Multilocus phylogeography of the ﬂightless darkling beetle … · 2015. 8. 11. · Caterino, M....

Multilocus phylogeography of the flightless darklingbeetle Nyctoporis carinata (Coleoptera: Tenebrionidae)in the California Floristic Province: deciphering anevolutionary mosaic

MAXI POLIHRONAKIS* and MICHAEL S. CATERINO

Department of Invertebrate Zoology, Santa Barbara Museum of Natural History, 2559 Puesta del SolRoad, Santa Barbara, CA 93105, USA

Received 16 July 2009; accepted for publication 9 September 2009bij_1360 424..444

The California Floristic Province (CFP) is considered a global biodiversity hotspot because of its confluence of highspecies diversity across a wide range of threatened habitats. To understand how biodiversity hotspots such as theCFP maintain and generate diversity, we conducted a phylogeographic analysis of the flightless darkling beetle,Nyctoporis carinata, using multiple genetic markers. Analyses of both nuclear and mitochondrial loci revealed aneast–west genetic break through the Transverse Ranges and high genetic diversity and isolation of the southernSierra Nevada Mountains. Overall, the results obtained suggest that this species has a deep evolutionary historywhose current distribution resulted from migration out of a glacial refugium in the southern Sierra Nevada via theTransverse Ranges. This finding is discussed in light of similar genetic patterns found in other taxa to develop afoundation for understanding the biodiversity patterns of this dynamic area. © 2010 The Linnean Society ofLondon, Biological Journal of the Linnean Society, 2010, 99, 424–444.

ADDITIONAL KEYWORDS: cytochrome oxidase I (COI/CoxI) – guftagu – southern Sierra Nevada.

INTRODUCTION

The California Floristic Province (CFP) is simulta-neously a region of high biodiversity and one in whichdevelopment and other land uses severely threatenparts of the native biota. As such, it has been recog-nized as a globally significant biodiversity hotspotby Conservation International in an effort to raiseawareness and establish its needs as a top conserva-tion priority (Conservation International, 2009). TheCFP comprises approximately 70% of the state ofCalifornia and extends north across the state bound-ary into Oregon and south into the Baja Californiapeninsula. Understanding the range of diversitypatterns of species in this region is essential forpreservation of its delicate ecological systems. Byreconstructing the evolutionary history of species andintegrating this information with geographic data, we

will develop a better understanding of how theseregions accumulate and maintain diversity (Avise,2000). The dynamic geographic history and highhabitat diversity of the CFP are frequently invoked ascauses of increased species diversification and havebeen used to explain phylogenetic patterns acrossmany plant and vertebrate taxa (Calsbeek, Thompson& Richardson, 2003). Recent comparative phylogeo-graphic analyses of the CFP have revealed relativelyconcordant spatial barriers and high genetic diversityrelated to the orogeny of the Transverse Ranges,Tehachapi Mountains, and the Sierra Nevada (Cals-beek et al., 2003; Feldman & Spicer, 2006; Rissleret al., 2006; Chatzimanolis & Caterino, 2007a), andhave used this information to identify diversification‘hotspots’ within the larger CFP ‘hotspot’ (Davis et al.,2008). Although these generalized patterns provide auseful comparative framework for phylogeographicinvestigation of the CFP, fine-scale analyses ofpatterns within species continue to provide the*Corresponding author. E-mail: [email protected]

Biological Journal of the Linnean Society, 2010, 99, 424–444. With 5 figures

© 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 99, 424–444424

foundation for our understanding of how biodiversitypatterns arise in this region because concordance ofgenetic barriers across broad spatial scales amongtaxa does not necessarily imply parallel patterns oflocal adaptation and diversification (Feldman &Spicer, 2006).

In the past decade, the Transverse Ranges havereceived much of the attention of phylogeographers inthe CFP (Calsbeek et al., 2003; Lapointe & Rissler,2005; Chatzimanolis & Caterino, 2007a). Theirunusual orientation, extending from the Californiacoastline inland across southern California, and thediversity of habitats that they encompass have beensuggested as primary contributors to phylogeographiccomplexity in this region. Although this intriguingarea deserves further attention, empirical studiesyielding interesting results from populations in thesouthern Sierra Nevada and Tehachapi Mountainshave also been accumulating (Macey et al., 2001;Segraves & Pellmyr, 2001; Jockusch & Wake, 2002;Matocq, 2002; Feldman & Spicer, 2006; Kuchta &Tan, 2006; Chatzimanolis & Caterino, 2007a, b; Daviset al., 2008; Caterino & Chatzimanolis, 2009). Manyof these studies provide evidence for deep divergencebetween southern Sierra clades and coastal and moresoutherly populations, leading some studies to recog-nize the southern Sierras as a glacial refugium (Rich,Thompson & Fernandez, 2008), although evidence forthis is equivocal (Kuchta & Tan, 2006). Divergencetimes estimated for a Sierra Nevada break rangefrom approximately 2 Mya (Feldman & Spicer, 2006)to 9.9 Mya (Segraves & Pellmyr, 2001), furtheremphasizing that concordant spatial genetic patternsmay not imply concurrent chronology (Feldman &Spicer, 2006), especially when long persisting geo-graphical features are subject to climactic fluctua-tions over an extended period of time. This long anddynamic history coupled with increased habitat com-plexity resulting from the extremely varied topogra-phy of the Sierra Nevada highlight the importance ofthis area as a centre of genetic diversity and lineagediversification in the CFP.

