Ecology and Evolution. 2017;7:1919–1935.  | 1919www.ecolevol.org

Received:11November2016  |  Revised:10January2017  |  Accepted:24January2017DOI: 10.1002/ece3.2811

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

Species distribution modeling and molecular markers suggest longitudinal range shifts and cryptic northern refugia of the typical calcareous grassland species Hippocrepis comosa (horseshoe vetch)

Martin Leipold | Simone Tausch | Peter Poschlod | Christoph Reisch

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

InstituteofPlantSciences,UniversityofRegensburg,Regensburg,Germany

CorrespondenceChristophReisch,InstituteofPlantSciences,UniversityofRegensburg,Regensburg,Germany.Email:christoph.reisch@ biologie.uni-regensburg.de

AbstractCalcareousgrasslandsbelongtothemostdiverse,endangeredhabitatsinEurope,butthere isstill insufficient informationabouttheoriginoftheplantspeciesrelatedtothesegrasslands.Inordertoilluminatethisquestion,wechoseforourstudytherep-resentativegrasslandspeciesHippocrepis comosa(Horseshoevetch).Basedonspeciesdistributionmodelingandmolecularmarkers,weidentifiedtheglacialrefugiaandthepostglacialmigrationroutesofthespeciestoCentralEurope.WeclearlydemonstratethatH. comosafollowedalatitudinalandduetoitsoceanityalsoalongitudinalgradi-entduringthelastglacialmaximum(LGM),restrictingthespeciestosouthernrefugiasituatedonthePeninsulasofIberia,theBalkans,andItalyduringthelastglaciation.However,wealsofoundevidenceforcrypticnorthernrefugiaintheUK,theAlps,andCentralGermany.Bothspeciesdistributionmodelingandmolecularmarkersunderlinethatrefugiaoftemperate,oceanicspeciessuchasH. comosamustnotbeexclusivelylocatedinsouthernbutalsoinwesternofpartsofEurope.Theanalysisshowedadis-tinctseparationofthesouthernrefugiaintoawesternclusterembracingIberiaandaneasterngroup including theBalkansand Italy,whichdeterminedthepostglacial re-colonizationofCentralEurope.AttheendoftheLGM,H. comosaseemstohaveex-pandedfromtheIberianrefugium,toCentralandNorthernEurope,includingtheUK,Belgium,andGermany.

K E Y W O R D S

AFLP,geneticstructure,grassland,phylogeography

1  | INTRODUCTION

Calcareous grasslands are among themost species-rich ecosystemsinCentral Europe (Korneck, Schnittler,&Klingenstein, 1998; Sadlo,Chytry, & Pysek, 2007;WallisDeVries, Poschlod, &Willems, 2002),andmany studies havebeen conducted to understand the ecologi-cal mechanisms behind this observation (Cornish, 1954; Dutoit &

Alard,1995;Gibson&Brown,1991;Kahmen,Poschlod,&Schreiber,2002;Poschlod,Kiefer,Trankle,Fischer,&Bonn,1998;Römermann,Bernhardt-Römermann,Kleyer,&Poschlod, 2009).Notwithstandingtheseefforts, there isstillamajorgapabout theoriginof theplantspeciesassociatedwiththishabitattype.

Calcareous grasslands are regarded as seminatural landscapes(Karlik & Poschlod, 2009), created and maintained by human

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activities—particularly forest clearing and subsequent grazing withlivestock(Kahmen&Poschlod,2004;Poschlod&WallisDeVries,2002;Schmidt, Fischer, & Becker, 2007).However, theymay also alreadyhaveexistedintheperiglacialzoneinCentralEuropeduringthegla-cialperiod(Kunes,Pelankova,&Chytry,2008;Pokorný,2005).Recentstudiesevenpostulatedtheoccurrenceoftemperatetreespeciesinso-calledcrypticrefugiaprobablytill45°Nlatitude(Kunesetal.,2008;Magri, Vendramin, & Comps, 2006; Tzedakis, Emerson, & Hewitt,2013;Willis,Rudner,&Sumegi,2000).

Inthetraditionalview,glacial refugiaareknowntobesouthernrefugiafortemperatespeciesfromallgroupsoforganisms:theIberian,Italian, Balkan peninsulas in Southern Europe (Hewitt, 1999, 2004;Taberlet, Fumagalli,Wust-Saucy,&Cosson, 1998),where the influ-enceof theglacial cycleswasalleviated (Tzedakis, Lawson,Frogley,Hewitt,&Preece,2002)andspeciescouldescapefromcolddrycli-matesandpersistuntiltheycouldrepopulateEuropeintheintergla-cialsandafterthelastglacial.Nevertheless,recentstudiessuggestedseveral additional refugia for temperate species beyond thesepen-insulas.Thesecrypticnorthernrefugiaarepostulatedforhigherlat-itudesthantheexpectedsouthernrefugia(Bhagwat&Willis,2008;Bylebyl, Poschlod, & Reisch, 2008;Magri etal., 2006;Willis &vanAndel,2004)andaredefinedasclimaticislandswithfavorablecon-ditions(Stewart&Lister,2001),surroundedbyunsuitableconditions.Inthecaseofcalcareousgrasslands,thismayhavebeenrockyout-cropsorsteepsunnyslopeswithshallowdrysoils (Ellenberg,1988;Poschlod,Baumann,&Karlik,2009)indeeplyincisedvalleysprovidingmicroclimatesfortemperatespecies,allowingtosurvivewheretheynormallywouldhaveperished(Flojgaard,Normand,Skov,&Svenning,2009; Stewart& Lister, 2001).As soon as climate becamewarmerin the postglacial, recolonization of the surrounding steppe–tundravegetationmayhavestarted from there. In this context, itmustbementionedthatthefollowingreforestationmighthavebeenheldbackbeforetheNeolithic(seeBush,1988),eitherbyhumanwithfireorbymegaherbivoresenlargingthepotentialhabitatsofcalcareousgrass-landspeciesbesidesnaturallytreelesssiteslikecliffs(Pokorný,Chytrý,&Juřičková,2015;Svenning,2002).Consequently,bothwildanimalsandhumanswithdomesticatedanimalsmayhavecontributedtospe-cies’rangesandcontributions.

Stewart, Lister, Barnes, andDalen (2010) postulated a longitu-dinaloceanic-continentalgradientthat isoften ignoredwhenspec-ulating about recolonization of species along the latitudinal axis.The longitudinal gradient explains the expansionof steppe speciesand their inclusion in the Late Pleistocene ‘steppe–tundra’ biome.Accordingly, theoccurrenceofcurrentpostglacialsteppespecies islimited to eastern continental interglacial refugiawhich are deter-mined by the longitudinal gradient.Occurrences in thewest couldtherefore be interpreted as cryptic refugia (compare Kunes etal.,2008;Schmitt&Varga,2012).On theotherhand, thereshouldbecounterpartexamplesforoceanicspecies,becauseextensionofaridclimatesduringthelatePleistocenewouldhavebeenanimpedimentto some taxa likewise cold climates.The rangesofoceanic specieswouldhaveexpandedduringthemoisterinterglacialsandcontractedto Western Europe during the glacial periods. Together with the

latitudinalgradient,bothworkintandemindefiningsuitablehabitatsofaspecies(Stewartetal.,2010).

