusedstructuralequationmodels(SEM)toexam-
thesurfacewhichapproximatesaveragefieldwatertable ine the influence ofplant traitsand environmental fac-
depth. Species traits were measured in June 2005. All torsonbiomassNanddenitrificationpotential.Priorto
traitswerecontinuousvariables.Wemeasuredphotosyn- performingtheSEManalysis,somevariableswerelog
thesis and transpiration at 1000 μmol sec−1 m−2 and at transformed to better conform to assumptions of nor-
ambient levels of CO to calculate water use efficiency malityandlinearity(Grace2006).Wealsocheckedfor
2
(WUE) using a LiCor-6400 (LI-COR Biosciences, multivariateoutliersusingMahalanobisdistanceinthe
Lincoln,Nebraska,USA)(Diaz2001).Weestimatedroot software package Amos 5.0.1 (Arbuckle 1995). Based
lengthusingWinRhizosoftwaretocalculatespecificroot on this analysis we removed three outliers from the
length(SRL,cmg−1)(Cornelissenetal.2003).Wealso biomass N data and two from the denitrification data.
measuredspecificleafarea(SLA,cm2g−1)(Cornelissen Nextweconfirmedthatourdatamettheassumptionsof
et al. 2003), plant height, and belowground biomass multivariate kurtosis and normality using the critical
distribution (BGR), the ratio of root biomass above the ratio (C.R. < 5 means data are multivariate normal
watertable(whichwasfixedat15cmbelowthesurface (Byrne 2010)). After determining that our construct
inourgreenhousetreatments)torootbiomassbelowthe model was significant and therefore not a good repre-
watertable.C:Nratioofbelowgroundbiomass(BGCN) sentationofthestructureinthedata,followingstandard
wasmeasuredonaaFlashEA1112ElementalAnalyzer SEM protocols we modified our model based on the
(ThermoScientific,Waltham,MA,USA.),androotpo- modification indices suggested by AMOS (Arbuckle
rosity(POR)wasmeasuredusingthepycnometermeth- 1995) in order to develop a non-significant model for
od(Jensenetal.1969). biomass N and denitrification potential (note that in
For WUE, SLA, Height, and AGCN, average trait SEM, the significance test indicates whether the pro-
values for each plot were calculated by multiplying the posed model structure and the structure ofthe dataare
traitvalueforeachspeciesbytheweightedaverageofthe significantly different, thus a significant model is one
aboveground biomass of each species in the plot. SRL, thatdoesnotadequatelyexplaintheunderlyingstructure
BGR,andBGCNweightedaveragesforeachplotwere inthedata)(Arbuckle1995).Standardizedcoefficients
calculated based on belowground biomass estimates as are presented inthe results tofacilitatethe comparison
calculatedusingtheabovegroundbiomassfromthefield oftherelativestrengthsoftherelationshipsbetweenthe
andtheroot:shootratiofromourgreenhouseharvest. predictorandresponsevariables.
Based on the results of our SEM analysis, we also
Statistical analyses performedanon-metricmultidimensionalscaling(NMS)
ordinationusingtheimportanttraitsfromtheSEMmod-
Because we measured denitrification potential in the elstoexaminetherelationshipsbetweendifferentspecies
sameplotsthroughoutthegrowingseason,weverified in trait space. Prior to the ordination, data were trans-
thatourresidualsfromeachsamplingdatewereinde- formedtoz-scoressothatallvariableswouldhaveequal
pendent and normally distributed. The median value weight in the analysis and then we used a Euclidean
andspreadoftheresidualsfromeachsamplingmonth distancemetric.Analysiswasperformedinthestatistics
were very similar (data not shown). QQ plots also packagePCOrd4.41(McCuneandMefford1999).
confirmed that residuals from each month did not
differ greatly from those in the overall data. This
analysis confirmed that there was no obvious month- Results
to-month shift in terms of model performance. As a
result, we combined all dates into one dataset that Plant traits
represents the denitrification potential in the second
growingseason.BecauseweonlysampledbiomassN Traits measurements for all species for both the bio-
onceinSeptember,thesoilsdatausedinthebiomassN mass N and DNP models are shown in Table 3. The
analysiswasanaverageofthesoilsvariablescollected range in values for each trait demonstrate that our
PlantSoil
efforts to select species expressing a variety of trait
valueswerereasonablysuccessful.Traitvaluestended
to vary by at least a factor of 2.5 across all eleven
species, as is the case for BGCN, and up to about a
factorof 10 forSRL and POR.
Biomassnitrogenresults
Once we revised the construct model based on the
modification indices, the biomass N model had good
fit (chi-square p-value00.848, CFI01.0, TLI01.08,
RMSEA00.0,Hoelter0238).Thetwosignificantpre-
dictors of biomass N were soil N (positive relation-
ship)andWUE(negativerelationship)(Fig.2a).Both
variables have similar coefficients meaning that the
environmental variable and the plant trait variable are
equallyimportantpredictorsofbiomassN.Intermsof
the correlations between the explanatory variables,
soil N and soil OM were positively correlated, and
plant height and BGR were negatively correlated.
Bivariate relationships between traits, environmental
factors, and biomass N also support these significant
relationships intheSEM model (Figure S1insupple-
mentalmaterial).Interestingly,therewasalsoasignif-
icantnegativecorrelationbetweensoilOMandWUE.