Comparative phylogeography demands a broadperspective, based on many taxa and geneticmarkers. To date, our understanding of regionalpatterns in the CFP is based heavily on both ver-tebrates and mitochondrial DNA. In the presentstudy, we expand both perspectives in a multilocusanalysis of the flightless darkling beetle Nyctoporiscarinata LeConte (Tenebrionidae) throughout itsrange in central and southern California. Previouscomparative analysis of N. carinata using mitochon-drial DNA (mtDNA) (Caterino & Chatzimanolis,2009) revealed several interesting patterns withinrecognized evolutionary hotspots of the CFP (Daviset al., 2008). First, althlough many flightless organ-

isms exhibit a north–south genetic break attributedto the Transverse Ranges (Maldonado, Vila &Wayne, 2001; Calsbeek et al., 2003; Sgariglia &Burns, 2003; Forister, Fordyce & Shapiro, 2004;Feldman & Spicer, 2006; Chatzimanolis & Caterino,2007b), the mtDNA gene tree of N. carinatarevealed a prominent east–west break in the middleof the Transverse Ranges (as also observed in Cali-fornia mountain king snakes: Rodríguez-Robles et al.1999; pond turtles: Spinks & Shaffer, 2005; andSepedophilus rove beetles: Chatzimanolis &Caterino, 2007a). Second, the deepest split in the N.carinata mtDNA gene tree separated the southernSierra Nevada and Tehachapi populations from thecentral and southern California populations, corre-sponding to the Sierran break discussed above. Inaddition, one of two sampling localities in the Teh-achapi Mountains had two highly divergent mtDNAhaplotypes (0.11 uncorrected, 0.25 corrected) withone haplotype sharing a more recent common ances-tor with populations to the south, and the otherbeing more closely related to populations in thenorth. These results suggest that high diversity inthis region may result from mtDNA contact zonebetween formerly isolated populations. A detailedmultilocus analysis investigating these patterns willadd to a growing body of research aiming to under-stand and document the biodiversity of these impor-tant ecological regions.

The present study uses two independently evolvingsequence markers to evaluate phylogeographichypotheses using N. carinata. It has become apparentthat the validity of phylogeographic hypotheses isincreased when biparentally inherited markers fromthe nuclear genome are used in conjunction withuniparentally inherited mitochondrial markers. Theo-retical and empirical studies have documented bothstochastic and deterministic factors that limit theability of mtDNA to accurately represent the historyof a species (Irwin, 2002; Zhang & Hewitt, 2003;Godinho, Crespo & Ferrand, 2008). Most significantly,these include asymmetrical migration rates of malesand females (Jockusch & Wake, 2002; Miller, Haig &Wagner, 2005; Berghoff et al., 2008; Kerth et al., 2008;Ngamprasertwong et al., 2008), genetic drift, and/orlineage sorting (Maddison, 1997), with all of thempotentially resulting in discordance among markers.Multilocus analysis of N. carinata phylogeographyprovides a robust basis to test several hypotheses andto examine the generality of patterns documentedto date. Specifically, we investigate whether bothmarker types support: (1) the east–west geographicbreak through the middle of the Transverse Ranges(sensu Chatzimanolis & Caterino, 2007a); (2) the iso-lated southern Sierra Nevada clade with putativecontact to the Transverse Ranges through the

PHYLOGEOGRAPHY OF N. CARINATA 425


Tehachapi Mountains; (3) designated evolutionaryhotspots within the CFP (Davis et al., 2008); and (4)lineages of cryptic species within N. carinata(Caterino & Chatzimanolis, 2009).

MATERIAL AND METHODSFIELD COLLECTION

Nyctoporis carinata is abundant and widely distrib-uted throughout much of central and southern Cali-fornia, and can be found in detritus material andunder rocks and loose debris. Both sexes of N. cari-nata lack wings and are thus flightless. Specimenswere collected throughout the Transverse Ranges,central and southern Sierra Nevada, and the SantaLucia mountains of the outer coast ranges. Mostspecimens clearly represent the nominal species N.carinata LeConte. We sampled a small number ofspecimens from the central and southern SierraNevada which correspond to Nyctoporis vandykeiBlaisdell, and one specimen from Lake County (northof the San Francisco Bay) representing Nyctoporisaequicollis Eschscholtz. Based on morphology, thelatter appears quite distinct, and was initially treated

as a potential outgroup. The differences between N.vandykei and N. carinata, on the other hand, are veryminor, and preliminary mitochondrial analyses didnot clearly separate these two species; thus, we con-sidered them as probable synonyms in the presentstudy. All specimens were collected directly into 100%ethanol and stored at -70 °C prior to extraction.

Collection localities are grouped into regional ‘popu-lations’ in accordance with previous studies (Fig. 1)(Chatzimanolis & Caterino, 2007a; Caterino & Chat-zimanolis, 2009). These areas correspond to geologi-cally discrete subunits of the mountainous areassurrounding the Transverse Ranges, although bound-aries between a few, particularly in the westernTransverse Ranges, are less pronounced. Most ofthese, excluding the Santa Lucia Mountains to thenorth-west, and the Tehachapi, Breckenridge andSierra Nevada Mountains to the north-east, collec-tively constitute the Transverse Ranges (Fig. 1).

FOCAL GENES

Genes from both the mitochondrial and nucleargenome were targeted in this study to compare infor-

1

2

3

45

6 7 8 910 11

12

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9.1

10.1 10.210.3

11.111.2 11.3

12.1

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4.1

3.13.2

2.1

Figure 1. Map of southern California with population designations and specific collecting localities as coded in theAppendix (i.e. 1.1 = locality one from population one; 1.2 = locality two from population one, etc.) (1, northern Santa LuciaMountains; 2, southern Santa Lucia Mountains; 3, south-western Sierra Nevada; 4, Breckenridge Mountains; 5,Tehachapi Mountains; 6, Santa Ynez Mountains; 7, north-west Transverse Ranges; 8, central Transverse Ranges; 9, SierraPelona; 10, San Gabriel Mountains; 11, San Bernardino Mountains; 12, San Jacinto Mountains; 13, Santa Cruz Island).A few numbers cover multiple proximate populations, indistinguishable at this scale. Populations 6–12 comprise theentire Transverse Range region.

426 M. POLIHRONAKIS and M. S. CATERINO


mation from two independently evolving loci. Thewell-studied mitochondrial protein coding cytochromeoxidase I (COI) gene has been used in a large numberof phylogeographic and phylogenetic analyses, espe-cially among insect and arthropod groups (Caterino,Cho & Sperling, 2000). The nuclear marker used wasfrom an intron in the Guftagu (GFT) gene, identifiedduring a screening of Tribolium introns (M. S.Caterino, M. Polihronakis, I. Ouzounov, unpubl. data)provided by V. Krauss (Krauss et al., 2008). In Droso-phila, the GFT gene is important during the devel-opment of adult morphological structures and isprimarily associated with the external sensoryorgans. The targeted intron is located after the firstregion of coding sequence relative to both Drosophilaand Tribolium annotated genomes.