Inordertogaininsightintheoriginofatypicalcalcareousgrasslandspecieswith submediterranean and oceanic requirements,we choseHippocrepis comosa L. (horseshoevetch). Ithasalreadybeendemon-stratedthathumanactivitieshavecontributedatleastsincetheearlyNeolithic to the migration of crops, weeds, and animals (Beebee &Rowe,2000;Fjellheim,Rognli,Fosnes,&Brochmann,2006;Poschlod&Bonn,1998;Rosch,1998;Willerding,1986).ItseemsthereforequitepossiblethatthemigrationofH. comosaisalsorelatedtohumanmigra-tionprocesses.TheoccurrenceofH. comosawasfirsttimedocumentedfortheRomanageinthelowerRhineValley(Knörzer,1996).Therefore,thequestioniswhetherornotthespeciescametoCentralEuropeviaRomansettlers.AsMediterraneanspecies,thereisalsothepossibilityof spreading from Iberian orBalkanPeninsula. Exemplarily Poschlod(2014)claimsthemigrationofdrygrasslandspeciesfromtheEasternMediterranean regionor southeast Europe through themigration ofthe first farmers of the linearware ceramic culture (LBK) toCentralEuropeorfromWesternEuropethroughtheLaHoguetteculture.

Consideringtheclimaticconditions inCentralEuropeduringthePleistocene,wepostulatethatH. comosashiftedwestwardsinthegla-cial periods due to the lateral expansionof continental climate andadditional to its submediterranean character also southwards. Weassume thatH. comosa survived glaciations inwestern or southernrefugia,butitcannotfullybeexcludedthatthespeciesalsooccurredincrypticrefugiainCentralEurope.

Ouraimwasto identifyglacial refugiaandpostglacial immigra-tion routesofH. comosa toCentralEurope,andweapplied, there-fore,twoscientificapproaches.Firstly,weusedspeciesdistributionmodeling (SDM) to predict suitable refugia during the Pleistocenewith climate data.WithinMaxEnt, amachine-learning application,we initially calibrated a model containing actual distribution dataofH. comosa in combinationwith a setof today’s climateparame-ters(Elith&Leathwick,2009).Thismodelwasthenusedtoprocessclimate data prevailing during the last glacial maximum to predictsuitable refugia. Secondly, we applied amplified fragment lengthpolymorphisms(AFLPs)asmolecularmarkerstoanalyzethegeneticvariationwithin and among 38 populations ofH. comosa from thewholedistributionrangetogaininformationaboutglacialrefugiaandrecolonizationroutesofthespecies.Morespecificallyweaskedthefollowingquestions:(i)Whichrefugialareasservedassourceforthepostglacial immigrationofH. comosa toCentral Europe? (ii)WherewerethemainmigrationroutesfromtherefugiatoCentralEurope?(iii)Isthereevidenceforthelong-termsurvivalofH. comosaincryp-ticnorthernrefugia?

2  | MATERIALS AND METHODS

2.1 | Study species

For this study, we selected Hippocrepis comosa (horseshoe vetch),whichisatypicalcalcareousgrasslandspecieswithsubmediterraneanandoceanicrequirements.AsmentionedbySchmidtetal.(2007),the

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speciesoccursprimaryinnaturalhabitatsandsecondaryinseminatu-ralhabitats. Italsooccurs inrecentandancientgrasslands(Karlik&Poschlod,2009)andseedexchangebygrazingwasshownpossible(Müller-Schneider,1938).ThenativerangeofH. comosacoversmid-dleandsouthEuropeandryor semidrybasiphilousandcalciphilousgrasslandsor rocky cliffs (Brometalia erecti) and springheath—Pinewoods (Ericio-Pinetum)oralpinecalcareousgrasslands (Seslerietaliaalbicantis).

2.2 | Species distribution modeling

Information containing georeferenced occurrences of H. comosa was downloaded from the Global Biodiversity Information Facility(GBIF).Thetotalnumberofdownloadeddatawas17,934withabout7,000 locations clustered in the northern half of France. Thereforeandbecauseof the fact that thedatasetshowedamixtureofgridbaseddata(mainlyinGermany,France,Spain,andUK)andpinpointoccurrences, an uniform raster was created with a point distanceof2.5min inanunprojectedcoordinate referencesystem (WGS84)encompassingthetotaldistributionareaoftheH. comosa.Withthisapproach,samplingbiascanbeavoided(Wisz,Hijmans,&Li,2008).Thenewdistributionmapwasreducedto2,794points,38ofthemwere additionally added from another study focusing on the samespecies(unpublisheddata).Geologicalparametershadtobeexcludedfromoursurvey,asduetoourgridbasedapproach,theoccurrencedataofH. comosawouldhavebeen linkedto incorrectedaphicval-ues. Furthermore, to our knowledge, geologicalmaps that describetheedaphicconditionsduringtheLGM,especiallyinregardtocurrentunderseaareas,arenotavailable.

TodescribetheclimaticcircumstancesofthepresentageandtheLGM(about22,000yearsago),weused19bioclimaticvariables(listedinTableA1,Appendix).Thevariablesarederivedfrommonthlymeantemperature andprecipitation and represent climatic annual trends,seasonality,andextremeconditions.Providedasseparateclimatelay-ersatWorldClim(http://worldclim.org,Version1.4,release3,Hijmans,Cameron, Parra, Jones, & Jarvis, 2005), data were downloaded asgrid(raster)format.Theresolutionofthedatawas2.5min(WGS84,unprojected).Thecurrentconditionsinvolveinterpolationofobserveddatafrom1950to2000.Asclimaticdataforthelastglacialmaximumconditions,twodifferentreconstructionswereused:CCSM4andMPI-ESM-P.AllgatheredfromWorldClimtheoriginaldatawereprovidedby the CoupledModel Intercomparison Project (CMIP5). The reso-lutionwas 2.5min (WGS84, unprojected).AsH. comosa exclusivelyoccurs in Europe, geographic data were reduced to the Europeanregion.Toavoidgeographicbias,datawereprojectedtoanequalareaprojection(EuropeAlbersEAC).AllGIS-relatedworkwascarriedoutinArcGis10.2.2(ESRI,Redlands,CA,USA).

Ecologicalnichemodelingandthesubsequentcreationofthegeo-graphicdistributionmapsofH. comosaatpresentandpasttimewascomputedwiththeprogramMaxent,version3.3.3(Phillips&Dudik,2008).TheMaxentsoftwareusesamaximumentropyalgorithmwhichiswell suited for specieshabitatmodelingusingpresence-onlydata(Elith,Graham,&Anderson,2006).Therefore,itisapropermethodfor

predictingspeciesdistributions forbothpast-and future-orientatedscenarios(Hijmans&Graham,2006).Inafirststep,wecalibratedthemodelwiththeactualoccurrencedataofH. comosatogetherwithallcurrent 19 bioclimatic variables (TableA1,Appendix). The resultingpotentialspeciesdistributionmodelwasthenprojectedontothecli-mateconditionsprevailingduringthelastglacialmaximum.Tovalidatetheinformativevalueofthemodelregardingspeciesdistribution,weusedtheareaunderthereceiveroperatingcharacteristic(ROC)curve(AUC)(Fielding&Bell,1997),whichisanimplementedvalidationrou-tinewithinMaxent.Theoccurrencedatawere randomlypartitionedintotwogroups:Onegroupcontaining75%ofthedatawasusedforthemodelcalibration;theremaining25%wereusedformodeltesting(Phillips,Anderson,&Schapire, 2006).HighAUCvalues (>0.7) indi-cateagoodmodelperformance(Fielding&Bell,1997).Withfollowingexceptions,weusedthedefaultsettingsinMaxent(Phillips&Dudik,2008).Theconvergencethresholdwassetto10−5,themaximumnum-berofiterationswas5,000,and15replicateswiththereplicatedruntype “subsample”weremade.The selection of the relevant climatedatawasautomated.Asthresholdrule,wechosemaximumtestsensi-tivityandspecificity(MTSS)tooptimizethecorrectdiscriminationofpresencesandpseudoabsencesinthetestdata(Hernandez,Graham,Master,&Albert,2006;Jimenez-Valverde&Lobo,2007).Thecontin-uouslogisticoutputofMaxentwastransformedinabinarypresence–absencemap.ThethresholdvalueforpresencewasbasedonMTSSvalueswhichwereaveragedover15runs.

