Ecology and Evolution. 2017;7:9473–9484.  | 9473www.ecolevol.org

Received:15December2016  |  Revised:17August2017  |  Accepted:31August2017DOI: 10.1002/ece3.3436

O R I G I N A L R E S E A R C H

Tree species distribution in temperate forests is more influenced by soil than by climate

Lorenz Walthert1  | Eliane Seraina Meier2

ThisisanopenaccessarticleunderthetermsoftheCreativeCommonsAttributionLicense,whichpermitsuse,distributionandreproductioninanymedium,providedtheoriginalworkisproperlycited.©2017TheAuthors.Ecology and EvolutionpublishedbyJohnWiley&SonsLtd.

1SwissFederalInstituteforForest,SnowandLandscapeResearchWSL,Birmensdorf,Switzerland2Agroscope,InstituteforSustainabilitySciencesISS,Zurich,Switzerland

CorrespondenceLorenzWalthert,SwissFederalInstituteforForest,SnowandLandscapeResearchWSL,ResearchUnitForestSoilsandBiogeochemistry,Birmensdorf,Switzerland.Email:lorenz.walthert@wsl.ch

AbstractKnowledgeof theecological requirementsdetermining tree speciesdistributions is apreconditionforsustainableforestmanagement.Atpresent,theabioticrequirementsand the relative importanceof thedifferent abiotic factors are still unclear formanytemperatetreespecies.Wetherefore investigatedtherelative importanceofclimaticandedaphicfactorsfortheabundanceof12temperatetreespeciesalongenvironmen-tal gradients. Our investigations are based on data from 1,075 forest stands acrossSwitzerlandincludingthecold-inducedtreelineofallstudiedspeciesandthedrought-inducedrangeboundariesofseveralspecies.Fourclimaticandfouredaphicpredictorsrepresentedtheimportantgrowthfactorstemperature,watersupply,nutrientavailabil-ity,andsoilaeration.Theclimaticpredictorswerederivedfromthemeteorologicalnet-workofMeteoSwiss,andtheedaphicpredictorswereavailablefromsoilprofiles.Speciescoverabundanceswererecordedinfieldsurveys.Theexplanatorypowerofthepredic-torswasassessedbyvariationpartitioninganalyseswithgeneralizedlinearmodels.Forsixofthe12species,edaphicpredictorsweremoreimportantthanclimaticpredictorsinshapingspeciesdistribution.Overallspecies,abundancesdependedmainlyonnutrientavailability, followed by temperature, water supply, and soil aeration. The often co-occurringspeciesrespondedsimilartothesegrowthfactors.Droughtturnedouttobeadeterminantofthelowerrangeboundaryforsomespecies.Weconcludethatoverall12studiedtreespecies,soilpropertiesweremoreimportantthanclimatevariablesinshap-ingtreespeciesdistribution.Theinclusionofappropriatesoilvariablesinspeciesdistri-butionmodelsallowedtobetterexplainspecies’ecologicalniches.Moreover,ourstudyrevealedthattheecologicalrequirementsoftreespeciesassessedinlocalfieldstudiesandinexperimentsarevalidatlargerscalesacrossSwitzerland.

K E Y W O R D S

drought,ecologicalniche,gradientanalysis,nutrients,soilaeration,speciesabundance,speciesdistributionmodels,variationpartitioning

1  | INTRODUCTION

Knowledgeontheecological requirementsdeterminingtreespeciesdistributions is a precondition for profitable and sustainable forest

management and for forest conservation under current and futureenvironmental conditions. In the last decades, numerous studieshavebeenconductedtodevelopandtorefinethemethodologyforassessingtreespeciesresponsestotheenvironment.Thus,areliable

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conceptual framework forassessing theecological requirements fortreespeciesdistributionsshouldconsidermainly twomethodical is-sues.First,anadequatenumberofobservations(Coudun&Gegout,2006)shouldbeinvestigatedalongdiverseandstrongenvironmentalgradientsincludingspeciesrangeboundaries(Beauregard&deBlois,2014).However,speciesdonotalwaysreachtheirdistributionedgeswithintheinvestigationareaofstudies,and,thus,thispreconditionisoftennotfulfilled.Second,environmentalfactorswithadirectimpactonplantperformance(e.g.,temperatureandnutrients)shouldbepre-ferredtoindirectfactors(e.g.,elevationandgeology),becausedirectfactorsareecologicallymorecomprehensibleandhavealargerspatialapplicability (Austin & Cunningham, 1981; Guisan & Zimmermann,2000).However, thedataavailablefordirectfactorsaremostoftennot spatially explicit, for example, biotic interactions, disturbances,and soil (Mod,Scherrer, Luoto,Guisan,&Scheiner,2016).Thus, di-rect factors and, more specifically, soil characteristics are, despitetheirimportance,rarelyconsideredinpredictionsofspeciesdistribu-tions(Diekmann,Michaelis,&Pannek,2015;Thuiller,2013).Incaseswheresoil-relatedinformationhasbeenusedinstudies,ithasoftenbeenderivedfrombioindication (e.g.,Piedallu,Gegout,Lebourgeois,& Seynave, 2016) and is therefore considered to suffer from ambi-guityandthusfromlimitedcomparabilitytomeasuredsoilvariables(Szymura,Szymura,&Maciol,2014).

Only a few studies have considered measured soil variablesin species distribution models (SDM) for characterizing ecologicalniches(e.g.,Dubuisetal.,2013;Pinto&Gegout,2005;Walthert,GrafPannatier,&Meier,2013)orforpredictingspeciesdistributions(e.g.,Beauregard&deBlois,2014;Coudun,Gegout,Piedallu,&Rameau,2006; Piedallu,Gegout, Perez,& Lebourgeois, 2013).These studieshave shown that the statistical model performance was better formostofthestudiedplantspeciesifedaphicvariableswereaddedtoclimaticfactors.

FormanyEuropeantreespeciesgrowing inthetemperatezone,itisstillunclearifandhowtheyreacttosoilproperties,astheexist-ingstudiesincludedonlyafewtreespeciesordidnotfullyconsiderthemethodicalrequirementsmentionedabove;that is,observationsshouldcoverstrongenvironmentalgradientsincludingspeciesdistri-butionedges,useofdirectinsteadofindirectfactors,anduseofmea-suredsoilvariablesinsteadofecologicalindicatorvalues.

Climateandsoilareamongthemostimportantgrowthfactorsandthus drivers of tree species distributions. Important climatic factorsaretemperatureandwater,andimportantedaphicfactorsarewater,nutrients,andprobablysoilaeration.Thus,inourstudyweinvestigatethe importanceofclimatic (i.e., temperatureandwater)andedaphicfactors (i.e.,water, nutrients, and soil aeration) for the cover abun-danceof12 temperateEuropean tree species (i.e., threeconiferousandninedeciduousspecies).Morespecifically,ourstudyencompassesthethreefollowingthematicfields:First,weaimattestingforwhichtreespeciesthemodelperformancecanbe improvedbyaddingsoilvariablestoclimaticvariables,andhowmuchthespeciesdifferintheirsensitivitytoclimateandsoil.Weexpectthatincludingsoilvariablesimprovesthemodelperformanceforallspeciesandthatthecommonspecieshave the lowest sensitivity to soil properties.Moreover,we

wanttoassessthedegreeofredundancybetweentheclimateandtheedaphicvariablesinexplainingspeciesdistribution.Second,weanalyzetherelativeimportanceofthefourgrowthfactors(i.e.,temperature,water,nutrients,andsoilaeration)inshapingspeciesabundances.Weexpect similar responsepatterns for co-occurring species.Third,weevaluatespecies’sensitivitytodroughtandsoiloxygenshortage.Weexpectthatdrought isan importantfactor indeterminingthe lowerrangeboundaryofseveraltreespecies.Inaddition,wereviewwhethertheresultsofoursensitivityassessmentthatisbasedonlarge-scalein-ventorydataagreewiththeoutcomesofcorrespondingresearchfromlocalcasestudiesandfromexperiments.Asourstudyfocusesontherelative importanceof soil and climatevariables,wedonot includebioticinteractionsanddisturbances.

2  | MATERIALS AND METHODS

2.1 | Study area

ThestudyareaincludedallofSwitzerland(circa45–47°Nand6–10°E),whichislocatedinthecenterofWesternEurope(Fig.1).Duetothehighvariabilityoftopography,climate,andgeology,Switzerlandhasrelativelystrongenvironmentalgradientscomparedto itssmallsur-face.Roughly30%ofthecountry(12,000km2)iscoveredwithforest,half ofwhich is located above1,000m a.s.l. Forestmanagement ismainlypracticedatlowelevations,wherenolarge-scaleclear-cuttingis applied andnatural regeneration isoften fosteredby silviculturalmanagement(Brassel&Brändli,1999).Fertilizingorlimingtostimu-latesoilfertilityhasalwaysbeenforbiddeninSwissforests.