GENERATING DNA SEQUENCE DATA

Genomic DNA was extracted from forebodies (headand prothorax) of field collected specimens usingDNeasy Tissue extraction kits (Qiagen). Apart fromdissection, extractions were minimally destructive,and chitinous parts were mounted as vouchers afterextraction. Full collection data, including vouchernumbers and corresponding DNA extractions, can beaccessed through the California Beetle Project data-base (http://www.sbnature.org/calbeetles). Amplifica-tion of the latter half of COI was performed usingpreviously described polymerase chain reaction (PCR)protocols and reagents (Caterino & Chatzimanolis,2009) with the primers C-J-2183 and C1-N-3014(Simon et al., 1994). Amplification of the GFT intronrequired a nested PCR reaction with an initial ampli-fication using degenerate primers GFT37F (5′-AAAATG MGN ATH CGN GCN TTT CC-3′) and GFT86R(5′-TCN ACA TAC TTY TCR TCC AT-3′). The nestedamplification was done using an internal speciesspecific forward primer located approximately 35 bpdownstream of the degenerate forward primer(NycaGFT) with the initial reverse primer. PCR prod-ucts were purified using Qiaquick PCR Purificationkits (Qiagen) or ExoSAP-IT® (USB Corp.). Forwardand reverse sequencing reactions and sequence visu-alization were performed by Macrogen, Inc. (Korea).All DNA sequences were edited in GENEIOUS Pro,version 4.5.4 (Biomatters Ltd) and aligned in SE-AL,version 2.0a11 (http://tree.bio.ed.ac.uk/software/seal/).Both genes were easy to align manually; the COI genedid not have any indels, and the GFT intron hadseveral small indels that were accommodated byinserting gaps. Sequence identity was checked byperforming BLAST searches in GenBank (Altschulet al., 1997).

To test for patterns of selection at either locus,Tajima’s D (Tajima, 1989) was estimated in ARLE-

QUIN, version 3.1 (Excoffier, Laval & Schneider,2005). Significance was evaluated by comparing theobserved test statistic to a distribution generatedfrom 1000 permutations of the original data such thatP-values represent the proportion of the distributionthat is less than or equal to the observed value.Recombination in the nuclear gene was assessedusing the PHI (pairwise homoplasy index) statistic(Bruen, Phillipe & Bryant, 2006) implemented usingthe software SPLITSTREE, version 4.10 (Huson,1998; Huson & Bryant, 2006).

ANALYSES OF POPULATION STRUCTURE AND

SPATIAL AUTOCORRELATION

To test for significant genetic breaks across certaingeographic barriers, we performed analysis ofmolecular variance (AMOVA) and spatial autocorre-lation analyses. The degree of population divergenceand haplotype exclusivity was determined using FST

values (which differ from traditional FST valuesbecause they incorporate both overall similarity andhaplotype frequencies; Excoffier, Smouse & Quattro,1992), as calculated in ARLEQUIN. Spatial autocor-relation analyses were performed to examine therelationship between genetic and geographic dis-tance at the individual level, and to comparemarkers for possible differences related to sex-biaseddispersal. These analyses provide a finer-scale evalu-ation of isolation-by-distance (IBD) than matrix cor-relation tests because it is possible to assess thebehavior of the slope of autocorrelation values asthe size of distance classes increase. These wereperformed in GENEALEX, version 6.1 (Peakall &Smouse, 2006), with each marker treated individu-ally as a single population using the even samplesize option with 20 distance classes. Significance ofthe autocorrelation coefficient was assessed using999 permutations of the data in addition to 1000bootstrap replicates.

Of the seven evolutionary hotspots designated byDavis et al. (2008), our samples cover three of them(Santa Lucia Mountains; north-eastern TransverseRanges, Tehachapi and Piute Mountains; and SanBernardino and San Jacinto Mountains). To deter-mine whether genetic diversity within N. carinatawas consistently higher or more divergent in theseareas, we generated a genetic landscape shape foreach of the markers using the software ALLELES INSPACE (AIS) (Miller, 2005). The genetic landscapeinterpolation method implemented in AIS uses aDelaunay triangulation-based connectivity networkfrom X and Y geographic coordinates and assigns realand interpolated genetic distances to the Z-axis toproduce a three dimensional landscape (Miller et al.,2006), which provides a visual representation of



genetic diversity patterns across a sample area.Unlike AMOVA, this method requires no a priorigroup designations. Genetic landscapes were interpo-lated using raw genetic distances and a 50 ¥ 50 gridwith a distance weight value of one. Sensitivity of theanalysis to changes in grid size, weighting parametervalues, and genetic distance measure were evaluated,but resulted in minimal changes to the resultinglandscape plot. We also calculated genetic diversitywithin populations using uncorrected and correcteddistances, as calculated in PAUP* (Swofford, 2002).

PHYLOGENETIC ANALYSIS AND

NETWORK CONSTRUCTION

Relationships among individuals or haplotypes wereinferred using phylogenetic and network methods toobtain both bifurcating and nonbifurcating perspec-tives of the gene lineages. Models of molecular evolu-tion for each individual marker were evaluatedin MRMODELTEST, version 2.3 (Nylander, 2004).According to the Akaike information criterion, the COIdata best fit the HKY + I + G model, and the GFT databest fit the GTR + G model. Gene trees were inferredin MRBAYES, imposing the model specified by AIC,using default priors for all parameters. Branch lengthpriors for each marker were determined through com-parison of branch lengths of maximum likelihood treesgenerated in GARLI, version 0.96 (Zwickl, 2006) tothose obtained in the Bayesian analyses. Haplotypenetworks were built using COMBINE TREES(Cassens, Mardulyn & Milinkovitch, 2005), whichimplements the union of maximum parsimonioustrees approach (UMP), using parsimony trees asinput. For the nuclear data set, parsimony trees weregenerated with gaps coded as a fifth state, using aheuristic search in PAUP* (Swofford, 2002) with treebisection–reconnection branch swapping and 500 rep-licates of random sequence addition, saving 200 treesper replicate. Heuristic search options for the mito-chondrial data set were modified to accommodate thelarge number of trees generated (500 replicates ofrandom sequence addition saving a maximum of 20trees per replicate). Because of the computationaldemands of the COMBINE TREES software, the first1000 trees from the parsimony search were analysedin two sets of 500 trees each. Ten networks wereconstructed for each data set which were then used togenerate a ‘best network’ with the fewest number ofcycles (Cassens et al., 2005). The best networks foreach set of 500 trees were compared to ensure concor-dance when only subsets of trees from the parsimonysearch were used.

