Wing shape is influenced by environmental variability in Polietina orbitalis (Stein) (Diptera: Muscidae)

REVISTA

BRASILEIRA

DE

Entomologia

w w w . r b e n t o m o l o g i a . c o m

Systematics,

Morphology

and

Biogeography

Wing

shape

is

influenced

by

environmental

variability

in

Polietina

orbitalis

(Stein)

(Diptera:

Muscidae)

Victor

Michelon

Alves

,

Maurício

Osvaldo

Moura,

Claudio

J.B.

de

Carvalho

DepartmentofZoology,UniversidadeFederaldoParaná,Curitiba,PR,Brazil

a

r

t

i

c

l

e

i

n

f

o

Articlehistory: Received6May2015 Accepted13February2016 Availableonline2March2016 AssistantEditor:MarcioR.Pie

Keywords: Diptera Geometricmorphometrics Muscidae Phenotypicplasticity

a

b

s

t

r

a

c

t

WemeasuredvariationandcovariationinwingmorphologyinsixpopulationsoftheflyPolietina orbitalis(Stein)(Diptera:Muscidae)totestforgeographicmorphologicalstructure.Additionally,we examinedtheroleofenvironmentalvariablesindetermininggeographicvariationinwingshape.We sampledfivepopulationsinthestateofParaná,southernBrazil(Colombo,Fênix,Guarapuava,Jundiaí doSulandPontaGrossa),andoneinParaguay(Mbaracayú).Wechoose15landmarkstodescribethe wingshapeandsizeand19environmentalvariablestodescribethelocalenvironmentalconditions.Our resultsshowedthatP.orbitaliswingshape,butnotsize,variesgeographically.Acanonicalvariate anal-ysisshowedtheexistenceoftwoclustersofpopulationsbasedonwingshape.Thesegroupscompare populationsinwhichthewingisslenderwithgroupsinwhichthewingsarebroad.Theseshape differ-enceswerecorrelatedwithvariationinelevation,precipitationandtemperaturebutwerenotallometric. Takentogether,theseresultssuggestthatwingshapedifferencesinP.orbitalispopulationsareduetoa plasticresponsetolocalenvironmentalconditions.

©2016SociedadeBrasileiradeEntomologia.PublishedbyElsevierEditoraLtda.Thisisanopen accessarticleundertheCCBY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Introduction

Phenotypicplasticityistheabilityofanorganismtoexpress differentphenotypesdependingontheenvironmentalconditions faced (Agrawal, 2001). Such plasticity is therefore the conse-quenceoftheinteractionbetweenenvironmentalvariabilityand thedevelopmentalprogram(genotypic×environmental interac-tion; Scheiner, 1993)and is thus, one solutionto theproblem ofadaptationtoheterogeneousenvironments(Viaet al.,1995). Plasticityarisesbecauseenvironmentalvariabilityinduces devel-opmental changes, which alter the expression and connection betweentraits(Relyea,2004).

Asa developmentalresponse, plastic traits allowspecies to copewithenvironmentalvariability(temporalorspatial),enabling a fitnessoptimization(such as acclimatization)to these condi-tions(Ghalamboretal.,2007).Althoughphenotypicplasticityhas aneffectonfitness,thiseffectcouldbeadaptive,maladaptiveor neutral(Scheiner,1993;Pigliucci,2005;Ghalamboretal.,2007). Becauseanyenvironmentallyinducedplasticityplacesphenotypes intodifferentselectiveregimes,thefitnessconsequence,inboth theshortand longrun,willdeterminewhethertheresponse is adaptive.Forexample,environmentalvariabilitythatispersistent, causingpersistentselectionpressure,mayleadtolocalpopulation

∗ Correspondingauthor.

E-mail:vmichelonalves@gmail.com(V.M.Alves).

adaptation,withfitnesspeaksthataredifferentforeachpopulation (KaweckiandEbert,2004).

