Plant trait variation along environmental indicators to infer global change impacts

2018·2.5 MB·English

by  M. Dalle Fratte

Flora xxx (xxxx) xxx–xxx
ContentslistsavailableatScienceDirect
Flora
journal homepage: www.elsevier.com/locate/flora
Plant trait variation along environmental indicators to infer global change
☆
impacts
M. Dalle Frattea,⁎, G. Brusaa, S. Pierceb, M. Zanzotteraa, B.E.L. Cerabolinia
aUniversitàdeglistudidell’Insubria,ViaDunant3,Varese,21100,Italy
bDepartmentofAgriculturalandEnvironmentalSciences(DiSAA),UniversityofMilan,ViaG.Celoria2,Milan,20133,Italy
ARTICLE INFO ABSTRACT
EditedbyHermannHeilmeier Oneofthekeyprinciplesformodelingfutureimpactsofanthropogenicandclimatechangesonvegetationisto
Keywords: identifyclearpatternsandtrendsbetweenplantfunctionaltraitsandecologicaldrivers.Theglobalspectrumof
Plantglobalspectrum plant form and function outlines the major axes of variation and coordination among plant traits. We hy-
Ecologicalgradients pothesizedthatinter-specificplanttraitvariationattheregionalscaleshouldmatchaxesofplantadaptation
Landolt highlighted by the global spectrum, and that plant trait–environmental associations should be evident over
Climatechange ranges of environmental indicators corresponding to key drivers of future climate and land use changes in
Landusechange SouthernEurope.Totestourhypotheses,weanalyzedasampleof1095plantspeciesfromNorthernItaly,also
characteristicofSouthernEuropeanvegetation.Weanalyzedtrendsforfourplanttraits(canopyheight,leaf
area, specific leaf area, leaf nitrogen content) along meso- (temperature, continentality) and micro-climatic
(light) ranges, as well as along soil ranges (moisture, reaction and nutrient content), by means of Landolt’s
environmentalindicators.Attheregionalscale,theinter-specificvariationofmeanplanttraitsoverrangesof
environmentalindicatorsconfirmedthemainaxesofplantadaptationemphasizedbytheplantglobalspectrum.
Leafareaandspecificleafareashowedthelargestsensitivityoverrangesofallenvironmentalindicators,while
canopyheightswerethemostresponsivetotemperaturevalues.Temperature,lightconditionsandnutrients
wereassociatedwithcleareffectsonplanttraits,underliningthatresponsestochangesinlanduseandincreased
soilnutrientloading(suddenandabruptchanges)couldtriggerandstrengthenresponsestoclimatealteration
(gradualchanges).ScenariosforthenextfewdecadesinSouthernEuropeindicateincreasedtemperature,nu-
trient availability and forest coverage that, according to our findings, will favor more ‘acquisitive and fast
growing’plants,alsorepresentedbysubtropicalinvasivespecies.
1. Introduction Responsesofplantcommunitiestoclimatechangedependstrongly
onthefunctionalecologyofspecies(Sudingetal.,2008),anaspectof
In the Anthropocene epoch, human activities have changed the theirbiologythathasbeenincreasinglyinvestigatedinrecentdecades,
global climate, land cover and biodiversity at unprecedented rates, thankstoanincreasingnumberofstudiesonPlantTraits(PTs)(Garnier
exacerbating negative effects on plant diversity (Parmesan, 2006; et al., 2017). PTs are among the best proxies used to observe plant
Waltheretal.,2002).Worldwideimpactsofglobalchangeareacause responses to changes in environmental conditions (Lamarque et al.,
ofconcernforfuturescenariosinplantdiversityalteration(Parmesan 2014; Vandewalle et al., 2010). They integrate the ecological and
and Hanley, 2015), and its cascade effects on ecosystem functioning evolutionary history of a species, thereby reflecting trade-offs among
andtheprovisionofservices(Chapinetal.,2000;Naeemetal.,2009). different functions within a single plant and determining species eco-
In Southern Europe, in future decades a rise in temperatures and eu- logical roles in the environment (Violle et al., 2007). PTs influence
trophication are expected due to an increase in nitrogen deposition, plant fitness via their effects on resource acquisition, growth, re-
whiletheconcomitantincreaseinforestedareaswillleadtoanoverall productionandsurvival(DıazandCabido,2001).Indeed,theyquantify
decline in light availability (EEA, 2017a, 2017b; Kovats et al., 2014; thefunctionaldimensionsofplantsandarefundamentaltodetecttheir
Leipetal.,2015;Rounsevelletal.,2006;Tilmanetal.,2001). ecologicalstrategies(GrimeandPierce,2012;Pierceetal.,2017).
☆Thisarticleispartofaspecialissueentitled:“Functionaltraitsexplainingplantresponsestopastandfutureclimatechanges”publishedatthejournalFlora254C,
2019.
⁎Correspondingauthor.