2.3 | AFLP analysis

Forthemolecularanalysis,plantmaterialofH. comosawassampledthroughout the whole species range distribution on the Europeancontinentandembracedintotal588individualsfrom38populations(Table1).Eachsampleencompassedmultiplefreshandhealthyleaveswhichweredriedinsilicagel.

DNAextractionfollowedCTAPprotocolfromRogersandBendich(1994)adaptedbyReisch (2007)using15mgofthedried leafsam-ples. DNA contentswere photometrically determined and adjustedto7.8ngDNAper1μl H2O.Wechosethedominantmarkeranaly-sisofamplified fragment lengthpolymorphisms (AFLP,Vos,Hogers,& Bleeker, 1995; Zabeau & Vos, 1993) to produce loci over thewholegenome(standardizedAFLPprotocolfromBeckmannCoulter(Brea, USA)).Molecular analyseswere conducted as described pre-viously (Bylebyl etal., 2008). For the selective DNA amplification,we chose three pairs of primer (D2: GATGAGTCCTGAGTAACTA-GACTGCGTACCAATTCAAC,D3:GATGAGTCCTGAGTAACAC-GACT GCGTACCAATTCAGG,andD4:GATGAGTCCTGAGTAACAC-GACTG CGTACCAATTCACA). PCR products were separated using capillaryelectrophoreses (CEQ 8000, Beckmann Coulter, USA). Data wereexportedascurve-filesandmanuallyanalyzedfortheoccurrenceofstrong, well-defined fragments in Bionumerics 6.6 (Applied Maths,Kortrijk,Belgium).Thepresenceorabsenceoffragmentswastrans-formedintoabinary(1-0)matrix,whichservedasbasisforallfurtheranalysis. Individualsshowingnoclearbandingsignalswererepeatedorultimatelyexcluded.

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2.4 | Statistical analysis of the AFLP data

Based on the allele frequencies from the 0/1 matrix Nei′s GeneDiversity (Nei,1972),Shannon′s InformationIndex(Shannon1948),

thenumberandpercentageofpolymorphic lociwerecalculatedforeachpopulationusingPOPGENEv.1.31(Yeh,Yang,&Boyle,1999).Inorder tomake the resultsofNei’sgenediversitymorecompara-ble,anadditionalcalculationwasconductedatwhichthenumberoftestedindividualswassetto12foreachpopulation(lowestavailableamount).The12sampleswerechosenrandomlywith50,000 itera-tions,andameanvaluewascalculated.Theresultswereplottedontothegeographiccoordinatesofthesamplelocations.Abasemappro-videdby“NaturalEarth”servedasbackgroundforthisandallfollow-ingmaps.

As an additional measure of divergence, the rarity of markerswas calculated by frequency-down-weighted marker values (DW)(Schönswetter&Tribsch,2005).Thecalculationof theDWvalueswas performed via the r-script AFLPdat (Ehrich, 2006). To grantequal sample sizes, for each population, 12 individuals were ran-domlyselectedwith10iterationsandameanvalueofDWwascal-culated.The resultswereplottedonto thegeographiccoordinatesof the sample locations.The value ofDW is expected to be highin long-termisolatedpopulationswhereraremarkersshouldaccu-mulateduetomutationswhereasnewlyestablishedpopulationsareexpected to exhibit low values, thus helping in distinguishing oldvicariancefromrecentdispersal(Schönswetter&Tribsch,2005).WecalculatedaPearsoncorrelationcoefficientforNei′sGeneDiversityandDWvalue.

An analysis of molecular variance (AMOVA, Excoffier, Smouse,& Quattro, 1992) should give information about the genetic vari-ance within and between populations. The two-level AMOVA wasperformed within the program GENALEX v6.5 (Peakall & Smouse,2012) and included588 individuals of all 38populations.BasedonEuclideanpairwisegeneticdistances, thesumsofsquareswerecal-culated(SSWP)anddividedbythedegreesoffreedom(SSWP/n−1).The resultingAMOVA-SS diversityvalues per sample locationwerealso presented cartographically. Permutation tests (9,999 iterations)wereconductedtoshowsignificance.

The genetic structure and group assignment of the populationswas investigated with Bayesian clustering in STRUCTURE v 2.3(Pritchard, Wen, & Falush, 2009; Pritchard, Stephens, & Donnelly,2000).TheprogramperformsaMarkovchainMonteCarlo (MCMC)algorithmtoassignthetestedindividualsintokgroupsbasedonlyonitsgeneticdata,andnotonpopulationaffiliation.Theprogramwasrunwith followingparameters:noadmixtureancestrymodel, correlatedallele frequencymodel, k from2 to 40, a burn-in period of 10,000followedby10,000iterations,10replicateruns.Themostlikelynum-berofgroups in thedata setwasdeterminedvia the calculationofΔkfollowingthemethodofEvanno,Regnaut,andGoudet(2005).Theresultswereplottedonto thegeographiccoordinatesof thesamplelocations.

Toidentifyspatialgeneticpatternswithinthedataset,amultivari-ateapproachwasconductedusingspatialprincipalcomponentanaly-sis(sPCA).sPCAwascarriedoutinR(DevelopmentCoreTeam,2014)usingpackageadegenet(Jombart,Devillard,Dufour,&Pontier,2008).Forthisanalysis,all588individualsof38populationswereused.Thepopulations’ geographic coordinates (WGS1984)wereprojected to

TABLE  1 Summaryofthelocationsofallsampledpopulations.Longitudinalandlatitudinalcoordinatesaregivenasdecimalcoordinates(WGS84)

Pop ID Latitude Longitude Country

1 −5.945 42.932 Spain

2 −4.414 43.398 Spain

3 −3.485 41.977 Spain

4 −3.404 42.796 Spain

5 0.613 42.096 Spain

6 −0.107 50.901 UnitedKingdom

7 −0.399 44.762 France

8 1.010 49.321 France

9 2.182 42.875 France

10 4.339 44.972 France

11 4.609 44.802 France

12 4.778 50.298 Belgium

13 7.427 47.409 Switzerland

14 8.882 45.961 Switzerland

15 7.385 45.636 Italy

16 7.750 45.423 Italy

17 8.749 44.511 Italy

18 9.527 44.256 Italy

19 10.763 43.745 Italy

20 10.791 45.556 Italy

21 10.836 46.237 Italy

22 12.274 43.117 Italy

23 12.337 43.795 Italy

24 13.021 42.956 Italy

25 13.238 42.745 Italy

26 9.191 51.479 Germany

27 10.143 50.226 Germany

28 10.251 47.288 Germany

29 10.252 47.375 Germany

30 10.415 49.524 Germany

31 11.686 48.951 Germany

32 11.720 51.216 Germany

33 13.119 48.651 Germany

34 13.882 45.100 Croatia

35 16.368 43.894 Croatia

36 13.999 45.724 Slovenia

37 18.945 47.467 Hungary

38 21.655 41.368 Rep.ofMacedonia

Pop.ID.,Populationidentifier;Latitude/Longitude,geographicposition.