Althoughmorphologicaltraitshavebeenoneofthecharacters mostwidelyusedtostudyphenotypicplasticity,anymeasurable trait,suchaslifehistoryfeatures,physiologyandbehaviorcould alsobeused(WhitmanandAnanthakrishnan,2009).Insectbody sizeandshapestronglyrespondtochangesintemperature,with responsesrangingfrompopulationdifferences(e.g.,Hoffmannand Shirriffs,2002)tothermalclinesinbodysize(Griffithsetal.,2005; vanHeerwaardenandSgrò,2011).Assizeandshapeimpact perfor-manceandfitness(vanHeerwaardenandSgrò,2011),thesetraits areofinterestforthestudyofphenotypicplasticity.

Environmentalvariablesarespatiallystructured,andthis struc-turingcouldalsoleadtohierarchicallystructuredmorphological variation that could be either continuous (such as a cline) or discontinuous.Therefore, therecognitionof suchmorphological discontinuitiescanleadtoanunderstandingoftheshapingofnot onlyspeciesboundariesbutalsointraspecificpatternsofvariation andcovariation(Mateusetal.,2013).

Thestudyofmorphologicalvariationhasadvancedatthesame paceasthedevelopmentofthecorrespondinganalyticalmethods, thusallowingforamoreaccuratetreatmentofavarietyof hypothe-ses(Adamsetal.,2013).Somephenotypicalterationcanbevery subtleanddifficulttodetect,andmorphometrycanbeausefultool fordetectingsuchchange,mainlythatinvolvingthesizeandform oforganisms(StraussandBookstein,1982).Themainstrengthof geometricmorphometryovertraditionalmorphometricmethods http://dx.doi.org/10.1016/j.rbe.2016.02.003

0085-5626/© 2016 Sociedade Brasileira de Entomologia. Published by Elsevier Editora Ltda. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1 2 3 6 5 4 100 km

Fig.1.MapshowingsiteswhereP.orbitalispopulationswerecollected:1.Mbaracayú(25◦₁₇_S,54◦₄₉_{W)inParaguayand2.Fênix(23}◦₅₄_S,51◦₅₈_{W),3.JundiaídoSul}

(23◦₂₆_S,50◦₁₄_{W),4.Guarapuava(25}◦₂₃_S,51◦₂₇_{W),5.PontaGrossa(25}◦₀₅_S,50◦₀₉_{W)and6.Colombo(25}◦₁₇_S,49◦₁₃_W)inBrazil.

isitsabilitytodetectevensubtlemorphologicalvariation. Addi-tionally,geometricmorphometrycanseparatethesizeandshape componentsofform(Adamsetal.,2013)bypermittinganalysisina high-dimensionalmorphologicalspace,whichenablesagraphical interpretationofshapechanges.

Dipteraprovide a standard model system tostudy environ-mentally induced phenotypic changes. Several species exhibit variationsinfitness-relatedandmorphologicaltraits(wingsizeand shape)thatcorrelatewithlocalselectivepressures,mainlythermal selection(Reeveetal.,2000;HoffmannandShirriffs,2002;Soto etal.,2007;Aytekinetal.,2007;Marstelleretal.,2009;Devicari etal.,2011;Demircietal.,2012;Kjaersgaardetal.,2013;Hidalgo etal.,2015).However,theseresultsarebiasedtowardgroupsof medicalandveterinaryimportanceaswellasmodelspecies,such asthosebelongingtothegenusDrosophila.Althoughweexpect thatthelifehistorytraitsofallectothermsrespondtotemperature (Angillettaetal.,2004),thoseectothermalspeciesthatarebroadly distributedareexpectedtofacesteeperclimatechangesalongtheir geographicrange.Polietinaorbitalis(Stein,1904)(Diptera: Musci-dae)arebroadlydistributed inforestedareasofSouthAmerica, includingBrazil,Argentina,Bolivia,PeruandParaguay( Löwenberg-neto and de Carvalho, 2013).Thus, thegeographic range of P. orbitalisencompassesseveralbiometypes,eachonedefinedbyaset ofspecificenvironmentalcharacteristics.Additionally,theirrange spanstropicalandsubtropical/temperateclimates.Therefore,this speciesseemsa model candidatetodeterminetheinfluenceof environmentalconditionsonmorphologybecauseitspopulations alongalatitudinal/longitudinalgradientareunderdifferent ther-malregimes.Consequently,weusedgeometricmorphometryto characterizegeographicpatternsinP.orbitalismorphology(wing shape),expectingthem tovaryaccording totheenvironmental conditionsunderwhicheachpopulationevolved.Specifically,we testedforthefollowingpatternsandassociations:(a) morphologi-calinterpopulationalvariation,(b)anassociationofmorphological changeswithlatitudeorlongitudeand(c)theclimaticvariables moststronglycorrelatedwithmorphologicalchanges.