E-mailaddress:[email protected](M.DalleFratte).
https://doi.org/10.1016/j.flora.2018.12.004
Received27April2018;Receivedinrevisedform26November2018;Accepted6December2018
0367-2530/ © 2018 Elsevier GmbH. All rights reserved.
Please cite this article as: M. Dalle Fratte, et al., Flora, https://doi.org/10.1016/j.flora.2018.12.004
M.DalleFratte,etal. Flora xxx (xxxx) xxx–xxx
Extensive studies focused on the plant economics spectrum found selected two “size” traits (Canopy Height and Leaf Area, respectively
evidencethatsupportthe‘traitsmanifesto’(Reich,2014).Globallythe CANHandLA),andtwotraitslinkedtothe“leafeconomicsspectrum”
two main axes of functional variation in vascular plant traits (Díaz (Specific Leaf Area and Leaf Nitrogen Content per leaf dry mass, re-
etal.,2016)arethoserelatedtothesizeoforgansandwholeplant,and spectivelySLAandLNC).PTdatawereselectedfromAuthorsdatasets
those associated with leaves, the leaf economics spectrum (Wright available in TRY (Kattge et al., 2011; https://www.try-db.org/: see
etal.,2004).Thefirstdimensiondenotestheabilityofplantstomake datasetsn.227,228,229,371,372andrelatedreferences),augmented
useoflightresources,dispersediaspores,andtheircolonizationability withsomepreviouslyunpublisheddatafromourowndatabase.
inspaceandtime.Itrunsfromshortandsmall-leavedspeciestendingto Foreachspecies,withinthesamepopulationwesampledfrom5to
havesmalldiasporestotallandbroad-leavedspeciestendingtohave 15fullyexpandedleavesselectedrandomlyfromtheoutercanopyof
large diaspores, broadly reflecting the r (colonization) versus K (ex- different individual adult plants growing under optimal conditions,
ploitation)continuum.Theseconddimensionindicateshowplantsac- followingastandardizedmethodologicalprotocol(Perez-Harguindeguy
quire and use resources. It represents the variation from large leaves etal.,2016).WemeasuredCANHasthemaximumvegetativeheight:
with high specific leaf area and leaf nitrogen content,to smallleaves distance between the groundand the highest vegetative leaves of the
withlowspecificleafareaandleafnitrogencontent,whichisatrade- plant(i.e. excludinginflorescences; Perez-Harguindeguyetal., 2016).
offamong‘fastandacquisitive’to‘slowandconservative’growth. For this measure, we selected the tallest individual of each species
PTshavebeenshowntovaryalongecologicalgradientsforatleast amongthoseidentifiedforleafsampling.Plantsweremeasuredintheir
some sites and combinations of species (e.g., Ackerly et al., 2002; normalstate,i.e.withoutstretchingthem.CANHwasquantifiedinsitu,
Herbenetal.,2018;Shipleyetal.,2017).Nevertheless,thereisstillno while all the other PTs were determined from analysis of material
clearconsensusaboutwhichPTsbestpredictclimatechangeresponses collectedinthefieldandanalysedinthelaboratory.ForLNCwepro-
(ParmesanandHanley,2015).Thelinkswithasingleecologicaldriver cessedthreereplicates(eachfromasingleindividualshoot)fromdry
haveonlyrarelybeensystematicallyevaluated,sinceitrequiresalarge leaf material with a CHNS-analyzer (FlashEA 1112 series Thermo-
samplingeffort(Rosbakhetal.,2015),althoughitisanecessarystep Scientific). More detailed methodological protocol concerning collec-
towardanaccuratemappingofcurrentorfuturespatialdistributionof tionofleafmaterialandsampleprocessingproceduresarereportedin
PTs(Butleretal.,2017;vanBodegometal.,2014;Wrightetal.,2017). Cerabolinietal.(2010).
Selection pressures of defined ecological drivers should generate pre- Sampling sites (n=154) are widespread over an area of approxi-
dictablevariationofPTsfollowingthemainaxesofadaptationwithin mately89,000km²,covering22provincesofNorthernItaly.Almostall
the plant economics spectrum, at least at the inter-specific level recordsofourdataset(96%)arerepresentativeofvegetationtypesof
(Vellend et al., 2014), providing useful opportunities to explore the Alpine(SouthernAlps)andContinental(PoPlain)biogeographiczones
abilityofplantstorespondtoglobalchanges. (ETC/BD, 2006) (Fig. 1). Except for some samples from the Medi-
In this context, Environmental Indicators (EIs) (Ellenberg et al., terraneanbiogeographicregion,theclimaterangesfromcontinentalto
1992;Landoltetal.,2010)couldbesuitable,sincetheyarescoresfor oceanic regimes (Pesaresi et al., 2014), although the complex mor-
eachsinglespeciesrepresentingtheiraveragepositionwithintherange phology of the study area results in considerable variability of meso-
ofkeyecologicaldrivers.Theyhavebeencriticizedsincetheyarede- climates, from warm climates close to the main lakes, to middle-Eur-
rived from the personal observations of authors and do not rely on opean oflowland areas andto cold Alpine regimes of high elevation.