2.2 | Study plots, species, and environmental data

2.2.1 | Study plots

SpeciesandenvironmentaldataoriginatefromadatabaseoftheSwissFederal Institute for Forest, Snow and Landscape Research (WSL)containingdataon~1,200mainly forestedplotsacrossSwitzerland.Foreachplot,datafromafloristicinventoryconductedaccordingtoBraun-Blanquet (Braun-Blanquet,1964)andsoildatafromasoilpitwereavailable.Formostplots, thevegetation surveyand soil sam-plingwerecarriedout inthesameyearorwithamaximumtimeof5yearsbetween them.Mostplotswere selectedaccording toeco-logicalcriteriaforforestsites;thatis,speciescompositionandstandstructureshouldbeclosetothoseobservedinnaturalforests.Fromthis dataset,we excluded about 90 forest plots according to threecriteria.First,weonlyselectedmatureforeststandsbecauseweex-pectedthatthesiterequirementsoftreespeciesaremoreapparentin mature than in juvenile stands.We therefore removed all plotswith juvenile forests (n=32) from the dataset by excluding forestswherethetallesttreesweresmallerthan20m.However,maturefor-estsondrysitesorathighelevationswith limitedtreeheightwerenotexcluded.Inaddition,weexcludedallplotswherethewaterbal-ancewasnotcomputableduetounfavorablesoilproperties,mainlyboulder-richsoilswithhollowspaces.Finally,weeliminatedseveral

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early successional forest plots dominated by birch trees and someheavilymanagedchestnutgroves.Theselectionprocedure resultedin1,060seminatural,predominantly latesuccessionalmature foreststands.Wefurtherincluded15treelessplotsinordertoextendthedroughtandthesoilaerationgradient.Tenoftheseplotsarelocatedindrygrassysteppesonextremelyshallowsoils,andthefiveremain-ingplotsareonmarshes.Thus,atotalof1,075plotswereinvestigatedinourstudy(Fig.1).

2.2.2 | Species data

Species data on the study plots were collected by ~35 authorsand partly originate from the Swiss Forest Vegetation Database(Wohlgemuth,2012).Theabundanceofthespeciesinthetreelayer(maturetrees)wasassessedduringthevegetationsurvey.Therefore,

alltheplantspeciesoccurringintheherb,shrub,andtreelayersinanarearangingfrom100to500m2(avg.200m2)wererecordedusingthe Braun-Blanquet cover abundance scale (Braun-Blanquet, 1964;MuellerDombois&Ellenberg,1974).Aspartofthevegetationsur-vey,theheightoftheforeststandwasestimated.Thevegetationsur-veyformostoftheplotswascarriedoutbetween1987and2014;however, 34 surveys were completed before 1987. We restrictedourassessmentto12treespeciesthatwerepresentonasufficientnumberofplotswithregardtospeciesdistributionmodels,forwhichaminimumof approximately50observations is necessary (Coudun&Gegout,2006).Threespecieswerepresent inslightly fewer than50plots (Table1).The12speciesbelong to two functionalgroups:broadleaveddeciduous (Europeanbeech,Fagus sylvatica; sycamore,Acer pseudoplatanus; European ash, Fraxinus excelsior; wych elm,Ulmus glabra; cherry tree, Prunus avium; pedunculate oak,Quercus

F IGURE  1 Locationsofthe1,075studyplotsinSwitzerland.Reddotsshowthe1,060matureforeststands,yellowsquaresthe10treelessplotsonextremelydrysoils,andbluetrianglesthefivetreelesssitesonmarshes.Forestareaisshowningreen

km

TABLE  1 Numberandelevationofforestplotswithspeciescoverabundancedataforthe12studiedtreespecies.Theminimumandmaximumelevationofallstudied1,060matureforeststandsacrossSwitzerlandwas240and2,200ma.s.l.,respectively

SpeciesNumber of plots where present

Elevation (m a.s.l.) where presentCover abundance (%) where present

Minimum Mean Maximum Mean Maximum

Picea abies 684 280 1,005 2,040 33 88

Abies alba 410 390 854 1,540 25 88

Pinus sylvestris 168 310 820 1,980 24 88

Fagus sylvatica 621 340 728 1,490 46 88

Fraxinus excelsior 206 240 613 1,360 19 63

Acer peseudoplatanus 200 290 811 1,600 17 88

Ulmus glabra 61 410 742 1,360 10 63

Prunus avium 39 240 515 820 6 38

Quercus robur 66 240 528 950 15 63

Quercus petraea 97 320 604 1,360 17 88

Quercus pubescens 41 330 727 1,360 31 88

Carpinus betulus 40 370 506 710 19 88

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robur; sessile oak,Quercus petraea; downy oak,Quercus pubescens; andEuropeanhornbeam,Carpinus betulus),andneedle-leavedever-green(Norwayspruce,Picea abies;silverfir,Abies alba;andScotspine,Pinus sylvestris).Thethreeoakspecieswereidentifiedafterarevisita-tionofallplotswhereoaksoccurbyjointlyconsideringmorphologyand molecular-genetic markers according to Rellstab, Bühler, Graf,Folly,andGugerli(2016).

2.2.3 | Environmental data

Toestimatetherelativeimportanceofthesoilandclimatevariables,andtoavoidmulticollinearityproblems inouranalyses,weselectedeightpredictorsaccordingtotheirsuitabilityforexplainingtreespe-cies distributions, as described inWalthert etal. (2013). Briefly, aninitial set of 87 environmental variables was evaluated and all thevariablesthatwereselectedhadars<|0.7|.Halfoftheeightselectedpredictorswere climatic, and theotherhalfwere closely related tothesoil.

Asclimatepredictors,weselectedmeanyearlydegree-dayswitha5.56°Cthreshold(DD),meantemperatureamplitudebetweenJanuaryandJuly(T-Cont),meanamountofprecipitationfromJunetoAugust(RR),andmeanrelativeairhumidityfromJunetoAugust(RH;Table2).AirhumidityandtemperatureamplitudewerenottestedinWalthertetal.(2013),butbothhaveanimpactonplantperformanceandspe-cies distributions (for RH, see Lendzion and Leuschner (2008) andKöcher, Horna, and Leuschner (2012); forT-Cont, see Jobbagy andJackson(2000)andNishimuraandLaroque(2011)).

Climate data for the period 1981–2010 were derived fromweather data from themeteorological network ofMeteoSwiss.DDvalues were produced with Daymet (Thornton, Running, & White,1997).T-Cont,RR,andRHwerecalculatedusingtherespectivelong-termmeanmonthlyvalues(1981–2010)providedbyRemund,Rihm,andHuguenin-Landl(2014).

Assoilpredictors,weselectedthedroughtindex(AT/PT)forsoilwater availability, theC/N ratio (C/N), and base saturation (BS) forsoilnutrientavailability,and thedepthof thewater level in thesoil(W-level)forsoilaeration(Table2).Themethodsforassessingthesesoilpredictorsarebrieflydescribedinthefollowingtext.Foradetaileddescriptionofthesepredictorsanda justificationfortheirselection,seeAppendixS1.

Soilsamplesweretakenfromsoilprofiles,onaverage1.2mdeep,accordingtopedogenetichorizons.

BS in0–50cmsoildepth:Exchangeablecations (Na,K,Mg,Ca,Mn,Al,Fe)wereextractedina1mol/LNH4Clsolutionanddeterminedbyatomicemissionspectroscopy(ICP-AES).Contentsofexchangeableprotonswere determined after extracting the soil in a 1mol/LKCLsolution.Theeffectivecation-exchangecapacity(CEC)wascalculatedbysumming thechargeequivalentsofexchangeableNa,K,Mg,Ca,Mn,Al,Fe,andH.ThebasesaturationisthepercentofexchangeableNa,K,Mg,andCaoftheCEC.

C/Nin0–10cmsoildepth:C/Nistheratiobetweenorganiccar-bonandtotalnitrogen.Theircontentsweredeterminedbydrycom-bustion.PossiblecarbonateswereremovedbyHClvaporpriortodrycombustion.