Materialandmethods

WerestrictedouranalysistofemalespecimensofP.orbitalis fromsouthernBrazilandnearbyParaguay.Thegeographicalextent

ofsamplingwasdefinedbytheavailabilityofanadequatesample size,andonlyfemalesweresampledtoavoidtheeffectsofsexual dimorphismandamalesamplesizethatwastoosmall.Thesample comprised thefollowing specimens:28 fromParaguay (Mbara-cayú) andJundiaí do Sul,24from Colomboand 30fromFênix, Guarapuava,andPontaGrossa(Fig.1).

AllthespecimenswerepreviouslycollectedusingMalaisetraps. TheParaguayanpopulationwascollectedattheReservaNatural delBosqueMbaracayú,andtheBrazilianpopulationswere sam-pledwithintheproject“LevantamentodaFaunaEntomológicano EstadodoParaná”(PROFAUPAR),fromAugust1986toJuly1988 (MarinoniandDutra,1993).AllthespecimensbelongtothePadre JesusSantiagoMoureentomologicalcollectionhousedinthe Zool-ogyDepartmentoftheUniversidadeFederaldoParaná(DZUP).

Totestourhypotheses,weusedrightwingsasamorphological proxy.Thewingswerecarefullyremovedandplacedinabsolute alcoholfor20min,xylenefor10minandthenmountedbetween 14mmcoverslipswithEntellan®.Thedorsalsideofthewingwas photographedusingaDino-LitePro® microscopeat15_× magni-fication. Voucher specimens, togetherwith wingmounts, were depositedintheDZUP.

We defined the wing shape through the placement of 15 anatomicallandmarksatwingveinintersections(Table1,Fig.2). These15landmarkswerechosenbecausetheycouldcapture mor-phologicalvariationsalongtheentirewingarea(thebase,center andtipofthewing).AllthelandmarksweredigitizedusingtpsDig version2.16(Rohlf,2010)andtpsUtilversion1.53(Rohlf,2012), freesoftwareavailableathttp://life.bio.sunysb.edu/morph/.

Thelandmarkconfigurationsweresuperimposedusing gener-alizedProcrustesanalysis(GPA,Rohlfand Slice,1990).Thefirst stepofGPAistocentereachlandmarkconfigurationattheorigin thatisaligningallthelandmarkconfigurationstotheircentroids, eliminatingtheeffectofposition.Then,thelandmark configura-tionisscaledtounitcentroidsize,thuseliminatingtheeffectof size. Next, thelandmark configurationsare rotated around the origintominimizethesummedsquaredistancebetween homol-ogouslandmarks,whichremovestheeffectoforientation.After this procedure, thedistances(Procrustesdistance) betweenthe superimposedconfigurationscorrespondtotheextentstowhich theconfigurationsdifferinshape,andthesuperimposed coordi-natescontain information regardingthe shape. Toquantify the

Numberanddescriptionofthe15anatomicallandmarksusedtocharacterizewing shapeinP.orbitalis.

Anatomicallandmark Description(intersectionbetweenveins)

1 Cwithh 2 CwithSc 3 CwithR1 4 CwithR2+3 5 CwithR4+5 6 CwithM 7 bm-cuwithM

8 bm-cuwithRear1

9 R4+5withr-m

10 Mwithr-m

11 R2+3withR4+5

12 Mwithdm-cu

13 CuA1withdm-cu

14 CuA1withCuA2

15 A1withCuA2

Abbreviations:A1,anal1;C,costa;CuA,anteriorcubital;CuA1,anteriorcubital1; CuA2,anteriorcubital2;M,medial;R,radial;R1,radialanterior1;R2+3,radial2+3; R4+5,radial4+5;Sc,subcosta;bm-cu,basalmediancubital;dm-cu,distalmedian cubital;h,humeral;r-m,radialmedial.