objective measurements (Shipley et al., 2017). However, they have a Substrates vary from silicatic or carbonatic rocks to sedimentary de-
longhistoryofvalidation(e.g.,Klausetal.,2012;ScherrerandKörner, posits of various sources, with additional extensive variability at the
2011; Thompson et al., 1993; Wamelink et al., 1998), so they may localscale.
provideanearlywarningsignalofchangesintheenvironment.Forthis
reason,theyhavebeenwidelyappliedinstudiesofvegetationecology
aswellasstudiesconcerningglobalchanges(Diekmann,2003;Scherrer 2.2. Environmentalindicators(EIs)
andKörner,2011).Inthecontextoftheplant’scompetitivesituation,
PTs should vary accordingly to EIs, depending on the environmental EIs for the vascular plants of Central Europe (see Ellenberg et al.,
filters,sinceEIsrepresentcoordinatesofplants’nicheinnaturalcom- 1992)havealsobeendeterminedformanyflorasofadjacentregions.
munitiesasdefinedbytheirphysiologicalgrowthlimits(Landoltetal., Thosepublished fortheSwissflora havealong traditionculminating
2010).Hence,insightsfromtheanalysisofmeanvaluesofPTsalongEIs withthe2010editionoftheFloraIndicativa(Landoltetal.,2010),that
rangescouldreconciletwomainfieldsofecology.Thiscouldprovidea weconsideredthemostsuitabletodescribespeciesdistributionalong
deeper understanding of plant functional responses to future climate ranges of ecological drivers in our study area, since it neighbours
and land use changes, especially considering trends in PTs and their Switzerland(Fig.1).
responsesattheextremesofEIranges. Foreachspecies,weconsideredtwomeso-climatic(temperature,T;
Inthisstudy,weevaluatedtheextentofinter-specificvariationof continentality,K),onemicro-climatic(light,L)andthreesoilindicators
keyPTsoftheplantglobalspectrum(Díazetal.,2016)alongarangeof (moisture, F; reaction, R; nutrients N), allocating them to indicators’
ecological drivers. To this end, we studied a large sampleof vascular classes as reported in Flora Indicativa (Landolt et al., 2010). T char-
plantsformSouthernEurope,andestimatedecologicaldriversusingthe acterises the average air temperature during the growth period of a
EIscoresofthestudyspecies.Firstly,we hypothesizedthatinter-spe- plant, which largely corresponds with the average elevational dis-
cificvariationofPTsattheregionalscaleshouldmatchthemajoraxes tributionofspecies.Itrangesfromalpineandnival(1)toverywarm
of plant adaptation highlighted by the plant global spectrum (Díaz colline (5). K indicates whether the air is humid and the daily and
etal.,2016).Secondly,variationoverrangesofEIsshouldcorrespond annual variation of temperature occur within small ranges (small va-
to PT variation along gradients of key ecological drivers of global lues),ortheairisdryandthereisalargevariationintemperature(high
changes (temperature, rainfall, light and N availability, soil reaction values). It ranges from oceanic (1) to continental (5). L specifies the
andmoisture). averagelightquantityreceivedintherespectivehabitats.Itrangesfrom
deep shade (1) to full light (5). Concerning soil indicators: F is the
2. Methods averagesoilmoistureduringthegrowthperiod;itrangesfromverydry
(1)toflooded(5);RisthecontentoffreeH-ionsinthesoils,itranges
2.1. Planttraits(PTs) fromextremelyacid(1)toalkaline(5);Nisthenutrientcontentinthe
soil, referring mostly to nitrogen but also to phosphorus, and spreads
Inaccordancewiththeplantglobalspectrum(Díazetal.,2016),we fromveryinfertile(1)toveryfertile(5).
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M.DalleFratte,etal. Flora xxx (xxxx) xxx–xxx
Fig.1.Mapofthespatialdistributionoftheanalyseddataset(referencesystemWGS84/UTM32N).The10km×10kmgridhasbeencategorizedusingthesumof
allrecordscontainedineachpixel:1–15(white),16–30(lightgrey),31–50(darkgrey),and51–150(black).Theblacklineindicatestheadministrativeborders,i.e.
borders of France and Switzerland, and the administrative regions of Italy. Biogeographic regions (ETC/BD, 2006) are indicated: Alpine=dotted grey; Con-
tinental=strikethroughhorizontalgrey,Mediterranean=strikethroughdiagonalgrey.Theblackstarintheinsetmapindicatesthelocationofthestudyarea.