AT/PT: This index represents the ratio of actual to potentialtranspiration and corresponds to the average reduction in tran-spiration due to soilwater shortage from June toAugust in theperiod1981–2010.Thewaterbalancewasmodeledonall1,075studyplotsusingacoupledmassandheattransfermodelforsoil–plant–atmosphere systems (Coupmodel). The model was drivenby daily weather data and used soil hydraulic parameters thatwe derived from measured soil properties like texture, density,andstonecontent.Vegetationwasimplementedasforadynamicmodel forest,andmaximumrootingdepthwasset to1.5m.Dueto relatively high amounts of precipitation (>900mm/a) in mostpartsofSwitzerland,droughtpredominantlyoccursonsoilswitha

TABLE  2 Environmentalpredictorsatthe1,075studyplotsacrossSwitzerland

Category Name Class Description Min Q0.25 Med Q0.75 Max Unit

Climate DD Temperature Meanyearlydegree-days>5.56°C(1981–2010)

575 1,318 1,743 2,025 2,870 °C

T-Cont Temperature MeantemperatureamplitudeJanuary/July(1981–2010)

14.6 17.1 18.0 18.4 20.5 °C

RR Water MeanprecipitationJunetoAugust(1981–2010)

151 342 414 510 792 mm

RH Water MeanrelativeairhumidityJunetoAugust(1981–2010)

63.7 71.8 73.0 74.3 80.9 %

Soil AT/PT Water Droughtindex;meanratiobetweenactualandpotentialtranspirationJunetoAugust(1981–2010)

0.23 0.94 0.99 1.00 1.00 —

C/N Nutrients MeanC/Nratio(Corg/Ntot)in0–10cmsoildepth

7.5 14.5 16.9 20.1 42.5 —

BS Nutrients Meanbasesaturationin0–50cmsoildepth

2.7 18.5 85.9 99.6 100.0 %

W-level Soilaeration Meandepthofthesoilwaterlevelinthevegetationperiod

20 200 200 200 200 cm

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lowstoragecapacityofplantavailablewater.Inthehumidsuboce-anicclimateofcentralEurope,edaphicfactors,suchaslowwaterholdingcapacity,areexpectedtoimposemoreseriousconstraintson treewater relations thanclimate (Backes&Leuschner,2000).Therefore,we assigned the drought indexAT/PT to the edaphicpredictors.Thus, inourstudy,wateravailabilityisrepresentedbytwo climatic predictors (RR and RH) and one edaphic predictor (AT/PT).

W-Level:Thisvariablewasusedasaproxyforsoiloxygenshort-age.Itisderivedfromredoximorphicpropertiesandtimeseriesofthemeasuredsoilwaterlevelinthesoilpits.

2.3 | Data analysis

Toestimatetherelativeimportanceofclimate(temperature[DD,T-Cont] and water [RR, RH]) and soil (water [AT/PT], nutrients[C/N,BS],andaeration[W-level]),weconductedavariationpar-titioning analysis (Borcard, Legendre, & Drapeau, 1992; Mood,1971).Weestimated thepurecontributionofeachpredictor setby subtracting the model fit of the opposite set of predictorsfrom the full setofpredictors, so thatVPredictor subset i = VFullModel–VFullModelwithoutPredictorsubseti.Asstatisticalmodels,weusedgener-alized linearmodels (GLM,McCullagh&Nelder,1989)with logitlinks,assumingabinomialdistributionbecausetheresponsevari-ableswere proportions (i.e., species abundance data).Model fitswereevaluatedusingtheadjustedD2(adj.D2),followingWeisberg(1980).ResponsecurveswerederivedforeachtreespeciesfrompredictionsofGLMswithlogitlinksassumingabinomialdistribu-tion.Asresponsevariable,weusedthecoverabundanceofmaturetreesandaspredictorsthesinglevariablesinthelinearandquad-raticforms.ToestimatetheGLMperformanceforeachcombina-tionofsinglepredictorandspecies,weestimatedwiththehelpofanANOVAthesignificanceofthedifferencebetweentheperfor-mancesofthefullmodelandthemodelwithoutthespecificsinglepredictor.AlldatawerepreparedandanalyzedusingR (R,2016)andArcGIS9.2(ESRI,2006).

3  | RESULTS

3.1 | Relative importance of climate and soil

Thestatisticalperformance (model fits)of the fullmodels includ-ing all climatic and edaphic variables varied considerably amongtreespecies.Theexplaineddeviance(adj.D2) rangedfrom0.28to0.59(Table3).Thebestmodelfits (i.e.,adj.D2>0.45)werefoundfor P. sylvestris, F. excelsior, and Q. pubescens. Poor model fits(i.e.,adj.D2<0.30)were foundforP. abies,A. pseudoplatanus, andA. alba.

Across all 12 tree species, the eight single predictors (in total96 combinations) had a significant amount of information for thedistribution of the tree species in 15 cases (significance level ofp<.05;Table3).Formostspecies,p-valuesandadj.D2werehighlycorrelated.

Foralltreespecies,modelsincludingedaphicandclimaticpredic-torshadabettermodel fit thanmodels includingonlyclimaticpre-dictors (Table4).Further,forallspecies,themeanpurecontributionof the edaphic predictors (adj.D2 0.13±0.09) was larger than thecorresponding pure contribution of the climatic predictors (adj.D2 0.11±0.06).However,thepurecontributionsoftheedaphicpredic-torsvaried strongly among the tree species: Forhalf of the species(F. excelsior,A. pseudoplatanus,U. glabra,Q. pubescens,P. sylvestris,andQ. petraea),thepurecontributionoftheedaphicpredictorswaslarger,while for the other half of the species (A. alba,F. sylvatica,Q. robur,C. betulus, P. avium, and P. abies), the pure contribution of the cli-maticpredictorswaslarger(Fig.2).ThemostcommontreespeciesinSwitzerland,P. abies,A. alba,andF. sylvaticarespondedratherweaklytosoilcharacteristics.

Acrossall species, the jointcontribution (redundancy)ofclimateandsoil (adj.D20.15±0.10;Table4)wassimilar to thepureclimate(adj.D20.11±0.06)andthepuresoilcontribution(adj.D2 0.13 ± 0.09). Redundancyofsoilandclimatevariableswasobservedforallspecies,andthedegreeofthisredundancy,however,variedbetweenspecies.

3.2 | Relative importance of temperature, water, nutrients, and soil aeration

Themeanvalueof thepurecontributionsofall12speciesshowedthat the abundance depended mainly on nutrient status (adj.D2 0.09±0.09), followed by temperature (adj.D2 0.07±0.05), wateravailability(adj.D20.04±0.04),andsoilaeration(adj.D2 0.01 ± 0.01; Table4).Basedon the individual responsepatterns, the12 speciesweregroupedasbeingmainlysensitivetonutrientstatus,totempera-tureortowateravailability(Fig.3).

Species with a predominant response to nutrient status wereA. pseudoplatanus, U. glabra, F. excelsior, P. avium, and P. sylvestris (Fig.3).AbundanceofA. pseudoplatanus, U. glabra, F. excelsior,and,toalesserextent,P. aviumwaspositivelycorrelatedwithnutrientavail-ability.These speciespreferred soilswithhighbase saturations andlowC/Nratios(Table3).WhileP. sylvestrisalsorespondedpositivelytohighbasesaturations,thisspeciesreachedthehighestabundanceon soilswith low nitrogen availabilities (high C/N ratios).After nu-trient status, temperature, andwater availabilitywere of secondaryimportanceforthesefivespecies.WhileA. pseudoplatanusrespondedonlymarginallytotemperature,thefourotherspeciesrequiredhighertemperatures.Highairhumidityandagoodwatersupplywereben-eficial forA. pseudoplatanusandforU. glabra. Incontrast,F. excelsior, P. avium,and P. sylvestriswerelesswaterdemanding(Table3;seealsoSection3.3.1).

Species that predominantly responded to temperature wereQ. robur, C. betulus, F. sylvatica, A. alba,andP. abies (Fig.3).Whiletheabundance of Q. robur and C. betulus increased with temperature,F. sylvatica, A. alba, and P. abies reached the highest abundance atmoderatetemperatures; the lowestheatrequirementwasfoundforP. abieswithamaximalabundanceat1,100°Cdegree-days,followedby A. alba and then F. sylvatica with corresponding degree-days of1,500°Cand1,900°C,respectively(Table3).Aftertemperature,water

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andnutrientstatuswereofsecondaryimportanceforthesefivespe-cies.P. abies, A. alba, and F. sylvatica had higherwater requirementsthan Q. robur and C. betulus (Table3; see also Section3.3.1), withF. sylvaticaandA. albabenefitingmostfromhighairhumidity.Allfivespecieswere rather indifferent to soil nutrient status,most notablywithrespecttobasesaturation(Table3).

ApredominantresponsetowateravailabilitywasfoundforQ. pu-bescens andQ. petraea (Fig.3).Bothspeciesweremostabundantatsiteswherewatersupplyandairhumiditywereratherlow(Table3;seealsoSection3.3.1).Moreover,bothspeciespreferredrelativelywarmsites.Thenutrientstatusofthesoilhadonlyasmallinfluenceontheseoakspecies.

3.3 | Responses of species abundance along drought and soil aeration gradients

3.3.1 | Drought sensitivity

Amongthethreewater-relatedpredictorsofourstudy(RR,RH,andAT/PT),thedroughtindexAT/PTwasmostabletoexplainspeciescoverabundances;thatis,thepurecontributionofthisvariablewasrelativelyhigh for themajorityof thespecieswithsignificantvalues,however,onlyforP. abies,F. sylvatica,andQ. pubescens(p<.05;Table3).