1

A

B

Fig.2.DorsalsideoftherightwingofP.orbitalisshowing(A)the15(numbered points)anatomicallandmarksand(B)thegeneralshapeofthewingbasedontheir positions.

measurementerror,eachwingwasdigitizedthreetimeson sep-aratedays. Wethen applieda multivariateanalysisofvariance (MANOVA)totestfordeparturefromrandommeasurementerrors amongthereplicates.

Asageneralmeasureofwingsize,weusedthecentroidsizeof eachspecimen,whichisthesquarerootofthesumofthesquared distancesofallthelandmarksfromtheircentroid,thecenterof eachconfiguration(Bookstein,1991).Totestthepopulationsfor differencesin averagesize, we compared wingsizeamong the populationsusingananalysisofvariance(ANOVA).Toassessthe degreeofcorrelationbetweensizeandshape,wefitalinear regres-sionbetweentheProcrustescoordinatesandthecentroidsize.

Thepopulationswereorderedinareducedspaceusing canon-icalvariableanalysis(CVA).CVA isa techniquethat maximizes theseparationofpopulationsbecauseeachaxisisconstrainedto representthemaximumbetween-groupvariance.Totestwhether thepopulationshavedivergedinshape,weusedaMANOVAwith theProcrustescoordinates astheresponse variable.The differ-encebetweengroupswascharacterizedbydiscriminantfunction

minedbycross-validation.

Wetestedforaneffectofclimateonphenotypicchangesusing a two-blockpartialleastsquares(PLS)analysis.ThisPLS analy-sistestedforcovariationbetweenblocksofvariables(wingshape withinpopulations),usingtheProcrustescoordinatesand environ-mentalvariables(RohlfandCorti,2000).Climatewassummarized by19variablesrepresentingvariationsintemperatureand rain-fall: (1)annual meantemperature; (2) meandiurnal range;(3) isothermality;(4)temperatureseasonality;(5)maximum temper-ature of warmest month;(6) minimum temperatureof coldest month;(7) annualtemperaturerange; (8)averagetemperature ofthewettestand(9)driestthree-monthperiods;(10)average temperatureofthehottestquarter;(11)averagetemperatureof thecoldestquarter;(12)annualprecipitation;(13)precipitation in the wettest and (14)driest months; (15) seasonal variation inprecipitation; and (16)precipitationin thewettest,(17) dri-est,(18)hottestand(19)coldestquarters.Elevation,latitudeand longitudewerealsoincludedintheenvironmentalmatrix.These climaticvariables representa 50-year mean trend in historical data(1950–2000)andwereobtainedfromtheWorldClimdatabase (http://www.worldclim.org/)usingDIVA-GISversion7.5(Hijmans et al., 2001). Allthe variables were standardized before analy-sis. All analysis was performed using MorphoJ® version 1.05d (Klingenberg,2011)andPAST®version2.17b(Hammeretal.,2001).

Results

Theerrorduetolandmarkdigitizationwasrandomfor both shape (ANOVA; p=0.971, SS=0.004, F=0.36, df=32) and size (ANOVA; p=0.988, SS=1905, F=0.010,df=2); thus, any differ-encesbetweenpopulationswereunrelatedtoimageordigitization errors.Therewasnosize(wingcentroidsize)variationbetween among the P.orbitalis populations (ANOVA; F=0.004, p=0.998, df=2),withthesizerangingfrom5to6mm.Incontrastwithwing size,wingshape differedamongall thepopulations(MANOVA; Wilks’=0.030,p<0.001,df=150, F=5.38).Therewasno allo-metriccomponentbecausetheshapeandsizevariableswerenot correlated(r=0.02,p=0.1873).Therefore,thewingshape differ-enceamongthepopulationswasindependentofthesizeofthe specimens.

The ordination of the populations by CVA showed that the firstandsecondcanonicalvariablesjointlyexplained81%ofthe observedvariation.Thefirstcanonicalaxis(accountingfor51%of thevariance)orderedthepopulationsinawidertonarrowerwing shapegradient.Thepopulationswithpositiveloadings(positive CV1values,Fig.3),suchasPontaGrossa,GuarapuavaandColombo, hadcertainlandmarksofthecostalmargin(landmarks1,2and3) andoftheposteriorwingmargin(landmarks8and15)displaced towardthecenterofthewing.Additionally,landmarks4 and7 displacedtowardthewingtip.Together,theselandmark displace-mentsshortenthewingblade,resultinginanarrowwing(Fig.4). ThepopulationswithnegativeloadingsonCV1,suchasMbaracayú andFênix(Figs.3and4),showedacontrastingwingshape com-paredwiththoseinthepositivequadrant.Thedisplacementofthe costal(landmarks1,2and3)andposterior(landmarks8and15) wingmarginlandmarksexpandedthewingblade(Fig.4). Addi-tionally,landmarks4and7movedtowardthewingbase.These rearrangementsresultedinanenlargedwing.