2.3. Dataanalysis theShapiroWilkmantest.WecompareddifferencesinPTsamongthe
fiveclassesofeachEIbymeansofanalysisofvariance(ANOVA).We
We selected 1095 species from 50 families (Peruzzi, 2010), ex- usedalinearmixed-effectsmodel,consideringthefamilyasarandom
cludingspeciesofunderrepresentedfamiliesintheoveralldataset(i.e. effect to exclude influences of phylogenetic correlation. For each
withlessthanfivespecies);thusthedatasetsubmittedtoanalysisin- combinationofPTsvsEIs,we testedtheeffectoffamilyviaANOVA,
cluded108trees,76shrubs,209graminoidsand702forbs(Appendix comparingtheresultsofthelinearmixed-effectsmodelwiththoseofa
A). generallinearmodel,consideringonlytheclassesofEIsasfactors.The
LNCanalysiswereperformedonasmallerdataset,duetothelackof addition of family on the models was always highly significant
available data (838taxa analyzed),while hydrophytes wereexcluded (p < 0.01).Post-hoccomparisonsontheresultsoflinearmixed-effects
from CANH analysis (1057 taxa analyzed). We also excluded species modelswerethencalculatedbyTukey'stest.ForPTsshowingstatisti-
indifferenttoasingleEI,markedas“x”inLandoltetal.(2010),from cally significant differences following post-hoc comparison, we esti-
analysisconcerningthatEI(AppendixB).However,theproportionof matedtheregressiontrendforthemeansofPTsalongtheclassesofEIs
“x”specieswasalwayssmall,rangingfrom0%(LandN)to1.2%(LNC using linear or non-linear regression (quadratic) models. We selected
vsT). the most significant regression trend comparing the two models
AccordingtoLandoltetal.(2010),eachEIhasitsownscaleofin- through ANOVA. We then compared the effect sizes of EIs and their
dicatorvalues,mainlyaunitincrementrangingfrom1to5.InTandF interactionsoneachPTthroughamultifactorialANOVAwithinterac-
the1to5rangeissplitinto0.5unitincrements,resultingin9classes. tions, using eta squared (η²) to quantify the effect sizes (Levine and
TobeabletocomparetheresultsamongEIs,weassignedspecieswith Hullett,2002).AllthestatisticalanalyseswerecomputedwithRsoft-
“intermediate”valuestothenextunitclasstowardtheextremesofthe ware(RCoreTeam,2017).
range(i.e.1.5to1,2.5to2,3.5to4,and4.5to5).
Datawerefirstnormalizedbylogarithmic(LA,CANHandSLA)or
squareroot(LNC)transformation,andnormalitycheckedaccordingto
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M.DalleFratte,etal. Flora xxx (xxxx) xxx–xxx
Fig. 2.Mean (±1.96*SE) of Plant Traits–PTs (CANH=Canopy Height, LA=Leaf Area, SLA=Specific Leaf Area, LNC=Leaf Nitrogen Content) vs climatic
EnvironmentalIndicatorclasses–EIs(T=Temperature,K=Continentality,L=Light)accordingtoLandoltetal.(2010).Lower-caselettersindicatetheresultsfrom
Tukeypost-hoccomparisonfollowingANOVA.RegressiontrendsbetweenPTsandEIsareplottedonlywhenANOVAwithTukeypost-hoccomparisonwassta-
tisticallysignificant:(ns)p-value>0.05;(*)p-value≤0.05;(**);p-value≤0.01.
3. Results showed an overall significant linear decrease along the L range: they
werehigherinmoderateshadeclasses(L=2andL=3)andlowerin
3.1. Canopyheight(CANH) well-lit (L=4) and mostly in full light (L=5) classes. On the other
hand, deep shade species displayed a slightly opposing trend, but re-
Withregardtomeso-andmicro-climaticEIs(Fig.2),CANHshowed presentedasmallproportionofthedataset(about0.2%)(AppendixB),
highlysignificantdifferencesintermsofT(F4,995=111.7,p < 0.001) andthismayhaveaffectedtheoutcome.
and L (F4,1006=65.7,p < 0.001) ranges, while differences were less AmongsoilEIs(Fig.3),CANHexhibitedsignificantdifferencesonly
distinct for K (F4,1003=4.8, p < 0.001). Mean canopy heights were over the R (F4,995=24.2, p < 0.001) and the N (F4,1006=46.3,
significantly shorter in species of the lowest temperature regimes p < 0.001)ranges,sincewefoundweaksignificantdifferencesamong
(T=1)andincreasedprogressivelyreachingthemaximuminspecies F classes (F4,1001=2.4, p=0.05) with no significant post-hoc com-
of higher temperature regimes (T=4 and T=5). Mean canopies parisons (p > 0.05). However, while the CANH differences over R
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M.DalleFratte,etal. Flora xxx (xxxx) xxx–xxx
Fig.3.Mean(±1.96*SE)ofPlantTraits–PTs(CANH=CanopyHeight,LA=LeafArea,SLA=SpecificLeafArea,LNC=LeafNitrogenContent)vssoilconditions
EnvironmentalIndicatorclasses–EIs(F=Moisture,R=Reaction,N=Nutrients)accordingtoLandoltetal.(2010).Lower-caselettersindicatetheresultsfrom
Tukeypost-hoccomparisonfollowingANOVA.RegressiontrendsbetweenPTsandEIsareplottedonlywhenANOVAwithTukeypost-hoccomparisonwassta-
tisticallysignificant:(ns)p-value>0.05;(*)p-value≤0.05;(**);p-value≤0.01.
