the genetic network of greater sage‐grouse: range‐wide ...5,950 individuals from 1,200 greater...

Ecology and Evolution 20181ndash19 emsp|emsp1wwwecolevolorg

Received4August2017emsp |emsp Revised9March2018emsp |emsp Accepted13March2018DOI101002ece34056

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

The genetic network of greater sage- grouse Range- wide identification of keystone hubs of connectivity

Todd B Cross12emsp|emspMichael K Schwartz1emsp|emspDavid E Naugle2emsp|emspBrad C Fedy3emsp|emsp Jeffrey R Row3emsp|emspSara J Oyler-McCance4

ThisisanopenaccessarticleunderthetermsoftheCreativeCommonsAttributionLicensewhichpermitsusedistributionandreproductioninanymediumprovidedtheoriginalworkisproperlycitedcopy2018TheAuthorsEcology and EvolutionpublishedbyJohnWileyampSonsLtd

1USDAForestServiceNationalGenomicsCenterforWildlifeandFishConservationRockyMountainResearchStationMissoulaMontana2CollegeofForestryandConservationUniversityofMontanaMissoulaMontana3SchoolofEnvironmentResourcesandSustainabilityUniversityofWaterlooWaterlooONCanada4USGeologicalSurveyFortCollinsScienceCenterFortCollinsColorado

CorrespondenceToddBCrossUSDAForestServiceNationalGenomicsCenterforWildlifeandFishConservationRockyMountainResearchStationMissoulaMTEmailtbcrossfsfedus

Funding informationThisstudywassupportedbygrantsfromtheMontanaandDakotasBureauofLandManagement(07-IA-11221643-34310-IA-11221635-027and14-IA-11221635-059)theGreatNorthernLandscapeConservationCooperative(12-IA-11221635-132)andtheNaturalResourcesConservationServicemdashSage-grouseInitiative(13-IA-11221635-054)AnyuseoftradefirmorproductnamesisfordescriptivepurposesonlyanddoesnotimplyendorsementbytheUSGovernmentTheviewsinthisarticlearethoseoftheauthorsandoftheUSGeologicalSurveyhowevertheseviewsdonotnecessarilyreflectthoseofotheremployers

AbstractGeneticnetworkscancharacterizecomplexgeneticrelationshipsamonggroupsofindividualswhichcanbeusedtoranknodesmostimportanttotheoverallconnec-tivityofthesystemRankingallowsscarceresourcestobeguidedtowardnodesin-tegral to connectivity The greater sage-grouse (Centrocercus urophasianus) is aspeciesofconservationconcernthatbreedsonspatiallydiscreteleksthatmustre-main connected by genetic exchange for population persistence We genotyped5950individualsfrom1200greatersage-grouseleksdistributedacrosstheentirespeciesrsquogeographicrangeWefoundasmall-worldnetworkcomposedof458nodesconnectedby14481edgesThisnetworkwascomposedofhubsmdashthatisnodesfa-cilitatinggeneflowacrossthenetworkmdashandspokesmdashthatisnodeswhereconnectiv-ityisservedbyhubsItiswithinthesehubsthatthegreatestgeneticdiversitywashousedUsingindicesofnetworkcentralityweidentifiedhubnodesofgreatestcon-servation importanceWe also identified keystonenodeswith elevated centralitydespite low localpopulationsizeHubandkeystonenodeswerefoundacrosstheentire speciesrsquo contiguous range although nodes with elevated importance tonetwork-wideconnectivitywerefoundmorecentralespeciallyinnortheasterncen-tralandsouthwesternWyomingandeasternIdahoNodesamongwhichgenesaremostreadilyexchangedweremostlylocatedinMontanaandnorthernWyomingaswellasUtahandeasternNevadaThelossofhuborkeystonenodescouldleadtothedisintegrationofthenetworkintosmallerisolatedsubnetworksProtectingbothhubnodesandkeystonenodeswillconservegeneticdiversityandshouldmaintainnet-workconnectionstoensurearesilientandviablepopulationovertimeOuranalysisshowsthatnetworkmodelscanbeusedtomodelgeneflowofferinginsightsintoitspatternandprocesswithapplicationtoprioritizinglandscapesforconservation

K E Y W O R D S

Centrocercus urophasianusgraphtheorymultiscaleconservationprioritization

2emsp |emsp emspensp CROSS et al

1emsp |emspINTRODUC TION

Understanding population structure and quantifying genetic con-nectivity are important for guidingongoing conservation and res-torationefforts(CrooksampSanjayan2006)Traditionallypopulationstructure isfirstanalyzedandsubpopulationsdelineatedthenge-netic connectivity among subpopulations is quantified HoweverthisprocessneednotbecompletedintwostagesGeneticnetworkmodelscanbeusedtosimultaneouslygainanunderstandingofpop-ulationstructureandtoquantifygeneticconnectivityamongpopu-lationsinnaturalsystems(BunnUrbanampKeitt2000Dyer2007DyerampNason2004)

Geneticnetworksareconstructedofcomponentscallednodesand edges where nodes may represent populations and edgesrepresent genetic connectivity among nodes (Sallaberry Zaidi ampMelanccedilon 2013) Each node can be weighted by the genetic di-versitywithin thenodesandeachedgeby thegeneticcovarianceamonglocalpopulations(Bunnetal2000)Theoverallstructureofthenetworkprovidesameansbywhichtoranktheimportanceofhoweachcomponentcontributes tomaintainingnetworkconnec-tivity(JacobyampFreeman2016)OnecanthinkofnetworkstructureintermsofthecommercialairlinemodelInsuchmodelsnodesandedgesareknownasarenodesofhighandlowconnectivity(hence-forthhubnodesandspokenodesrespectively)Intheairlineindus-tryhubnodesarestrategically selected tomaximizeefficiencyofairtrafficwhilespokenodesareselectedbasedonlimitedneedforservicesForwildlifepopulationswherepopulationsserveashubnodesandwherepopulationsserveasspokenodesareunknown

Qualifyinggeneticnetworkstructureandidentifyingnodesthatact ashubscanbevery informative toconservationandmanage-mentofwildlifespecies(GarrowayBowmanCarrampWilson2008LookingbillGardner FerrariampKeller 2010)Knowledgeofwhichnodesareconnectedtooneanotherandwhichnodes rankhighlyin network centrality can facilitate prioritization for management(JacobyampFreeman2016)Priornetworkmodelingofwildlifepop-ulationshaveshownthatwhichnodesfunctionashubsandwhichfunction as spokes is not intuitive (Bunn etal 2000 GarrowayBowmanampWilson 2011Garroway etal 2008 Koen BowmanampWilson2015)Onemightexpectanodersquosproximitytothecen-terofthespeciesrsquorangewouldinfluencethatnodersquosimportancetoconnectivity where centrally located nodes have greater geneticexchangethanperipheralnodesHowever ithasbeenshownthatpopulationsat theperipheryofaspeciesrsquo rangecanactascriticalhub nodes connecting populations across the network and thatpopulationslocatedtowardthecenteroftherangedonotnecessar-ilyfunctionashubnodes(Bunnetal2000)

Emergentpropertiesofgeneticnetworkscanbeusedto iden-tify hub nodes and spoke nodes and the sensitivity of the entirenetwork to the lossofconnectivity (DunneWilliamsampMartinez2002)Therearethreecommonnetworkstructures(1)single-scale(ldquoregularrdquo) (2) broad-scale (ldquorandomrdquo) and (3) small-worldmdasha sub-setofwhichareknownasscale-free(AmaralScalaBartheacuteleacutemyampStanley2000Bray2003)Regularnetworksarehighlystructured

suchthatproximalnodestendtobelinkedtoeachotherwhiledis-tantnodestendnottobelinkedastructurecomparabletotheiso-lationbydistancepatterncommonlydiscovered in thepopulationgenetics literature (eg a stepping-stonemodelWright 1943) Ina regular geneticnetwork genetic connectivity isbetweenneigh-boring nodes and nodes separated by a greater number of edgeswill bemore isolated from one another In regular networks hubnodesarenonexistentasallnodesareequallyconnectedRandomnetworks are unstructured such that proximity of nodes is irrele-vanttowhethernodesareconnectedornotandtothestrengthofconnectionsastructuremostsimilartothetheoreticalislandmodelfirst proposed byWright (1931) and analogous to the populationgeneticconceptofpanmixiaInarandomgeneticnetworkgeneticconnectivity is unencumbered across the entire network becausethenumberofstepsbetweenanytwonodesisrelativelysmallsuchthatcloseanddistantnodeshaveequalchancesofbeinglinkedInrandomnetworkstherearenohubnodesbutthereexistthorough-faresthroughthenetworkthat fosterquicktransitamonganysetof nodes In contrast small-world networks are composedof fewhighlyconnectednodes(hubnodes)andagreaternumberofmoreisolated nodes (spoke nodes) much like the hub-and-nodemodelcharacteristicofthefamiliarcommercialairlinemodelMostnodescanbereachedfromeveryothernodebyasmallnumberofstepsoftenroutedthroughcentralhubnodeswhichfosterconnectivityamongthespokenodesRedundancyisanimportantcharacteristicof small-worldnetworks In small-worldgeneticnetworksgeneticconnectivityisgreatestamongnearestneighbornodesbutgeneticconnectivitycanexistbetweenanytwonodesbyasmallnumberofstepsthroughhubnodeswhicharenodesatwhichgeneticconnec-tivityisconcentratedsuchthatthesenodesservetoconnectotherdistalnodes(alsoknownasspokenodes)Anextremeformofsmall-worldnetworksisscale-freenetworksInscale-freenetworksthereis lessredundancy in internodeconnectionsandgreatercentralityforthehubnodes

Withinanynetworkrsquos structure individualnode importance tonetworkconnectivitycanbequantifiedbycentralityindicesThereare several centrality indices eachofwhich quantifies the impor-tanceofanodetonetworkconnectivityinadifferentway(Table1)Some centrality indices rank nodes based on local connectivityand some based on network-wide connectivity Therefore thesefunction-valued centrality indices can be easily transformed intonode-specificrankingsandtheserankingscanbeusedtoprioritizeconservation(JacobyampFreeman2016)

The greater sage-grouse (Centrocercus urophasianus hereaftersage-grouseFigure1)isalekkinggallinaceousbirdofconservationconcernanindicatorspeciesforsagebrush(Artemisiaspp)commu-nities(RowlandWisdomSuringampMeinke2006)andanindicatorspeciesforlandscape-scaleconnectivityacrossthewesternUnitedStatesandsouthernCanada(Aldridgeetal2008)Sage-grouseonceoccupied over 12millionkm2 (Edminster 1954 Schroeder etal2004)Thespeciesnowoccupies lessthan067millionkm2across11 western states and two Canadian provinces (Patterson 1952Schroederetal2004)mdash56ofitsrangecomparedtopre-Western

emspensp emsp | emsp3CROSS et al

Settlement (Schroeder etal 2004) An additional 29 of the re-mainingspeciesrsquorangeislikelyatriskofextirpation(Aldridgeetal2008) Increased geographic isolation and declines in sage-grousepopulations range-wide coincide with fragmentation and loss ofsagebrushduetochangesin landuse(CopelandDohertyNauglePocewicz amp Kiesecker 2009 Knick etal 2003 Schrag KonradMillerWalkerampForrest2011)

Sagebrush provides essential cover is a staple of the speciesrsquodiet iswherethebirdnestsandrears itsbroodsandcongregatesinthespringtodisplayandbreedonleks(BeeverampAldridge2011

Hagen Connelly amp Schroeder 2007 Patterson 1952 RemingtonampBraun1985WallestadampEng1975)OnleksmalesbattlewithoneanothertoclaimthecenterandenergeticallydisplaytopotentialmatesLekattendancebymales issignificantlycorrelatedwithfe-malelekattendance(BradburyVehrencampampGibson1989)

Despite long seasonal migratory movements (up to 240kmSmith2012)and largehomeranges (4ndash195km2ConnellyHagenampSchroeder2011ConnellyRinkesampBraun2011)fidelitytoleksand stability in lek location are well documented (Cross NaugleCarlson amp Schwartz 2017 Dalke Pyrah Stanton Crawford ampSchlatterer 1963 Dunn amp Braun 1985 Emmons amp Braun 1984Patterson1952SchroederampRobb2003WallestadampSchladweiler1974)Howeversage-grousemayshiftorabandonleksbecauseofpersistent disturbance or alteration of sagebrush cover (HolloranKaiserampHubert2010WalkerNaugleampDoherty2007)

Sage-grouseareknowntodisperseduringthebreedingseasonand are capable of long-distance breeding dispersal movements(Cross etal 2017) While the fashion of long-distance dispersalmovements is unknown most migratory movements are made instepping-stonefashion(Tack2009)andshort-distanceabruptsin-gularmovementsarecommonwhensuitablehabitatislacking(DunnampBraun1986)

Thelekmatingsystemofsage-grouseiswellsuitedtonetworkanalyses because leks are fairly fixed spatial locations Given thespeciesrsquopatternsofdispersalwewouldexpectthatnetworkstruc-tureshouldbecomposedofclusteredhubnode-likenodescharac-teristicofasmall-worldnetwork(Garrowayetal2008)

TABLE 1emspNetworkparametersusedtoquantifyconnectivitytheunitforwhicheachiscalculatedandthedefinitionoftheparameterandrelationoftheparameteraspertainstothegreatersage-grousepopulationnetworkAllbutcharacteristicpathlengthandweightarecentralityindices

Network parameter Network unit Definition source Ecological interpretation

Characteristicpathlength

Entirenetwork Themeanofallpairwisenetworkdistancesconnectingnodesb

Meannumberofstepsforgeneticexchangeamongallnodesalongallpossiblepaths

Betweennesscentrality

Node Thenumberofshortestpathsuponwhichaparticularnodeliesb

Theimportanceofnodetomaintainingnetwork-widegeneticexchangealongthemostdirectroutes

Closenesscentrality Node Themeanshortestpathbetweennodeandallothernodes(connectednetwork)

Meannumberofstepsforthemostdirectpathofgeneticexchangebetweenanytwonodes

Clusteringcoefficient

Node Theprobabilitythattwonodesconnectedtoagivennodearealsoconnected(rangesfrom0ndash1)b

Anindexofgeneticconnectivityamongnodesthatarebothconnectedtoanothernode

Degreecentrality Node Thenumberofedgesconnectedtoanodeb Thenumberofothernodeswithwhichagivennodeexchangesgenes

Eigenvectorcentrality

Node Thedirectandindirectconnectivitypernodeandimmediateneighborsa

Anindexofhowwellconnectedagivennodesrsquoconnectionsareasfollowsthatishowmuchgeneticexchangeoccursatanodersquosimmediateconnections

Strength Node Thesumofalledgeweightsb Anindexofthemagnitudeofgeneticexchangewithallnodesconnectedtoagivennode

Weight Edge Themagnitudeofcovariancebetweencon-nectednodes

Themagnitudeofgeneticexchangebetweenanytwoconnectednodes

aGarrowayetal(2008)bNewman(2006)

F IGURE 1emspAmalegreatersage-grouse(Centrocercus urophasianus)displaysonalekintheearlymorningPhotographcreditRickMcEwan