Thesecondcanonicalaxis(CV2,accountingfor30%ofthe vari-ance)showedonlylocalchanges(Fig.5),incontrastwithCV1,for whichthemorphologicalchangeswereglobal.Thelandmark dis-placementoccurredmostlyinthebasalregion(landmarks1,2,and 11).Thewingsofpopulationsloadedwithpositivescores(Fênix, ColomboandGuarapuava)hadaslightexpansionoftheanterior

4 2 _Fênix Jundiaí do sul Mbaracayú - Ponta grossa - Guarapuava - Colombo Canonical v a ri ate 2 (30%) 0 –2 –4 –6 –4 –2 0 Canonical variate 1 (51%) 2 4 6

Fig.3.PositionoftheP.orbitalispopulationsinthespacedeterminedbythefirst twocanonicalaxes.Theellipsesindicate95%confidenceintervals.

1 2

A

B

Fig.4.Graphicreconstructionofthewingshapeofindividualswith(A)positiveand (B)negativescoresonthefirstcanonicalaxis(CV1,increased10times).Thelinesin grayrepresenttheaverageconfigurationofthewing,andthoseinblackrepresent thecanonicalfirstvariable.

Table2

PercentcorrectclassificationofpopulationsofP.orbitalisbasedonwingmorphology anddiscriminantfunctionanalysis(DFA).

Population Allocationvalue(%)

Colombo 97 Fênix 95 Guarapuava 99 JundiaídoSul 98 Mbaracayú 97 PontaGrossa 96

region(landmarks1and2)andatlandmark11inamoreapical position.ThepopulationswithnegativeloadingsonCV2 (Mbara-cayúandPontaGrossa)showedlessbasalareathanthatshown bypopulationsonthepositiveCV2quadrantduetothebasal dis-placementoflandmark11andaslightlyposteriordisplacement oflandmarks1and2.Thecrossvalidationtestsindicatedthatan averageof97%(rangingbetween95%and99%)ofindividualswere correctlyallocatedtotheirrespectivepopulations(Table2).

AlldimensionsofthePLSanalysis(Table3)werestatistically significant(allwithp<0.01),butthePLS1axisaloneexplained92%

1 2 3 4 5 6 4 8 7 10 9 11 12 14 13 15 2 3 9 11 12 14 13 10 7 5 6 8 CV2 15

A

B

Fig.5.Graphicreconstructionofthewingshapeofindividualswith(A)positiveand (B)negativescoresonthesecondcanonicalaxis(CV2,increased10times).Thelines ingrayrepresenttheaverageconfiguration,andthoseinblackrepresentthesecond canonicalvariable.

ofthevariationintheshapevariables.Elevationandprecipitation inthedriestmonthwerenegativelycorrelatedwithPLS1(−0.79 and−0.20,respectively),whereastheminimumtemperatureof thecoldestmonth andaveragetemperatureof thedriest quar-terwerepositivelycorrelated(0.23and 0.25,respectively)with PLS1(Fig.6).Thepopulationsclusteredintwogroupsalongthis axis:Guarapuava,PontaGrossa and Colomboin onegroupand Mbaracayú,FênixandJundiaídoSulinanother.Thevariationin shapewasindependentoflatitudeandlongitude.Thus,higher ele-vationsandgreaterrainfallwereassociatedwithnarrowerwings, andlowerelevationsandtemperaturewereassociatedwithwider wings(Fig.7).