Denitrificationpotential results
Average, minimum and maximum site DNP values Fig.2 Modelresultsfor(a)biomassNand(b)denitrification
can be found in Appendix A. The revised model for potential (DNP). The significant parameters (p-value≤0.05) in
DNP also had good fit (chi-square p-value00.614, eachmodelareinboldandhavepathcoefficientswhilethenon-
significant parameters are gray without path coefficients. The
CFI01.0, TLI01.02, RMSEA00.0, Hoelter0521). twoexceptionstothisarethepathsfromBGCNandLogAGB
ThesignificantpredictorsofDNPweresoilN(positive toDNPwhichhadp-values00.07,sotheircoefficientsarelisted
relationship), soil OM (positive relationship), BGB butitalicized.Bothmodelshavegoodfitasshownbythenon-
significantχ2(Chi-squaredteststatistic)andmodelp-value;p-
(positive relationship) and POR(negative relationship)
values>0.05indicatethemodelstructureandthedatastructure
(Fig. 2b). In general the plant traits were weaker pre- donotdiffer.Directpathsaresolid,straightlineswhilecorrela-
dictors ofDNP thanthe environmentalfactors.BGCN tions are curved, dashed lines. Path coefficients are standard-
andAGBweremarginallysignificant(p-values00.067 ized.Thewidthofthearrowsindicateseachparameter’srelative
importance with thicker lines being parameters with a greater
and 0.066, respectively) with BGCN demonstrating a
influence. The models explained 56.8% of the variability in
negative relationship and AGB a positive relationship biomassNand61.4%ofthevariabilityinDNP
with DNP. There were also many significant correla-
tionsbetweentheexplanatoryvariables.Bothsoilprop- relationshipsbetweentraits,environmentalfactors,and
erties were positively correlated, and two plant traits, DNP(FigureS2insupplementalmaterial).
BGCNandPOR,werenegativelycorrelated.Inaddition
to these relationships, soil N and soil OM were both
positivelycorrelatedwithAGB,soilOMwaspositively NMS results
correlatedwithBGB,andsoilNwasnegativelycorre-
lated with BGCN. The significant relationships in the The three NMS axes accounted for 96.1% of the
SEM model are also supported by the bivariate variability in species traits. Axis 1 explained 22.4%
PlantSoil
of the variability and was characterized by a negative correlated with WUE, which is roughly in the same
relationship with WUE and a positive relationship area of trait space as species with traits similar to the
with AGB (Fig. 3). Axis 2 was characterized by neg- sedges and rushes (the middle, right side of Fig. 3).
ativerelationshipswithBGR,BGB,andBGCN,anda DNP, on the other hand, was negatively correlated
positive relationship with POR, and accounted for with POR and positively correlated with BGB sug-
57.3% ofthe variability. gesting that DNP can be maximized by species with
The members of the related sedge (Cyperaceae- traits similar to E. fistulosum (bottom, left corner of
Carex crinita, Carex lurida, and Scirpus cyperinus) Fig. 3). However, if we account for the two other
and rush (Juncaceae-Juncus effusus) families gener- traits, BGCN and AGB, which were marginally sig-
ally clustered together in trait space on Axes 1 and nificantly related to DNP, BGCN was negatively
2. The other species did not cluster based on life correlated to DNP and AGB was positively corre-
form or any other kind of taxonomic grouping that lated. These traits indicate that DNP is maximized
we perceived. in the opposite part of trait space (upper, right
Based on the relationships between the plant corner of Fig. 3). These results suggest that no
traits and ecosystem functions, increases in each single position in trait space could maximize all
function are related to specific regions of trait trait values that were correlated with an increase
space. For example, biomass N was negatively in DNP. Furthermore, the traits that maximize bio-
mass N are not in the same region of trait space as
those that maximize DNP and do not overlap with
the same species; these results indicate that the
same species do not maximize both ecosystem
functions.
Discussion
Anumberofstudiessuggestthatplanttraitsinfluence
ecosystem functioning (see, for example, Diaz and
Cabido 1997; Diaz et al. 2004). We determined that
plant traits significantly influenced both N removal
ecosystem functions, biomass N and DNP, and in the
case of biomass N were of approximately equal im-
portance to environmental variables. However, many
generally accepted trait relationships were not sup-
ported suggesting that the standard models of plant-
soil relationships need tobecritically evaluated.
Which specific traitsand environmental factors
controlbiomassN?
FourofourfivebiomassNplanttraitswerenotimpor-
tant predictors of biomass N. Neither the traits that
represent resource acquisition (SLA and SRL) nor the
Fig. 3 Non-metric multidimensional (NMS) ordination of the
architectural growth traits (Height and Belowground
species in trait space. Species abbreviations are as listed in
Table3.Traitsusedintheordinationwerethoseintherevised Rooting ratio) had significant relationships with bio-
SEMmodelsforbothbiomassNandDNP.Traitsdisplayedare mass N. This was unexpected given that many studies
those with loadings>0.2 on each axis. Axis 1 accounted for
have demonstrated a tight relationship between plant
22.4% of the variation and Axis 2 accounted for 57.3%. The
traitsrepresentingresourceacquisitionandplantgrowth
NMS ordination with all three axes (third axis not shown)
explained96.1%ofthevariabilityinspecies architectureatbothregionalandglobalscales(Craineet