Newconnectivityinsightswouldcomeatacriticaltimeforsage-grouseIn2010sage-grousewereaddedtothefederalEndangeredSpeciesAct(ESA)candidatelistfollowingseveralpetitionsforpro-tection(USFishandWildlifeService2010)InSeptember2015aUSFishandWildlifeServicedeterminationfoundcurrenteffortsbystateandfederalagenciesandotherpartnersadequatetoobviatetheneedforlistingHoweversignificantconservationchallengesre-main and the speciesrsquo statuswill againbe reviewed in2020 (USFishandWildlifeService2015)UnderstandinggeneticconnectivityisacriticalsteptowardcomprehendingtherelationshipbetweenthedistributionandabundanceofextantpopulationsacrossfragmentedlandscapesPerhapsmoreimportantlynetworkanalysiswillgreatlybenefitplanninginidentifyingconservationtargetswiththegreat-estbenefittomaintaininggeneticconnectivity

In this studywehad twoprimaryobjectives Firstwe soughtto determine the network structure of connectivity among leksweightingedgesbygeneticdivergencebasedongeneticcovarianceamongleksSecondwesoughttoidentifywhichlekswereimport-ant tomaintainingoverallpopulationconnectivityandpersistenceusingnetworkcentrality indicesWithin this secondobjectivewealso sought to identify keystone nodes that is nodes that act asmoreimportanttomaintaininggeneflowthantheirsizeorlocationwithinthespeciesrangealonewouldindicate

2emsp |emspMETHODS

21emsp|emspStudy area and sampling

Weused16420spatiallyreferencedsage-grousefeatherandbloodsamples collected from2139 leks (meanof 768 samples per lek)across the entire contiguous range of the species in the UnitedStatesofAmericaandCanadafrom2005to2015Thespatialdis-tributionofoursamplingwasoptimizedasdescribedinHanksetal

(2016)followingasmallerpilotsampleFeathersampleswerecol-lectedfromleksusingnoninvasivemethods(BushVinskyAldridgeampPaszkowski2005Segelbacher2002)afterhavingbeendroppedbysage-grouseduringbreedingactivitywhilebloodsampleswerecollected fromsage-grouseon leksaspartof radiotelemetry fieldresearchTheonlylocationthroughouttheentiredistributionofthespeciesthatwedidnotusewasWashingtonStatebecausesamplesfrom this location were collected during a different period (from1992to1999)thantherestofthesamples

22emsp|emspDNA extraction

Genetic analysiswas conducted at twomolecularbiological labo-ratories the National Genomics Center for Wildlife and FishConservation at theUSFS RockyMountain Research Station andtheMolecularEcologyLabattheUSGSFortCollinsScienceCenterProtocolswereestablishedattheinceptionofthestudytoensureconsistencyamonglaboratorygenotypingandaredescribedbelowFeatherDNAwasextractedfromthequill(calamus)usingQIAGENDNeasyBloodandTissueKitandtheuser-developedprotocolforpurificationoftotalDNAfromnailshairorfeathersTheprotocolwasmodifiedbyincubatingsamplesforaminimumof8hafterad-ditionofProteinaseKtomaximizetissuelysisandbyelutingDNAwith100μlofBufferAEtoincreasethefinalDNAconcentrationintheeluateBloodsampleswereextractedusingQIAGENDNeasyBloodandTissueKitandtheprotocolfornucleatedbloodAtUSGSparts of the extractionprocesswere automatedusing aQIAcube(Qiagen)

23emsp|emspMicrosatellite DNA amplification and genotyping

Webasedouranalysisuponapanelofneutralpolymorphicmicros-atellitelocibothtoidentifyindividualsfromnoninvasivelycollectedsamples(ofunknownindividualorigin)andtoquantifyrelatedness(iefunctionalmovementresultingingeneflow)Weamplified15variablemicrosatellite loci (BG6SGMS064SGMS066SGMS068MSP11 MSP18 SG28 SG29 SG36 SG39 SGCA5 SGCA11SGCTAT1 TUT3 and TUT4) and one sex-diagnostic locus [CHDgeneusingtheprimers1237Land1272H(KahnStJohnampQuinn1998)]PrimerdesignPCRconditionsandelectrophoresisusedatUSFSandUSGSaredetailedinCrossNaugleCarlsonandSchwartz(2016)Crossetal(2017)andRowetal(2015)

Toensurecorrectgenotypingfromlow-qualityandlow-quantityfeatherDNAsampleseachsamplewasPCR-amplifiedtwiceacrossthe15variablemicrosatellitelocitoscreenforalleledropoutstut-terartifactsandfalsealleles (DeWoodyNasonampHipkins2006)Tominimizegenotypingerrorat least two independentobserversscoredeachsampleIfanylocusfailedtoamplifyineitherreplicateor if therewas adiscrepancybetween locus genotypes as scoredby the twoobserversPCRamplificationandgenotypingwere re-peatedtwicemoreIfagenotypewasconfirmedbythisrepeatanal-ysisthenitwasretainedIfagenotypefailedagainthesamplewas

TABLE 2emspSamplesummarylistedbyUSstateorCanadianprovinceforallsamplesusedtoconstructrange-widegreatersage-grousegeneticnetworkTotalindividualssampledperstateprovincelekssampledperstateprovinceandtotalnumberofnodesperstateprovince

StateProvince Individuals Leks sampled Nodes

CA 53 14 6

CAN(SASK) 6 2 1

CO 679 106 38

ID 988 281 80

MT 1881 358 130

ND 7 2 1

NV 430 116 45

OR 296 52 31

SD 75 15 6

UT 607 114 44

WY 902 120 76


assignedamissingscoreatthefailedlocusToensureconsistencyamonglaboratoriesbothlaboratoriesgenotypedthesame70indi-viduals(approximately1oftheretainedgenotypes)Eachlaborato-ryrsquosgenotypesfortheseindividualswerecomparedandnecessaryshiftstosynchronizeallelecallsweremadeforallsamples

Toscreensamplesforqualitycontrolweremovedfromanaly-sisany individual forwhichamplificationfailedatone-thirdof theloci(iefiveloci)Afterremovalofpoor-qualitysamplesgenotypeswere screened to ensure consistency between allele length andlength of the microsatellite repeat motif using MICROCHECKERv223 (Van Oosterhout Hutchinson Wills amp Shipley 2004) Toidentify and removemultiple captures of the same individual andto screen for and correct genotyping error we used DROPOUT23(McKelveyampSchwartz2005asimplementedinSchwartzetal2006)MICROCHECKERv223 (VanOosterhoutetal2004)andpackageALLELEMATCH25(GalpernManseauHettingaSmithampWilson2012) inprogramR330 (RCoreTeam2016)Finallywequantifiedthepowerofourmicrosatellitelocuspaneltodiscernin-dividualsusingprobability identity (PIDEvettampWeir1998)whichcalculatestheprobabilitythattwoindividualsdrawnatrandomfromthepopulationhavethesamegenotypeacrossallloci

24emsp|emspNetwork construction

Aminimumoffourormoreindividualspernodeisrequiredtocalcu-latewithin-nodegeneticvariation(Dyer2014b)Thereforebeforeconstructing the network we performed a hierarchical clusteringanalysisof leklocationsFirstwecalculatedadistance-basedtreeusingthegeographiccoordinatesfortheleksfromwhichthe6242individual samples were collected (using the HCLUST function inbaseR)Secondweclusteredallleklocationswithin15kmofoneanother (cut distance implemented using theCUTREE function inbaseR)Weselected15kmasthecutdistanceasthis isthebestestimateofmedianbreedingdispersaldistanceamongleksforsage-grouse(Crossetal2017)Thirdweremovedanyresultantclustersoflekscomposedoffewerthanfourindividuals

Followingclusteringweconstructedaweightedpopulationnet-workamongtheresultingclusterswhichwehenceforthrefertoasnodesForallclusteredsamplesandforallnodeswecalculatedthemeanandstandarddeviationfornumberofallelesperlocuseffec-tivenumberofallelesexpectedandobservedheterozygosityandFISWe estimated genetic covariance amongnodeswheremicro-satellitegeneticcovariancerepresentstheweightofeachnetworkedge connecting nodes We used the packages GSTUDIO (Dyer2014a)andPOPGRAPH(Dyer2014b)inprogramRtoestimatetheconditional genetic covariance network following themethods ofDyerandNason(2004)usingdefaultparameters(α=005andtol-erance=1times10minus4Garroway etal 2008) Following pruning usingtherecommendedsettings theresultantminimal incidencematrixcontained the smallest set of edges that sufficiently capture theamong-node genetic covariance structure (Dyer amp Nason 2004)Wealsocalculatedtheminimumspanningtreewhichisthesubsetofnetworkedgesthatconnectallnodestogetherwiththemaximum

geneticcovarianceamongnodes(edgeweight)withoutanycyclesTotestforstructurewithintheminimumspanningtreewetestedforcorrelationbetweenweighted(factoringgeneticcovariance)dis-tancesamongnodesintheminimumspanningtreeandgeographicdistance (great circle distance) among all nodes calculated usingtheRDISTEARTHfunctionintheFIELDSpackage(NychkaFurrerPaigeampSain2015)inR

25emsp|emspNetwork structure determination

Todeterminethenetworkstructurewecomparedthedegreedistri-butionclusteringcoefficientandcharacteristicpathlengthofthesage-grouse genetic network to that of 1000 ErdosndashRenyi modelrandomnetworkswiththesamenumberofnodesedgesandedgeweightdistributionastherange-widesage-grousegeneticnetworkThecharacteristicpathlengthisdefinedastheaverageshortestpathlengthbetweenallpairsofnodesinthenetworkanditprovidesanunderstandingofhowlongittakesallelestotraversethenetworkWegeneratedtherandomnetworksusingpackageIGRAPH(CsardiampNepusz2006)inprogramRandtestedforsignificantdifferencesbetweenthedegreedistributionclusteringcoefficientandcharac-teristic path lengthof the true sage-grousenetwork and the ran-dom networks using permutation tests following themethods ofGarrowayetal (2008)Weused the resultsof thesecomparisonstodeterminewhethernetworkstructurewaspurelya functionofthenumberofnodesandedgesorwhethernetworkstructurewasaresultofnonrandomprocessesForexampleifwefoundachar-acteristicpathlengththatdidnotdeviatesignificantlyfromthatoftherandomnetworkscoupledwithasignificantlyhigherclusteringcoefficientthanthatoftherandomnetworks thenwecouldcon-cludethatthenetworkhadsmall-worldorscale-freecharacteristics(WattsampStrogatz1998)Furthermoreifwefoundadegreedistri-butionthatdidnotfollowthepowerlaw(whichwouldindicatescale-free network) was not binomial (which would indicate a randomnetwork)orfixed(whichwouldindicatearegularnetwork)butin-steadthatwasfat-tailedwewouldconcludethatthelikelynetworkstructurewasthatofthehubnode-and-spokesmall-worldnetwork

Wequantified pairwise conditional genetic distance among allnodes Conditional genetic distance is the length of the shortestpathconnectingeachpairofnodesconditionedonnetworkstruc-ture (DyerNasonampGarrick2010)ortherelativestrengthofthegenetic covariance between nodes along the connecting edges(Koen Bowman Garroway ampWilson 2013) When compared togeographicdistanceamongnodesconditionalgeneticdistancecanprovide insight intonetworkstructureForexample if conditionalgenetic distance is correlated with geographic distance one canconcludethattheprocessofisolationbydistanceshapedageneticnetwork(Dyeretal2010)

We calculated six centrality indices (Table1) and used thesemetricstoquantifyconnectivityandrelativeisolationofeachnodeacross the network To calculate standard error (SE) of themeanand median as well as their respective 95 confidence intervals(CI)wecalculated1000resamplednetworksof75ofthenodes


Betweenness centrality quantifies the importance of a node interms of the bottleneck to gene flow it creates eigenvector cen-tralityquantifieshowconnectedanodeisandstrengthquantifieshowstrongtheconnectionisbetweenanodeandallitsneighboringnodes(Garrowayetal2008)Eigenvectorcentralityisanindexofbothhowwellanodeisconnectedandhowwellanodersquosimmedi-ateconnectionsareconnectedmdashinessencemeasuringbothdirectand indirect connectivity These properties make eigenvector abetter index than betweenness if one is interested in quantifyingthe strength of connections This is because eigenvector central-ity increases not only just with increased immediate connectivityofthenodeofinterestbutalsotheimmediateconnectivityofthenodestowhichthenodeofinterestisconnectedWhenquantifyingconnectivityweusedthecentrality indexofnodestrengthratherthannodedegree asKoenetal (2015) found that strengthmoreadequatelydepictsmigrationandgeneflowthandegreecentralityTo examine relationships between network centrality indices wetestedforpairwisecorrelationbetweenallindicesusingSpearmanrsquosrankcorrelationWealsotestedforcorrelationbetweeneachnet-workcentralityindexandmeanpeakmalecountpernode(calcula-tiondescribedbelow)AllnetworkcentralityindiceswerecalculatedusingtheIGRAPHpackageinRandallcorrelationswerecalculatedinbaseR

To identify hub nodes of genetic exchange we screened allnodeswithinthetop1ofeachnetworkcentralityindex(Table1)andwithin the top50ofallnodecentrality indicescombined inordertoidentifythosenodesthatweremostimportanttolocalnet-work(regional)andnetwork-wide(range-wide)connectivityThesenodesrepresentthetophubnodesofgeneticexchangethatmain-tainconnectivityatallscalesToidentifyspokenodesweidentifiedthenodeswiththelowestrankingforeachcentralityindex

26emsp|emspKeystone nodes

Wehypothesizedthatnodescomposedofthemosthighlyattendedleksandthemostgeographicallycentralnodesinthespeciesrsquorangewould rank highest for centrality (ie node abundance and rangecentralitywould be positively correlatedwith node centrality) Tocalculateabundanceweusedtheper-lekhighmalecountsrecordedbetween2005and2015(WAFWA2015)andcalculatedthemeanpeakmalecountpernodeovertheseyearsusingallleksconstitut-ingeachnode (male lekattendance issignificantlycorrelatedwithfemalelekattendanceBradburyetal1989)Usingmeanpeakmalecountwetestedforcorrelationwithnetworkcentralityindices

We defined range centrality as the great circle distance fromthe center of the geographic center of the sampling distribution

Wecalculated range centrality as thedistanceof eachnode fromthe centroid of a minimum convex polygon enveloping all nodessuch that increased magnitude of distance was equivalent to in-verserangecentralityWecalculatedtheminimumconvexpolygonusingtheGCONVEXHULLfunctionandcalculatedthecentroidofthe minimum convex polygon using the GCENTROID function intheRGEOSpackage(BivandampRundel2017)inRFinallywecalcu-latedthedistanceofeverynodefromthecentroidusingtheRDISTEARTHfunction in theFIELDSpackage (Nychkaetal2015) inRUsingrangecentralitywetestedforcorrelationwithnodecentralityindices

Finallywesoughttoidentifynodeswithgreaterimportancetogenetic connectivity than themagnitudeof lek attendancewithinthenodeornodelocationwithinthespeciesrangealonemightin-dicateWecallthesenodeskeystonenodesWeidentifiedkeystonenodes as those that were low in attendance or peripheral to therangebut that still rankedhigh in centralityTo identifykeystonenodesweplottedboththemeanpeakmalecountwithinanodeandthe rangecentralityofeachnodeagainsteachnetworkcentralityindexWethencalledtheoutliersoftheseplotskeystonenodes