Discussion

OurresultsshowedthatP.orbitaliswingshapevaries geograph-ically,butnosuchvariationwasdetectedforwingsize(centroid size). Additionally, all the populations differed in wing shape, whichledtoahighsuccessrate(anaverageof97%)in predict-ingthepopulationinwhicheachindividualbelonged.Although allthepopulationshadaveragewingshapesthatdifferedfromone another,theCVAshowedtwoclustersofpopulationsbasedonwing shape(narrowwingandbroadwingclusters),withtheshapeof bothgroupscorrelatedwithelevation,precipitationand tempera-turebutnotwithwingsize.Takentogether,thesefindingssuggest thatshapedifferencesinP.orbitalisresultfromaplasticresponse tolocalenvironmentalconditions.

Althoughelevationwasthesinglevariablethatmostinfluenced thePLSaxis,ingeneral,theaxiscouldbeviewedasa tempera-ture/precipitationgradientbecauseelevationandtemperatureare negativelycorrelated.Temperatureisoneofthemostimportant driversofphenotypicplasticityininsects(LomônacoandPrado, 1994;Imashevaetal.,1995;WhitmanandAnanthakrishnan,2009). Thetemperature-sizeruleproposesthatinsectsdevelopingunder hightemperaturehaveasmallersize(Atkinson,1994).Aswingsize correlateswithbodysize,wewouldexpectsmallerwingsathigh temperatures,ahypothesiswerejectbecausewedidnotfindany differenceinwingsizeamongthepopulations.However, temper-aturealsoaffectswingload(GilchristandHuey,2004),whichis dependentontheshapeofthewing.Thus,wewouldexpectthat apopulationfromcoolerareaswouldhaveareducedwingload

PLSresultbetweentheshapeandenvironmentalvariables.NoticethatthegreaterPLS1hasthestrongestcorrelation(allp<0.01). Variable Dimension 1 2 3 4 5 Latitude 0.033 −0.083 0.057 −0.067 0.227 Longitude −0.051 −0.059 −0.054 0.159 −0.091 Altitude −0.790 0.254 0.090 0.145 0.089

Annualmeantemperature 0.178 0.007 0.064 0.123 0.091

Meandiurnalrange 0.117 −0.005 0.409 −0.221 0.111

Isothermality 0.011 −0.104 0.279 −0.264 0.233

Temperatureseasonality 0.121 0.304 −0.197 0.129 −0.282

Maximumtemperatureofwarmestmonth 0.149 0.057 0.090 0.142 −0.038

Minimumtemperatureofcoldestmonth 0.235 −0.056 −0.038 0.392 0.167

Temperatureannualrange 0.111 0.106 0.146 0.032 −0.128

Averagetemperatureofwettestquarter 0.116 −0.007 0.060 0.158 0.029

Averagetemperatureofdriestquarter 0.253 −0.094 0.145 0.063 0.407

Averagetemperatureofwarmestquarter 0.163 0.064 0.040 0.134 −0.031

Averagetemperatureofcoldestquarter 0.184 −0.036 0.145 0.144 0.101

Annualprecipitation −0.007 0.156 −0.206 −0.154 0.384

Precipitationwettestmonth 0.057 0.097 −0.454 −0.244 −0.055

Precipitationdriestmonth −0.201 0.420 −0.060 0.542 0.319

Precipitationseasonality 0.141 −0.521 −0.298 0.165 0.214

Precipitationofwettestquarter 0.034 −0.011 −0.332 −0.096 −0.097

Precipitationofdriestquarter −0.122 0.442 −0.000 −0.028 0.221

Precipitationofwarmestquarter 0.007 −0.105 −0.404 −0.130 0.364

Precipitationofcoldestquarter −0.045 0.314 0.032 −0.349 0.249

Singularvalues 0.00138 0.00040 0.00011 0.00007 0.00001

Covariation(%) 92 7 0.6 0.3 0.1

Correlations 0.678 0.401 0.315 0.362 0.213

Coefficientofassociationbetweenblocksis0.2179.

tocompensateforreducedflightperformanceduetothesmaller adultsize(Dudley,2000).Areducedwingloadingcouldbeachieved byincreasingthewingarea(Dudley,2000),butpopulationsfrom colderclimates(positiveCVAquadrantandnegativecorrelations withPLS)hadlongandslenderwings,whichdidnotmatchthe pre-dictionofthewingloadhypothesis.Thus,thewingshapechanges wedescribedarenottotallyrelatedtoanaerodynamichypothesis. Ourfindingshighlightthecomplexinteractionbetweengenotype andenvironment.