The12specieswerearrangedinfivegroupsaccordingtotheirre-sponsetoAT/PT.Thefirstgroup,withP. abies, A. alba,andF. sylvatica,

hadamaximalabundanceundergoodwatersupply, that is,athigh AT/PT levels, and faded away at AT/PT values of approximately 0.6–0.7(Fig.4).Thesecondgroup,withA. pseudoplatanusandU. glabra,behaved similar to the first group, but hadmuch lower abundancelevels, as is typical for these tree species that rarely dominate inSwiss forests (Table1). Compared to the speciesmentioned above,the abundance of C. betulus and Q. robur peaked on drier sites (AT/PT 0.8). The species of group 4,Q. pubescens, P. sylvestris, andQ. petraeapeakedinabundanceinwhatwasclearlythedriestrange(AT/PT 0.6–0.7) and all extendedmost toward the dry end of thedroughtaxis.Finally,auniqueresponsewasfoundforF. excelsior.Thisspecies,thoughoftenpresentonhydromorphicsoils,extendedintotheverydryrangeofthedroughtaxis.

BasedontheirresponsetothedroughtindexAT/PT,that is,thepositionoftheabundancemaximumandtheshapeofthecurveto-ward thedryendof this gradient (Fig.4), thedrought sensitivityofthestudiedspecieswasrankedasfollows:U. glabra, A. pseudoplatanus, F. sylvatica, P. abies, A. alba > C. betulus, Q. robur > P. avium, Q. petraea, P. sylvestris > F. excelsior, Q. pubescens.

3.3.2 | Sensitivity to limited soil aeration

Asmeasuredbythepurecontributions,soilaerationhadlittleinfluenceonthecoverabundanceofmosttreespecies(Table3).ForF. sylvatica,however,thepredictiveabilityofthisvariablewasquitehigh(p < .001).

TABLE  3 Purecontributionsofindividualpredictorsandofthefullsetofpredictors(fullmodel)forthe12studiedtreespecies,derived fromvariableGLMswithcoverabundanceofmaturetreesastheresponsevariableandenvironmentalpredictorsbasedondatafrom1,075 studyplotsacrossSwitzerland.Numbersindicatethedevianceexplainedbytheindividualpredictors(adj.D2multipliedby100)andbythefull model(adj.D2).Thedirectionofthetrend(T)betweenthepredictorandtheresponsevariableisindicatedaspositivelinear“+,” negativelinear“−,”positiveunimodal“+/−,”orwithnocleartrend“N.”“Optimum”(Opt.)forDD,RH,AT/PT,andC/Nspecifiesthe environmentalconditionsforwhichmaximalabundancewaspredictedifthetrendwaspositiveunimodal.Boldfaceindicatesadj.D2≥1.5%for individualpredictors.Forp-values,see(p).AbbreviationsandunitsofpredictorsareexplainedinTable2.Notethatthesumofthepure contributionsoftheindividualpredictorsdiffersfromthedevianceexplainedbythefullmodelduetojointcontributions

Species

Full model

Climate Soil

Temperature Water Nutrients Soil aeration

Full modelDD TOpt. °C p T- Cont T p RR T p RH T

Opt. % p AT/PT T Opt. p C/N T Opt. p BS T p W- Level T p

P. abies 5.70 +/− 1100 .00 0.10 − .64 −0.10 + .95 0.00 + .76 1.60 + .02 1.70 +/− 27 .02 0.50 − .26 0.40 − .32 0.28

A. alba 5.50 +/− 1500 .00 0.00 +/− .86 0.40 + .40 3.30 +/− 76 .00 0.70 + .28 0.60 +/− 18 .34 0.60 − .31 0.60 +/− .29 0.30

P. sylvestris 2.60 +/− 1900 .06 0.60 + .47 −0.10 N .95 1.20 − .28 0.50 +/− 0.65 .53 9.50 + .00 2.60 + .07 0.50 + .54 0.45

F. sylvatica 10.10 +/− 1900 .00 1.20 +/− .01 0.30 + .29 0.30 +/− 73 .27 1.90 +/− 0.90 .00 1.40 +/− 15 .01 0.50 N .12 3.30 + .00 0.37

F. excelsior 2.60 + .09 0.20 +/− .77 0.00 − .90 0.40 − .62 0.70 − .48 8.00 − .00 2.80 + .07 1.10 − .33 0.55

A. ps.platanus −0.10 N .97 −0.10 − .98 0.20 + .73 1.60 +/− 75 .24 0.20 + .74 7.30 − .00 2.50 + .11 1.10 N .36 0.29

U. glabra 4.80 + .35 3.20 N .49 0.20 + .94 2.10 +/− 74 .63 4.30 + .39 1.50 − .71 7.20 + .21 3.50 N .46 0.39

P. avium 4.00 + .66 1.40 + .86 0.70 N .92 1.60 − .84 0.80 − .92 2.90 − .74 0.70 N .92 1.40 N .86 0.39

Q. robur 8.50 + .04 0.80 +/− .71 1.00 N .66 0.60 − .78 1.90 +/− 0.80 .47 0.60 − .78 0.70 N .73 0.20 N .90 0.40

Q. petraea 1.90 + .35 0.10 + .88 1.00 N .57 1.90 +/− 68 .34 4.00 +/− 0.70 .12 1.50 +/− 19 .42 0.40 N .75 0.50 + .72 0.30

Q. pubescens 0.60 + .72 1.20 + .52 1.40 N .49 2.50 − .27 9.70 +/− 0.60 .01 2.50 +/− 18 .28 0.20 + .87 0.20 + .89 0.59

C. betulus 3.20 + .36 0.20 +/− .92 4.50 N .24 0.90 − .72 2.20 +/− 0.80 .49 2.20 − .48 0.10 N .92 0.40 N .86 0.40

     |  9479WALTHERT And MEIER

Ourdata enabledus to assess the sensitivity to limited soil aerationforfivespecies.Basedontheshapeoftheresponsecurves,werankedF. sylvaticaassensitiveandP. abies,A. alba,F. excelsior,andA. pseudopla-tanusasnotsensitivetolimitedsoilaeration(Fig.5).AswedidnothaveenoughobservationsonstronglyhydromorphicsoilsforP. sylvestrisandforQ. petraea,wedidnotassessthesensitivitytosoilaerationforthesespecies.ThesamewasthecaseforC. betulus, U. glabra, P. avium, Q. pu-bescens,andQ. robur,whichhadnearlylinearandhorizontalresponsecurvesatlowabundancelevelsandwerethusnotevaluable.

4  | DISCUSSION

4.1 | Relative importance of climate and soil

Variationpartitioningrevealedthattheadditionofedaphicvariablestotheclimaticvariablesimprovedthemodelperformanceforallspe-cies.Similar resultswerealready found inearlier research,both fortreespecies(e.g.,Beauregard&deBlois,2014;Coudunetal.,2006;Piedalluetal.,2013)and forherbaceousspecies (e.g.,Dubuisetal.,2013).Overallspecies,soilpropertieswereevenmoreimportantthanclimate variables in explaining species distribution. Diekmann etal.(2015) found that thedistributionofmanyplant species is stronglydrivenbysoilconditions inregionswith lowclimaticheterogeneity.Ourstudyshowsthatthisseemstobethecasenotonlyinclimaticallyhomogeneous landscapesbut alsoeven inheterogeneousones like

thoseinSwitzerland.Therefore,appropriatesoilpropertiesshouldbeincludedinspeciesdistributionmodels,especiallyforthespecieswithastrongresponsetoedaphicvariables.Forthemostcommonspecies,P. abies,A. alba,andF. sylvatica,however,theinclusionofsoilvariablesislesseffective,astheirwidespreaddistributioncanbeinterpretedasaresultoftheirrelativelylowsensitivitytosoilcharacteristics.

Climateisasoil-formingfactoraffectingnumeroussoilprocessesandsoilproperties,whichcouldresult inacertaindegreeof redun-dancybetweensoilandclimatevariablesinexplainingspeciesdistri-bution,asrecentlyhypothesizedbyThuiller(2013).Afirstindicationof such a redundancy in our dataset is given by the relatively highcorrelationbetween the four climate and the four soil predictors insomecases(Pearsonrrangingfrom−0.40to0.55forthe16combi-nations).Duetothesepartlysubstantialcorrelations,theredundancyinexplainingspeciesdistribution,measuredasjointcontributionofallclimateandallsoilvariablesoverallspecies,wasofsimilarmagnitudethanthepureclimateandthepuresoilcontributionsandthus,con-firmingthehypothesisofThuiller(2013).

Themodelperformancewiththefullsetofpredictorswasratherweak for some species. Part of the unexplained deviance could beattributed tobiotic factorsnot included inourmodels (Meieretal.,2010).Finally, it isnoteworthythattheresponseswereall inaccor-dancewith theniche theory (Austin&Smith,1990),withnomulti-modalorU-shapedresponsecurvesbutonlylineartrendsorunimodalorskewedresponses(Table3andFig.4).