3emsp |emspRESULTS

31emsp|emspGenotyping and network construction

After genotyping the same 70 individuals at each laboratory wecomparedallelecallsFortwolocithelaboratorieshadatwobasepairdifferenceacrossallallelecalls(BG6andSGCA5)foronelocusthere was a four base pair difference (SGCA11) and for anotherlocusasevenbasepairdifference(SGCTAT1)aswellastwoallelescalledoff-steponadimerrepeatmotifchangedtocomplywiththetetramer repeatmotifEach laboratoryrsquosgenotypes for these indi-vidualswere shifted to synchronize allele calls for all samples fortheselociWiththeadditionalALLELEMATCHanalysiswediscov-erednoadditionalmultiplematches (identical genotypes resultingfrom the same sample being genotypedby both laboratories) PID for the complete microsatellite panel was 220times10-22 providingevidencethatourmicrosatellitepanelwasadequatefordistinguish-ingindividualsOurgenotypingeffortsresultedin6729individualgenotypesfrom1388leks(median=3individualsperlekIQR=4individualsperlekrange=1ndash62individualsperlek)followingdupli-cateremovalandqualitycontrol

Hierarchical clustering and removal of nodes with fewer thanfour individuals yielded 5924 samples from 1180 leks clusteredinto 458 nodes from 2006 to 2015 (median=10 individuals pernodeIQR=700ndash1575range=4ndash90individualspernodeTable2)

F IGURE 2emspThegreatersage-grouserange-widegeneticnetworkminimumspanningtreeTheminimumspanningtreeisprunedsuchthatonlythemosthighlyweightededges(ietheconnectionsrepresentativeofthegreatestgeneticcovariance)areshownbetweenallnodes(n = 458)Distanceamongnodesintheminimumspanningtreewashighlycorrelatedwithgeographicdistancebetweennodes(rs=061p lt 22times10minus16)(a)Fruchterman-Reingoldplot(layoutwithminimaledgeoverlap)(b)Geographicmapoftherange-widegreatersage-grousegeneticnetworknodesconnectedbyedgesretainedwithintheminimumspanningtreeNodecolorindicatesgeographiclocationbystateEdgesareshownasblacklinesThespeciesrsquorangeisshownaslightgraypolygons


36

40

44

48

minus125 minus120 minus115 minus110 minus105

StateCA

CAN (SASK)

CO

ID

MT

ND

NV

OR

SD

UT

WY

(a)

(b)


Wewereabletouse1057meanpeakmalecountsfor399nodesForall clusteredsamplesandacrossallnodesaveragenumberofalleleswas170plusmn748(meanplusmnSD)and643plusmn147fortheeffectivenumberofalleleswas730plusmn288and441plusmn075forexpectedhet-erozygositywas084plusmn006and074plusmn005forobservedheterozy-gositywas078plusmn005andforFISwas007plusmn002andminus006plusmn006Weconstructedanetworkcomposedof458nodesconnectedby14481 edges The minimum spanning tree (Figure2a) was cor-relatedwithgeographicdistance(rs=061p lt 22times10minus16)Montananodescomposealargegroupwithintheminimumspanningtreeandare joined to the rest of the network through nodes inWyomingand Idaho (Fig2)Nodes inColorado also group together and arelinkedbynodesinWyomingandUtah(Figure2)Therewasstrongevidenceforapositivecorrelationbetweenconditionalgeneticdis-tanceandgeographicdistance(Table4)


Thesage-grousegeneticnetworkdeviatedfromrandomnetworkstruc-tureinbothmeanclusteringcoefficientandmeancharacteristicpathlengthBothindicesweresignificantlygreaterforthesage-grousege-neticnetworkthanforthe1000randomnetworkswiththesamenum-berofnodesedgesandedgeweightdistributionasthesage-grousenetworknoneoftherandomnetworkshadagreatermeanclustering

coefficientorcharacteristicpathlengththanthesage-grousenetwork(clustering coefficient p lt 001 characteristic path length p lt 001)Boththemeanclusteringcoefficient(019plusmn335times10minus3[SE])andthemeancharacteristicpath length (188plusmn704times10minus3 [SE] [188191])wereshortandthenodedegreedistributionwasfat-tailed(Figure4d)

33emsp|emspNode properties

InordertodescribenodelocationweusedUSGShydrologiccata-logingunitsalsoknownaswatersheds(httpswaterusgsgovGISmetadatausgswrdXMLhuc250kxml)We discovered hub nodeswithintheCJStrikeReservoirwatershedinIdahoandtheBigHornLakeandUpperGreen-SlatewatershedsinWyomingThesenodesrankhighlyacrossmultiple centrality indices indicating the impor-tance of these regions tomaintaining genetic connectivity acrossthe network Collectively these two basins contained nodeswiththemaximumcentralityindices(Figure3)Manyotherregionscon-tainedhubnodesasdeterminedwithoneormorecentralityindicesNotablytheBigHornLakewatershedinWyomingtheBullwhacker-DogandMiddleMusselshellwatersheds inMontana theFremontwatershedinUtahandtheMiddleSnake-SuccorwatershedinIdahoandOregoncontainnodesthatrankhighforcentralityindicesindic-ativeoftheirfunctioningashubnodesthatmaintainlocalconnectiv-ityTheCrazyWomanwatershedtheUpperGreen-Slatewatershed

F IGURE 3emspThetop1ofnodesforeachofthesixcentralityindices(n = 20perindex)Nodesinthetop1ofmorethanoneindexareoffsettotheleftorrighttorevealbothNodecolorindicatescentralitymeasureShadedpolygonsdepictthewatershedwithinwhichthesetop-rankingnodesarelocatedThespeciesrsquorangeisshownaslightgraypolygons

36

40

44

48

ndash125 ndash120 ndash115 ndash110 ndash105

Centrality MeasureBetweenness

Clustering Coefficient (weighted)

Closeness

Eigenvector

Strength

Bullwhacker-Dog

Big Horn Lake

Crazy Woman

Upper Green-Slate

Middle North Platte-Casper

Upper Green

Lake Abert

Upper Malheur

Crowley LakeFremont

Middle Snake-Succor

CJ Strike Reservoir

Upper Snake-Rock

Lake Walcott

Provo

Upper Bear

Diamond-Monitor Valleys

Little Snake


and the Middle North Platte-Casper watershed inWyoming theLakeWalcottwatershedinIdahoandtheUpperBearwatershedinUtahallcontainnodesthatrankhighforcentralityindicesindicativeoftheirfunctioningashubnodesthatmaintainnetwork-widecon-nectivity(Figure3)

Only fivenodes ranked in the top50thpercentileof all net-work centrality indices indicating their importance to geneticconnectivitybothlocallyandnetwork-wideaswellastherarityofthiscombinationoflocalandnetwork-wideimportanceTherangeofeachcentrality indexfor thesehubnodeswas large (Table3)These nodeswere locatedwithin the Idaho Falls LakeWalcottand Salmon Falls watersheds in Idaho and the Middle NorthPlatte-CasperandSweetwaterwatershedsinWyoming(Figure3)locationscentraltoboththesamplingextentandthegeographicrangeofthespecies

Nodeswithhighbetweennesscentralityactasbridgesbetweendifferentpartsofthenetworksotheirlosscanhavenetwork-wideimpactsongeneticconnectivity (Garrowayetal2008)We iden-tifiedseveralhubnodeswhosebetweennessrankingwashigh lo-catedtowardthecenterofthespeciesrsquorangeThreeofthetop1ofnodesrankedbybetweennesscentralitywerelocatedinWyomingmdashone in the Crazy Woman watershed one in the Middle NorthPlatte-CasperandoneintheUpperGreen-SlatewatershedOftheremainingtwonodesonewas located in theLakeWalcottwater-shedinIdahoandonewaslocatedintheUpperBearwatershedin

Utah(Figure3)Ofthesethenodewiththegreatestbetweenness(1491)was located justsouthofGrandTetonNationalPark intheUpper Green-Slate watershed This same node also indexed highfor strength (70089)Mostnodes in thenetworkwere importantto network-wide connectivity (right-skewed distribution Table3Figure4)Howeverseventeennodeshadabetweennessofzeroin-dicatingrelativelylowimportancetofosteringgeneticconnectivityacrossthenetworkThesespokenodeshadanaveragestrengthof4292 (plusmn2183 [SD]) indicating strong connections to other nodesdespitelowimportancetonetwork-widegeneticconnectivityManyofthesespokenodeswiththelowestbetweennessindiceswerelo-catedtowardtheperipheryofthespeciesrsquorange

ToidentifynodesthatcovariedthegreatestwithallothernodesinthenetworkwerankednodesbyclosenesscentralityClosenessis an index of the average shortest path between a node and allothernodesinthenetworkHenceasmallerclosenessindexindi-catesshorterpathsonaverageandthereforegreaterconnectivityTherewereasmallnumberofverycloselycovaryingnodes inthenetwork(left-skeweddistributionTable3Figure4)Thetop-rankedclosenessnodeswerecentraltothespeciesrsquorangeawayfromtheperipheryTwoof thenodes in the top1ofclosenesscentralitywerelocatedintheLakeWalcottwatershedinIdahoTheremain-ingnodeswere located in theLittleSnakewatershed inColoradoandtheSweetwaterandUpperGreen-SlatewatershedsinWyoming(Figure3)Thenodewiththeminimumcloseness(491times10minus5)was

TABLE 3emspNetworkcentralityindices(betweennessclosenessclusteringcoefficientdegreeandeigenvector)andnetworkconnectivity(strengthandweight)fortherange-widegreatersage-grousegeneticnetwork(a)andnetworks(b)calculatedfrom1000networksconstructedfromaresampleof75(n = 343nodes)oftheoriginallysampled458nodes(sampledwithoutreplacement)Listedarethenetworkcentralityindexthecomponentforwhicheachindexwascalculatedminimummeanmedianstandarderror(SE)ofthemeanandmedianand95confidenceintervals(CI)ofthemeanandmedian

(a)

Centrality index Component Min Mean plusmn SD Median (IQR) Max

Betweennesscentrality Node 000 20360plusmn24963 9050(2925ndash27050) 149100

Closenesscentrality Node 491times10minus5 134times10minus4plusmn151times10minus5 137times10minus4 (128times10minus4ndash145times10minus4)

159times10minus4

Clusteringcoefficient Node 012 019plusmn0022 018(017ndash020) 033

Eigenvectorcentrality Node 007 055plusmn018 057(043ndash069) 100

Strength Node 8819 61910plusmn18151 63470(48860ndash75250) 108500

Weight Edge 302 982plusmn223 968(842ndash1100) 3561

(b)

Centrality index Component

Resampled networks

Mean plusmn SE [95 CI] Median plusmn SE [95 CI]

Betweennesscentrality Node 15577plusmn128[1532815826] 8700plusmn487[78009800]

Closenesscentrality Node 175times10-4plusmn183times10-6[171times10-4178times10-4]

178times10-4plusmn187times10-6[175times10-4182times10-4]

Clusteringcoefficient Node 018plusmn00032[018019] 018plusmn00032[017019]

Eigenvectorcentrality Node 055plusmn0025[049059] 056plusmn0027[050060]

Strength Node 44946plusmn882[4311946640] 45571plusmn1022[4350647558]

Weight Edge 986plusmn0066[974999] 974plusmn0069[960987]


located in the Spring-Steptoe Valleys watershed in Nevada andhad lowbetweenness (0)Thenodewiththegreatestcloseness inthe network (159times10minus4) was located just south of Grand TetonNationalParkintheUpperGreen-SlatewatershedinWyomingandhadarelativelyhighbetweenness(1521)andstrength(70089)

Toidentifynodesthatanchortightlyknitgroupsconnectedbyahighnumberofedgesweexaminednoderankingsbyclusteringcoefficient Increased clustering coefficient is indicative of small-world characteristicsNetwork-wide therewas a lowchance thatanytwonodesconnectedtoagivennodewerealsoconnectedto

oneanother(right-skeweddistributionTable3Figure4)Thenodesinthetop1ofclusteringcoefficientwerefoundacrossthespeciesrsquorangeandweremostlytoward itsperiphery (Figure3)Thesouth-ernmosthubnodeinthetop1wasintheFremontwatershedinUtahOtherhubnodesinthetop1ofcentralitywerelocatedinthe Middle Snake-Succor watershed in Idaho the Big Horn Lakewatershed inWyoming and the Bullwhacker-Dog watershed andMiddleMusselshellwatershedinMontanaThehubnodewiththegreatestclusteringcoefficient(033)wasfoundintheMiddleSnake-SuccorwatershedThisnodealsohadlowbetweenness(0)strength

F IGURE 4emspCentralityindexdistributionsforallnodes(andashfn = 458)andedges(gn = 14433)inthegreatersage-grousegeneticnetworkThesolidverticalblacklineshowsthemedianforeachindex

0

30

60

90

0 500 1000 1500Betweeness

Num

ber o

f Nod

es

0

20

40

60

015 020 025 030Clustering Coefficient

Num

ber o

f Nod

es

0

10

20

30

25 50 75 100

Degree

Num

ber o

f Nod

es

0

20

40

60

000008 000012 000016Closeness

Num

ber o

f Nod

es

(a) (b)

(c) (d)


(22365) and eigenvector centrality (021) The spoke node withthelowestclusteringcoefficient(012)wasfoundintheShoshonewatershedinnorthcentralWyomingandhadlowbetweenness(7)strength(32734)andeigenvectorcentrality(025)

ToidentifythemosthighlynetworkednodesweexaminednoderankingsbyeigenvectorcentralityEigenvectorcentralityincreasesfor nodes that are highly connected to other highly connectednodesAll butoneof the toponepercentofnodes rankedbyei-genvectorcentralitywerelocatedintheGreatBasinindicatingin-creasedgeneticconnectivitythereinThesehubnodesarelocatedintheCrowleyLakewatershedinCaliforniatheLakeAbertwatershedinOregontheDiamond-MonitorValleyswatershedinNevadaand

theUpperMalheurwatershed inOregon(Figure3)TheexceptionwasasinglenodeoutsidetheGreatBasinintheCJStrikeReservoirwatershed in Idaho This hub node had the greatest eigenvectorcentrality(100)andhadveryhighstrength(106407)butverylowbetweennesscentrality(3)Thespokenodewiththelowesteigen-vectorcentrality(0067)waslocatedintheEscalanteDesertwater-shedinUtahandwasaterminalnodeontheminimumspanningtreeThisspokenodewasalsolowforstrength(882)andbetweenness(69)lowcentralitybothlocallyandnetwork-wideEigenvectorcen-tralitywasnormallydistributed(Table3Figure4)

To determine which nodes covaried closely with many othernodes we calculated the strength of each node The top 1 of

0

10

20

30

000 025 075 100050

Eigenvector

Num

ber o

f Nod

es

0

10

20

30

300 900600

Node Strength

Num

ber o

f Nod

es

0

1000

2000

3000

10 20 30Edge Strength

Num

ber o

f Edg

es

(e)

(g)

(f)

F IGURE 4emsp (continued)


nodesrankedbystrengthwaslocatedtowardtherangeperipheryinboththecentralandsouthwesternpartofthespeciesrsquorangein-dicating increased genetic covariance among a greater number ofnodes(Figure3)Ofthehubnodeswiththegreateststrengthonehubnodewas intheCrowleyLakewatershed inCaliforniaone intheDiamond-MonitorValleyswatershedinNevadaoneintheCJReservoirStrikewatershedinIdahooneintheProvowatershedinUtahandoneintheUpperGreenwatershedinWyomingAllfiveofthetop1ofnodesbystrengthwereterminalnodesonthemin-imumspanningtreeThenodeswiththegreatestandleaststrength(10850and882)locatedintheCJStrikeReservoirwatershedinIdahoandtheEscalanteDesertwatershedinUtahwerealsosimi-larlyrankedforeigenvectorcentrality(twoindicesforwhichtherewasverystrongevidenceforapositivecorrelation(Table4))Nodestrengthwasnormallydistributed(Table3Figure4)