classes did not show a clear trend, over the N range they showed a significantly smaller in species of the lowest temperature regimes
statisticallysignificantnon-linearincrease,consistingofasaturationin (T=1) and progressively larger in warmer classes, showing a slight
the two higher N classes of fertile and over-rich soils (N=4 and decrease in the warmest one (T=5). Mean LA was also significantly
N=5). The lowest mean canopy heights were those of very infertile higherinspeciesofintermediatemoistureclimates(K=3)butaclear
soils(N=1). patternofleavessizewasnotdetectabletowardstheextremesoftheK
range. Concerning light availability, along L range mean LA progres-
sively decreased movingfrom shade tofulllight conditions(i.e. from
3.2. Leafarea(LA) L=2toL=5),althoughthetrendwasnotsignificant.Outcomesmay
be affected by the shortage of deep shade species in the dataset, as
Mean LA showed significant differences over all the climatic EI mentionedabove.
(Fig. 2) ranges: T (F4,1030=36.8, p < 0.001), K (F4,1038=2.9, LAdisplayedhighlysignificantdifferencesoverallthesoilEIranges
p=0.02)andL(F4,1041=54.0,p < 0.001).Leaveswereonaverage
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M.DalleFratte,etal. Flora xxx (xxxx) xxx–xxx
(Fig.3):F(F4,1036=16.2,p < 0.001),R(F4,1030=12.5,p < 0.001) significantdifferencesemergedonlybetweenspeciesfromneutralsoils
and N (F4,1041=67.0, p < 0.001), although the evident non-linear (R=3)andthoselinkedtoalkalinesoils(R=5),thefirstonesshowing
layoutshighlightedalongFandRrangesbypost-hoccomparisonswere a higher mean LNC value. Ultimately, mean LNC values revealed a
notsupportedbysignificantregressions.MeanLAwassmallerinspe- significant marked linear increase along N range, with lower values
ciesgrowingindryandverydrysoils(F=1)andinwetandflooded belonging to species of very infertile and infertile soil classes (N=1,
soils (F=5), if compared to moist soils species (F=4). At the same N=2)andhighervaluesinspecieslinkedtoveryfertileandover-rich
time, mean LA was significantly smaller in species growing in acid soils(N=5).
(R=2) and alkaline soils (R=5) but larger for species from soils at
intermediatereaction(R=3andR=4).Aclearprogressiveincrease
of mean LA was evident throughout the range from very infertile 4. Discussion
(N=1)toveryfertileandover-richsoils(N=5).
Our analyses, based on a large dataset, broadly confirmed at the
3.3. Specificleafarea(SLA) regionalscaleofSouthernEuropethemainaxesofadaptationofvas-
cular plants determined by the global spectrum of plant form and
MeanSLAshowedhighlysignificantdifferencesalongallthemeso function (Díaz et al., 2016). Whole plant size, represented by CANH,
andmicro-climaticEIranges(Fig.2):T(F4,1030=13.2,p < 0.001),K andorgansize,representedbyLA,showedsimilarresponsesandcon-
(F4,1038=14.8,p < 0.001)andL(F4,1041=37.2,p < 0.001).Leaves sistenttrendsthroughoutalltheEIranges,althoughwithslightlydif-
were on average tougher (low values of SLA) in species from low ferentlevelsofsignificance.Similarly,butmoreevidently,SLAandLNC
temperatureregimesofhighelevation(T=1andT=2)comparedto variedoverEIrangesinagreementwiththeleafeconomicsspectrum
thosefromwarmerclimatesofthemontaneandcollinebelts(T=3and (Wrightetal.,2004).
T=4). Species proper of warmest climates (T=5) showed a slight At a broad scale, we observed a general response of PTs to en-
decreaseofmeanSLA,andtheyweresignificantlydifferentonlyfrom vironmental associations depicted by Landolt’s EIs. Among these, we
thoseofthecollinebelt(T=4).Nevertheless,thetrendofmeanSLA foundsubtlepatternsofPTvariationassociatedwithcontinentality(K)
over the T range was not significant. Mean SLA values were sig- whilethemostremarkablepatternsconcernedsoilnutrientcontent(N).