TABLE  3 Purecontributionsofindividualpredictorsandofthefullsetofpredictors(fullmodel)forthe12studiedtreespecies,derived fromvariableGLMswithcoverabundanceofmaturetreesastheresponsevariableandenvironmentalpredictorsbasedondatafrom1,075 studyplotsacrossSwitzerland.Numbersindicatethedevianceexplainedbytheindividualpredictors(adj.D2multipliedby100)andbythefull model(adj.D2).Thedirectionofthetrend(T)betweenthepredictorandtheresponsevariableisindicatedaspositivelinear“+,” negativelinear“−,”positiveunimodal“+/−,”orwithnocleartrend“N.”“Optimum”(Opt.)forDD,RH,AT/PT,andC/Nspecifiesthe environmentalconditionsforwhichmaximalabundancewaspredictedifthetrendwaspositiveunimodal.Boldfaceindicatesadj.D2≥1.5%for individualpredictors.Forp-values,see(p).AbbreviationsandunitsofpredictorsareexplainedinTable2.Notethatthesumofthepure contributionsoftheindividualpredictorsdiffersfromthedevianceexplainedbythefullmodelduetojointcontributions

Species

Full model

Climate Soil

Temperature Water Nutrients Soil aeration

Full modelDD TOpt. °C p T- Cont T p RR T p RH T

Opt. % p AT/PT T Opt. p C/N T Opt. p BS T p W- Level T p

P. abies 5.70 +/− 1100 .00 0.10 − .64 −0.10 + .95 0.00 + .76 1.60 + .02 1.70 +/− 27 .02 0.50 − .26 0.40 − .32 0.28

A. alba 5.50 +/− 1500 .00 0.00 +/− .86 0.40 + .40 3.30 +/− 76 .00 0.70 + .28 0.60 +/− 18 .34 0.60 − .31 0.60 +/− .29 0.30

P. sylvestris 2.60 +/− 1900 .06 0.60 + .47 −0.10 N .95 1.20 − .28 0.50 +/− 0.65 .53 9.50 + .00 2.60 + .07 0.50 + .54 0.45

F. sylvatica 10.10 +/− 1900 .00 1.20 +/− .01 0.30 + .29 0.30 +/− 73 .27 1.90 +/− 0.90 .00 1.40 +/− 15 .01 0.50 N .12 3.30 + .00 0.37

F. excelsior 2.60 + .09 0.20 +/− .77 0.00 − .90 0.40 − .62 0.70 − .48 8.00 − .00 2.80 + .07 1.10 − .33 0.55

A. ps.platanus −0.10 N .97 −0.10 − .98 0.20 + .73 1.60 +/− 75 .24 0.20 + .74 7.30 − .00 2.50 + .11 1.10 N .36 0.29

U. glabra 4.80 + .35 3.20 N .49 0.20 + .94 2.10 +/− 74 .63 4.30 + .39 1.50 − .71 7.20 + .21 3.50 N .46 0.39

P. avium 4.00 + .66 1.40 + .86 0.70 N .92 1.60 − .84 0.80 − .92 2.90 − .74 0.70 N .92 1.40 N .86 0.39

Q. robur 8.50 + .04 0.80 +/− .71 1.00 N .66 0.60 − .78 1.90 +/− 0.80 .47 0.60 − .78 0.70 N .73 0.20 N .90 0.40

Q. petraea 1.90 + .35 0.10 + .88 1.00 N .57 1.90 +/− 68 .34 4.00 +/− 0.70 .12 1.50 +/− 19 .42 0.40 N .75 0.50 + .72 0.30

Q. pubescens 0.60 + .72 1.20 + .52 1.40 N .49 2.50 − .27 9.70 +/− 0.60 .01 2.50 +/− 18 .28 0.20 + .87 0.20 + .89 0.59

C. betulus 3.20 + .36 0.20 +/− .92 4.50 N .24 0.90 − .72 2.20 +/− 0.80 .49 2.20 − .48 0.10 N .92 0.40 N .86 0.40

9480  |     WALTHERT And MEIER

4.2 | Relative importance of temperature, water, nutrients, and soil aeration

Asweexpected, theoftenco-occurring treespeciesshowedsimi-lar response patterns to the four growth factors. Accordingly, thefour species F. excelsior, A. pseudoplatanus, U. glabra, and P. avium reached highest abundances on nutrient-rich soils,Q. petraea andQ. pubescensweremostabundantonsiteswitha lowwateravail-ability,whereasQ. roburandC. betuluswererestrictedtowarmsiteswith frequently changing soil water and soil oxygen availabilities.Thethreemostwidespreadspecies,P. abies,A. alba,andF. sylvatica,showed a similar response pattern as well. The characteristic forthese specieswasa lownutrientdemandanda relativelyhigh re-sponsetotemperature.

Acrossall12species, temperatureandnutrientavailabilityweremost important in shaping species ecological niches. Consistentlywiththealtitudinaldistributionofthestudiedtreespecies,variationpartitioningrevealedthedifferentheatdemand(degree-days)ofthespecies (e.g., F. sylvatica>A. alba>P. abies; Q. robur>Q. petraea). Temperaturewasa relevant factornotonly inour studybutalso inmostotherSDMstudies(Austin&VanNiel,2011).Contrarytodegree-days,thermalcontinentalitywasaweakpredictorformostspeciesinour study.The impact of soil nutrient availability on tree growth isspeciesspecific(Lévesque,Walthert,&Weber,2016).Therefore,itis

logicalthatspeciesdifferedintheirresponsetonutrientsinourstudy.However, there isanongoingdebateabout thedegreeof feedbackbetween plants and soils. The soil does not only influence speciescomposition,but speciesalsoaffect soilproperties suchasnutrientstatus, mainly by species specific litter input. This bidirectional linkbetween plants and soils is discussed inmore detail in Section4.4.Afternutrientstatusandtemperature,wateravailabilitywasalsoanimportantfactorforspeciesnichedifferentiation,muchmoresothansoil aeration. Among the three predictors that represented wateravailability,thedroughtindexAT/PTwasbyfarmostimportant(seeSection4.3.1),followedbyairhumidityandprecipitation,whichwasaweakpredictorformostspecies.Speciesthatpreferredoceaniccli-matewithhighmeanairhumiditywereA. alba,A. pseudoplatanusandU. glabra,whereasQ. pubescens,P. sylvestris, andP. aviumweremostabundantincontinentalregionswithoftenrelativelylowairhumidity.Theimportanceofsoilaerationcouldbeevaluatedonlyforaminorityofthestudiedspecies(seeSection4.3.2).

4.3 | Responses of species abundance along drought and soil aeration gradients

Futuresummerswillprobablybewarmeranddrier,whileprecipitationinwinterandspringmaybeenhanced(Lindneretal.,2014).Increasing

F IGURE  3 Relativeimportanceoftemperature,water,nutrients,andsoilaerationinexplainingthedistributionofthe12studiedtreespecies.RelativeimportanceisbasedonpurecontributionsderivedfromgroupedvariableGLMswithcoverabundanceofmaturetreesastheresponsevariableandfourgroupedenvironmentalpredictors—temperature,water,nutrients,andsoilaeration—basedondatafrom1,075studyplotsacrossSwitzerland.Togetrelativeimportance,thepurecontributionsofthefourgroupedpredictorswereconvertedtorelativeonessothatthesumofthepurecontributionsofthefourgroupedpredictorswassetto100%foreachspecies.Forpurecontributions(adj.D2)ofthefourgroupedpredictorsandofthefullmodels,seeTable4

0

20

40

60

80

100

Ace.p

se

Fra.e

xc

Pin.s

yl Ul

m.gla

Pr

u.avi

Que.r

ob

Fag.s

yl Ab

i.alb

Pic.a

bi Ca

r.bet

Que.p

ub

Que.p

et

Relat

ive im

porta

nce (

%)

Nutrientdominated

Temperaturedominated

Waterdominated

Nutrients Soil aerationWaterTemperature

F IGURE  2 Climateversussoilsensitivityforthe12studiedtreespecies.AbsoluteandrelativepurecontributionsofclimateandsoilvariableswerederivedfromgroupedvariableGLMswithcoverabundanceofmaturetreesastheresponsevariableandgroupedenvironmentalpredictors(fourclimateandfoursoilpredictors)basedondatafrom1,075studyplotsacrossSwitzerland.Speciesbelowthe1:1lineareconsideredrathersensitivetoclimate,whereasspeciesabovethislineareconsideredrathersensitivetosoilvariables.Therelativepurecontributionsrefertothefullmodel

0.1

0.2

0.3

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0.6

0.7

0.8

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Soil v

ariab

les

Climate variables

Fag.syl

Absolute pure contributionRelative pure contribution

Abi.albQue.rob

Car.bet

Pru.aviPic.abi

Que.petPin.syl

Fra.excUlm.gla

Ace.pse

Que.pub

     |  9481WALTHERT And MEIER

summerdroughtsanddecreasingoxygenavailabilityduetowaterlog-gingduringwinter and springmayadversely affect thegrowthandcompetitive ability of sensitive tree species on many forest sites

(Kreuzwieser&Gessler,2010).Withthesefutureprospects,itisim-portanttoknowthesensitivityofspeciestodroughtandtosoiloxy-genshortage.