Wefoundevidenceforastronglypositivesignificantcorrela-tion between betweenness and closeness and eigenvector andstrength (Table4) All other significant correlations among cen-trality indiceswereweakandnegativeWefoundevidenceforastronglypositivesignificantcorrelationbetweennumberofallelespernodeandbetweennessandclosenessalthoughallothersignif-icantcorrelationsweremoderatelynegativeorweakandpositiveTheevidenceforacorrelationbetweenmeanpeakmalecountandcentralityindiceswasweakwhensignificantFinallywefoundev-idenceforastronglypositivesignificantcorrelationbetweenthenumberofsamplesinanodeandbetweennessbutonlymoderateorweakrelationshipswhentestingforcorrelationwithothercen-tralityindices

34emsp|emspEdge properties

Edgeweightisanindexofthemagnitudeofgeneticcovariancebe-tweennodesandcanbeusedtoidentifynodesmostcloselylinkedOverallgeneticconnectivityamongnodeshasledtoincreasednet-work connectivitywith theoccurrenceof somehighly connectednodes evidenced by a skewed right distribution of edge weight(Table3Figure4)Thetop01ofedgeswiththegreatestgeneticcovariance emanated from a node in the Spring-Steptoe ValleyswatershedinNevadaThisnodealsohasthelowestclosenesscen-tralityandloweigenvectorcentrality(033)andverylowbetween-ness(0)TheedgeofleastweightconnectedtwonodeswithintheFremontwatershedinUtah(inthesouthcentralUTgroupofnodesinFigure3)Thisnodewasofmoderateimportancetonetwork-wideconnectivity(betweenness115)buthadlowconnectivitytoothernearbynodes(eigenvectorcentrality011)


Itwascommonthatthehubnodesmdashthosewiththehighestcentral-ity rankingsmdashwere also those with lower mean peak male count(Figure5)Therewas strongevidence for aweakpositive correla-tionbetweenmeanpeakmalecountandeigenvectorcentralityandmeanpeakmalecountandstrengthandstrongevidenceforweakTA

BLE 4emspCorrelationbetweennetworkcentralityindicesrangecentralityandlekattendancepernodefortherange-widegreatersage-grousegeneticnetworkSpearmanrsquosrank

correlation(r s)isshownbelowthediagonalwithsignificance(p)above

AH

eBe

twee

nnes

sCl

osen

ess

Clus

terin

g co

effic

ient

Eige

nvec

tor

Stre

ngth

Sam

ples

in

clus

ter

Ove

rall

mea

n pe

ak

mal

e co

unt

Rang

e ce

ntra

lity

(inve

rse)

A―

000

00

000

000

00

000

000

00

000

000

00

002

000

0

He

087

2―

000

00

000

000

00

000

000

00

000

002

80

000

Betweenness

0706

0565

―0

000

074

90

000

000

00

000

002

80237

Closeness

0670

0595

091

4―

0380

000

80

007

000

00046

0005

Clustering

coefficient

0341

027

8minus0015

minus0041

―0

000

000

00

000

0314

042

9

Eigenvector

minus0515

minus0307

minus0280

minus0124

minus0499

―0

000

000

00003

000

0

Strength

minus0551

minus0359

minus0281

minus0125

minus0567

0983

―0

000

0006

000

2

Samplesincluster

0860

0602

0665

0547

0340

minus0577

minus0613

―0

021

0310

Overallmeanpeak

malecount

minus0157

minus0110

minus0110

minus0100

minus0051

014

90137

minus0116

―0

449

Rangecentrality

(inverse)

minus0234

minus0388

minus0055

minus0131

0037

minus0206

minus0147

minus0048

0038

―


F IGURE 5emspRelationshipsbetweencentralityindex(y-axis)andmeanpeakmalecountpernode(x-axis)RedcirclesenvelopekeystonenodesThefittedlinearmodelandconfidenceintervalareshown(bluelinewithshadedconfidenceinterval)

0

500

1000

1500

0 20 40 60 80Mean Peak Male Count

Bet

wee

nnes

s

000008

000012

000016


Clo

sene

ss

015

020

025

030

0 20 40 60 80

Mean Peak Male Count

Clu

ster

ing

Coe

ffici

ent

025

050

075

100

0 20 40 60 80


Eig

enve

ctor

300

600

900


Nod

e S

treng

th

(a) (b)

(d)(c)

(e)


negativecorrelationbetweenmeanpeakmalecountandbetween-nessandbetweenmeanpeakmalecountandcloseness(Table4andFigure5)

Acrossallcentralityindiceswediscovered26nodesthatrankedhigh for network centrality despite having lowermean peakmalecount thannodesof similar ranking (Figure6) These26 keystonenodeswerelocatedacrosstheentirespeciesrsquorangeFourofthesekeystonenodes rankedhighly formore thanone centrality indexwithhighrankingscoupledforeigenvectorcentralityandstrengthandforclosenessandclusteringcoefficientInallcasesthesenodeswere keystone for betweenness and closeness or for eigenvectorandstrength

4emsp |emspDISCUSSION

41emsp|emspEmergent network properties

Thegreatestutilityofournetworkanalysisisitsabilitytobeusedtoprioritizeandtargetconservationeffortstothenodesmostim-portant tomaintaining network connectivity at any desired scaleOur network approach allowsnodes tobe ranked acrossmultiple

centralityindicesindicativeofdifferentscalesandpatternsofcon-nectivityeachwithuniqueimportancetoconservation

Wediscoveredthatthesage-grouserange-widegeneticnet-workisbestcharacterizedashub-and-spoketopologymostre-sembling the structure of a small-world network and not thatof a random or regular network Both themean clustering co-efficient (019plusmn335times10minus3 [SE]) and the mean characteristicpath length (188plusmn704times10minus3 [SE] [188 191]) were shorterthanhasbeen reported forother species (eg 0254and226in Garroway etal 2008) The fat-tailed distribution of nodedegree (Figure4d)confirmedsmall-worldnetworkstructurebyrulingoutscale-freestructureforwhichthedegreedistributionfollowsapowerlaw

Manyhubnodesofconnectivitywithinthenetworkarelocatedacrossthespeciesrsquorange(Figure3)withmostspokenodeslocatedalong the periphery of the range This hub-and-spoke topology isevident in theminimum spanning treewith important hub nodesof genetic connectivity occurring in nearly every state across thecontiguousrange(Figure2)Lossofoneofthesehighlyconnectedhubnodeswithinseveralmajorbasinscouldseverelyaffectoverallnetworkconnectivity

F IGURE 6emspKeystonenodes(n = 26)nodeswithgreaterimportancetogeneticconnectivitythanthemagnitudeoflekattendancewithinthenodeornodelocationwithinthespeciesrangealonemightindicateThesenodeswerelowinmeanpeakhighmalecountrelativetotheirnetworkcentralityrankingsPointsrepresentingkeystonenodesformorethanonecentralityindexareoffsettotheleftorrightsuchthattheseoffsettouchingpointsrepresentthesamenodeNodecolorindicatescentralitymeasureShadedpolygonsdepictthewatershedwithinwhichthesetop-rankingnodesarelocatedThespeciesrsquorangeisshownaslightgraypolygons

36

40

44

48



Closeness


Eigenvector

StrengthLake Abert

Crowley Lake

Bullwhacker-Dog

Middle Musselshell

Upper Malheur

Middle Snake-Succor

CJ Strike Reservoir

Lake Walcott

Upper Bear

Fremont

Upper Green-Slate

Upper GreenMiddle North Platte-Casper

Crazy Woman

Big Horn Lake

Price

ProvoLower Weber

Spring-Steptoe ValleysRockUpper Snake-Rock

Upper Little MissouriSmith

Ruby


Shoshone


Wedocumentedstrongconnectivityacrosstheentirenetworkevidenced by high-ranking nodes and edges across the speciesrsquorangeThismeansthatsomeofthenodesmaybeabletorecovershould theybeextirpatedbut thehabitat remain intactorbere-stored (eg following a local extinction caused byWestNile vi-rusmdashegNaugleetal2004orreturnafterrestorationfollowingnatural resourceextractionmdashegNaugle etal 2011) Theabilitytorecoverisexhibitedinthenetworkrsquostraversability(ietheap-parent low resistance togene flow)However to thebestofourknowledgenoderecoveryhasnotbeenpreviouslyinvestigatedinwildlifenetworksTheminimumspanningtreecanserveasapow-erfulguideinmakingmanagementdecisionsrelatedtotherelativeimportanceof individual nodes tooverall landscape connectivity(UrbanampKeitt 2001) as it is possible tomodelwhich nodes orwhichpartsoftherangewillmostlikelybeaffectedbythelossofanygivennode

Our results suggest that distance plays an important role instructuring genetic connectivity (also known as isolation by dis-tanceWright 1943) The vastmajority of edges in theminimumspanningtreemdashthoseconnectionsthatrepresentthegreatestcova-riancemdashconnectgeographicallyproximalnodes(Figure2a)Similarlytherewas a correlation between conditional genetic distance andgeographicdistanceThese results support prior findingsof isola-tionbydistanceacrossthespeciesrsquorange(Bushetal2011Crossetal2016DavisReeseGardnerampBird2015FedyRowampOyler-McCance2017Oyler-McCanceTaylorampQuinn2005SchulwitzBedrosianampJohnson2014)Howevernodeswithgreatercentral-ityimportanttobothlocalandnetwork-widegeneticexchangearelocatedacrossthespeciesrsquorange

BothCrossetal(2016)andOyler-McCanceetal(2005)foundthat sage-grouse subpopulations in southwesternMontanawerediverged from populations in the rest of the stateWe confirmthispriorfindingshowingthatsage-grousefromthesesamesub-populationsaremorecloselyrelatedtoconspecificsinIdahothantosubpopulations inMontanaas isevident inedgeconnectivitywithin theminimumspanningtree (Figure2b)Crossetal (2016)alsofoundthatthepopulationinNorthernMontanawasdivergedfromthesubpopulationinSoutheasternMontanaandtheDakotasandfromthesouthcentralMontanasubpopulation(theSE-Wsub-populationinCrossetal2016)Weconfirmthesefindingshereshowingnodeswithveryhighclusteringcoefficient(indicativeofhighly interconnectednetworksubunits)withinthesameregions(Figure3)We expect that the other top-ranked nodes for clus-teringcoefficientintheMiddleSnake-SuccorwatershedinIdahoand the Fremontwatershed inUtahmight also be embedded atthecoreoftheirrespectivesubpopulationsSchulwitzetal(2014)found that the subpopulations in southern and southeasternMontanaandtheDakotaswerebothhighlyconnectedto leks innorthernWyomingWe also found the samepattern of connec-tivity evident in the hub-and-spoke topology of the minimumspanning tree Inourcaseahubnode inWyomingsouthcentralMontanaislocatedintheBigHornLakewatershedofnorthcentralWyoming and a hub node forWyomingsoutheasternMontana

subpopulations is locatedwithintheCrazyWomanwatershedofnortheasternWyoming(Figures2band3)Davisetal(2015)foundthat the small northernCalifornia population known tohave ex-periencedpopulationdeclineshadretainedgeneticdiversityWeconfirmthisunderstandingbyfindingthatthenodes inthisareashowelevatedlocalconnectivity(covariance)withintheareaWealso found thatgeneticconnectivity into thenorthernCalifornianodescomesfromnodestothenorthinOregon(Figure2b)Oyler-McCance Casazza Fike and Coates (2014) discovered a north-ernandasouthernsubpopulationwithin theBi-Statepopulationin southern California and southwesternNevadaWe found thesame break evidenced by a lack of edges connecting these twounitsintheminimumspanningtree(Figure2b)Thislackofinter-connectivityamongnodesinthenorthernandsoutherngroupsisespeciallysurprisinggiventhatbothgroupsexhibitgreatercovari-ancewithfarmoregeographicallydistantnodesFedyetal(2017)documentedgeneticdifferentiationbetweenbirdsintheBighornandPowderRiverBasinsofWyomingaswellasdifferentiationbe-tween thenorthernand southernpartsof the statedifferencesreflectedinouranalysisasevidencedbyedgeconnectivitywithintheminimumspanningtree

42emsp|emspHubs of genetic exchange

Weidentifiednodeswithhighimportancetolarge-scalenetwork-widegeneticconnectivity (ienodeswithhighbetweenness)andnodeswithinthetop50ofallcentralityindicesimportanttobothnetwork-wideand localconnectivityThesetop-rankedhubnodesare located across the entire range of the species The locationsofthesehubnodesimportanttonetwork-wideconnectivityareinareasthatshouldfosterrange-widegeneticconnectivityduetotheirlocationinthetopographyofthewesternlandscape

Connectivityofthesehubsisapparentintheminimumspanningtree(Figure2b)whereconnectivityacrosstherangeappearscres-centshapedwithonepointofthecrescentinnorthernMontanaSaskatchewanandtheotherinOregonTheUpperSnakeBasinofIdaho(LakeWalcottwatershed)formsathumbterminatinginsouth-western Montana to the northeast Hub connectivity opens upovertheColumbiaPlateauofIdaho(UpperSnake-RockCJStrikeReservoir and Middle Snake-Succor watersheds) Connectivityextends south into the Great Basin composing most of easternCalifornia all of Nevada and western Utah Here the SouthwestRiverBasininIdaho(MiddleSnake-SuccorandCJStrikeReservoirwatersheds)connectstotheMalheurBasin(UpperMalheurwater-shed)tothewestandtotheSouthLahontanRiverBasin(CrowleyLakewatershed)bywayof theCentralNevadaDesert (Diamond-MonitorValleyswatershed)tothesouthwestTheGreenRiverBasinofWyoming(UpperGreenandUpperGreen-Slatewatersheds)sitsjustwestofalowsectionoftheNorthAmericanContinentalDivideconnecting theUpper SnakeBasin andGreatBasin to the rest ofWyomingandfartherupintothenortheasternpartofthespeciesrangeTheGreenRiverBasinalsositsjustnorthoftheYampaandWhite River Basins in Colorado (Little Snakewatershed) and the


BearRiverBasin inUtah (UpperBearwatershed)bothconnectedbylowvalleystothesouthSimilarlythePowderandTongueRiverBasinsofWyoming(CrazyWomanwatershed)connecttothenorthwithbothoftheDakotasandeasternMontanaTheBighornRiverBasinwhich ranks lower for other connectivity indices connectsto the southeastern-west subpopulation in the Yellowstone RiverBasinofMontana (Crossetal2016)mdashnodes intheBigHornLakewatershedanchorbothbasinsWesuspectthatthetopologyofthegenetic network is largely shaped by the topography of the land-scapeahypothesispreviouslypositedforsage-grouse(Crossetal2016Rowetal2015Schulwitzetal2014)andwhichhasbeenfound to influencegenetic structure inother species (eg Roffleretal2014)