nificantlyhigherinspeciesfromclimatesunderoceanicinfluence(from Inaveryschematicwaywecanstatethat,‘size’PTs(CANHandLA)
K=1 to K=3) and lower in those linked to continental regimes wereinfluencedmorebymeso-andmicro-climaticEIs(TandL),while
(K=5),asunderlinedbythehighlysignificantnon-lineartrend.Mean PTsrepresentingtheleafeconomicsspectrum(SLAandLNC)exhibited
SLA values decreased linearly as light availability increased, being evidentresponsestosoilEIs(F,RandN).However,theoccurrenceof
higherinspeciesgrowinginshade(L=2)andlowerinthoselinkedto synergiceffectsduetoshiftsinenvironmentaldriversshouldalwaysbe
fulllightsites(L=5).TheslightdecreaseofmeanSLAindeepshade taken into account, although our analysis concerning the interactions
species(L=1)canlikelybeduetotheshortageofdeepshadespecies betweencouplesofEIsmainlyexhibitednosignificanceorsmalleffect
inthedataset,asmentionedabove. sizes(Table1).Ontheotherhandwemustconsiderthatenvironmental
MeanSLAalsodisplayedsignificantdifferencesthroughoutallthe driverswilllikelyactinanasynchronousway,sinceclimatewarming
soilEIranges(Fig.3):F(F4,1036=26.0,p < 0.001),R(F4,1030=12.3, may occur more gradually compared to more abrupt changes in land
p < 0.001)andN(F4,1041)=27.4,p < 0.001).Specieslinkedtovery use, especially with regard to local soil nutrients loadings (eu-
drysoils(F=1)showedthelowestvalueofmeanSLA,whichincreased trophication),sothattheformerwillactonvegetationassetsmodified
linearlyinspeciesgrowinginmoderatedrysoils(F=2)andagainin bythelatter.
those from moderately moist to flooded soils classes (F=3, F=4,
F=5),butwithnofurtherdifferences.AlongtheRrange,thehighest Table1
value of mean SLA occurred in species of the intermediate class re- Effectsizes(etasquared,η²)andsignificance(p-value)ofeachenvironmental
indicator, and their interactions, on plant traits (CANH=Canopy Height,
presenting almost neutral soils (R=3), while comparable low mean
LA=Leaf Area, SLA=Specific Leaf Area, LNC=Leaf Nitrogen Content) as
SLA values belonged to species of extremely acid (R=1) or alkaline
inferredbymultifactorialANOVAwithinteractions.
soil (R=5) classes. Mean SLA values clearly increased alongside nu-
trient availability, until an asymptotic saturation appeared from CANH LA SLA LNC
mediumfertiletoover-richsoils(N=3,N=4,N=5).
η² p-value η² p-value η² p-value η² p-value
3.4. Leafnitrogencontent(LNC) T 0.269 *** 0.124 *** 0.052 *** 0.009 ns
K 0.016 *** 0.016 *** 0.028 *** 0.003 ns
AmongtheclimaticEIranges(Fig.2),meanLNCshowedsignificant L 0.065 *** 0.090 *** 0.043 *** 0.037 ***
F 0.012 *** 0.052 *** 0.027 *** 0.033 ***
differences over T (F4,774=2.9, p=0.02) or L (F4,784=7.9, R 0.003 ns 0.017 *** 0.021 *** 0.029 ***
p < 0.001)classes,whilenosignificantdifferenceswerefoundoverK N 0.015 *** 0.064 *** 0.016 *** 0.041 ***
classes (F4,782=1.9, p=0.11). However, mean LNC values did not T*K 0.019 ** 0.009 ns 0.012 ns 0.026 *
displayasignificanttrendalongTrangeandthepost-hoccomparisons T*L 0.012 * 0.008 ns 0.025 ** 0.026 *
T*F 0.012 ns 0.007 ns 0.013 ns 0.02 ns
showed significant differences only between species of two classes
T*R 0.011 ns 0.015 ns 0.013 ns 0.014 ns
(T=3 vs T=5). On average, mean LNC values were significantly T*N 0.008 ns 0.012 ns 0.012 ns 0.014 ns
higherinspecieslinkedtomoderateshade(L=2,L=3)comparedto K*L 0.014 * 0.011 ns 0.016 ns 0.016 ns
thoseassignedtowell-litandfulllightclasses(L=4,L=5),resulting K*F 0.007 ns 0.021 ** 0.017 ns 0.014 ns
in a linear decreasing trend of mean LNC as light availability rose. K*R 0.006 ns 0.010 ns 0.006 ns 0.014 ns
K*N 0.004 ns 0.013 * 0.009 ns 0.008 ns
Speciesofdeepshadedidnotshowanydifferences.
L*F 0.011 ns 0.009 ns 0.018 * 0.028 *
Conversely,meanLNCdisplayedsignificantdifferencesalongallthe L*R 0.012 ns 0.002 ns 0.015 ns 0.017 ns
soil EI ranges (Fig. 3): F (F4,781=14.8, p < 0.001), R (F4,777=4.1, L*N 0.005 ns 0.005 ns 0.004 ns 0.006 ns
p < 0.003) and N (F4,784=14.1, p < 0.001). It was significantly F*R 0.015 * 0.015 ns 0.011 ns 0.013 ns
F*N 0.014 ns 0.009 ns 0.014 ns 0.011 ns
lower in species linked to dry soils (F=1), then increased until sa-
R*N 0.005 ns 0.006 ns 0.005 ns 0.006 ns
turationinspecieslinkedtoclassesofFindicatingmoderatemoistto
flooded soils (from F=3 to F=5). Along the R range, a non-linear Legend: (ns) p>0.05; (*) p≤0.05; (**) p≤0.01; (***) p≤0.001.
trend was readily detectable, although in the post-hoc comparisons Emboldenedfactorsarethosestatisticallysignificant.