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ETRS1989LCC.Toavoidsamecoordinatesofindividualsinthesamepopulation,thecoordinateswereshiftedrandomlybyafactorof0.5.UnliketheanalysiswithSTRUCTURE,thedataforasPCAdonothavetomeetHardy–Weinbergexpectationsorlinkageequilibrium.Forthemethod,twomatricesarenecessary.Thefirstonecontainstherelativeallelefrequenciesofallindividualsandthesecondembracesallspa-tialproximityinformationfromtheprojectedcoordinates.ThespatialproximityinformationmatrixwasgainedfromaconnectionnetworkusingDelaunaytriangulation.Also,thesecondmatrixwasusedtocal-culateaspatialautocorrelationusingMoran’sI(Moran,1948).Moran’sIrangesfrom+1to−1,indicatingastrongpositiveornegativespatialautocorrelation,respectively.Incaseofapositivespatialautocorrela-tion, a global structure in the data can be assumed.AMoran’s I ofzeroindicatesatotallyrandompattern.Foravisualverificationoftheoccurrence of spatial structures, a screeplotwas drawn by plottingthevarianceof the sPCAagainst spatial autocorrelation (Moran’s I).Supplementary,tostatisticallystrengthenthepreviousvisualfindings,twoMonteCarlotestswith9,999permutationseachwereconductedinordertodetectglobalorlocalstructuresinthedataset.Asdisplay,the genetic differentiationof theprincipal componentswasplottedontothegeographiccoordinates.

3  | RESULTS

3.1 | Species distribution modeling

Speciesdistributionmodelingresultedinthreemaps,whichshowthepredictedgeographicdistributionofHippocrepis comosa forpresenttime and the twodifferent climatic assumptions (CCSM4andMPI-ESM-P)fortheLGM.Themodelfortoday’sdistributionofH. comosa displayed a good prediction of the reported locations (FigureA1,Appendix).Aberrationscanbearesultofanimprecisesamplingdesign,duetothefactthatfirstwehadtorasterizealldataandsecondanunsteadyparticipationofEuropeancountriesinprovidingoccurrencedataoftheinvestigatedplantspecies.Also,geologicalaspectswerenot included in themodel. If taken into consideration, they wouldrule out areaswith no occurrences of calcareous substrates like inNorthernGermanyandtheNetherlandsorsilicateaffectedsubsoilsforexampleinFranceortheCzechRepublic.Nevertheless,whenthefocusliesonclimaticfactorsonly,theseregionsprovidesuitablecli-matichabitatsforH. comosa.

Of all 19 tested bioclimatic parameters, “Precipitation of DriestQuarter” (BIO17) had the highest influence on the prediction ofsuitablehabitatsof theactualoccurrenceofH. comosa,witha con-tribution to themodel of 45%.Togetherwith “TemperatureAnnualRange” (BIO7, percent contribution: 36%) and the “Isothermality”(BIO3,percentcontribution:8%,Isothermality=MeanDiurnalRange/TemperatureAnnualRange×100) rankedsecondand third the firstthreeparameterscontributewith89%tothefinalmodel.

BothmodelsforthedistributionofH. comosaattheLGM(about22,000yearsago)effectedgoodperformanceswithAUCsof0.890forCCSM4(FigureA2)and0.889forMPI-ESM-P(Figure1).Inbothpre-dictionsofsuitableclimatichabitatsforH. comosa,similardistribution

mapswerecomputed.SeparatedbytheAlpstwomajorclusterscouldbedistinguished:OnesituatedinthewestofEuropeinvolvingtoday’ssubmersed landswestofFranceand theUK,France,and thenorthofSpainandasecondembracingItaly,theAdriaticSea,andpartsoftheBalkanPeninsula.CorrespondingtothepostulatedrefugiaonthePeninsulasof Iberia, Italy,andBalkan,theseregionsformedsuitablehabitats forH. comosaduringtheLGM. Ingeneral, themodelbasedonMPI-ESM-PdatashowedastrongertendencyofshiftingsuitablehabitatstowardthesouthandwestthantheCCSM4model.

3.2 | AFLP analysis

In theAFLPanalysisof588 individualswith threeprimercombina-tions,271unambiguousfragmentswereselectedrangingbetween60and420bpandofwhich98.16%werepolymorphic (D2CTA-AAC:111fragments,D3CAC-AGG:87fragments,D4CAC-ACA:73frag-ments).Thewithin-populationgeneticvariationwascalculatedasfourdifferentmeasures(Table2).Allgeneticvariationvalueswereconsist-entlylowestinpopulationno.11inFranceandhighestinpopulationno.29inGermany.Meanpercentageofpolymorphicloci(%PPL)was56.7%, ranging between 46.3 and 64.7. Mean Shannon’s informa-tionindex(SI)was0.28,rangingbetween0.21and0.32.MeanNei’sgenediversityyieldedforallindividualswas0.18,rangingfrom0.14to0.21.MeanNei’sgenediversityfor12individualswas0.18,rang-ingfrom0.13to0.21.Highervaluesforgeneticvariation(He≥0.19)wererecordedonlyinsouthernpopulations,liketheIberian(He=0.2and 0.21), the Italian (He=0.19), the Balkan Peninsula (He=0.20),southoftheAlps(populationno.14,He=0.2),exceptfromonepopu-lationinthenorthernAlps(populationno.29,He=0.21),andnorthoftheAlpsinGermany(populationno.31).Lowervalues(He≤0.16)onlyoccurredinnorthernpopulationsandontheItalianpeninsula,butnotinthetwootherpeninsulas(Figure2).

Thesurveyof the rarityofmarkers revealedDWvalues rangingfrom4.83inanItalianpopulation(no.10)to10.5inaSpanishpop-ulation (no. 2),with an average of 7.66 (SE=0.2; seeTable2).Thehighest values were recorded either in populations of the IberianPeninsula (values between 8.35 and 10.5), the Balkan Peninsula(valuesbetween8.89and9.89),or insomenorthernpopulations, inCentralGermany (DW=10.3), BavarianAlps (DW=8.84 and8.36),andtheUnitedKingdom(DW=8.05).Lowvaluesoccurredinnorth-ernpopulationsandontheItalian,butnotontheIberianandBalkanpeninsulas (Figure2).Therewere highly significant positive correla-tions betweenDWand all geneticvariationmeasures (r=.729413,p<.0001, t=6.3977),meaning that a highnumber of raremarkers(highDW)wereassociatedwithhighgeneticvariation.

The analysis ofmolecularvariance (AMOVA) involving all popu-lations without classification of regions revealed a total molecularvariancewithin thepopulationsof68%.This leavesa strongdiffer-entiation among the populationswith amolecular variance of 32%(Figure3).Theresultswerehighlysignificant(p<.001).AllAMOVA-SSvaluesaregiveninTable2.