Weidentified26keystonenodesacrosstherangeofsage-grousethatstandoutwithincreasedimportancetogeneticconnectivityde-spitehavinglowermeanpeakmalecount(Figure6)Thesekeystonenodesdonotfollowthepresuppositionofincreasedcentralitywithincreasedmeanpeakmalecount(ieaproxyforpopulationsizeforanygivennode)andincludethehighestrankingnodesforeachcen-tralityindexregardlessofthepopulationsize(Figure5)Webelievethat thesekeystonenodesandotherhubnodes (Figure3)are topcandidatesfortargetedconservationeffortsastheirprotectionwillhelp secure range-wide genetic connectivity The keystone nodesarealsodistributedacrosstheentirespeciesrsquorangefromthecoretotheperiphery (Figure6)Thereforeneitherrangecentralitynorlocalpopulationsizealoneshouldbetrustedproxiesforprioritizingtargetedconservationactionsforsage-grouse

43emsp|emspLimitations of the study and future directions

Prior research has modeled range-wide sage-grouse connectiv-ity using a network approach Knick and Hanser (2011) weightednodesusinglekattendanceandlimitededgeconnectionsusinghy-pothesizeddispersal thresholdsHowever these imposeddispersalthresholdsmayhaveaffectedtheresultantnetworkstructureForexampleKnickandHanser(2011)usedanexponentialdecayfunc-tion todetermine theprobabilityof connectivityof leks Imposingdispersalthresholdslikelyoversimplifiedthecontributionthateachpriorityareaforconservationmadetonetworkconnectivitybyas-sumingdispersal limitations isequalamongallnodes regardlessofthe internal population dynamicswithin nodes and environmentalconditionswithinandamongnodesCristKnickandHanser(2017)usednetwork approaches to generate severalmodelsof hypothe-sized connectivity among sage-grousepriority areas for conserva-tionwhichareareas thatprotect larger leks (ie thosewithmoremales visible during breeding) and surrounding area They char-acterized the centrality of each priority area for conservation andconcludedthatseveralsubnetworksexistacrossthespeciesrsquorangeHoweverintheiranalysispatchsizeshapeandboundarylengthallhadaneffectonthepatternofconnectivityandcentralityOuranal-ysisprovidesinsightintogeneticconnectivityusingcentralityindicesbasedsolelyonthespeciesrsquobiologythegeneticcovarianceresultingfromcumulativedispersalandbreedingaquantitativemetric

Wehaveconfidenceinthecutdistanceweusedtoclusterleksintonodes as it isempiricallybasedondispersaldistancesdoc-umented over a vast area acrossmultiple years involving bothsexes (Crossetal2017)Ourclusteringapproach increasedge-neticvariancewithinnodesbutalsoincreasedcovarianceamongnodes (Dyer 2015) Choice of cut distance depends on the de-siredscaleofanalysisforconservationandmanagementapplica-tionWecouldhaveperformedthisanalysisusingindividualleksDoing sowouldhave resulted in finer resolution forour resultsHoweveritalsowouldhaveresultedinfewerindividualspernodewhichwouldhavelimitedourcharacterizationofwithin-nodege-neticvariationFurthermorewewouldhavehadtocutmanyleksfromouranalysisduetotheminimumnodecompositionrequire-mentof four individualsBy clustering leks intonodeswewererestrictedtomakingstatementsabouttheconnectivityof largerlandscapes that extendbeyond the size of an individual lek andwhichwere potentially representative of leks unsampledwithinthe same landscapes Furthermore our clustering approach re-flectsthebiologyofthespeciesaspriorresearchhasshownthatboth female andmale sage-grouse attendmultiple lekswithin abreedingseason(Crossetal2017DunnampBraun1985SempleWayneampGibson2001)

Wefoundevidenceforcorrelationbetweensomenetworkcentral-ityindicesandsamplespernodeandmeanpeakmalecountHoweverwhensignificanttheserelationshipswereonlymoderateorweakinallbutonecasethatofbetweennessandsampleswithinanode(Table4)ThereforewedonotbelievethatsamplesizedrovethecentralityofanodeLargerpopulationsactingashubnodesmightbeexpectedasthesehighlypopulatedhubnodeswouldbeexpectedtohousegreatergenetic diversity to be the sources of dispersers However as dis-cussedabovethehighestrankingnodesforeachcentralityindexwereneverthosewiththegreatestmeanpeakmalecount(Figure5)

Future work should examine the effect of the spatial distri-butionof individualscomposingnodeson the resultantnetworkmodel structure For example constructing a genetic networkwhere priority areas for conservation serve as nodes may helpprioritize conservation based on existingmanagement boundar-ies at a larger landscape scale It isworth noting that if priorityareasforconservationaretreatedasnodes largerpriorityareasforconservationmayscorehigherforcentralityindicesduetothewithin-nodeproportionof thegenetic covariancewhichwill in-creasecentrality

44emsp|emspApplications and future directions

WebelievethatthegreatestutilityofournetworkanalysiswillbeitsuseinprioritizingandtargetingconservationeffortstothenodesmostimportanttomaintainingnetworkconnectivityThisnetworkapproachallowsfortherankingofnodesbymultiplecentralityindi-cesindicativeofdifferentscalesanddifferentpatternsofconnectiv-ityTheseindicescanbeusedtolocatethetop-rankingnodesmdashandmoreimportantlythelekswhichcomposethosenodesmdashwhichcanthen be prioritized in accordancewithmanagement goals (Bottrill


etal2008)Ifgoalsaretoconservehubsofgeneticexchangethatconnect the greatest number of nodes range-wide then rankingnodesbasedonbetweennessismostrelevant Ifgoalsaretocon-serve hubs of genetic exchange that connect immediate connec-tions then rankingnodesbasedoneigenvector centrality ismostrelevant Ifgoalsaretoconservenodesthathavethegreatestge-netic exchange with their immediate connections then rankingnodesbasedonstrength ismostrelevant Ifgoalsaretoconservehubsoflocalconnectivitythenrankingnodesbasedonclosenessorclusteringcoefficientismostrelevant

Conservation actions may be targeted first toward the top-rankingnodesormanagersmay first choose tocombinenetworkcentralitywitheconomiccostbeforedecidingwheretoactWecanimaginemanyadditionalways inwhichnetworkcentralitymaybecombinedwithadditionalmetricstotargetconservationresourcesOurhopeisthattheempiricallybasedsage-grousegeneticnetworkweconstructedwillproveausefultooltoconservationplanners

ACKNOWLEDG MENTS

WethankRodneyDyerFredAllendorfJeffGoodJonGrahamandKatie Zarn for their comments on this manuscript This researchwould not have been possible without Steve Knickrsquos leadershipand the many state federal and nongovernmental organization(NGO) biologists and technicians who have spent lifetimes work-ingtirelessly inthefieldandbehinddesksfor theconservationofthe Greater sage-grouse We specifically acknowledge biologistsand technicianswith the Bureau of LandManagement CaliforniaDepartmentof FishampGameColoradoDivisionofWildlife IdahoFishampGameMontanaFishWildlifeampParksMontanaAudubonNevadaDivisionofWildlifeNorthDakotaGameandFishNaturalResources Conservation Service Sage-Grouse Initiative OregonFish ampWildlife South Dakota Game Fish amp Parks UtahWildlifeResourcesUSForestService (USFS) theWesternAssociationofFishandWildlifeAgencies (WAFWA)WashingtonDepartmentofFishandWildlifeandWyomingGameampFishDepartmentOurmo-leculargeneticsanalysesandmanuscriptpreparationwouldnothavebeen possible without Cory Engkjer Kevin McKelvey and KristyPilgrim with laboratory work by Kara Bates Nasreen BroomandTaylor Dowell Jennifer Fike Scott Hampton Randi LesagoniczIngaOrtloffSaraSchwarzKateWelchtheUSFSRockyMountainResearch StationNational Genomics Center forWildlife and FishConservationandtheUSGSFORTMolecularEcologyLaboratory

CONFLIC T OF INTERE S T

Nonedeclared

AUTHOR CONTRIBUTIONS

TBCconceivedtheideaanalyzedthedataconductedtheresearchandwrotethepaperTBCandMKSdevelopedanddesignedthe

methodsTBCandSJOgeneratedthedataTBCDENSJOJRRBCFSTKandMKSsubstantiallyeditedthepaper

DATA ACCE SSIBILIT Y

SamplemicrosatellitegenotypesandnodemembershipareavailableonUSGSScienceBasehttpsdoiorg105066f73n22pn

R E FE R E N C E S

Aldridge C L Nielsen S E Beyer H L Boyce M S Connelly JWKnickSTampSchroederMA(2008)Range-widepatternsofgreatersage-grousepersistenceDiversity and Distributions14983ndash994httpsdoiorg101111j1472-4642200800502x

Amaral L A N Scala A Bartheacuteleacutemy M amp Stanley H E (2000)Classes of small-world networks Proceedings of the National Academy of Sciences 97 11149ndash11152 httpsdoiorg101073pnas200327197

Beever E A amp Aldridge C L (2011) Influences of free-roamingequids on sagebrush ecosystems with a focus on greater sage-grouseInSTKnickampJWConnelly(Eds)Greater Sage-Grouse Ecology and conservation of a landscape species and its habitats Studies in avian biology (pp 273ndash290) 38th edn Berkeley CAUniversityofCaliforniaPress

BivandRSampRundelC(2017)RGEOSInterfacetoGeometryEnginendashOpenSource(GEOS)Rpackageversion03-26httpsCRANR-projectorgpackage=rgeos

BottrillMCJosephLNCarwardineJBodeMCookCGameEThellipPresseyRL(2008)Isconservationtriagejustsmartdeci-sionmakingTrends in Ecology amp Evolution23649ndash654httpsdoiorg101016jtree200807007

BradburyJVehrencampSampGibsonR(1989)Dispersionofdisplay-ingmalesage-grouseBehavioral Ecology and Sociobiology241ndash14httpsdoiorg101007BF00300112

BrayD(2003)MolecularnetworksThetop-downviewScience3011864ndash1865httpsdoiorg101126science1089118

BunnAGUrbanDLampKeittTH(2000)LandscapeconnectivityAconservationapplicationofgraphtheoryJournal of Environmental Management59265ndash278httpsdoiorg101006jema20000373

Bush K L Dyte C KMoynahan B J Aldridge C L SaulsH SBattazzoAMhellipCarlsonJ(2011)Populationstructureandgeneticdiversityofgreatersage-grouse(Centrocercus urophasianus)infrag-mentedlandscapesatthenorthernedgeoftheirrangeConservation Genetics12527ndash542httpsdoiorg101007s10592-010-0159-8

BushKLVinskyMDAldridgeCLampPaszkowskiCA(2005)AcomparisonofsampletypesvaryingininvasivenessforuseinDNAsex determination in an endangered population of greater sage-grouse (Centrocercus uropihasianus)Conservation Genetics 6 867ndash870httpsdoiorg101007s10592-005-9040-6

ConnellyJWHagenCAampSchroederMA(2011)Characteristicsanddynamicsofgreatersage-grousepopulationsInSTKnickampJWConnelly(Eds)Greater Sage-Grouse Ecology and conservation of a landscape species and its habitats Studies in avian biology(pp53ndash67)38thednBerkeleyCAUniversityofCaliforniaPress

ConnellyJWRinkesEampBraunC(2011)Characteristicsofgreatersage-grousehabitatsAlandscapespeciesatmicroandmacroscalesIn S TKnickamp JWConnelly (Eds)Greater Sage-Grouse Ecology and conservation of a landscape species and its habitats Studies in avian biology(pp69ndash83)38thednBerkeleyCAUniversityofCaliforniaPress

CopelandHEDohertyKENaugleDEPocewiczAampKieseckerJM (2009)Mappingoilandgasdevelopmentpotential intheUS


IntermountainWestandestimatingimpactstospeciesPLoS ONE4e7400httpsdoiorg101371journalpone0007400

CristMRKnickSTampHanserSE(2017)Range-wideconnectivityofpriorityareasforGreaterSage-GrouseImplicationsforlong-termconservationfromgraphtheoryCondor Ornithological Applications11944ndash57httpsdoiorg101650CONDOR-16-601

Crooks K R amp Sanjayan M (2006) Connectivity conservationMaintainingconnectionsfornature InKRCrooksampMSanjayan(Eds)Connectivity conservation Conservation biology series (vol14pp383ndash405)CambridgeMACambridgeUniversityPresshttpsdoiorg101017CBO9780511754821

Cross T B Naugle D E Carlson J C amp Schwartz M K (2016)Hierarchicalpopulationstructureingreatersage-grouseprovidesin-sightintomanagementboundarydelineationConservation Genetics171417ndash1433httpsdoiorg101007s10592-016-0872-z

Cross T B Naugle D E Carlson J C amp Schwartz M K (2017)Genetic recapture identifies long-distance breeding disper-sal in Greater Sage-Grouse (Centrocercus urophasianus) Condor Ornithological Applications 119 155ndash166 httpsdoiorg101650CONDOR-16-1781

CsardiGampNepuszT(2006)TheIGRAPHsoftwarepackageforcom-plexnetworkresearchInterJournal Complex Systems169538

DalkePDPyrahDBStantonDCCrawfordJEampSchlattererEF(1963)Ecologyproductivityandmanagementofsage-grouseinIdahoJournal of Wildlife Management27811ndash841

DavisDMReeseKPGardnerSCampBirdKL (2015)Geneticstructure of Greater Sage-Grouse (Centrocercus urophasianus) in adecliningperipheralpopulationCondor Ornithological Applications117530ndash544httpsdoiorg101650CONDOR-15-341

DeWoodyJNasonJDampHipkinsVD(2006)MitigatingscoringerrorsinmicrosatellitedatafromwildpopulationsMolecular Ecology Notes6951ndash957httpsdoiorg101111j1471-8286200601449x

DunnPOampBraunCE(1985)Nataldispersalandlekfidelityofsage-grouse Auk102621ndash627

DunnPOampBraunCE (1986)LatesummerndashspringmovementsofjuvenileSage-grouseWilson Bulletin9883ndash92

Dunne JAWilliamsR JampMartinezND (2002)Networkstruc-ture and biodiversity loss in food webs Robustness increaseswith connectance Ecology Letters 5 558ndash567 httpsdoiorg101046j1461-0248200200354x

Dyer R J (2007) The evolution of genetic topologies Theoretical Population Biology 71 71ndash79 httpsdoiorg101016jtpb200607001

DyerRJ(2014a)GSTUDIOSpatialutilityfunctionsfromtheDyerlab-oratoryRpackageversion12-

DyerRJ(2014b)POPGRAPHThisisanRpackagethatconstructsandmanipulatespopulationgraphsRpackageversion14

Dyer R J (2015) Population graphs and landscape genetics Annual Review of Ecology Evolution and Systematics46 327ndash342 httpsdoiorg101146annurev-ecolsys-112414-054150

DyerRJampNasonJD(2004)PopulationgraphsThegraphtheoreticshapeofgeneticstructureMolecular Ecology131713ndash1727httpsdoiorg101111j1365-294X200402177x

Dyer R J Nason J D amp Garrick R C (2010) Landscape mod-elling of gene flow Improved power using conditional ge-netic distance derived from the topology of populationnetworks Molecular Ecology 19 3746ndash3759 httpsdoiorg101111j1365-294X201004748x

EdminsterFC(1954)American game birds of eld and forest Their habits ecology and managementNewYorkScribner

Emmons S R amp Braun C E (1984) Lek attendance of male sage-grouse Journal of Wildlife Management481023ndash1028httpsdoiorg1023073801461

EvettIWampWeirBS(1998)Interpreting DNA evidence Statistical ge-netics for forensic scientistsSunderlandMAUSASinauerAssociates