6
M.DalleFratte,etal. Flora xxx (xxxx) xxx–xxx
Wearewellawareofthecriticalpointsofourbroadapproachthat rangedidnotrevealasignificanttrendattheinter-specificlevelcon-
needtobefollowedupbymoredetailedanddeeperanalysestovalidate sidered by our analysis. Leaf area shifts with light availability are
andtofine-tuneouroutcomes.Forexample,differentialresponsesover probablyrelatedtobothsinglespeciesandgrowthforms(Ackerlyetal.,
EI ranges may be expected by dividing the traits dataset used here 2002). An almost stable pattern of LA along irradiance gradient was
among growth forms (Shipley et al., 2017) or plant strategies (Pierce observed for herbs and shrubs, while graminoids and trees showed a
etal.,2017),orfurtherconsideringintra-specifictraitsvariationalong remarkabledeclineasirradianceincreased(Shipleyetal.,2017).
better defined gradients (Vellend et al., 2014) to gain insights with AccordingtothescenariomodeledforSouthernEurope(Rounsevell
regardtospecies’chancesofacclimatizationwhenfacedwithchanging etal.,2006),futuredecadesshouldbecharacterizedbyanincreaseof
ecologicalconditions. forested areas at the expense of grasslands, so species that are large,
‘acquisitiveandfastgrowing’and/orshadetolerant,willbefavoredby
4.1. Climateindicators futurelandusecircumstances.
Our data showed the largest effect size scores in relation to the T 4.2. Soilindicators
range for CANH and LA (Table 1), evidence that agrees with the fact
that temperature rise will undoubtedly be the major environmental From very dry to moist soils, as expected (Reich, 2014), we ob-
driver of climate change determining future vegetation distributions. servedagradualshiftfrom‘slowandconservative’to‘fastandacqui-
From low to high temperature regimes we found, in accordance with sitive’ species, denoted by the trends ofleaf economics PTs (i.e. from
previous studies, groups of species with higher mean canopy height lowtohighSLAandLNC).Similartrendshavealreadybeenreportedin
(Sandel et al., 2016; Tardella et al., 2016) and bigger mean size of the literature (Fraser et al., 2016; Garnier and Navas, 2012; Herben
leaves(Hodgsonetal.,2011;Scoffonietal.,2011;Wrightetal.,2017). et al., 2018). However, theincreases ofmean valuesof SLA andLNC
Temperature regimes had much less effect on traits linked to the leaf wereevidentonlyinthefirstpartoftherangeuntilthemesiccondition
economics spectrum, even though we recorded a mean SLA increase wasreached,andbeyondthispointtheincreaseofsoilmoisturedidnot
fromlowtohighTclasses:i.e.passingfrom‘slowandconservative’to provideanysignificantmodificationofleafeconomicsPTs.Acompar-
‘fast and acquisitive’ leaves (Borgy et al., 2017; Fontana et al., 2017; able pattern was also shown by leaf area (Table 1) although this de-
Rosbakhetal.,2015;Sandeletal.,2016;Wrightetal.,2004). creasedonwetsoils,probablyduetothecontributionofleaflessspecies
WecanthereforeassumethatwarmerclimateexpectedinSouthern (seeCyperaceaeandJuncaceae).Smallleaveswerethusassociatedwith
Europe infuturedecades(EEA,2017a;Kovatsetal., 2014)willfacil- bothextremesofsoilmoisture,accordingtoShipleyetal.(2017).Re-
itatelarge-leavedspecieswithtallercanopies,andthathighelevation gardless of local presence of water bodies (rivers, lakes, ponds, mires
vegetationoftheSouthernAlpswillalsobeaffected,asrecentfindings etc.) soil moisture availability is strongly linked to temperature and
across the tundra biome have highlighted (Bjorkman et al., 2018). precipitation regimes, so that models performed for Southern Europe
Despitethis,atthewarmestextremeoftheTrange,LA,SLAandLNC predict a decrease of soil moisture in future decades (EEA, 2017a;
showedamoderatedecreasethatfitswellwiththe“laurophyllisation” Kovats et al., 2014), which will presumably favour species with rela-
phenomenon of lower vegetation belts in Southern Switzerland tivelyconservativestrategies.