TheresultoftheBayesianclusteringconductedwithSTRUCTUREsupportsaclearassignmentoftheindividualsintotwogroups(k=2,

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FigureA3).Besides thismost likelynumberof clusters, avery smallprobability for 13 groups was found, one group involving popula-tionsofWesternandCentralEurope(Spain,France,UnitedKingdom,Belgium,Switzerland,Germany),andasouthEasternEuropeangroup(Italy,Slovenia,Croatia,Macedonia).Onlyat theboundarypointsofthetwogroupsinSpain(no.3and5),Germany(no.28,29,and33),andHungary (no.37), the100%assignment intooneof thegroupswasdiluted,resultinginsomeadmixedpopulations(Figure4).

Likewise,theresultsofthesPCAintheassignmentofthepopu-lationsintotwogroupsrevealedasimilarpattern.All588individualswere included in thespatialanalysis.ThespatialPCAwasbasedonDelaunay triangulation as connection network. The eigenvalues ofthesPCAindependenceofitsMoran’sIandvarianceareshowninascreeplot(FigureA4).Theeigenvalueofthefirstglobalscoreλ1couldclearlybedistinguished fromallothereigenvaluesdue to itshigherlevelsofvarianceandspatialautocorrelation.Thisindicatestheexis-tenceofspatialstructuresinthedata,whichwassubsequentlytestedwithglobalandlocalMonteCarlotests(9,999iterations).Astheglobaltestshowedasignificant(p<.0001)result,andwiththescreeplotinmind,aglobalspatialstructurewasassumedforthedataset.ThelocalMonteCarlotestwasnotsignificant.

Figure5 shows the eigenvalues of the first global score plottedagainst geographic coordinates. Black squares indicate positive andwhitesquaresnegativevaluesofthescores.Thesizeofthesquaresrepresents different absolute values. Therefore, large-sized squaresfrombothcolorsarehighlydifferentiated,while small-sizedsquaresindicate only small differentiation. A clear distinction between twoclusters can be drawn, one involving populations of Western andCentralEurope(Spain,France,UnitedKingdom,Belgium,Switzerland,Germany)andtheotheroneincludingpopulationsfromsouthEasternEurope(Italy,Hungary,Slovenia,Croatia,Macedonia).AMonteCarloManteltest(10,000iterations)foracorrelationbetweengeographicandgeneticdistanceswashighlysignificant(p<.0001).

4  | DISCUSSION

4.1 | Species distribution modeling

As thepredictedpresent-daydistributionofH. comosa showed agoodmatchwiththeactualdistribution,weconsideredthefollow-ingSDMspredictionssuitablehabitatsduringthelastglacialmaxi-mum as convincing.Our findings support the generally accepted

F IGURE  1 SpeciesdistributionmodelprojectionofH. comosaatthelastglacialmaximum(21,000ya)basedontheoutputoftheMPI-ESM-Pscenario.Darkgrayareasindicatesuitablehabitatswithintheecologicalniche;lightgrayareaareunsuitablehabitatsforH. comosa. Ice shieldsareshowninwhitewithadarkoutline.Nationalboundariesrepresenttoday’sEuropeanlandarea

     |  1925LEIPOLD Et aL.

assumptionof northern temperate speciesoutlasting the LGM insouthlocatedEuropeanrefugia(Bennett,Tzedakis,&Willis,1991;Hewitt,1999,2000;Huntley&Birks,1983;Taberletetal.,1998).Additionally, results of the species distribution models suggest

theexistenceofpossible refugia inFranceandalongtheAtlanticcoastup to theUK.Bothmodels (CCSM4andMPI-ESM-P) showdifferences in this area.While CCSM4 predicts suitable habitatsfor almost entire France and even parts of southwest Germany,

TABLE  2 GeneticvariationwithinthepopulationsofH. comosa

Pop. ID CC N PL PPL He He12 SI DW12 SSWP/n − 1

1 ES 14 171 62.9 0.21 0.208 0.32 8.965 29.95

2 ES 16 165 60.7 0.20 0.189 0.30 10.453 28.28

3 ES 15 171 62.9 0.21 0.201 0.31 8.531 30.94

4 ES 16 159 58.5 0.19 0.183 0.29 9.131 27.27

5 ES 15 159 58.5 0.18 0.176 0.27 8.842 27.40

6 UK 15 152 55.9 0.19 0.182 0.28 8.055 25.79

7 FR 15 139 51.1 0.16 0.153 0.24 7.906 22.50

8 FR 16 150 55.2 0.18 0.176 0.28 6.981 26.68

9 FR 16 153 56.3 0.18 0.173 0.27 7.944 26.35

10 FR 16 134 49.3 0.14 0.137 0.22 4.831 20.96

11 FR 16 126 46.3 0.14 0.132 0.21 4.907 20.21

12 BE 15 133 48.9 0.16 0.154 0.24 6.727 22.56

13 CH 16 152 55.9 0.18 0.171 0.27 7.484 26.81

14 CH 15 164 60.3 0.21 0.200 0.31 7.337 28.66

15 IT 16 155 57.0 0.19 0.181 0.28 7.782 27.13

16 IT 16 160 58.8 0.19 0.186 0.29 7.297 27.28

17 IT 16 174 64.0 0.20 0.195 0.31 7.760 30.28

18 IT 16 144 52.9 0.17 0.164 0.26 7.046 23.66

19 IT 16 134 49.3 0.16 0.153 0.24 6.153 22.82

20 IT 16 138 50.7 0.17 0.164 0.25 6.853 24.88

21 IT 16 159 58.5 0.19 0.185 0.29 6.829 27.07

22 IT 16 140 51.5 0.17 0.163 0.25 6.590 22.90

23 IT 16 163 59.9 0.20 0.194 0.30 7.379 28.75

24 IT 16 148 54.4 0.18 0.171 0.26 7.566 27.50

25 IT 16 159 58.5 0.17 0.163 0.26 6.762 26.05

26 DE 16 143 52.6 0.18 0.171 0.27 8.361 26.11

27 DE 16 156 57.4 0.18 0.174 0.27 7.096 27.99

28 DE 15 162 59.6 0.20 0.189 0.29 8.835 28.42

29 DE 16 176 64.7 0.21 0.206 0.32 10.339 31.96

30 DE 16 146 53.7 0.17 0.167 0.26 6.743 25.19

31 DE 16 174 64.0 0.20 0.197 0.31 7.775 30.55

32 DE 16 159 58.5 0.17 0.163 0.26 6.665 26.10

33 DE 12 147 54.0 0.18 0.181 0.27 7.578 27.79

34 HR 15 159 58.5 0.18 0.174 0.27 7.663 26.16

35 HR 14 164 60.3 0.20 0.198 0.31 9.890 27.62

36 SL 15 165 60.7 0.19 0.182 0.29 7.880 27.90

37 HU 13 153 56.3 0.19 0.183 0.28 7.180 27.67

38 MK 16 154 56.6 0.18 0.175 0.27 8.888 25.96

Mean 15.5 154 56.7 0.18 ± 0.01 0.177 ± 0.01 0.28 ± 0.02 7.658 ± 0.200 26.63±0.43

Pop.ID.,Populationidentifier;CC,countrycode;N,samplesize;PL,numberofpolymorphicloci;PPL,percentageofpolymorphicloci;He,Nei’sgenedi-versity(withstandarderror);He12,Nei’sgenediversityfor12randomlychosenindividuals(withstandarderror);SI,ShannonIndex(withstandarderror),rarityvalue(DW12),AMOVA-SS(SSWP/n−1).