FedyBCRowJRampOyler-McCanceSJ(2017)Integrationofge-neticanddemographicdata toassesspopulationrisk inacontinu-ouslydistributedspeciesConservation Genetics1889ndash104httpsdoiorg101007s10592-016-0885-7

Galpern PManseauMHettinga P Smith K ampWilson P (2012)ALLELEMATCH An R package for identifying unique multi-locusgenotypes where genotyping error and missing data may bepresent Molecular Ecology Resources 12 771ndash778 httpsdoiorg101111j1755-0998201203137x

GarrowayCJBowmanJCarrDampWilsonPJ(2008)Applicationsofgraph theory to landscapegeneticsEvolutionary Applications1620ndash630

GarrowayCJBowmanJampWilsonPJ(2011)Usingageneticnet-work to parameterize a landscape resistance surface for fishersMartes pennanti Molecular Ecology 20 3978ndash3988 httpsdoiorg101111j1365-294X201105243x

Hagen C A Connelly J W amp Schroeder M A (2007) A meta-analysis of greater sage-grouse Centrocercus urophasianus nestingandbrood-rearinghabitatsWildlife Biology13 42ndash50 httpsdoiorg1029810909-6396(2007)13[42AMOGSC]20CO2

HanksEMHootenMBKnickSTOyler-McCanceSJFikeJACrossTBampSchwartzMK(2016)Latentspatialmodelsandsamplingdesignfor landscapegeneticsAnnals of Applied Statistics101041ndash1062httpsdoiorg10121416-AOAS929

HolloranM JKaiserRCampHubertWA (2010)Yearlinggreatersage-grouseresponsetoenergydevelopmentinWyomingJournal of Wildlife Management7465ndash72httpsdoiorg1021932008-291

JacobyDMPampFreemanR (2016)Emergingnetwork-based toolsin movement ecology Trends in Ecology amp Evolution 31 301ndash314httpsdoiorg101016jtree201601011

KahnNStJohnJampQuinnTW(1998)Chromosome-specificintronsizedifferencesintheavianCHDgeneprovideanefficientmethodfor sex identification in birdsAuk Ornithological Applications1151074ndash1078

Knick S TDobkinD S Rotenberry J T SchroederMAVanderHaegenWMampVanRiperCIII(2003)TeeteringontheedgeortoolateConservationandresearchissuesforavifaunaofsagebrushhabitatsCondor105611ndash634httpsdoiorg1016507329

KnickSTampHanserSE (2011)Connectingpatternandprocess ingreatersage-grousepopulationsandsagebrushlandscapes InSTKnickampJWConnelly(Eds)Greater Sage-Grouse Ecology and con-servation of a landscape species and its habitats Studies in avian biology (pp383ndash405)38thednBerkeleyCAUniversityofCaliforniaPress

KoenELBowmanJGarrowayCJampWilsonPJ(2013)Thesen-sitivityofgenetic connectivitymeasures tounsampledandunder-sampled sitesPLoS ONE8 e56204 httpsdoiorg101371jour-nalpone0056204

Koen E L Bowman JampWilson P J (2015)Node-basedmeasuresofconnectivityingeneticnetworksMolecular Ecology Resources1669ndash79

Lookingbill T R Gardner R H Ferrari J R amp Keller C E (2010)CombiningadispersalmodelwithnetworktheorytoassesshabitatconnectivityGeography and the Environment20427ndash441

McKelvey K S amp Schwartz M K (2005) DROPOUT A pro-gram to identify problem loci and samples for noninva-sive genetic samples in a capture-mark-recapture frame-work Molecular Ecology Notes 5 716ndash718 httpsdoiorg101111j1471-8286200501038x

NaugleDEAldridgeCLWalkerBLCornishTEMoynahanBJHolloranMJhellipMayerRT(2004)WestNilevirusPendingcri-sisforgreatersage-grouseEcology Letters7704ndash713httpsdoiorg101111j1461-0248200400631x

NaugleDEDohertyKEWalkerBLCopelHEHolloranMJampTackJD(2011)Sage-grouseandcumulativeimpactsofenergydevelopment InDENaugle (Ed)Energy development and wildlife


conservation in western North America (pp 55ndash70) WashingtonIslandhttpsdoiorg105822978-1-61091-022-4

NewmanME J (2006)Modularityandcommunitystructure innet-worksProceedings of the National Academy of Sciences1038577ndash8582wwwpnasorgcgidoi101073pnas0601602103

Nychka D Furrer R Paige J amp Sain S (2015) FIELDS Tools forspatial data httpsdoiorg105065d6w957ct (URL httpsdoiorg105065d6w957ct)Rpackageversion810wwwimageucaredufields

Oyler-McCanceSJCasazzaMLFikeJAampCoatesPS (2014)Hierarchical spatial genetic structure in a distinct population seg-mentofgreatersage-grouseConservation Genetics151299ndash1311httpsdoiorg101007s10592-014-0618-8

Oyler-McCance S J Taylor S E amp Quinn T W (2005) A mul-tilocus population genetic survey of the greater sage-grouseacross their rangeMolecular Ecology 14 1293ndash1310 httpsdoiorg101111j1365-294X200502491x

PattersonRL (1952)The Sage-grouse in WyomingDenverCOUSASageBooks forWyomingGame andFishCommissionCheyenneWYUSA

RCoreTeam(2016)R A language and environment for statistical comput-ingViennaAustriaRFoundationforStatisticalComputinghttpwwwR-projectorg

RemingtonT EampBraunC E (1985) Sage-grouse food selection inwinter North Park Colorado Journal of Wildlife Management 491055ndash1061httpsdoiorg1023073801395

Roffler GH Talbot S L Luikart GH SageG K Pilgrim K LAdams L G amp Schwartz M K (2014) Lack of sex-biased dis-persal promotes fine-scale genetic structure in alpine ungulatesConservation Genetics 15 837ndash851 httpsdoiorg101007s10592-014-0583-2

RowJROyler-McCanceSJFikeJAOrsquoDonnellMSDohertyKEAldridgeCLhellipFedyBC (2015)Landscapecharacter-istics influencing the genetic structure of greater sage-grousewithinthestrongholdoftheirrangeAholisticmodelingapproachEcology and Evolution 5 1955ndash1969 httpsdoiorg101002ece31479

RowlandMMWisdomMJSuringLHampMeinkeCW (2006)Greatersage-grouseasanumbrellaspeciesforsagebrush-associatedvertebrates Biological Conservation 129 323ndash335 httpsdoiorg101016jbiocon200510048

Sallaberry A Zaidi F amp Melanccedilon G (2013) Model for generat-ing artificial social networks having community structures withsmall-worldandscale-freepropertiesSocial Network Analysis and Mining31ndash13

SchragAKonradSMillerSWalkerBampForrestS(2011)Climate-changeimpactsonsagebrushhabitatandWestNilevirustransmissionriskandconservationimplicationsforgreatersage-grouseGeoJournal76561ndash575httpsdoiorg101007s10708-010-9369-3

SchroederMAAldridgeCLApaADBohneJRBraunCEBunnellSDhellipHilliardMA(2004)Distributionofsage-grouseinNorthAmericaCondor106363ndash376httpsdoiorg1016507425

SchroederMAampRobbLA(2003)FidelityofGreaterSage-GrouseCentrocercus urophasianus to breeding areas in a fragmented land-scapeWildlife Biology9291ndash299

Schulwitz S Bedrosian B amp Johnson J A (2014) Low neutral ge-netic diversity in isolated Greater Sage-Grouse (Centrocercus urophasianus) populations in northwest Wyoming Condor Ornithological Applications 116 560ndash573 httpsdoiorg101650CONDOR-14-541

Segelbacher G (2002) Noninvasive genetic analysis in birds TestingreliabilityoffeathersamplesMolecular Ecology Notes2367ndash369

Semple K Wayne R K amp Gibson R M (2001) Microsatelliteanalysis of female mating behaviour in lek-breeding sage-grouse Molecular Ecology 10 2043ndash2048 httpsdoiorg101046j0962-1083200101348x

SmithRE(2012)ConservingMontanarsquossagebrushhighwayLongdis-tancemigrationinsage-grouseMSthesisUniversityofMontanaMissoulaMTUSA

SchwartzMKCushmanSAMcKelveyKSHaydenJampEngkjerC (2006) Detecting genotyping errors and describing AmericanblackbearmovementinnorthernIdahoUrsus17138ndash148httpsdoiorg1021921537-6176(2006)17[138DGEADA]20CO2

Tack J D (2009) Sage-grouse and the human footprint Implicationsfor conservation of small and declining populations MS thesisUniversityofMontanaMissoulaMTUSA

Urban D L amp Keitt T (2001) Landscape connectivity A graph-theoretic perspective Ecology 82 1205ndash1218 httpsdoiorg1018900012-9658(2001)082[1205LCAGTP]20CO2

USFishandWildlifeService(2010)Endangeredandthreatenedwildlifeandplants12-monthfindingsforpetitionstolisttheGreaterSage-Grouse (Centrocercus urophasianus) as Threatened or EndangeredproposedruleFederal Register7513910ndash14014

USFishandWildlifeService(2015)Endangeredandthreatenedwild-lifeandplants12-monthfindingonapetitiontolistGreaterSage-Grouse(Centrocercus urophasianus)asanEndangeredorThreatenedspeciesproposedruleFederal Register8059858ndash59942

VanOosterhoutCHutchinsonWFWillsDPampShipleyP(2004)MICRO-CHECKERSoftwareforidentifyingandcorrectinggenotyp-ingerrorsinmicrosatellitedataMolecular Ecology Notes4535ndash538httpsdoiorg101111j1471-8286200400684x

Walker B L Naugle D E amp Doherty K E (2007) Greater sage-grouse population response to energy development and habitatloss Journal of Wildlife Management 71 2644ndash2654 httpsdoiorg1021932006-529

WallestadRampEngRL(1975)Foodsofadultsage-grouseinCentralMontanaJournal of Wildlife Management39628ndash630httpsdoiorg1023073800409

WallestadRampSchladweilerP(1974)Breedingseasonmovementsandhabitatselectionofmalesage-grouseJournal of Wildlife Management38634ndash637httpsdoiorg1023073800030

WattsDampStrogatzSH(1998)Collectivedynamicsofldquosmall-worldrdquonetworksNature393440ndash442httpsdoiorg10103830918

Western Association of Fish andWildlife Agencies [WAFWA] (2015)Greatersage-grousepopulationtrendsananalysisoflekcountdata-bases1965ndash2015ReportWesternAssociationofFishandWildlifeAgenciesCheyenneWYUSA

Wright S (1931) Evolution in Mendelian populations Genetics 1697ndash159

WrightS(1943)IsolationbydistanceGenetics28114

How to cite this articleCrossTBSchwartzMKNaugleDEFedyBCRowJROyler-McCanceSJThegeneticnetworkofgreatersage-grouseRange-wideidentificationofkeystonehubsofconnectivityEcol Evol 2018001ndash19 httpsdoiorg101002ece34056


1emsp |emspINTRODUC TION

Understanding population structure and quantifying genetic con-nectivity are important for guidingongoing conservation and res-torationefforts(CrooksampSanjayan2006)Traditionallypopulationstructure isfirstanalyzedandsubpopulationsdelineatedthenge-netic connectivity among subpopulations is quantified HoweverthisprocessneednotbecompletedintwostagesGeneticnetworkmodelscanbeusedtosimultaneouslygainanunderstandingofpop-ulationstructureandtoquantifygeneticconnectivityamongpopu-lationsinnaturalsystems(BunnUrbanampKeitt2000Dyer2007DyerampNason2004)

Geneticnetworksareconstructedofcomponentscallednodesand edges where nodes may represent populations and edgesrepresent genetic connectivity among nodes (Sallaberry Zaidi ampMelanccedilon 2013) Each node can be weighted by the genetic di-versitywithin thenodesandeachedgeby thegeneticcovarianceamonglocalpopulations(Bunnetal2000)Theoverallstructureofthenetworkprovidesameansbywhichtoranktheimportanceofhoweachcomponentcontributes tomaintainingnetworkconnec-tivity(JacobyampFreeman2016)OnecanthinkofnetworkstructureintermsofthecommercialairlinemodelInsuchmodelsnodesandedgesareknownasarenodesofhighandlowconnectivity(hence-forthhubnodesandspokenodesrespectively)Intheairlineindus-tryhubnodesarestrategically selected tomaximizeefficiencyofairtrafficwhilespokenodesareselectedbasedonlimitedneedforservicesForwildlifepopulationswherepopulationsserveashubnodesandwherepopulationsserveasspokenodesareunknown

Qualifyinggeneticnetworkstructureandidentifyingnodesthatact ashubscanbevery informative toconservationandmanage-mentofwildlifespecies(GarrowayBowmanCarrampWilson2008LookingbillGardner FerrariampKeller 2010)Knowledgeofwhichnodesareconnectedtooneanotherandwhichnodes rankhighlyin network centrality can facilitate prioritization for management(JacobyampFreeman2016)Priornetworkmodelingofwildlifepop-ulationshaveshownthatwhichnodesfunctionashubsandwhichfunction as spokes is not intuitive (Bunn etal 2000 GarrowayBowmanampWilson 2011Garroway etal 2008 Koen BowmanampWilson2015)Onemightexpectanodersquosproximitytothecen-terofthespeciesrsquorangewouldinfluencethatnodersquosimportancetoconnectivity where centrally located nodes have greater geneticexchangethanperipheralnodesHowever ithasbeenshownthatpopulationsat theperipheryofaspeciesrsquo rangecanactascriticalhub nodes connecting populations across the network and thatpopulationslocatedtowardthecenteroftherangedonotnecessar-ilyfunctionashubnodes(Bunnetal2000)

Emergentpropertiesofgeneticnetworkscanbeusedto iden-tify hub nodes and spoke nodes and the sensitivity of the entirenetwork to the lossofconnectivity (DunneWilliamsampMartinez2002)Therearethreecommonnetworkstructures(1)single-scale(ldquoregularrdquo) (2) broad-scale (ldquorandomrdquo) and (3) small-worldmdasha sub-setofwhichareknownasscale-free(AmaralScalaBartheacuteleacutemyampStanley2000Bray2003)Regularnetworksarehighlystructured

suchthatproximalnodestendtobelinkedtoeachotherwhiledis-tantnodestendnottobelinkedastructurecomparabletotheiso-lationbydistancepatterncommonlydiscovered in thepopulationgenetics literature (eg a stepping-stonemodelWright 1943) Ina regular geneticnetwork genetic connectivity isbetweenneigh-boring nodes and nodes separated by a greater number of edgeswill bemore isolated from one another In regular networks hubnodesarenonexistentasallnodesareequallyconnectedRandomnetworks are unstructured such that proximity of nodes is irrele-vanttowhethernodesareconnectedornotandtothestrengthofconnectionsastructuremostsimilartothetheoreticalislandmodelfirst proposed byWright (1931) and analogous to the populationgeneticconceptofpanmixiaInarandomgeneticnetworkgeneticconnectivity is unencumbered across the entire network becausethenumberofstepsbetweenanytwonodesisrelativelysmallsuchthatcloseanddistantnodeshaveequalchancesofbeinglinkedInrandomnetworkstherearenohubnodesbutthereexistthorough-faresthroughthenetworkthat fosterquicktransitamonganysetof nodes In contrast small-world networks are composedof fewhighlyconnectednodes(hubnodes)andagreaternumberofmoreisolated nodes (spoke nodes) much like the hub-and-nodemodelcharacteristicofthefamiliarcommercialairlinemodelMostnodescanbereachedfromeveryothernodebyasmallnumberofstepsoftenroutedthroughcentralhubnodeswhichfosterconnectivityamongthespokenodesRedundancyisanimportantcharacteristicof small-worldnetworks In small-worldgeneticnetworksgeneticconnectivityisgreatestamongnearestneighbornodesbutgeneticconnectivitycanexistbetweenanytwonodesbyasmallnumberofstepsthroughhubnodeswhicharenodesatwhichgeneticconnec-tivityisconcentratedsuchthatthesenodesservetoconnectotherdistalnodes(alsoknownasspokenodes)Anextremeformofsmall-worldnetworksisscale-freenetworksInscale-freenetworksthereis lessredundancy in internodeconnectionsandgreatercentralityforthehubnodes