Although mitochondrial DNA showed N. aequicollisto be rather divergent from other samples, tentativelysupporting its outgroup status, this was not the case

in GFT, where both haplotypes from this individualwere minimally different from those in many popula-tions of N. carinata. Therefore, we consider thespecies status and relationship of this entity uncer-tain and do not treat it as an outgroup. For thisreason, our trees should be interpreted as unrooted,although ‘midpoint rooting’ (as implemented inPAUP*) is used for presentation.

RESULTSSEQUENCE DATA

A total of 130 and 109 individuals were sequenced forthe COI (826 bp) (Genbank accessions EU37099-EU37190 and GU049332-GU049339) and GFT(497 bp) (Genbank accessions GU049270-GU049331)genes, respectively. Of the 109 samples with GFTsequence, 34 had polymorphic sites (Appendix S1).Individuals with one polymorphic site were phasedmanually and both haplotypes were included in theanalysis. Specimens with two or more polymorphicsites were phased using the software PHASE, version2.1 (Stephens, Smith & Donnely, 2001; Stephens &Scheet, 2005). There were three cases that resulted inambiguous phase calls and so autapomorphic poly-morphic sites for these individuals were coded asmissing data. For all three cases, this resulted in justone parsimony informative polymorphic site thatwas phased manually. Thus, further discussion ofsequences from both genes will refer to haplotypes.

The mean Tajima’s D-values for COI and GFTestimated across all populations were not significant(P = 0.55 and 0.52, respectively). At the populationlevel, the northern Santa Lucia population had asignificant Tajima’s D-value (P = 0.01) for the COIgene, and the north-west Transverse Range popula-tion had a significant Tajima’s D-value (P = 0.05) forthe GFT gene, potentially indicating past selection orpopulation bottlenecks for these two populations.There was no evidence for recombination in the GFTintron according the PHI test (P = 0.80).

The latter half of the COI gene targeted for ampli-fication had 224 variable sites, with 207 of thosebeing parsimony informative. The COI data set had atotal of 100 unique haplotypes with no one haplotypedominating more than 10% of the entire sample. Ofthe 497 bp sequenced of GFT, 53 were variable, with27 of those being parsimony informative. The GFTdata set had a total of 62 unique haplotypes, althoughthe three most common haplotypes dominated 49% ofthe data set. Interestingly, approximately 20% of thehaplotype diversity in the GFT data came from withinthe south-western Sierra population, whereas thispopulation only accounted for 9% of the haplotypediversity of the COI gene.



POPULATION STRUCTURE AND

AUTOCORRELATION ANALYSIS

High differentiation among populations was sup-ported by both markers. All FST values based on theCOI data were significant, except for some of thecomparisons with the Breckenridge Mountains popu-lation where only two individuals were sampled(Table 1, lower diagonal). Pairwise FST values basedon the GFT locus were also almost all significant(Table 1, upper diagonal), with exceptions betweenthe Sierra Pelona and central Transverse Rangepopulations, as well as between the southern SantaLucia and north-west Transverse Range populations.Both of these insignificant values are betweengeographically adjacent areas, suggesting gene flowbetween them, although insufficient sampling cannotbe ruled out as an explanation.

Spatial autocorrelation analyses showed a strongdecline in relatedness with increasing distance

(Fig. 2). A decreasing slope of the spatial autocorre-lation graphs is indicative of an IBD trend (i.e.autocorrelation values decrease as distance classsize increases), whereas significant r-values indicatehigher or lower genetic relatedness than predicted ifindividuals were randomly distributed (Smouse &Peakall, 1999; Peakall, Ruibal & Lindenmayer,2003). COI haplotypes showed significant IBD withdecreasing r-values from 0–165 km. The increasein r-values at approximately 200 km likely reflectsthe close relationship of some individuals fromthe central Transverse range population with indi-viduals from the San Bernardino and San Jacintopopulations. Beyond 218 km, autocorrelation valueswere relatively unchanging and significantly nega-tive. The GFT locus exhibited a very similarpattern, showing a general IBD trend up to appro-ximately 150 km, at which point r-values re-mained negative with some fluctuation. The similarrelationships between genetic and geographic

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Distance (km)

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A)

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Figure 2. Spatial autocorrelation graphs of COI haplotypes (A) and GFT haplotypes (B). Dotted lines represent the 95%confidence interval with respect to the null hypothesis that individuals are randomly distributed.



Tab

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distance between the two genes suggest that dis-persal is not sex-biased.

Comparison of the genetic landscape shapes for thetwo genes provided a visual representation of thegenetic diversity patterns observed over the samplingarea (Fig. 3). Peaks on each landscape shape repre-sent the degree of genetic discontinuity between twolocalities as determined by the Delaunay triangula-tion method (Miller et al., 2006). Thus, when collect-ing localities are geographically close in proximity(short distances between vertices), high peaks repre-

sent areas of high genetic diversity. On the otherhand, when localities are further apart (long linesbetween vertices), high peaks are more appropriatelyinterpreted as genetic barriers. Both markers demon-strated high diversity among the south-westernSierra populations as a result of high genetic diver-gence among several geographically proximate locali-ties, as well as a barrier across the Central Valley.The landscape based on the COI data (Fig. 3B) alsoexhibited genetic discontinuity in the Transverseranges, beginning at the southern tip of the Central

X (Southern edge)

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Figure 3. Landscape shapes from ALLELES IN SPACE analyses. A, connectivity network based on Delaunay triangu-lation overlayed onto map of southern California (the vertex of each triangle represents a collecting locality). B, landscapeinterpolation of COI sequence data. C, landscape interpolation of GFT sequence data. The Z-axis in (B, C) represents thedegree of genetic discontinuity between two localities.