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the MPI-ESM-P model draws the restriction further west. Thereason liesmostprobably in the fact thatbothmodelsmakedif-ferent assumptions regarding annual precipitation.WhileCCSM4model only shows drier summer in Central Spain and NorthernItaly but not in Central Europe, the opposite conditions are pre-dicted by theMPI-ESM-Pmodel (PMIP3, Braconnot,Harrison,&Kageyama, 2012).Given that themain contributing parameter inourmodelswas“PrecipitationofDriestQuarter”,thisassumptionseemsreasonable.Nevertheless,bothSDMpredictedvastareasofnowadayssubmerged landas suitablehabitats forH. comosa asaspecies adjusted to oceanic climate. The up to 110m lower sealevel(Ruddiman&Thomson,2001)revealedice-freelandwestofFranceandtheUKofseveral100kmwide.Beyondclimaticparam-eters,itisuncertainwhetherthislandmassesprovidedthepropercalcareous substrates for H. comosa and therefore could haveserved as refugia. Otherwise several studies including flora andfauna(Boston,Montgomery,Hynes,&Prodohl,2015;Ohlemüller,Huntley, Normand, & Svenning, 2012; Svenning, Normand, &Kageyama,2008)aresupportingtheexistenceofthesenorthwest-ernpotentialrefugia.

4.2 | Eastern and western lineages and postglacial migration

AFLPanalysisisapowerfultoolinrevealingglacialrefugiaandpost-glacial immigration processes, as we show here on an example ofthecalcareousgrasslandspeciesH. comosa.Baseduponourresults,weassumethatH. comosafollowedacontraction–expansionmodel,whereby the species was restricted to traditional southern refugiaduringtheLGMfollowingalatitudinaltemperatureandalongitudinalhumidity gradient andexpanded into the rest of Europe afterward.Ourresults indicateadditionalcrypticrefugiaatthewesternshoresofFranceandUK.

Both spatial analyses (STRUCTURE and sPCA) identified twoalmostcompletelydistinctlineagesofH. comosa:onegroupinvolv-ingpopulationsoftheIberianpeninsulaandWesternEurope(Spain,France, United Kingdom, Belgium, Switzerland, Germany) and theother includingpopulations fromthe ItalianandtheBalkanpenin-sulas (Italy, Croatia, The Former Yugoslav Republic of Macedonia,Slovenia).Only a fewadmixedpopulationsweredetected, locatedinHungary,Germany,andSpain.Accordingtothesespatialresults,

F IGURE  2 MapofNei’sgenediversity(leftsemicircle)andfrequency-down-weightedmarkervalues(DW,rightsemicircle)foreachsurveyedpopulation.Thedifferentsizesofthecirclesindicatedifferentabsolutevalues.Iceshieldsareshowninwhitewithadarkoutline.Nationalboundariesrepresenttoday’sEuropeanlandarea

     |  1927LEIPOLD Et aL.

two traditional southern refugia (Hewitt, 1999, 2004; Taberletetal., 1998) could be confirmed: Iberia as a southwestern refugiaand Italy and the Balkans as southeastern refugia, enclosing theadmixedpopulationsinSpain,SouthGermany,andHungaryascon-tactzones.ContactzoneshavebeenreportedtoclusterintheAlps,CentralEurope,northernBalkans,andthePyrenees(Hewitt,2000;Taberlet etal., 1998), resulting in an accumulation of genotypesfrombothgroups.Therefore,weassumethatH. comosarepopulatedCentral Europe from Iberia (Pyrenees) to France, Britain, Belgium,Switzerland,andGermanyuntil itseasternborder,wherethepop-ulations admixedwith populations that were migrating from Italyand the Balkans up north. It has been shown before that in con-trasttoIberianlineages,ItaliangenomesrarelypopulatedNorthernEurope, as the ice-cappedAlps prevented their northward expan-sion (Hewitt, 2000; Taberlet etal., 1998). This barrier is regardedasexplanationoftherelativelylowspecies’andgeneticdiversityofnorthern populations compared to southern populations (Hewitt,2000).ConsideringthevaluesforNei’sgenediversityandtherarityindex (DW)-values,H. comosa populationsof theBalkanPeninsulamay have served as refugium fromwhere the Italian populationswerefoundedsubsequently.

4.3 | Southern and cryptic northern refugia

Themostimportantrefugialareasaregeographicregionswherespe-ciespersistedthroughoutseveralfullglacial/interglacialcycles(each100–120kyr in duration). These so-called true refugia (Stewart &Dalen,2008)areexpectedtopossesshighergeneticvariabilitycom-paredwithsurroundingrecolonizedregions(Comes&Kadereit,1998;Taberlet etal., 1998; Tzedakis etal., 2013). In contrast, recent dis-persalmight lead to genetic depauperation due to founder effects.Supporting this theory, the highest values for genetic variation ofH. comosawererecordedalmostentirelyinsouthernpopulations,likethe Iberian, the Italian, theBalkanPeninsula,andsouthoftheAlps.Otherabove-averagegeneticallydiversepopulationswerelocatedintheAlpsandinGermany.Theformercanbeascribedtotheabove-mentionedhybridizationofthewesternandthesoutheasternlineage(Petit, Aguinagalde,& deBeaulieu, 2003; Provan&Bennett, 2008;Tzedakisetal.,2013).Wefounddifferentexplanations for thehighgeneticvariationofthe latter.Firstly, itmaybetheresultofrecentgeneticexchangedue tograzingmanagement,which ismoreprev-alent in the areaof the Jurassicmountains inGermany than in thenorthernGermanpopulations.Paun,Schonswetter,Winkler,Tribsch,

F IGURE  3 MapofAMOVA-SSvaluesforeachsurveyedpopulation.Thedifferentsizesofthecirclesindicatedifferentabsolutevaluesofmolecularvariance

1928  |     LEIPOLD Et aL.

and Consortium (2008) criticized the use of genetic variation foridentificationofrefugiaastheymayinfactreflectcurrentprocesses(geneticexchangeandpopulationsizes)insteadofhistoricalprocesses.Secondly,thehighgeneticvariationsmayalsohaveresultedfromaphalanxwayof recolonization fromsouth tonorthor canbe inter-pretedasa legacyfromYoungerDryascoldreversal (Hewitt,1999,2000, 2004). A reduction of northern populations during this coldperiodcouldhaveledtohighdiversitypopulationsduetothemixturewithrecolonizinglinagesduringtheHolocene(Tzedakisetal.,2013).Inordertocircumventtheseconfusions,asecondparameter,therarityindex(DW)wasused,asitisabetterindicatorofhistoricalprocesses(Paunetal.,2008).ThevalueofDWisexpectedtobehighinlong-termisolatedpopulationswhererarefragmentscouldaccumulateduetomutations,whereasyoungpopulationsareexpectedtoshowlowvalues,thushelping indistinguishingoldvicariancefromrecentdis-persal.Refugialpopulationsandrecolonizedregionswouldontheonehandcontainidenticalfragmentsbutduetodriftrarefragmentswouldaccumulateanddistinguish themfromother refugialareasandsur-roundingyoungerpopulationsthatwouldbelessdivergent(Provan&Bennett,2008;Schönswetter&Tribsch,2005;Tzedakisetal.,2013).According to this, we identified additional northern populations of