4.3.1 | Drought

Almost every physiological process in plants is affected directly orindirectlybywater supply (Kramer&Boyer,1995). In thepast, thedroughttoleranceofmanytreespecieshasbeenthoroughlyexplored,mainlyonthebasisofcasestudies.However, it isunclearhowwelltheselocalassessmentsreflectthelarge-scalesituationinforesteco-systems.Moreover,previousstudieshaveoftenfocusedonjuvenileplants,althoughjuvenileandmatureplantsmaydiffer inphysiology(Kolb&Matyssek,2001;Ryan&Yoder,1997).Furthermore,growthconditions simulated in experiments diverge from themultifactorialfieldconditions(Kolb&Matyssek,2001).

Basedonlarge-scaleinventorydatafromabout1,000maturefor-eststands,weshowedthatthe12studiedtreespeciesvariedgreatlyin theirdrought sensitivity, that is, in their response to thesummerdrought indexAT/PT.Our results are ingoodqualitativeagreementwithcomparableresearchexploringspeciesdroughtsensitivity,suchas (1) case studies investigating physiological characteristics ofma-turetrees inCentralEuropeanforeststands (e.g.,Zweifel,Rigling,&Dobbertin, 2009) or juvenile trees in greenhouse experiments (e.g.,Arend,Brem,Kuster,&Gunthardt-Goerg,2013);(2)aSwisscasestudymeasuringthecanopyfoliagetemperatureofmaturetreeswithinfra-redthermography(Scherrer,Bader,&Körner,2011);(3)casestudiesongrowthdynamicsofmaturetreesinold-growthEuropeanforestsusingtreerings(e.g.,Cavin,Mountford,Peterken,&Jump,2013);and(4)large-scaletreeregenerationandmortalityassessmentsinmature

F IGURE  4 Speciessensitivitytodrought.ResponsecurveswerederivedfromsinglevariableGLMswithcoverabundanceofmaturetreesastheresponsevariableandadroughtindex(meanAT/PTbetweenJuneandAugust,1981–2010)aspredictorbasedondatafrom1,075studyplotsacrossSwitzerland.Thedifferentspeciesabundancemaximareflectthedifferentmeanabundancethatspeciesreachedonthestudyplots(Table1).Prunus aviumisnotillustratedbecauseitsresponsecurverunshorizontallyatalevelofabout0%abundance.IfmodeledAT/PTissmallerthanabout0.3–0.4,forestsarenotabletopersistduetoexcessivewatershortage

0.4

0.3

0.2

0.1

0.0

0.4 0.5 0.6 0.7 0.8 0.9 1.0

Pred

icted

abun

danc

e

Drought index AT/PT during summerDry Moist

Fag.syl

Abi.al

b

Fra.exc

Pic.ab

iPin.syl

Que.pet

Que.pub

Que.rob

Car.bet

Ulm.glaAce.pse

TABLE  4 Pureandjointcontributionsofgroupedpredictorsforthe12studiedspecies,derivedfromgroupedvariableGLMswithcoverabundanceofmaturetreesastheresponsevariableandenvironmentalpredictorsbasedondatafrom1,075studyplotsacrossSwitzerland.Numbersindicatethedevianceexplainedbythegroupedpredictors(adj.D2).Notethatthesumofthepurecontributionsofthegroupedpredictorsdiffersfromthedevianceexplainedbythefullmodelduetojointcontributions

Species

Pure contributions adj.D2

Joint contribu-tions adj.D2

Full model Climate Soil Temperature Water Nutrients S. aeration Climate and soil

P. abies 0.28 0.08 0.07 0.06 0.02 0.04 0.00 0.13

A. alba 0.30 0.15 0.04 0.09 0.05 0.02 0.01 0.10

P. sylvestris 0.45 0.07 0.13 0.04 0.02 0.10 0.01 0.24

F. sylvatica 0.37 0.17 0.08 0.14 0.03 0.02 0.03 0.11

F. excelsior 0.55 0.12 0.34 0.07 0.01 0.29 0.01 0.09

A. ps.platanus 0.29 0.02 0.21 0.00 0.03 0.21 0.01 0.06

U. glabra 0.39 0.08 0.24 0.05 0.08 0.21 0.03 0.07

P. avium 0.39 0.14 0.11 0.07 0.03 0.08 0.01 0.14

Q. robur 0.40 0.19 0.05 0.17 0.02 0.03 0.00 0.15

Q. petraea 0.30 0.06 0.09 0.02 0.08 0.04 0.00 0.16

Q. pubescens 0.59 0.05 0.13 0.02 0.14 0.03 0.00 0.41

C. betulus 0.40 0.16 0.07 0.05 0.03 0.05 0.00 0.16

9482  |     WALTHERT And MEIER

Swissforests(Riglingetal.,2013).However,thecomparisonbetweenourstudyandtheabove-mentioneddrought-orientedresearchisnotexhaustive.First,comparabledatawerehardlyavailableforsomeofthe 12 studied species, especially forU. glabra, P. avium, andC. bet-ulus. Second,withonly three speciesonaverage, thenumberof si-multaneously investigated specieswas rather low inmost of thesepreviousstudiesfocusedondrought.Nevertheless,ourstudybasedonlarge-scaleinventorydataindicatesthatspeciesdroughtsensitivi-ties,asassessedincasestudiesandexperiments,arewellreflectedinspeciescoverabundanceinmatureforeststandsacrossSwitzerland.Moreover,species’responsepatternstothedroughtindexAT/PTin-dicatedthatdroughtwasanimportantdeterminantofthelowerrangeboundaryforsomespecies,ashypothesizedbyNormandetal.(2009)andAndereggandHilleRisLambers(2016).

4.3.2 | Soil aeration

Treesareaerobicorganisms thatdependonasteadysupplyofox-ygen toall livingcells.A shortageofoxygen thereforedisturbs theplant metabolism. During waterlogging, oxygen diffusion into thesoil andoxygen supply to the roots are reduced (Kozlowski, 1986).Manyenergy-consumingprocesses,includingshootandrootgrowth,aresloweddowntoovercometheenergycrisiscausedbywaterlog-ging (Kreuzwieser&Rennenberg,2014).Currentknowledgeon theresponses of European tree species to waterlogging and oxygen

shortagehasbeenacquiredmainlyincasestudiesbasedon(1)physi-ological, morphological, and growth reactions of seedlings undercontrolledconditions (e.g.,Dreyer,1994;Schmull&Thomas,2000),(2)abovegrounddamagetomaturetrees inopen land (reviewfromKreuzwieser&Rennenberg, 2014), and (3) time until plant diebackoccurred(reviewfromNiinemets&Valladares,2006).Ingeneral,spe-ciescoverabundanceontheapproximately1,000studyplotsacrossSwitzerlandreflectedspeciessensitivityratingsfromthesecasestud-ies.However, as alreadyobserved for drought sensitivity, compari-sonsbetweenpublishedresearchandourstudysufferedfromscarcedataavailability.Moreover,wecouldcompareonlyfivespecies(F. syl-vatica, P. abies, A. alba, F. excelsior,andA. pseudoplatanus),asoursen-sitivityassessmentwasfeasibleonlyforthesespecies.Discrepanciesbetween our and published resultswere only found in the case ofNiinemetsandValladares(2006),whostatedthatA. alba, P. abies,andA. pseudoplatanusareveryintoleranttowaterlogging.Inourstudy,incontrast,thesespeciesreachedarelativelyhighabundanceonmanystronglyhydromorphicsoils.TheabilityofP. abiestoadapttowater-loggingisremarkable.Instronglyhydromorphicsoils,thisspeciesde-velopsashallowrootingsystemtoavoiddeeperanaerobichorizons.

4.4 | Feedback between plants and soils

Thepostulatedrelevanceofsoilvariablesfortreespeciesdistribu-tionmayarisefromcircularreasoning.Vegetationisasoil-formingfactorandthusmodifiesdifferentsoilcharacteristicsmainlyinthetopsoil (Augusto, Dupouey, & Ranger, 2003). Even though suchtopsoilmodificationsmaybeconsiderableandmayoccurrelativelyfastwithinthelifetimeofatreespecies(Cools,Vesterdal,DeVos,Vanguelova,&Hansen,2014;Vesterdal,Schmidt,Callesen,Nilsson,&Gundersen, 2008), the influence of tree species on soil shouldnotbeoverratedasgeologicalcharacteristicsandothersitefactorsseemto influence soilpropertiesat leastasmuchas tree speciesdo (Augusto etal., 2003; Prescott, 2002). Therefore, the appar-entcircularreasoninginourstudymaybeonlyweakmeaningthatsoil datamaywell beusedas variables for analyzing tree speciesdistribution.