Valley separating the Tehachapis, BreckenridgeMountains, and south-western Sierra populationsfrom the eastern and western Transverse Rangepopulations. The plot of the GFT data (Fig. 3C) had abarrier that was not exhibited by the COI databetween the southern Sierra Nevada and northernTransverse Ranges. Thus, the areas of high geneticdiversity in N. carinata corresponded to the Trans-verse Ranges and southern Sierra hotspots desig-nated by Davis et al. (2008). However, in contrast toDavis et al. (2008) our samples from the Santa Lucia,San Bernardino, and San Jacinto Mountains did notexhibit increased levels of diversity.

PHYLOGENETIC ANALYSIS AND

NETWORK RECONSTRUCTION

mtDNAAnalyses of COI revealed very high levels of divergenceamong haplotypes, especially among regions. Uncor-rected distances were as high as 0.11 uncorrected (0.27corrected), or 0.07 uncorrected (0.11 corrected) if thenominally distinct populations of N. aequicollis and N.vandykei are excluded. The UMP method of networkbuilding resulted in one network but with a largenumber of unsampled nodes. Thus, because of the highhaplotype diversity and non-network-like behavior ofthe COI gene, these data are presented as a phyloge-netic tree only. The COI gene tree showed five mainclades, principally: the western Transverse Ranges(most) + Channel Islands + southern Santa Lucias;eastern, central, and small inclusions of westernTransverse Ranges and Tehachapis; San JacintoMountains (most); northern Santa Lucias; and SierraNevada (including the Breckenridge Mountains) +Tehachapis (most) (Fig. 4). The most strongly sup-ported relationship among the major clades linkedthe eastern and western Transverse Range clades(PP = 1.0). This supported a single lineage covering thebulk of the Transverse Ranges with two exceptions: (1)it did not include the San Jacinto Mountains and (2) itdid include the southern part of the Santa LuciaMountains, which is not considered part of the Trans-verse Ranges. The break between these two majorTransverse Range groups corresponds loosely to theVentura and Cuyama River drainages in the lowerelevations. However, at higher elevations, no suchobvious geographic barrier is observed.

Most populations were predominantly single lin-eages. However, almost all had individuals appearingelsewhere in the tree. The only two monophyleticpopulations were those from the northern SantaLucia Mountains (the north-western part of our sam-pling region) and from Santa Cruz Island. Most indi-viduals from the San Jacinto Mountains population(at the south-eastern extreme) fell out in an isolated

clade, although two San Jacinto individuals (haplo-type N21) were more closely related to a clade ofprimarily San Bernardino (the neighbouring regionto the north) haplotypes. The large clade containingthe remaining eastern Transverse Range samplescontained San Bernardino, San Gabriel, and SierraPelona haplotypes intermingled with minimal sitefidelity. Although the San Bernardino haplotypeswere the most cohesive of these, two of these indi-viduals were more closely related to haplotypes fromelsewhere in this broad region. The eastern Trans-verse Range clade included haplotypes from thecentral Transverse Ranges (the Tecuya Trail localityin the San Emigdio Mountains), as well as onehaplotype from the Tehachapis. Another interestingelement of this larger eastern Transverse Range cladewas a subclade of haplotypes from further west inOjai and the eastern-most Santa Ynez Mountains (theMurrietta Trail locality). Although the area coveredby the eastern Transverse Range clade is more or lesscontiguous, it is the most wide-ranging, with severalextensions beyond its core area. The mainly westernTransverse Range clade included all individuals fromthe north-western Transverse Range and Santa CruzIsland populations, as well as all individuals from thesouthern Santa Lucia Mountains. Most individualsfrom the Santa Ynez Mountains were also included,with the exception of the Murrietta Trail samplesmentioned above.

GFTDivergence among GFT alleles was much lower thanthat seen in mtDNA. Most haplotypes differed by onlyone or a few changes. For this reason, the phylogenetictree based on these sequences was largely unresolved.With such low divergence, the likelihood of directancestor–descendant relationships among sequenceswas high, and we therefore based our inferences onthe UMP network (Fig. 5). Although genetic variationwas generally low, three of the four main groups inthis network were recognized by a high frequency ofidentical haplotypes over broad (mostly) contiguousgeographic regions, along with linked derivativehaplotypes. The most widespread of these was thewestern group (orange), including all haplotypes fromthe northern and southern Santa Lucias, north-western Transverse Ranges, Santa Ynez Mountains,Santa Cruz Island, the central Tranverse Ranges, anda single individual from the San Gabriel Mountains;the central group (green) included most haplotypesfrom the Sierra Pelona, San Gabriel Mountains, Teh-achapi Mountains, and the northern California repre-sentative of N. aequicollis (G58 & G59); the easterngroup (blue) included all haplotypes from the SanJacinto Mountains, all but one from the San Bernar-dino Mountains, one haplotype from the San Gabriel



Mountains, and a disjunct group of haplotypes fromthe Ojai Valley and extreme eastern end of the SantaYnez Mountains. The fourth main group (yellow) rep-resented a diverse clade of related, mostly unique,haplotypes from the southern Sierra Nevada region.

Both the COI and GFT gene trees supported aneast–west break in the Transverse Ranges in theeastern Santa Ynez Mountains/western VenturaCounty area. However, the genetic break between thetwo clades supported by the COI data was positionedslightly west of the break supported by the GFT dataset. In addition, the geographic relationships of popu-lations within each clade were somewhat differentbetween genes in that the COI haplotypes from theeastern group were almost all closely related to hap-lotypes found in the Sierra Pelona and San Gabriel

Mountains, whereas GFT haplotypes in these areaswere more closely allied with those further east, inthe San Bernardino Mountains population. Althoughthe region where these discontinuities are observedmore-or-less coincides with the confluence of severalmajor drainages (principally the Ventura and SantaClara Rivers), it is only approximate, and neithercorresponds precisely to the Santa Clara break foundby Chatzimanolis & Caterino (2007a) in Sepedophilusbeetles, and among other species.

The results from an analysis of the COI and GFTmarkers differed in many other respects. Althoughboth gene trees revealed a few large clades with broadgeographic continuity, the exact regions definedby these clades showed little congruence. The GFTnetwork did not reveal a strong relationship between

Figure 4. A, COI gene tree inferred in MRBAYES (*posterior probability � 0.80). B, corresponding clade designations onsample map. Arrow from the yellow clade refers to the placement of the Nyctoporis aequicollis specimen from thenorthernmost Lake County sampling area (not visible on map).