Withinanynetworkrsquos structure individualnode importance tonetworkconnectivitycanbequantifiedbycentralityindicesThereare several centrality indices eachofwhich quantifies the impor-tanceofanodetonetworkconnectivityinadifferentway(Table1)Some centrality indices rank nodes based on local connectivityand some based on network-wide connectivity Therefore thesefunction-valued centrality indices can be easily transformed intonode-specificrankingsandtheserankingscanbeusedtoprioritizeconservation(JacobyampFreeman2016)

The greater sage-grouse (Centrocercus urophasianus hereaftersage-grouseFigure1)isalekkinggallinaceousbirdofconservationconcernanindicatorspeciesforsagebrush(Artemisiaspp)commu-nities(RowlandWisdomSuringampMeinke2006)andanindicatorspeciesforlandscape-scaleconnectivityacrossthewesternUnitedStatesandsouthernCanada(Aldridgeetal2008)Sage-grouseonceoccupied over 12millionkm2 (Edminster 1954 Schroeder etal2004)Thespeciesnowoccupies lessthan067millionkm2across11 western states and two Canadian provinces (Patterson 1952Schroederetal2004)mdash56ofitsrangecomparedtopre-Western

































2emsp |emspMETHODS











CA 53 14 6

CAN(SASK) 6 2 1

CO 679 106 38

ID 988 281 80

MT 1881 358 130

ND 7 2 1

NV 430 116 45

OR 296 52 31

SD 75 15 6

UT 607 114 44

WY 902 120 76




















3emsp |emspRESULTS






36

40

44

48


StateCA

CAN (SASK)

CO

ID

MT

ND

NV

OR

SD

UT

WY

(a)

(b)









36

40

44

48




Closeness

Eigenvector

Strength

Bullwhacker-Dog

Big Horn Lake

Crazy Woman

Upper Green-Slate


Upper Green

Lake Abert

Upper Malheur

Crowley LakeFremont

Middle Snake-Succor

CJ Strike Reservoir

Upper Snake-Rock

Lake Walcott

Provo

Upper Bear


Little Snake








(a)




159times10minus4





(b)


Resampled networks














0

30

60

90


Num

ber o

f Nod

es

0

20

40

60


Num

ber o

f Nod

es

0

10

20

30

25 50 75 100

Degree

Num

ber o

f Nod

es

0

20

40

60

000008 000012 000016Closeness

Num

ber o

f Nod

es

(a) (b)

(c) (d)






0

10

20

30

000 025 075 100050

Eigenvector

Num

ber o

f Nod

es

0

10

20

30

300 900600

Node Strength

Num

ber o

f Nod

es

0

1000

2000

3000


Num

ber o

f Edg

es

(e)

(g)

(f)











AH

eBe

twee

nnes

sCl

osen

ess

Clus

terin

g co

effic

ient

Eige

nvec

tor

Stre

ngth

Sam

ples

in

clus

ter

Ove

rall

mea

n pe

ak

mal

e co

unt

Rang

e ce

ntra

lity

(inve

rse)

A―

000

00

000

000

00

000

000

00

000

000

00

002

000

0

He

087

2―

000

00

000

000

00

000

000

00

000

002

80

000

Betweenness

0706

0565

―0

000

074

90

000

000

00

000

002

80237

Closeness

0670

0595

091

4―

0380

000

80

007

000

00046

0005

Clustering

coefficient

0341

027

8minus0015

minus0041

―0

000

000

00

000

0314

042

9

Eigenvector

minus0515

minus0307

minus0280

minus0124

minus0499

―0

000

000

00003

000

0

Strength

minus0551

minus0359

minus0281

minus0125

minus0567

0983

―0

000

0006

000

2

Samplesincluster

0860

0602

0665

0547

0340

minus0577

minus0613

―0

021

0310

Overallmeanpeak

malecount

minus0157

minus0110

minus0110

minus0100

minus0051

014

90137

minus0116

―0

449

Rangecentrality

(inverse)

minus0234

minus0388

minus0055

minus0131

0037

minus0206

minus0147

minus0048

0038

―



0

500

1000

1500


Bet

wee

nnes

s

000008

000012

000016


Clo

sene

ss

015

020

025

030

0 20 40 60 80


Clu

ster

ing

Coe

ffici

ent

025

050

075

100

0 20 40 60 80


Eig

enve

ctor

300

600

900


Nod

e S

treng

th

(a) (b)

(d)(c)

(e)











36

40

44

48



Closeness


Eigenvector

StrengthLake Abert

Crowley Lake

Bullwhacker-Dog

Middle Musselshell

Upper Malheur

Middle Snake-Succor

CJ Strike Reservoir

Lake Walcott

Upper Bear

Fremont

Upper Green-Slate


Crazy Woman

Big Horn Lake

Price

ProvoLower Weber



Ruby


Shoshone






















ACKNOWLEDG MENTS



Nonedeclared






R E FE R E N C E S























































































































2emsp |emspMETHODS











CA 53 14 6

CAN(SASK) 6 2 1

CO 679 106 38

ID 988 281 80

MT 1881 358 130

ND 7 2 1

NV 430 116 45

OR 296 52 31

SD 75 15 6

UT 607 114 44

WY 902 120 76




















3emsp |emspRESULTS






36

40

44

48


StateCA

CAN (SASK)

CO

ID

MT

ND

NV

OR

SD

UT

WY

(a)

(b)









36

40

44

48




Closeness

Eigenvector

Strength

Bullwhacker-Dog

Big Horn Lake

Crazy Woman

Upper Green-Slate


Upper Green

Lake Abert

Upper Malheur

Crowley LakeFremont

Middle Snake-Succor

CJ Strike Reservoir

Upper Snake-Rock

Lake Walcott

Provo

Upper Bear


Little Snake








(a)




159times10minus4





(b)


Resampled networks














0

30

60

90


Num

ber o

f Nod

es

0

20

40

60


Num

ber o

f Nod

es

0

10

20

30

25 50 75 100

Degree

Num

ber o

f Nod

es

0

20

40

60

000008 000012 000016Closeness

Num

ber o

f Nod

es

(a) (b)

(c) (d)






0

10

20

30

000 025 075 100050

Eigenvector

Num

ber o

f Nod

es

0

10

20

30

300 900600

Node Strength

Num

ber o

f Nod

es

0

1000

2000

3000


Num

ber o

f Edg

es

(e)

(g)

(f)











AH

eBe

twee

nnes

sCl

osen

ess

Clus

terin

g co

effic

ient

Eige

nvec

tor

Stre

ngth

Sam

ples

in

clus

ter

Ove

rall

mea

n pe

ak

mal

e co

unt

Rang

e ce

ntra

lity

(inve

rse)

A―

000

00

000

000

00

000

000

00

000

000

00

002

000

0

He

087

2―

000

00

000

000

00

000

000

00

000

002

80

000

Betweenness

0706

0565

―0

000

074

90

000

000

00

000

002

80237

Closeness

0670

0595

091

4―

0380

000

80

007

000

00046

0005

Clustering

coefficient

0341

027

8minus0015

minus0041

―0

000

000

00

000

0314

042

9

Eigenvector

minus0515

minus0307

minus0280

minus0124

minus0499

―0

000

000

00003

000

0

Strength

minus0551

minus0359

minus0281

minus0125

minus0567

0983

―0

000

0006

000

2

Samplesincluster

0860

0602

0665

0547

0340

minus0577

minus0613

―0

021

0310

Overallmeanpeak

malecount

minus0157

minus0110

minus0110

minus0100

minus0051

014

90137

minus0116

―0

449

Rangecentrality

(inverse)

minus0234

minus0388

minus0055

minus0131

0037

minus0206

minus0147

minus0048

0038

―



0

500

1000

1500


Bet

wee

nnes

s

000008

000012

000016


Clo

sene

ss

015

020

025

030

0 20 40 60 80


Clu

ster

ing

Coe

ffici

ent

025

050

075

100

0 20 40 60 80


Eig

enve

ctor

300

600

900


Nod

e S

treng

th

(a) (b)

(d)(c)

(e)











36

40

44

48



Closeness


Eigenvector

StrengthLake Abert

Crowley Lake

Bullwhacker-Dog

Middle Musselshell

Upper Malheur

Middle Snake-Succor

CJ Strike Reservoir

Lake Walcott

Upper Bear

Fremont

Upper Green-Slate


Crazy Woman

Big Horn Lake

Price

ProvoLower Weber



Ruby


Shoshone






















ACKNOWLEDG MENTS



Nonedeclared






R E FE R E N C E S










































































































3emsp |emspRESULTS






36

40

44

48


StateCA

CAN (SASK)

CO

ID

MT

ND

NV

OR

SD

UT

WY

(a)

(b)









36

40

44

48




Closeness

Eigenvector

Strength

Bullwhacker-Dog

Big Horn Lake

Crazy Woman

Upper Green-Slate


Upper Green

Lake Abert

Upper Malheur

Crowley LakeFremont

Middle Snake-Succor

CJ Strike Reservoir

Upper Snake-Rock

Lake Walcott

Provo

Upper Bear


Little Snake








(a)




159times10minus4





(b)


Resampled networks














0

30

60

90


Num

ber o

f Nod

es

0

20

40

60


Num

ber o

f Nod

es

0

10

20

30

25 50 75 100

Degree

Num

ber o

f Nod

es

0

20

40

60

000008 000012 000016Closeness

Num

ber o

f Nod

es

(a) (b)

(c) (d)






0

10

20

30

000 025 075 100050

Eigenvector

Num

ber o

f Nod

es

0

10

20

30

300 900600

Node Strength

Num

ber o

f Nod

es

0

1000

2000

3000


Num

ber o

f Edg

es

(e)

(g)

(f)











AH

eBe

twee

nnes

sCl

osen

ess

Clus

terin

g co

effic

ient

Eige

nvec

tor

Stre

ngth

Sam

ples

in

clus

ter

Ove

rall

mea

n pe

ak

mal

e co

unt

Rang

e ce

ntra

lity

(inve

rse)

A―

000

00

000

000

00

000

000

00

000

000

00

002

000

0

He

087

2―

000

00

000

000

00

000

000

00

000

002

80

000

Betweenness

0706

0565

―0

000

074

90

000

000

00

000

002

80237

Closeness

0670

0595

091

4―

0380

000

80

007

000

00046

0005

Clustering

coefficient

0341

027

8minus0015

minus0041

―0

000

000

00

000

0314

042

9

Eigenvector

minus0515

minus0307

minus0280

minus0124

minus0499

―0

000

000

00003

000

0

Strength

minus0551

minus0359

minus0281

minus0125

minus0567

0983

―0

000

0006

000

2

Samplesincluster

0860

0602

0665

0547

0340

minus0577

minus0613

―0

021

0310

Overallmeanpeak

malecount

minus0157

minus0110

minus0110

minus0100

minus0051

014

90137

minus0116

―0

449

Rangecentrality

(inverse)

minus0234

minus0388

minus0055

minus0131

0037

minus0206

minus0147

minus0048

0038

―



0

500

1000

1500


Bet

wee

nnes

s

000008

000012

000016


Clo

sene

ss

015

020

025

030

0 20 40 60 80


Clu

ster

ing

Coe

ffici

ent

025

050

075

100

0 20 40 60 80


Eig

enve

ctor

300

600

900


Nod

e S

treng

th

(a) (b)

(d)(c)

(e)











36

40

44

48



Closeness


Eigenvector

StrengthLake Abert

Crowley Lake

Bullwhacker-Dog

Middle Musselshell

Upper Malheur

Middle Snake-Succor

CJ Strike Reservoir

Lake Walcott

Upper Bear

Fremont

Upper Green-Slate


Crazy Woman

Big Horn Lake

Price

ProvoLower Weber



Ruby


Shoshone






















ACKNOWLEDG MENTS



Nonedeclared






R E FE R E N C E S
























































































36

40

44

48


StateCA

CAN (SASK)

CO

ID

MT

ND

NV

OR

SD

UT

WY

(a)

(b)









36

40

44

48




Closeness

Eigenvector

Strength

Bullwhacker-Dog

Big Horn Lake

Crazy Woman

Upper Green-Slate


Upper Green

Lake Abert

Upper Malheur

Crowley LakeFremont

Middle Snake-Succor

CJ Strike Reservoir

Upper Snake-Rock

Lake Walcott

Provo

Upper Bear


Little Snake








(a)




159times10minus4





(b)


Resampled networks














0

30

60

90


Num

ber o

f Nod

es

0

20

40

60


Num

ber o

f Nod

es

0

10

20

30

25 50 75 100

Degree

Num

ber o

f Nod

es

0

20

40

60

000008 000012 000016Closeness

Num

ber o

f Nod

es

(a) (b)

(c) (d)






0

10

20

30

000 025 075 100050

Eigenvector

Num

ber o

f Nod

es

0

10

20

30

300 900600

Node Strength

Num

ber o

f Nod

es

0

1000

2000

3000


Num

ber o

f Edg

es

(e)

(g)

(f)











AH

eBe

twee

nnes

sCl

osen

ess

Clus

terin

g co

effic

ient

Eige

nvec

tor

Stre

ngth

Sam

ples

in

clus

ter

Ove

rall

mea

n pe

ak

mal

e co

unt

Rang

e ce

ntra

lity

(inve

rse)

A―

000

00

000

000

00

000

000

00

000

000

00

002

000

0

He

087

2―

000

00

000

000

00

000

000

00

000

002

80

000

Betweenness

0706

0565

―0

000

074

90

000

000

00

000

002

80237

Closeness

0670

0595

091

4―

0380

000

80

007

000

00046

0005

Clustering

coefficient

0341

027

8minus0015

minus0041

―0

000

000

00

000

0314

042

9

Eigenvector

minus0515

minus0307

minus0280

minus0124

minus0499

―0

000

000

00003

000

0

Strength

minus0551

minus0359

minus0281

minus0125

minus0567

0983

―0

000

0006

000

2

Samplesincluster

0860

0602

0665

0547

0340

minus0577

minus0613

―0

021

0310

Overallmeanpeak

malecount

minus0157

minus0110

minus0110

minus0100

minus0051

014

90137

minus0116

―0

449

Rangecentrality

(inverse)

minus0234

minus0388

minus0055

minus0131

0037

minus0206

minus0147

minus0048

0038

―



0

500

1000

1500


Bet

wee

nnes

s

000008

000012

000016


Clo

sene

ss

015

020

025

030

0 20 40 60 80


Clu

ster

ing

Coe

ffici

ent

025

050

075

100

0 20 40 60 80


Eig

enve

ctor

300

600

900


Nod

e S

treng

th

(a) (b)