Variationsinsizebetweenpopulationscanbeassociatedwith alterationsinshape (Dujardin, 2008);however,we didnotfind thistrend.Theabsenceofallometryassociatedwithgeographical variationsinwingshape hasalsobeenfoundinDrosophila ser-rataMalloch(1927)(HoffmannandShirriffs,2002).Therefore,this absenceofallometricpatternsmeansthatfactorsotherthan allom-etryareinfluencingwingshape.Additionally,thePLSanalysisdid notshowsignificantcorrelationbetweenshapeandgeographical

distance,asobservedinotherstudies(Motokietal.,2012),perhaps asa resultofsamplingalargergeographicareainthosestudies comparedwiththatsampledbyourstudy(Monroyetal.,2003).

An effect of elevation has been reported in other studies. For instance, Belen et al. (2004) found that Phlebotomus pap-atasi(Scopoli,1786)populationsfromahigherelevationformed a distinct group.In studieswithCulex theileriTheobald(1903), Demircietal.(2012)alsofoundwingshapemodificationsin popu-lationsbetweenelevationsof808and2130m.Whileevaluatingthe traditionalandgeometricalmorphometryofDrosophila mediopunc-tataDobzhansky&Pavan(1943),Bitner-Mathéetal.(1995)also observedanassociationofformwithelevation,indicatingclinal variationwithelevationinthisspecies.Additionally,wecan estab-lishamorphologicalpatterninP.orbitalisrelatedtoelevation,in thatbeloworaboveanelevationof900m, populationsshowed abroad-orthin-wingphenotype,respectively.Thestandardthin wingfoundinpopulationsat higherelevations couldconferan

0.30 1 0.20 0.10 0.10 0.00 –0.10 0.00 –0.10 Bloc k 2 PLS 1 Block 1 PLS 1 –0.20 –0.30 3 2 5 1 2 3 4 5 6 100 km 6 4

Fig.6.ResultsofthePLSanalysis,withColombo(6),Guarapuava(4)andPontaGrossa(5)showingnegativevaluesandFênix(2),JundiaídoSul(3)andMbaracayú(1) showingpositivevalues.

0.30 A B 1 (229m) 3 (534m) 2 (700m) 5 (933m) 6 (929m) 4 (1128m) 0.20 0.10 0.00 –0.10 –0.10 0.00 Block 1 PLS 1 Bloc k 2 PLS 1 0.10 –0.20 –0.30

Fig.7.ComparisonbetweenthePLSanalysis(withelevation)andCVA(CV1)showingthatindividualswithawiderwingphenotypeoccurinpopulationslivingatamaximum elevationof700m(A)andthosewithathinnerwingphenotypeoccuratelevationsover900m(B).Localitiesareasfollows:1.Mbaracayú,2.Fênix,3.JundiaídoSul,4. Guarapuava,5.PontaGrossaand6.Colombo.

advantagebecausethereductionof airdensityat higher eleva-tionscaninterferewiththeaerodynamicforces ofinsectwings (Dudley,2000).Forpopulationsatlowerelevations,the character-isticbroad-wingphenotypecanleadtoanincreasedflightenergy cost(Ayalaetal.,2011).However,theorganismscanslowdown thewingtomaintainsustentationflight,whicheconomizesenergy (Dillonetal.,2006).

Intheaforementionedmanner,themorphometricvariations thatwefoundinP.orbitalisarebasicallyassociatedwith organ-ismalalterationsinthefaceofenvironmentalmodifications.Our resultsshowedtwobasicwingshapes(narrowandbroad),which arecorrelatedwithelevation,precipitationandtemperatureand donotscaleallometricallywithsize.

Conflictsofinterest

Theauthorsdeclarenoconflictsofinterest.

Acknowledgments

We thank two anonymous reviewerswho greatly improved anearlier version of themanuscript. Funding wasprovided by Conselho NacionaldeDesenvolvimento CientíficoeTecnológico (CNPq), an M.Sc. scholarship (V.M.A.); research grant (C.J.B.C.) [304713/2011-2], research grant (M.O.M.) [307947/2009-2]and Fundac¸ãoAraucaria,researchgrant(M.O.M.)[686/2014].

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