Figure 5. A, GFT union of maximum parsimonious trees approach network; sizes of boxes within coloured clades scaledto haplotype frequency, numbers in parentheses denote the frequency of the haplotype if > 1 (haplotypes in somehaplotype boxes have different numbers as a result of missing data but are otherwise identical). B, corresponding cladedesignations on sample map. Arrow from the green clade refers to the placement of the Nyctoporis aequicollis specimenfrom the northernmost Lake County sampling area (not visible on map); ‘X’ in population four denotes no nuclear dataare available for this region.



Sierran and Tehachapi populations as seen with COI,instead grouping the Tehachapis strongly with thecentral portion of the Transverse Ranges, the SanGabriel Mountains, and the Sierra Pelona (haplotypeG01 is the most common in all these areas). Sierranpopulations were more closely related to Tehachapiindividuals than to any other group, but were highlydivergent relative to the minimal divergence observedthroughout the remainder of the network. Most indi-viduals from the San Jacinto and San BernardinoMountains shared a GFT haplotype, whereas mostSan Jacinto COI haplotypes were highly divergent.Almost all northern Santa Lucia individuals had aGFT sequence identical to those from the southernSanta Lucias, as well as to most individuals from thenorth-western Transverse Ranges, and Santa YnezMountains, whereas the COI data supported thenorthern Santa Lucia population as being exclusiveand highly divergent. Finally, although the LakeCounty specimen (corresponding to N. aequicollis)differed by only a single mutation in GFT from theeastern Sierra Pelona group, its COI sequence washighly divergent from all other groups.

DISCUSSION

Detailed comparative phylogeographic analysesprovide a means to study how geography influencesthe evolutionary history and biodiversity patterns oforganisms. Within the CFP, there are many geo-graphic barriers that are hypothesized to have apervasive effect on species across different taxonomicgroups. Although evidence supporting this hypothesisis increasing, much of the data are focused on verte-brates, and, until recently, heavily dependent onsingle locus analyses of mitochondrial DNA. Thepresent study aimed to evaluate the phylogeographichistory of a dynamic part of the CFP through analysisof N. carinata, a flightless darkling beetle witha widespread distribution in central and southernCalifornia, using mitochondrial and nuclear genes.

THE PHYLOGEOGRAPHIC HISTORY OF N. CARINATA

Phylogeographic expectations of N. carinata areguided by two main ecological factors. The first istheir flightlessness; we expect populations to be rela-tively isolated as a result of their inability to traverseunsuitable habitat, and for lineage breaks to corre-spond well with major geographic or ecological fea-tures (although these may have varied considerablyover time). Second, we expect similar dispersal abili-ties of males and females and therefore do not expectpopulation structure to be significantly influencedby sex-biased dispersal (Jockusch & Wake, 2002;Matyukhin & Gongalsky, 2007).

The COI sequences of N. carinata were character-ized by high divergence, high haplotype diversity, lowpopulation connectedness, broad geographic cohesive-ness, and a combination of mono- and polyphyleticpopulations. This pattern signifies an old, broadly-distributed species with historically-isolated popula-tions, some of which have come into secondary contactwith subsequent gene flow (Jockusch & Wake, 2002).This was supported by the overall non-monophyly ofpopulations with otherwise high FST values. Interest-ingly, geographically peripheral populations from thesouthern Sierra Nevada, the northern Santa LuciaMountains, and the San Jacinto Mountains were themost isolated and exclusive (with one individualexception in the latter case), whereas more centrallylocated populations showed higher levels of admix-ture. This suggests that, if populations had remainedcompletely isolated, they would have had enough timeto achieve monophyly. Monophyly of the Santa CruzIsland population can also be cited in this respectbecause, although founder effects would be likely toexplain monophyly for this disjunct population, therewas no evidence for a bottleneck based on estimates ofTajima’s D.

The GFT data supported a somewhat different sce-nario. The results obtained based on this markershowed low levels of divergence and low haplotypediversity within and across most populations (with afew common haplotypes shared among, and dominat-ing most populations), but were similar to mitochon-drial results in low population connectedness, andshowed similar patterns of geographic relationshipsamong populations. This overall pattern is consistentwith a series of relatively rapid, stepwise coloniza-tions from one or more ancestral pools of diversityinto more peripheral areas, with historical continuitybetween populations that now appear to be isolated.

East–west Transverse Range breakThe COI gene tree and GFT gene network both sup-ported an east–west genetic break through the Trans-verse Ranges, although they did not completelyoverlap. The location of this break based on the COIgene followed the eastern boundary of the north-westTransverse Ranges but turned west to include thesouthernmost sampling locality from the Santa YnezMountains (Fig. 4B). The location of the genetic breakbased on the GFT network was further east betweenthe central Transverse Ranges and the Sierra Pelona.Although there are no significant geographic barriersthat would explain a genetic break in either of theseregions, there are two other examples of an east–westbreak further east in the Transverse Ranges, in Sepe-dophilus rove beetles (Chatzimanolis & Caterino,2007a) and California mountain king snakes (Lam-propeltis zonata) (Rodríguez-Robles et al. 1999), that



approximately correspond with N. carinata. Increasedsampling of N. carinata in these regions, in additionto further comparative work including other taxa,would help determine whether these genetic breaksare a result of stochastic processes (Irwin, 2002), orthe result of geography.

Isolation of southern Sierra Nevada populationOn the basis of the high diversity of the nuclear genein the southern Sierras, we propose that this popula-tion was ancestral and potentially served as a glacialrefugium (Hewitt, 2000; Rich et al., 2008). Further-more, the genetic data from both the mitochondrialand nuclear markers supported a south and westwarddispersal out of this area with an ancient steppingstone colonization through the Transverse Ranges.Potential geographic barriers reducing southwarddispersal for flightless N. carinata beetles are theKaweah and Kern River drainages (Segraves &Pellmyr, 2001; Feldman & Spicer, 2006). These drain-ages may have served as successive filters to dis-persal, resulting in reduced population sizes duringcolonization as demonstrated by a single derivednuclear haplotype in the Tehachapis (G01), whichlinked the larger western, central, and eastern groupsthroughout the rest of the range. Interestingly, all ofthe main groups in the GFT network, with the excep-tion of the southern Sierra population, were domi-nated by one main haplotype with several derivativehaplotypes that suggest there were subsequent filtersto dispersal east and west out of the TehachapisMountains and Sierra Pelona region.