(d)(c)

(e)











36

40

44

48



Closeness


Eigenvector

StrengthLake Abert

Crowley Lake

Bullwhacker-Dog

Middle Musselshell

Upper Malheur

Middle Snake-Succor

CJ Strike Reservoir

Lake Walcott

Upper Bear

Fremont

Upper Green-Slate


Crazy Woman

Big Horn Lake

Price

ProvoLower Weber



Ruby


Shoshone






















ACKNOWLEDG MENTS



Nonedeclared






R E FE R E N C E S































































































36

40

44

48




Closeness

Eigenvector

Strength

Bullwhacker-Dog

Big Horn Lake

Crazy Woman

Upper Green-Slate


Upper Green

Lake Abert

Upper Malheur

Crowley LakeFremont

Middle Snake-Succor

CJ Strike Reservoir

Upper Snake-Rock

Lake Walcott

Provo

Upper Bear


Little Snake








(a)




159times10minus4





(b)


Resampled networks














0

30

60

90


Num

ber o

f Nod

es

0

20

40

60


Num

ber o

f Nod

es

0

10

20

30

25 50 75 100

Degree

Num

ber o

f Nod

es

0

20

40

60

000008 000012 000016Closeness

Num

ber o

f Nod

es

(a) (b)

(c) (d)






0

10

20

30

000 025 075 100050

Eigenvector

Num

ber o

f Nod

es

0

10

20

30

300 900600

Node Strength

Num

ber o

f Nod

es

0

1000

2000

3000


Num

ber o

f Edg

es

(e)

(g)

(f)











AH

eBe

twee

nnes

sCl

osen

ess

Clus

terin

g co

effic

ient

Eige

nvec

tor

Stre

ngth

Sam

ples

in

clus

ter

Ove

rall

mea

n pe

ak

mal

e co

unt

Rang

e ce

ntra

lity

(inve

rse)

A―

000

00

000

000

00

000

000

00

000

000

00

002

000

0

He

087

2―

000

00

000

000

00

000

000

00

000

002

80

000

Betweenness

0706

0565

―0

000

074

90

000

000

00

000

002

80237

Closeness

0670

0595

091

4―

0380

000

80

007

000

00046

0005

Clustering

coefficient

0341

027

8minus0015

minus0041

―0

000

000

00

000

0314

042

9

Eigenvector

minus0515

minus0307

minus0280

minus0124

minus0499

―0

000

000

00003

000

0

Strength

minus0551

minus0359

minus0281

minus0125

minus0567

0983

―0

000

0006

000

2

Samplesincluster

0860

0602

0665

0547

0340

minus0577

minus0613

―0

021

0310

Overallmeanpeak

malecount

minus0157

minus0110

minus0110

minus0100

minus0051

014

90137

minus0116

―0

449

Rangecentrality

(inverse)

minus0234

minus0388

minus0055

minus0131

0037

minus0206

minus0147

minus0048

0038

―



0

500

1000

1500


Bet

wee

nnes

s

000008

000012

000016


Clo

sene

ss

015

020

025

030

0 20 40 60 80


Clu

ster

ing

Coe

ffici

ent

025

050

075

100

0 20 40 60 80


Eig

enve

ctor

300

600

900


Nod

e S

treng

th

(a) (b)

(d)(c)

(e)











36

40

44

48



Closeness


Eigenvector

StrengthLake Abert

Crowley Lake

Bullwhacker-Dog

Middle Musselshell

Upper Malheur

Middle Snake-Succor

CJ Strike Reservoir

Lake Walcott

Upper Bear

Fremont

Upper Green-Slate


Crazy Woman

Big Horn Lake

Price

ProvoLower Weber



Ruby


Shoshone






















ACKNOWLEDG MENTS



Nonedeclared






R E FE R E N C E S






























































































(a)




159times10minus4





(b)


Resampled networks














0

30

60

90


Num

ber o

f Nod

es

0

20

40

60


Num

ber o

f Nod

es

0

10

20

30

25 50 75 100

Degree

Num

ber o

f Nod

es

0

20

40

60

000008 000012 000016Closeness

Num

ber o

f Nod

es

(a) (b)

(c) (d)






0

10

20

30

000 025 075 100050

Eigenvector

Num

ber o

f Nod

es

0

10

20

30

300 900600

Node Strength

Num

ber o

f Nod

es

0

1000

2000

3000


Num

ber o

f Edg

es

(e)

(g)

(f)











AH

eBe

twee

nnes

sCl

osen

ess

Clus

terin

g co

effic

ient

Eige

nvec

tor

Stre

ngth

Sam

ples

in

clus

ter

Ove

rall

mea

n pe

ak

mal

e co

unt

Rang

e ce

ntra

lity

(inve

rse)

A―

000

00

000

000

00

000

000

00

000

000

00

002

000

0

He

087

2―

000

00

000

000

00

000

000

00

000

002

80

000

Betweenness

0706

0565

―0

000

074

90

000

000

00

000

002

80237

Closeness

0670

0595

091

4―

0380

000

80

007

000

00046

0005

Clustering

coefficient

0341

027

8minus0015

minus0041

―0

000

000

00

000

0314

042

9

Eigenvector

minus0515

minus0307

minus0280

minus0124

minus0499

―0

000

000

00003

000

0

Strength

minus0551

minus0359

minus0281

minus0125

minus0567

0983

―0

000

0006

000

2

Samplesincluster

0860

0602

0665

0547

0340

minus0577

minus0613

―0

021

0310

Overallmeanpeak

malecount

minus0157

minus0110

minus0110

minus0100

minus0051

014

90137

minus0116

―0

449

Rangecentrality

(inverse)

minus0234

minus0388

minus0055

minus0131

0037

minus0206

minus0147

minus0048

0038

―



0

500

1000

1500


Bet

wee

nnes

s

000008

000012

000016


Clo

sene

ss

015

020

025

030

0 20 40 60 80


Clu

ster

ing

Coe

ffici

ent

025

050

075

100

0 20 40 60 80


Eig

enve

ctor

300

600

900


Nod

e S

treng

th

(a) (b)

(d)(c)

(e)











36

40

44

48



Closeness


Eigenvector

StrengthLake Abert

Crowley Lake

Bullwhacker-Dog

Middle Musselshell

Upper Malheur

Middle Snake-Succor

CJ Strike Reservoir

Lake Walcott

Upper Bear

Fremont

Upper Green-Slate


Crazy Woman

Big Horn Lake

Price

ProvoLower Weber



Ruby


Shoshone






















ACKNOWLEDG MENTS



Nonedeclared






R E FE R E N C E S




























































































0

30

60

90


Num

ber o

f Nod

es

0

20

40

60


Num

ber o

f Nod

es

0

10

20

30

25 50 75 100

Degree

Num

ber o

f Nod

es

0

20

40

60

000008 000012 000016Closeness

Num

ber o

f Nod

es

(a) (b)

(c) (d)






0

10

20

30

000 025 075 100050

Eigenvector

Num

ber o

f Nod

es

0

10

20

30

300 900600

Node Strength

Num

ber o

f Nod

es

0

1000

2000

3000


Num

ber o

f Edg

es

(e)

(g)

(f)











AH

eBe

twee

nnes

sCl

osen

ess

Clus

terin

g co

effic

ient

Eige

nvec

tor

Stre

ngth

Sam

ples

in

clus

ter

Ove

rall

mea

n pe

ak

mal

e co

unt

Rang

e ce

ntra

lity

(inve

rse)

A―

000

00

000

000

00

000

000

00

000

000

00

002

000

0

He

087

2―

000

00

000

000

00

000

000

00

000

002

80

000

Betweenness

0706

0565

―0

000

074

90

000

000

00

000

002

80237

Closeness

0670

0595

091

4―

0380

000

80

007

000

00046

0005

Clustering

coefficient

0341

027

8minus0015

minus0041

―0

000

000

00

000

0314

042

9

Eigenvector

minus0515

minus0307

minus0280

minus0124

minus0499

―0

000

000

00003

000

0

Strength

minus0551

minus0359

minus0281

minus0125

minus0567

0983

―0

000

0006

000

2

Samplesincluster

0860

0602

0665

0547

0340

minus0577

minus0613

―0

021

0310

Overallmeanpeak

malecount

minus0157

minus0110

minus0110

minus0100

minus0051

014

90137

minus0116

―0

449

Rangecentrality

(inverse)

minus0234

minus0388

minus0055

minus0131

0037

minus0206

minus0147

minus0048

0038

―



0

500

1000

1500


Bet

wee

nnes

s

000008

000012

000016


Clo

sene

ss

015

020

025

030

0 20 40 60 80


Clu

ster

ing

Coe

ffici

ent

025

050

075

100

0 20 40 60 80


Eig

enve

ctor

300

600

900


Nod

e S

treng

th

(a) (b)

(d)(c)

(e)











36

40

44

48



Closeness


Eigenvector

StrengthLake Abert

Crowley Lake

Bullwhacker-Dog

Middle Musselshell

Upper Malheur

Middle Snake-Succor

CJ Strike Reservoir

Lake Walcott

Upper Bear

Fremont

Upper Green-Slate


Crazy Woman

Big Horn Lake

Price

ProvoLower Weber



Ruby


Shoshone






















ACKNOWLEDG MENTS



Nonedeclared






R E FE R E N C E S




























































































0

10

20

30

000 025 075 100050

Eigenvector

Num

ber o

f Nod

es

0

10

20

30

300 900600

Node Strength

Num

ber o

f Nod

es

0

1000

2000

3000


Num

ber o

f Edg

es

(e)

(g)

(f)











AH

eBe

twee

nnes

sCl

osen

ess

Clus

terin

g co

effic

ient

Eige

nvec

tor

Stre

ngth

Sam

ples

in

clus

ter

Ove

rall

mea

n pe

ak

mal

e co

unt

Rang

e ce

ntra

lity

(inve

rse)

A―

000

00

000

000

00

000

000

00

000

000

00

002

000

0

He

087

2―

000

00

000

000

00

000

000

00

000

002

80

000

Betweenness

0706

0565

―0

000

074

90

000

000

00

000

002

80237

Closeness

0670

0595

091

4―

0380

000

80

007

000

00046

0005

Clustering

coefficient

0341

027

8minus0015

minus0041

―0

000

000

00

000

0314

042

9

Eigenvector

minus0515

minus0307

minus0280

minus0124

minus0499

―0

000

000

00003

000

0

Strength

minus0551

minus0359

minus0281

minus0125

minus0567

0983

―0

000

0006

000

2

Samplesincluster

0860

0602

0665

0547

0340

minus0577

minus0613

―0

021

0310

Overallmeanpeak

malecount

minus0157

minus0110

minus0110

minus0100

minus0051

014

90137

minus0116

―0

449

Rangecentrality

(inverse)

minus0234

minus0388

minus0055

minus0131

0037

minus0206

minus0147

minus0048

0038

―



0

500

1000

1500


Bet

wee

nnes

s

000008

000012

000016


Clo

sene

ss

015

020

025

030

0 20 40 60 80


Clu

ster

ing

Coe

ffici

ent

025

050

075

100

0 20 40 60 80


Eig

enve

ctor

300

600

900


Nod

e S

treng

th

(a) (b)

(d)(c)

(e)











36

40

44

48



Closeness


Eigenvector

StrengthLake Abert

Crowley Lake

Bullwhacker-Dog

Middle Musselshell

Upper Malheur

Middle Snake-Succor

CJ Strike Reservoir

Lake Walcott

Upper Bear

Fremont

Upper Green-Slate


Crazy Woman

Big Horn Lake

Price

ProvoLower Weber



Ruby


Shoshone






















ACKNOWLEDG MENTS



Nonedeclared






R E FE R E N C E S
































































































AH

eBe

twee

nnes

sCl

osen

ess

Clus

terin

g co

effic

ient

Eige

nvec

tor

Stre

ngth

Sam

ples

in

clus

ter

Ove

rall

mea

n pe

ak

mal

e co

unt

Rang

e ce

ntra

lity

(inve

rse)

A―

000

00

000

000

00

000

000

00

000

000

00

002

000

0

He

087

2―

000

00

000

000

00

000

000

00

000

002

80

000

Betweenness

0706

0565

―0

000

074

90

000

000

00

000

002

80237

Closeness

0670

0595

091

4―

0380

000

80

007

000

00046

0005

Clustering

coefficient

0341

027

8minus0015

minus0041

―0

000

000

00

000

0314

042

9

Eigenvector

minus0515

minus0307

minus0280

minus0124

minus0499

―0

000

000

00003

000

0

Strength

minus0551

minus0359

minus0281

minus0125

minus0567

0983

―0

000

0006

000

2

Samplesincluster

0860

0602

0665

0547

0340

minus0577

minus0613

―0

021

0310

Overallmeanpeak

malecount

minus0157

minus0110

minus0110

minus0100

minus0051

014

90137

minus0116

―0

449

Rangecentrality

(inverse)

minus0234

minus0388

minus0055

minus0131

0037

minus0206

minus0147

minus0048

0038

―



0

500

1000

1500


Bet

wee

nnes

s

000008

000012

000016


Clo

sene

ss

015

020

025

030

0 20 40 60 80


Clu

ster

ing

Coe

ffici

ent

025

050

075

100

0 20 40 60 80


Eig

enve

ctor

300

600

900


Nod

e S

treng

th

(a) (b)

(d)(c)

(e)











36

40

44

48



Closeness


Eigenvector

StrengthLake Abert

Crowley Lake

Bullwhacker-Dog

Middle Musselshell

Upper Malheur

Middle Snake-Succor

CJ Strike Reservoir

Lake Walcott

Upper Bear

Fremont

Upper Green-Slate


Crazy Woman

Big Horn Lake

Price

ProvoLower Weber



Ruby


Shoshone






















ACKNOWLEDG MENTS



Nonedeclared






R E FE R E N C E S

























































































0

500

1000

1500


Bet

wee

nnes

s

000008

000012

000016


Clo

sene

ss

015

020

025

030

0 20 40 60 80


Clu

ster

ing

Coe

ffici

ent

025

050

075

100

0 20 40 60 80


Eig

enve

ctor

300

600

900


Nod

e S

treng

th

(a) (b)

(d)(c)

(e)











36

40

44

48



Closeness


Eigenvector

StrengthLake Abert

Crowley Lake

Bullwhacker-Dog

Middle Musselshell

Upper Malheur

Middle Snake-Succor

CJ Strike Reservoir

Lake Walcott

Upper Bear

Fremont

Upper Green-Slate


Crazy Woman

Big Horn Lake

Price

ProvoLower Weber



Ruby


Shoshone






















ACKNOWLEDG MENTS



Nonedeclared






R E FE R E N C E S

































































































36

40

44

48



Closeness


Eigenvector

StrengthLake Abert

Crowley Lake

Bullwhacker-Dog

Middle Musselshell

Upper Malheur

Middle Snake-Succor

CJ Strike Reservoir

Lake Walcott

Upper Bear

Fremont

Upper Green-Slate


Crazy Woman

Big Horn Lake

Price

ProvoLower Weber



Ruby


Shoshone






















ACKNOWLEDG MENTS



Nonedeclared






R E FE R E N C E S












































































































ACKNOWLEDG MENTS



Nonedeclared






R E FE R E N C E S























































































the genetic network of greater sage‐grouse: range‐wide ...5,950 individuals from 1,200 greater...

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