H. comosa thatare located far from the traditional southern refugiawhich possessed highDW values, supposing cryptic refugia inUK,theAlps,andCentralGermany.Suchcrypticrefugiahavepreviouslyalso been identified for other grassland species likeBromus erectus (Sutkowska,Pasierbinski,Warzecha,Mandal,&Mitka,2013)orgrass-landrelatedpineforestspecies likePolygala chamaebuxus.Althoughitmustnotbeignoredthattherarityindexmightbeoverestimated,asrelatedsouthernpopulationswiththeseallelesmaynothavebeeninvestigatedordistinctgenepatchesmightresultbygenesurfingontheleadingedge(Tzedakisetal.,2013).Nevertheless,wefoundasig-nificantcorrelationoftherarityindex(DW)andNei’sgenediversity(He),whichmeansthatinourstudy,rarefragmentsaccumulationandhighgeneticdiversity camealongwitheachother, pointing toward“true refugia”.TheGermanpopulationwithaveryhigh rarity indexandrelativelylowgeneticvariationmayindicateanisolatedrefugiumduringtheLGM,similar toalpinepopulationsofRanunculus glacialis (Paunetal.,2008).TheseresultsalsocoincidewithDengler,Janisova,Torok,andWellstein(2014),whoproposedacontinuousexistenceofpalearcticgrasslandat leastsincethePleistocene(2,400ka).Duringglaciations, grasslands covered most of the continent as steppe–tundraoverpermafrostandasxerothermicgrassland further in the

F IGURE  4 ResultsfromSTRUCTUREanalysis.ThesurveyedpopulationsofHippocrepis comosawereplottedontogeographiccoordinatesof.AsSTRUCTUREproposedatwo-groupsolution,eachpopulationwasassignedaccordingitsassociatedgroup

     |  1929LEIPOLD Et aL.

south.Duringtheinterglacials(Lang,1994;Pärtel,Bruun,&Sammul,2005),grasslandsweremostlyreplacedbyforests,apartfromsmall-scaleareasonsoilsthatimpedetreegrowth(drought,shallowground,instability (Ellenberg & Leuschner, 2010; Janišová, Bartha, Kiehl,&Dengler, 2011; Karlik & Poschlod, 2009) and reoccurring eventslikefire,windthrow(Hejcman,Hejcmanova,Pavlu,&Benes,2013),grazingbywildherbivores(Vera,2000),orhumanactivitysincetheMesolithic (Bush,1988;Simmons& Innes,1981).AsH. comosawasdescribed frost tolerant byHennenberg andBruelheide (2003) andit isgrowingup to2000ma.s.l. in theAlps, it seems reasonable toassumethatthespeciesmighthaveoccurredinCentralEuropeduringtheLGMonoutcropsunderfavorableconditions.

4.4 | Latitudinal and longitudinal constraints of its distribution

Onaglobalscale,itappliesforpast,present,andfuturethatwildspe-cies rangesareprimarilydeterminedbyclimate (Normand,Ricklefs,&Skov,2011;Pearson&Dawson,2003;Willis&Whittaker,2002).

Besides, there are additional parameters influencing the accessibil-ityofspecieslikelifeform,dispersalabilityandLGMrefugialocation(glacialcontraction),generationtime,habitatadaptation,competitionwith established vegetation, soil development, geographic barriersandhumanhabitatfragmentation(Normandetal.,2011),whichmayhaveresultedinacurrentdisequilibrium(postglacialcolonization)ofspecies rangeswithin theactualclimate (migrational lag).RegardingthedistributionofH. comosa inEurope,weassumethat ithas fullyexpandedto itspotential range.Thenortherndistribution limitationasitistodaywasinvestigatedonaregionalscalebyHennenbergandBruelheide(2003),showingareducedfitness(reducedseedsetting),whichcorrelatedwitheffectiveairtemperaturemeasuredataheightof10cmaboveground.Theareaofclimaticallysuitablehabitatswasalso shown in the present time species distribution model for thisspecies. In addition, soil composition impedes an expansion furthernorthandeastthanitspresentstatus(Hartmann&Moosdorf2012).Therefore,wedepicted that thecurrentoccurrenceofH. comosa ismainlylimitedduetoclimateandsoilfactorsandthatthedistributionpatternisnotlimitedbyreducedaccessibility.

F IGURE  5 Graphicaldisplayofthespatialdistributionofallsurveyedpopulationswiththevaluesofthefirstpositive(global)sPCAscore.Thedifferentsizesofthesquaresindicatedifferentabsolutevalues.Largeblacksquaresarewelldifferentiatedfromlargewhiteones,whilesmallsquaresshowlessdifferentiation.Onthemap,thegenotypesdifferentiateintwodistinctclusters,oneintheinthenorthwestandoneinthesoutheast.TheusedconnectionnetworkbasedonDelaunaytriangulationisshownwithgraylines.Onthetoprightpositionofthemap,thefirst25sPCA-positivescoresareshown

1930  |     LEIPOLD Et aL.

Thedecliningtemperatureatthebeginningofthelasticeagewaswithnodoubtthemaindrivingfactorinfluencingmigrationofplantsfollowingalatitudegradienttotheirsouthernrefugia(Taberletetal.,1998). Nevertheless, likewise Stewart etal. (2010), we emphasizethat not only this latitudinal gradient should be taken into consid-eration.The longitudinal gradient representing an increasing conti-nental climate fromwest toeastmighthavehadan impacton themigrationtosuitablehabitatsespeciallyforspeciesclassifiedasoce-anic likeH. comosa. Due to declining precipitations in combinationwithalowersealevel,whichcomprisedgreatlandmassesbetweenEngland and Europe, the oceanity of Central Europe decreased.Regardingthemigrationofplantsatthattime,itisouropinionthatoceanic plantswere not only forced tomove southwards but alsowestwards to maintain their climatic niche. Furthermore, the AlpsandtheCarpathianMountainshaveactedasnaturalbarrierhinderingexchangeandmigrationofplantstowardItaly.ThenorthandCentralEuropean populations ofH. comosa therefore might have followeda combination of bothvectors to the south and thewest pointingtoward apotential refugium inSpain,which canbe anexplanationfor our cluster including Spain, France, Belgium, UK, Switzerland,andGermany.AftertheLGM,H. comosamigratedtoCentralEurope,butno furthereastbecauseof theclimatic limitationsandedaphicbarriers.Nevertheless, followingtheresultsof thespeciesdistribu-tion modeling describing climatic niches during the LGM, there isalsoapossibilityof crypticnorthern refugia located in theUKandFrance,whichcouldhaveservedasadditionalplacesoforiginforarecolonization.