EVOLUTIONARY HOTSPOTS

Our finding of both high genetic divergence and diver-sity of N. carinata populations from the Sierra andTehachapi Mountains populations is interesting inlight of similar results from an increasing number ofother studies focusing on a wide variety of taxa dis-tributed throughout the CFP (Macey et al., 2001; Seg-raves & Pellmyr, 2001; Jockusch & Wake, 2002;Matocq, 2002; Feldman & Spicer, 2006; Kuchta &Tan, 2006; Chatzimanolis & Caterino, 2007a, b; Daviset al., 2008). In addition to evidence of divergentsouthern Sierran populations, two of these studieswith sufficient taxon sampling also support highergenetic diversity in this region (Kuchta & Tan, 2006;Rich et al., 2008). This contrasts with previous workrevealing higher genetic diversity within southern orcoastal populations (Law & Crespi, 2002; Matocq,2002), suggesting that refugia and/or genetic diversityhotspots have existed in multiple areas, althoughpossibly over different time periods.

The elevated diversity in the southern Sierranregion for both genes clearly supports the contention

that this area represents an evolutionary hotspot.Can these data shed light on any of the underlyingprocesses? The maintenance of high diversity inthese populations supports the hypothesis that thisregion once served as a glacial refugium that poten-tially sourced expansion into coastal and southernpopulations via the Transverse Ranges. However, thispopulation was paraphyletic in the mtDNA tree withrespect to haplotypes from the Tehachapi and Breck-enridge Mountains, but monophyletic and quitedivergent in the GFT network. Non-monophyly ofmtDNA combined with monophyly of the nDNA isconsidered characteristic of differential introgressionpatterns between the two genomes (Chan & Levin,2005; Weisrock et al., 2006; Linnen & Farrell, 2007;Peters et al., 2007; Spinks & Shaffer, 2007; Zhang &Sota, 2007; Gompert et al., 2008), and is suggestive ofsome interesting evolutionary dynamics in the Teh-achapi Mountains.

Conflicting relationships inferred by the nuclearand mitochondrial markers in the Tehachapi Moun-tains are consistent with differential introgressioncombined with several range fluctuations, whichresulted in unique contact zones for each of the twomarkers. The haplotype relationships of the twomarkers from the southern Sierra Nevada suggestthat migration out of this region comprised multipleevents that occurred over an extended period oftime. Under this scenario, an initial populationexpansion southward was followed by subsequentisolation (i.e. population contraction or relativelysimple isolation-by-distance) between populations inthe south-western Sierra, and those in the Breck-enridge and Tehachapi Mountains. The divergencepatterns of haplotypes in this region suggest thatthis isolation lasted long enough for severalpopulation-specific mutations to accumulate in boththe nuclear and mitochondrial genes. To explain thediscrepancy of relationships of the Tehachapi Moun-tains population between the two markers, wehypothesize secondary contact between these iso-lated populations with differential introgression ofmitochondrial genes from the south-western Sierrapopulations south into the Breckenridge and Teh-achapi Mountains populations. This scenario wouldexplain why individuals from the TehachapiMountains had a derived nuclear haplotype and anancestral mitochondrial haplotype. The data aresuggestive of asymmetrical mitochondrial introgres-sion during secondary contact as a result of a lackof derived nuclear or mitochondrial haplotypes inthe south-western Sierra population. We also didnot sample any individuals with an ancestralnuclear haplotype and a derived mitochondrial hap-lotype. Finer-scale sampling would help resolve his-torical connections in this area.



High genetic diversity of N. carinata from thesouthern Sierras and Transverse Ranges corre-sponded to evolutionary hotspots designated by Daviset al. (2008). However, only the southern Sierrasreceived support from both marker types, whereasdiversity in the Transverse Ranges was supported bythe COI data only. Evidence for other evolutionaryhotspots (sensu Davis et al., 2008) within the south-ern CFP is equivocal; other recognized areas forwhich our data might be pertinent (i.e. the centralcoast ranges, the San Bernardino Mountains, andSan Jacinto Valley) showed little above baseline levelsof diversity in either locus.

EVIDENCE FOR CRYPTIC SPECIES?

Although some of the suggested morphological char-acters separating the three species N. carinata, N.vandykei, and N. aequicollis are relatively discern-able, high variation in N. carinata precludes theirconsistent application. Two of the main charactersoriginally cited to distinguish N. vandykei from N.carinata are the former’s comparatively slender, lessrobust form, and less pronounced frontal carina(ridge) on the head (Blaisdell, 1931). However, boththese characters vary considerably within N. cari-nata, such that some specimens are easily diagnosed,whereas others are not. The uncertainty surroundingthe morphological distinctions was not necessarilyclarified by the genetic data. In the COI gene tree,‘N. vandykei’ specimens were paraphyletic withrespect to N. carinata specimens from the Tehachapiand Breckenridge Mountains, whereas, in the GFTnetwork, the ‘N. vandykei’ individuals are monophyl-etic and deeply diverged from the rest of the individu-als sampled (discussed above). The uncertaintysurrounding N. aequicollis is even greater as a resultof its very close ties with populations in the centralportion of the Transverse Ranges according to theGFT gene, at the same time as being quite distinctfrom most other lineages in the COI tree (but groupedwith the south-west Sierra and Tehachapi clade withmidpoint rooting).

Aside from the questionable status of the namedspecies, high divergence among mtDNA haplotypeswas also initially suggestive of additional crypticspecies within N. carinata. Where populations weremonophyletic, average uncorrected distances to neigh-bouring populations exceeded 0.05 in all cases(Caterino & Chatzimanolis, 2009). The high diver-gence of the two most northerly and southerly popu-lations (northern Santa Lucias and San JacintoMountains, respectively), in particular, suggested thatthese lineages represented genealogically distinctunits as a result of allopatric divergence. However, thishypothesis was not supported by the nuclear data,

which revealed that many of the individuals from bothof these populations shared identical haplotypes withneighbouring populations. Mitochondrial studies ofother arthropod taxa in the CFP have found levelsmuch lower than this to correspond with well differ-entiated subspecies (e.g. 0.014 in the yucca mothTegiticula maculata; Segraves & Pellmyr, 2001), andfor comparable divergences to correspond to fully dis-tinct species (0.08 in a species complex of the Califor-nia turret spider, Antrodiaetus riversi; Starrett &Hedin, 2007). In the latter case, nuclear data werefully concordant with the mitochondrial, offering astark contrast to the situation in N. carinata.