4.5 | Importance of forest management

Silvicultural interventionshavethepotentialtosmeartheecologicalniches of tree species. Past forest management probably changedtreespeciescompositiononmanyplotsofourstudy,althoughanear-natural tree species compositionwas a precondition for the selec-tionofmostplots. Inourstudy,thehighest influenceofpastforestmanagement isexpected forP. abies in lowlandswhere thisspecieshasbeenfosteredbysilviculturalactivitiesatmanyplaces.However,aroundhalfofthe684plotswithoccurrenceofP. abiesare locatedabove1,000ma.s.l.wheretheabundanceofthisspeciesisnaturallyimportantonmanyforestsitesduetoharshclimate.Thus,carefulplotselectionandmanyhighaltitudinalplotsmighthavedampened thespurious impactof forestmanagementonthedata, theresults,andthestudyconclusions.

F IGURE  5 Speciessensitivitytolimitedsoilaeration.ResponsecurveswerederivedfromsinglevariableGLMswithcoverabundanceofmaturetreesastheresponsevariableandmeandepthofasoilwaterlevelduringthevegetationperiod(aproxyforsoiloxygenavailability)asapredictorbasedondatafrom1,075studyplotsacrossSwitzerland.Thedifferentspeciesabundancemaximareflectthedifferentmeanabundancethatspeciesreachedonthestudyplots(Table1).Fivespecies(Carpinus betulus, Ulmus glabra, Prunus avium, Quercus pubescens,andQuercus robur)arenotillustratedbecausetheirresponsecurvesrunhorizontallyatalevelofabout0%abundance

0.4

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20015010050

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Depth of water level during vegetation period (cm)

Fag.syl

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     |  9483WALTHERT And MEIER

5  | CONCLUSIONS

The edaphic predictors used in our study improved the statisticalmodel performance (i.e., themodel fit) for all studied tree species.Moreover, with two of our edaphic predictors, which quantifieddrought and soil oxygen shortage,wewere able to show that spe-ciesecologicalrequirementsassessedinlocalfieldstudiesandexperi-mentsaregenerallywell reflected in forestson larger scalesacrossSwitzerland.Thesefindingsunderlinetheecophysiologicalrelevanceoftheedaphicpredictorsusedinourstudy.

Ourthoroughlydata-drivenassessmentisexpectedtodeliveranobjective insight into species ecological niches. Itmay complementtheratherqualitativeknowledgeprovidedbythesystemofindicatorvalues.Ourresultsmaybeusedasbasicinformationforforestman-agementandhabitatprotection ingeneralandspecificallyforforestconversionunderachangingclimate.

ACKNOWLEDGMENTS

Wethank theWSL technical staff for samplingandanalyzing thesoilprofilesusedinthisstudy,and35botanistsforthecorrespondingveg-etationsurveys.WearegratefultoStefanZimmermann,MarcoWalser,andPeterJakobfortheircontributionstotheWSLforestsoildatabase,toDirkSchmatzforprovidingtheDaymetclimatedataandtoElisabethGrafPannatier,ManfredStähli,andMartinScherlerfortheirhelpinmod-elingthewaterbalance.WefurtherthanktheSwissFederalOfficefortheEnvironment(FOEN)forfinancingpartofthedataandMelissaDawesforeditingtheEnglish.TheevaluationswerepartlybasedondatafromtheSwissLong-termForestEcosystemResearchProgrammeLWF(www.lwf.ch),whichispartoftheUNECECo-operativeProgrammeonAssessmentandMonitoringofAirPollutionEffectsonForestsICP-Forests(www.icp-forests.net).WeareparticularlygratefultoAnneThimonierandElisabethGrafPannatier,whoprovideddatafrom16LWFplots.

CONFLICT OF INTEREST

Nonedeclared.

AUTHOR CONTRIBUTIONS

L.W.conceivedtheideas,providedthedata,andledthewriting,andE.S.M.analyzedthedataandassistedinwriting.

DATA ACCESSIBILITY

ThedataofthisstudyarearchivedinthesoildatabaseoftheSwissFederalInstituteforForest,SnowandLandscapeResearchWSLandare available on request. Contact person: LorenzWalthert (lorenz.walthert@wsl.ch).

ORCID

Lorenz Walthert http://orcid.org/0000-0002-1790-8563

REFERENCES

Anderegg,L.D.L.,&HilleRisLambers,J.(2016).Droughtstresslimitsthegeographicrangesoftwotreespeciesviadifferentphysiologicalmech-anisms.Global Change Biology,22,1029–1045.

Arend, M., Brem, A., Kuster, T. M., & Gunthardt-Goerg, M. S. (2013).SeasonalphotosyntheticresponsesofEuropeanoakstodroughtandelevateddaytimetemperature.Plant Biology,15,169–176.

Augusto,L.,Dupouey,J.L.,&Ranger,J.(2003).Effectsoftreespeciesonunderstoryvegetationandenvironmentalconditionsintemperatefor-ests.Annals of Forest Science,60,823–831.

Austin,M.P.,&Cunningham,R.B.(1981).Observationalanalysisofenvi-ronmentalgradients.Proc. Ecol. Soc. Austr.,11,109–119.

Austin,M.P.,&Smith,T.M.(1990).Anewmodelforthecontinuumcon-cept.InG.Grabherr,L.Mucina,M.B.Dale,&C.J.F.TerBraak(Eds.),Progress in theoretical vegetation science (pp. 35–47). Netherlands,Dordrecht:Springer.

Austin,M.P.,&VanNiel,K.P.(2011).Improvingspeciesdistributionmod-elsforclimatechangestudies:Variableselectionandscale.Journal of Biogeography,38,1–8.

Backes, K., & Leuschner, C. (2000). Leaf water relations of competitiveFagus sylvatica andQuercus petraea trees during 4years differing insoildrought.Canadian Journal of Forest Research- Revue Canadienne De Recherche Forestiere,30,335–346.

Beauregard,F.,&deBlois,S.(2014).Beyondaclimate-centricviewofplantdistribution:Edaphicvariablesaddvaluetodistributionmodels.PLoS ONE,9,e92642.

Borcard,D.,Legendre,P.,&Drapeau,P.(1992).Partiallingoutthespatialcomponentofecologicalvariation.Ecology,73,1045–1055.

Brassel, P., & Brändli, U.-B. (1999). Schweizerisches Landesforstinventar. Ergebnisse der Zweitaufnahme 1993-1995, Eidgenössische Forschungsanstalt für Wald und Schnee und Landschaft, Bundesamt für Umwelt und Wald und Landschaft.Bern:HauptVerlag.

Braun-Blanquet, J. (1964). Pflanzensoziologie. Grundzüge der Vegetationskunde. 3. Aufl.Wien,NewYork:Springer.

Cavin,L.,Mountford,E.P.,Peterken,G.F.,&Jump,A.S.(2013).Extremedroughtalterscompetitivedominancewithinandbetweentreespeciesinamixedforeststand.Functional Ecology,27,1424–1435.

Cools,N.,Vesterdal, L.,DeVos,B.,Vanguelova,E.,&Hansen,K. (2014).TreespeciesisthemajorfactorexplainingC:NratiosinEuropeanfor-estsoils.Forest Ecology and Management,311,3–16.

Coudun,C.,&Gegout,J.C. (2006).Thederivationofspeciesresponsecurves with Gaussian logistic regression is sensitive to samplingintensity and curve characteristics. Ecological Modelling, 199,164–175.

Coudun,C.,Gegout,J.C.,Piedallu,C.,&Rameau,J.C. (2006).Soilnutri-tional factors improvemodels of plant species distribution:An illus-trationwithAcer campestre (L.) inFrance.Journal of Biogeography,33,1750–1763.

Diekmann,M.,Michaelis,J.,&Pannek,A.(2015).Knowyourlimits–Theneedforbetterdataonspeciesresponsestosoilvariables.Basic and Applied Ecology,16,563–572.

Dreyer,E.(1994).Comparedsensitivityofseedlingsfrom3woodyspecies(Quercus roburL,Quercus rubraLandFagus silvaticaL)towater-loggingandassociatedroothypoxia:Effectsonwaterrelationsandphotosyn-thesis.Annales des sciences forestières,51,417–428.

Dubuis,A.,Giovanettina, S., Pellissier, L., Pottier, J.,Vittoz, P.,&Guisan,A. (2013). Improving thepredictionofplant speciesdistributionandcommunitycompositionbyaddingedaphictotopo-climaticvariables.Journal of Vegetation Science,24,593–606.