ForasuccessfulrecolonizationfollowingtheLGM,H. comosacouldrelyonatleasttwomechanismsofseeddispersal.Endozoochoryviaherbivores (Fischer, Poschlod, & Beinlich, 1996; Müller-Schneider,1938;vonOheimb,Schmidt,Kriebitzsch,&Ellenberg,2005)andepi-zoochory, implyingatransportationwithsoilmaterial inthehooves,whichwasreportedforsheepandcattle(Fischeretal.,1996;Poschlod&Bonn,1998)andaffectsthegeneticstructureofplantpopulations(Willerding & Poschlod, 2002). However, thisway of transport canmostprobablyalsobetransferredtootherhoofedanimalslikeredorroedeer.Therefore,wewoulddesignateH. comosaasaspecieswithahighlong-distancedispersalpotential,especiallybecauseofthefact,that hoofed animals can bridge distances of several kilometers perday (Pépin,Adrados,Mann,&Janeau,2004).ThisbecomesobviouswhenconsideringtheworkofSkog,Zachos,andRueness(2009)andMeiri, Lister, andHigham (2013), presenting the recolonization andphylogeographyofEuropeanreddeer(Cervus elaphus).Basedonmod-ernandancientDNA,thisstudystrikinglyresemblesourfindingsforH. comosa,showingarestrictionofCervus elaphustosouthernrefugiaduringtheLGMandarecolonizationofWesternandNorthernEuropeoriginatingfromIberia.Thisverysimilarphylogeographicpatternmaylead to the assumption thatH. comosa expanded toCentral EuropeviaC. elaphus or other equivalent herbivores (wild horse, roe deer;Pakeman,2001).

WhetherandtowhatextenthumanactivitiesmayhavecontributedtotheexpansionofH. comosabecausethe latestagesoftheUpperPaleolithiccannotbeanswered.Butgrasslandsmayhavecontinuously

existedsince theLGM (Bush&Flenley,1987;Bush,1988;Pokornýetal.,2015),alsobecauseofhumanpractices(Poschlod,2014).TheLaHoguetteculture(~7,500y.a.)showedfirststepstowardnomadicgoatand sheep breeding (Gronenborn, 2003) and therefore could havedirectlyinfluencedthedispersalofH. comosaviaepizoochory(Müller-Schneider,1938)fromSouthernFrancetoGermany.Thisassumptiondemonstratesthatamoreinterdisciplinaryapproachtothissubjectisnecessary inorder to fullyunderstand the recolonizationofCentralEuropebyplants,animals,orhumans.Astrengthenedcooperationofphylogeneticsandarcheologycoulddeliverintriguingnewinsightsinthegenesisofourenvironment.

5  | CONCLUSIONS

Basedonthepresentstudyonthepreviousandcurrentdistribu-tion of H. comosa, we could demonstrate that a comprehensiveclimatic approach including a second driving factor can lead toa better understanding of historical and present developments.The traditional latitudinal temperature gradient as major param-eterwasextendedbyalongitudinalhumiditygradientwhichbothworkintandemdefiningthesuitablehabitatsduringtheLGM.Ofthe two detected clearly distinguished phylogeographic clusters,one inWestern Europe ranging from Spain to Germany and theotherembracingpartsofsouthEasternEurope,onlythewesternrefugiacontributedtotherecolonizationofCentralEurope.AstheclimatebecamedrierinCentralEuropeduringtheLGM,H. comosa evaded tomoister climates, which prevailed inWestern Europe.Theresultsofoursurveyfurthermoreprovideevidencefornorth-ernlocationsinFranceandtheUKthatcouldhaveservedascryp-tic refugia. For the postglacial recolonization, H. comosa couldhave benefited from habitats shapes by humans and zoochory,which provided a long ranged dispersal to Central Europe. Thusintegrated approaches incorporating multidisciplinary knowledgemightbethebestwaytoapproximateandilluminatehistoricalandpresentprocesses.

ACKNOWLEDGMENTS

We would like to thank John Dickie, Langlois Estelle, ChristineFrohnauer, Melanié Harzé, Daniela Listl, Anton Mayer, RichardMichalet,NicolaSchoenenberger,SebastianTeufel,andMolnarZsoltforcollectingplantmaterialfromsinglelocationsandPetraSchitkoforherassistanceinthelaboratory.

CONFLICT OF INTEREST

Theauthorsdeclarethattheyhavenoconflictofinterest.

AUTHOR CONTRIBUTIONS

M.L.andS.T.collectedtheplantmaterialandperformedtheanalyses.M.L.,S.T.,P.P.,andC.R.contributedtomanuscriptwriting.

     |  1931LEIPOLD Et aL.

DATA ACCESSIBILITY

DatafromAFLPsanddistributionmodelingareuponrequestavailablefromtheauthors.

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How to cite this article:LeipoldM,TauschS,PoschlodP,ReischC.SpeciesdistributionmodelingandmolecularmarkerssuggestlongitudinalrangeshiftsandcrypticnorthernrefugiaofthetypicalcalcareousgrasslandspeciesHippocrepis comosa (horseshoevetch).Ecol Evol.2017;7:1919–1935.https://doi.org/10.1002/ece3.2811

APPENDIX

TABLE  A1 Listofallclimaticvariablesusedtopredicttheclimaticconditionsinthisstudy.Thegridresolutionwas2.5min (http://worldclim.org)

BIO10=AnnualMeanTemperature

BIO20=MeanDiurnalRange(Meanofmonthly(maxtemp−mintemp))

BIO30=Isothermality(BIO2/BIO7)(*100)

BIO40=TemperatureSeasonality(standarddeviation*100)

BIO50=MaxTemperatureofWarmestMonth

BIO60=MinTemperatureofColdestMonth

BIO70=TemperatureAnnualRange(BIO5-BIO6)

BIO80=MeanTemperatureofWettestQuarter

BIO90=MeanTemperatureofDriestQuarter

BIO10=MeanTemperatureofWarmestQuarter

BIO11=MeanTemperatureofColdestQuarter

BIO12=AnnualPrecipitation

BIO13=PrecipitationofWettestMonth

BIO14=PrecipitationofDriestMonth

BIO15=PrecipitationSeasonality(CoefficientofVariation)

BIO16=PrecipitationofWettestQuarter

BIO17=PrecipitationofDriestQuarter

BIO18=PrecipitationofWarmestQuarter

BIO19=PrecipitationofColdestQuarter

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F IGURE  A1 DistributionmodelofH. comosainEuropebasedoncurrentclimatedata.Darkgrayareasindicatesuitablehabitatswithintheecologicalniche;lightgrayareaareunsuitablehabitatsforH. comosa.BlackdotsshowtheoccurrencedatagatheredfromGBIFandusedformodeling.Nationalboundariesaregiven

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F IGURE  A2 SpeciesdistributionmodelprojectionofH. comosaatthelastglacialmaximumbasedontheoutputoftheCCSM4scenario.Darkgrayareasindicatesuitablehabitatswithintheecologicalniche;lightgrayareaareunsuitablehabitatsforH. comosa.Iceshieldsareshowninwhitewithadarkoutline.Nationalboundariesrepresenttoday’sEuropeanlandarea

F IGURE  A3 AnalysisoftheSTRUCTUREruns.Relationshipbetweenthenumberofproposedgroupsk,ΔkandLn(k),respectively.ThemostlikelynumberofgroupswithhighestΔk=684.8wasfoundwithk=2

FIGURE A4 Screeplotofthetotaldataset.TheeigenvaluesofthesPCAarearrangedbyitsvariance(x-axis)andMoran’sI(spatialautocorrelation,y-axis).Asthey-axisrepresentsMoran’sI,positivescoresindicateglobalstructures.Moran’sIshowsahigh-spatialautocorrelation(>0.8).Duetoitslargestpositiveeigenvaluesλ1,thefirstglobalscorecanbeclearlydistinguishedfromallothervaluesandthereforewaschosenforfurtherinterpretation.ThemaximumattainablevariancefromanordinaryPCAisshownasthedashedverticallineontherightsideofthegraph.TherangeofvariationofMoran’sIislimitedbythehorizontaldashedlines