(Waltheretal.,2002),althoughleaflifespanwasnotincludedinour Overthesoilreactionrange(R)allthemeanvaluesofPTsgenerally
dataset. exhibitedaconvexunimodalrelationship,reachingmaximumvaluesin
Over the continentality range mean values of traits were the least the group of species growing in neutral soils. This humped pattern
responsive, probably because of the small amplitude of this range in highlightstheharshsoilconditionsexperiencedbyplantslivingatboth
Switzerland, obscured by the attribution of Mediterranean species to extremesoftherange,whichareassociatedwithconservativeandslow
the medium class instead of to the driest one (Landolt et al., 2010). growingspecies(Garnieretal.,2016),smallerinheightandinleafsize
Although an increase of plant height together with precipitation was (trendsofthesetwolatterPTswerenotsignificant).Althoughresponses
foundforgrasslands(Sandeletal.,2016),speciesinourdatasetshowed of plants to soil reaction range were clear, insights concerning future
higher mean values in intermediate classes, as observed by Fontana vegetationscenariosdrivenbythisfactoraredifficulttoassesssinceit
etal.(2017).Ouroutcomesalsocannotcorroboratetheevidencethat presumably will not change rapidly in the near future, being strictly
broad-leavedspeciesareassociatedwithwetterclimates(e.g.Hodgson associatedwiththegeologicalsubstrate.
etal.,2011;Scoffonietal.,2011;Wrightetal.,2017). Largerplantsareexpectedtobestronglylinkedtoveryfertilesoils,
Continentality range was associated with a significant decrease of consideringthatnitrogenandphosphorusavailabilities(towhichtheN
mean SLA values (Table 1) in agreement with previous observations indicator mainly refers; Landolt et al., 2010) are the main limiting
(Hodgsonetal.,2011;Rosbakhetal.,2015;Sandeletal.,2016;Shipley factorsofplantgrowth(Güsewell,2004;Stevensetal.,2018).Hencean
et al., 2017; Wright et al., 2004). Accordingly, we can assume that increaseinsoilnutrientavailabilitiescanleadtoaparallelincreaseof
specieswithmoreconservativeleaves(lowSLA)willbefavoredbythe valuesinallthePTsstudiedhere(Ackerlyetal.,2002;andreferences
average precipitation decrease predicted in future scenarios (EEA, therein). Specifically, from poor to very rich soils plants exhibited
2017a;Kovatsetal.,2014). consistentlytallercanopies(Tardellaetal.,2016;Herbenetal.,2018)
The light availability indicator exhibited significant effects on all andlargerleaves(McDonaldetal.,2003;Shipleyetal.,2017).Thetwo
PTs(Table1),whichshowedageneralprogressivedecreasefromshade ‘size’PTsdifferedeachotheronlyinthehighestNclass,inwhichmean
tofulllightconditions.Ouroutcomesareinagreementwithcompeti- valuesofcanopyheightsreachedsaturation.
tion forlight, as shaded habitats arenormallyassociated with‘acqui- Conservativestrategies(Stresstolerant,sensuGrime;seeGrimeand
sitiveandfastgrowing’species(Hodgsonetal.,2011;Rosbakhetal., Pierce,2012)arelinkedtonutrientpoorsoils,whilerelativelyacqui-
2015; Wright et al., 2004) with taller canopies (Fontana et al., 2017; sitivestrategies(CompetitiveandRuderal,sensuGrime;seeGrimeand
Tardellaetal.,2016).Thefunctionalvariabilityofspeciesadaptedto Pierce, 2012) to nutrient rich soils (Fraser et al., 2016; Garnier and
deepshadeisprobablyduetothewidearrayofresponsesdisplayedby Navas, 2012; Reich, 2014; Wright et al., 2004). This was also clearly
understoryplants(e.g.,Valladaresetal.,2002).Wemustalsoconsider showninourresultsconcerningmean‘leafeconomics’planttraits(SLA
thatlocalflorastypicallyincludefewerspeciesadaptedtodeepshade andLNC),alongtheentireNrange.
with respect to those living in higher light conditions. For example, SincesoilnitrogenandphosphorusloadingsinSouthernEuropewill
considering the whole flora of Switzerland (n=6472; Landolt et al., continue to increase in the future decades (EEA, 2017b; Leip et al.,
2010), only 0.5% of species are included in the deep shade class. 2015; Tilmanetal., 2001),we canexpect ariseoflarger ‘acquisitive
However, distribution of mean LA values along the light availability andfastgrowing’species,withanoverallenhancementofCompetitive
7
M.DalleFratte,etal. Flora xxx (xxxx) xxx–xxx
strategiesandcompetitionratesinplantcommunities.Thiscouldputa ChapinIII,F.S.,Zavaleta,E.S.,Eviner,V.T.,Naylor,R.L.,Vitousek,P.M.,Reynolds,H.L.,
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ofspeciestosharePTsisconnectedtotheirbroadhabitataffinitiesas Sheremet’ev,S.N.,Jactel,H.,Baraloto,C.,Cerabolini,B.,Pierce,S.,Shipley,B.,
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(Garnieretal.,2016).AmongthePTsconsideredinourwork,SLAand systemprocesses.TrendsEcol.Evol.16,646–655.
Diekmann,M.,2003.Speciesindicatorvaluesasanimportanttoolinappliedplant
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range, suggesting that species with taller canopies will very likely be
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