ESRI(2006).ArcGis 9.2.Redlands,CA:ESRI.Guisan,A., & Zimmermann, N. E. (2000). Predictive habitat distribution

modelsinecology.Ecological Modelling,135,147–186.Jobbagy,E.G.,&Jackson,R.B.(2000).Globalcontrolsofforestlineele-

vation inthenorthernandsouthernhemispheres.Global Ecology and Biogeography,9,253–268.

9484  |     WALTHERT And MEIER

Köcher,P.,Horna,V.,&Leuschner,C.(2012).Environmentalcontrolofdailystemgrowthpatternsinfivetemperatebroad-leavedtreespecies.Tree Physiology,32,1021–1032.

Kolb,T. E.,&Matyssek, R. (2001). Limitations andperspectives aboutscaling ozone impacts in trees. Environmental Pollution, 115,373–392.

Kozlowski,T.T.(1986).Soilaerationandgrowthofforesttrees.Scandinavian Journal of Forest Research,1,113–123.

Kramer,P.J.,&Boyer,J.S. (1995).Water relations of plants and soils.SanDiego,CA:AcademicPress.

Kreuzwieser,J.,&Gessler,A. (2010).Globalclimatechangeandtreenu-trition:Influenceofwateravailability.Tree Physiology,30,1221–1234.

Kreuzwieser,J.,&Rennenberg,H.(2014).Molecularandphysiologicalre-sponsesoftreestowaterloggingstress.Plant, Cell & Environment,37,2245–2259.

Lendzion,J.,&Leuschner,C.(2008).GrowthofEuropeanbeech(Fagus syl-vaticaL.)saplings is limitedbyelevatedatmosphericvapourpressuredeficits.Forest Ecology and Management,256,648–655.

Lévesque,M.,Walthert, L., &Weber, P. (2016). Soil nutrients influencegrowth response of temperate tree species to drought. Journal of Ecology,104,377–387.

Lindner,M.,Fitzgerald,J.B.,Zimmermann,N.E.,Reyer,C.,Delzon,S.,vanderMaaten,E.,…Hanewinkel,M.(2014).ClimatechangeandEuropeanforests:Whatdoweknow,whataretheuncertainties,andwhatarethe implications for forest management? Journal of Environmental Management,146,69–83.

McCullagh, P., & Nelder, J. A. (1989).Generalized linear models, 2nd ed.London:ChapmanandHall.

Meier,E.S.,Kienast,F.,Pearman,P.B.,Svenning,J.C.,Thuiller,W.,Araujo,M.B.,…Zimmermann,N.E.(2010).Bioticandabioticvariablesshowlittleredundancyinexplainingtreespeciesdistributions.Ecography,33,1038–1048.

Mod,H.K.,Scherrer,D.,Luoto,M.,Guisan,A.,&Scheiner,S.(2016).Whatweuseisnotwhatweknow:Environmentalpredictorsinplantdistri-butionmodels.Journal of Vegetation Science,27,1308–1322.

Mood,A.M. (1971).Partitioningvariance inmultipleregressionanalysesasatoolfordevelopinglearningmodels.American Educational Research Journal,8,191–202.

MuellerDombois,D.,&Ellenberg,H.(1974).Aims and methods of vegeta-tion ecology.NewYork,NY:JohnWiley&Sons.

Niinemets, Ü., &Valladares, F. (2006). Tolerance to shade, drought, andwaterlogging of temperate northern hemisphere trees and shrubs.Ecological Monographs,76,521–547.

Nishimura,P.H.,&Laroque,C.P. (2011).Observedcontinentality in ra-dialgrowth-climaterelationships inatwelvesitenetwork inwesternLabrador,Canada.Dendrochronologia,29,17–23.

Normand,S.,Treier,U.A.,Randin,C.,Vittoz,P.,Guisan,A.,&Svenning,J.-C.(2009). Importanceof abiotic stress as a range-limitdeterminant forEuropeanplants:Insightsfromspeciesresponsestoclimaticgradients.Global Ecology and Biogeography,18,437–449.

Piedallu,C.,Gegout,J.C.,Lebourgeois,F.,&Seynave, I. (2016).Soilaer-ation,water deficit, nitrogen availability, acidity and temperature allcontribute to shaping tree species distribution in temperate forests.Journal of Vegetation Science,27,387–399.

Piedallu,C.,Gegout,J.C.,Perez,V.,&Lebourgeois,F.(2013).Soilwaterbalance performs better than climaticwater variables in tree spe-cies distribution modelling. Global Ecology and Biogeography, 22,470–482.

Pinto,P.E.,&Gegout,J.C. (2005).AssessingthenutritionalandclimaticresponseoftemperatetreespeciesintheVosgesMountains.Annals of Forest Science,62,761–770.

Prescott,C.E.(2002).Theinfluenceoftheforestcanopyonnutrientcy-cling.Tree Physiology,22,1193–1200.

R (2016).R: A language and environment for statistical computing.Vienna(Austria):RDevelopmentCoreTeam.

Rellstab,C.,Bühler,A.,Graf,R.,Folly,C.,&Gugerli,F. (2016).Usingjointmultivariateanalysesofleafmorphologyandmolecular-geneticmark-ers for taxon identification in three hybridizing Europeanwhite oakspecies(Quercusspp.).Annals of Forest Science,73,669–679.

Remund, J., Rihm, B., & Huguenin-Landl, B. (2014). Klimadaten für die Waldmodellierung für das 20. und 21. Jahrhundert. Schlussbericht des Projektes im Forschungsprogramm Wald und Klimawandel. Eidg. Forschungsanstalt fürWald, Schnee und LandschaftWSL. Retrievedfromhttps://doi.org/10.3929/ethz-a-010693673

Rigling,A.,Bigler,C.,Eilmann,B.,Feldmeyer-Christe,E.,Gimmi,U.,Ginzler,C.,…Dobbertin,M.(2013).DrivingfactorsofavegetationshiftfromScots pine to pubescent oak in dry Alpine forests. Global Change Biology,19,229–240.

Ryan,M.G.,&Yoder,B.J.(1997).Hydrauliclimitstotreeheightandtreegrowth.BioScience,47,235–242.

Scherrer,D.,Bader,M.K.F.,&Körner,C.(2011).Drought-sensitivityrank-ingofdeciduoustreespeciesbasedonthermalimagingofforestcano-pies.Agricultural and Forest Meteorology,151,1632–1640.

Schmull,M.,&Thomas,F.M.(2000).Morphologicalandphysiologicalre-actionsofyoungdeciduoustrees(Quercus roburL.,Q-petraea [Matt.]Liebl.,Fagus sylvaticaL.)towaterlogging.Plant and Soil,225,227–242.

Szymura, T. H., Szymura, M., & Maciol, A. (2014). Bioindication withEllenberg’sindicatorvalues:AcomparisonwithmeasuredparametersinCentralEuropeanoakforests.Ecological Indicators,46,495–503.

Thornton,P.E.,Running,S.W.,&White,M.A.(1997).Generatingsurfacesofdailymeteorologicalvariablesoverlargeregionsofcomplexterrain.Journal of Hydrology,190,214–251.

Thuiller, W. (2013). On the importance of edaphic variables to predictplantspeciesdistributions–Limitsandprospects.Journal of Vegetation Science,24,591–592.

Vesterdal,L.,Schmidt,I.K.,Callesen,I.,Nilsson,L.O.,&Gundersen,P.(2008).CarbonandnitrogeninforestfloorandmineralsoilundersixcommonEuropeantreespecies.Forest Ecology and Management,255,35–48.

Walthert, L., Graf Pannatier, E., & Meier, E. S. (2013). Shortage of nu-trients and excess of toxic elements in soils limit the distribution ofsoil-sensitive tree species in temperate forests. Forest Ecology and Management,297,94–107.

Weisberg,S.(1980).Applied linear regression.NewYork,NY:Wiley.Wohlgemuth,T.(2012).Vegetationdatabasesforthe21stcentury:Swiss

ForestVegetationDatabase. InJ.Dengler,J.Oldeland,F.Jansen,M.Chytry,J.Ewald,M.Finckh,F.Glöckler,G.Lopez-Gonzalez,R.K.Peet,&J.H.J.Schaminée(Eds.),Biodiversity & ecology(pp.340).Biodiversity,Evolution and Ecology of Plants (BEE), Biocenter Klein Flottbek andBotanicalGarden,UniversityofHamburg.

Zweifel,R.,Rigling,A.,&Dobbertin,M. (2009).Species-specificstomatalresponseoftreestodrought–alinktovegetationdynamics?Journal of Vegetation Science,20,442–454.

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How to cite this article:WalthertL,MeierES.Treespeciesdistributionintemperateforestsismoreinfluencedbysoilthanbyclimate.Ecol Evol. 2017;7:9473–9484. https://doi.org/10.1002/ece3.3436