GENETIC VARIATION: MITOCHONDRIAL VERSUS

NUCLEAR DATA

It is often assumed that large-scale population-levelprocesses will result in relatively parallel patternsbetween marker types, although this is not alwaysthe case (Ballard, Chernoff & James, 2002; Benschet al., 2006; Weisrock et al., 2006). Thus, the mostdifficult questions for multilocus phylogeography arewhat differences are expected, what differencesexceed expectations, and what are the underlyingcauses in either case (Hudson & Turelli, 2003; Zink &Barrowclough, 2008). Most of the expected differencesbetween mitochondrial and nuclear markers in thepresent study likely arise from two factors: differen-tial mutation rates and uneven effective populationsizes (Forister et al., 2004). In general, the possibilityof recombination in nuclear loci and selection in bothmarker types should also be considered, although wefound no evidence for either of these in our dataset.There are little data on expected mutation rates innuclear introns relative to mitochondrial protein-coding sequences (Zhang & Hewitt, 2003) but thebackground rate should be significantly lower,perhaps one-tenth the mtDNA rate (Hare & Palumbi,2003). The interaction of mutation generation andmaintenance via larger Ne is generally expected toyield lower variation in nuclear haplotypes in a popu-lation sample (Zink & Barrowclough, 2008), althoughjust how much less will be determined by manyadditional factors (Hudson & Turelli, 2003).

The clearest finding supported by both genes wasthe generally low population connectedness. Both FST

and autocorrelation analyses indicated rapid attenu-ation of similarity with distance. This indicates thatthe shared haplotypes or related haplotypes weobserved across populations did not result from recentgene flow. This is further supported by COI relation-ships, where such geographic discordance was mostobvious (San Bernardino haplotypes in the San Jacin-tos; Central Transverse Range haplotypes in the Teh-achapis). In no cases were ‘rogue’ haplotypes identical



to any in their source areas, indicating that wesampled descendants of the original contact, and thatthe observed relationships resulted from eitherancient migration or the retention of ancestral poly-morphism from a more broadly-distributed historicalpopulation. In the GFT network, these particular setsof areas shared common haplotypes but did not shareany of the more recently derived variants within theirrespective groups. This supports the latter scenariosuggesting these populations were broadly connectedin the past.

Further work resolving details of the evolutionaryhistory in this group would continue to solidifysupport for the importance of this region with regardto the unique evolutionary dynamics and speciesdiversity patterns found in the present study. Specifi-cally, we need a more robust outgroup for this species,which may be difficult as a result of the fact that it isthe only genus in its tribe worldwide. However,because of the relatively low variation of the nucleargene in the present study, it may be possible to usespecies in related tribes of Pimeliinae to root the N.carinata nuclear gene tree. Increased taxon samplingin areas of high diversity, such as the southern SierraNevada, and throughout the more peripheral parts ofthe range would also provide further insight intoimportant evolutionary processes in the species andthe region.

ACKNOWLEDGEMENTS

Our field collections were assisted and facilitated bynumerous individuals and agencies. We thank R.Aalbu, S. Chatzimanolis, K. Hopp, P. Jump, S.Mulqueen, S. Russell, P. Russell, A. Short, K. Will, theCalifornia Department of Fish and Game, Los PadresNational Forest, Angeles National Forest, SequoiaNational Forest, San Bernardino National Forest,San Dimas Experimental Forest, Arroyo Hondo Pre-serve (Land Trust for Santa Barbara County), CampCedar Falls, the UC McLaughlin Reserve, UC Sedg-wick Reserve, UC Whitaker Forest, the UC JamesReserve, and the UC Santa Cruz Island Reserve. Weappreciate the assistance of I. Ouzounov and G.Betzholtz in the laboratory, J. Q. Richmond for helpwith the data interpretation, and V. Krauss forsharing his Tribolium intron data. This work wassupported by the Schlinger Foundation, a bequestfrom G. Ostertag, and National Science Foundationaward DEB0447694 to M. Caterino.

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APPENDIXSAMPLING LOCALITIES BY REGION, WITH HAPLOTYPES REPRESENTED IN EACH (LOCALITY NUMBERS REFER

TO POINTS ON FIG. 1)

Population N Locality Coordinates COI haplotypes GFT haplotypes

1. NorthernSanta Lucias

10 1.1 – CA: MontereyCo., Arroyo SecoCounty Park

36.2341°N,121.4886°W

N88, N89, N90, N91 G04, G55, G56, G57

Total 102. SouthernSanta Lucias

5 2.1 – CA: San LuisObispo Co., LPNF,Cuesta Ridge

35.3731°N,120.6859°W

N13, N14, N15 G04, G09, G10

1 2.2 – CA: San LuisObispo Co., SantaRosa Creek

35.5861°N,121.0057°W

N93 G60

Total 63. SouthwesternSierra Nevada

2 3.1 – CA: Fresno Co.,Sequoia NF, SE ofHume Lake

36.7718°N,118.8854°W

N78, N79 G39, G40, G41, G42

8 3.2 – CA: Tulare Co.,Sequoia NF, UCWhitaker Forest

36.7025°N,118.9322°W

N72, N73, N74, N75,N76, N77

G32, G33, G34, G35,G36, G37, G38, G49

1 3.2 – CA: Tulare Co.,Sequoia NF, F.S.14S75

36.6720°N,118.9798°W

N80 G43

Total 114. BreckenridgeMountains

2 4.1 – CA: Kern Co.,Sequoia NF, KernCyn.

35.4753°N,118.7284°W

N02 –

Total 25. TehachapiMountains

2 5.1 – CA: Kern Co.,Oak Creek Cyn.

35.0487°N,118.3605°W

N61, N62 G01

5 5.1 – CA: Kern Co.,Oak Creek Cyn.

35.0506°N,118.3567°W

N61, N63 G01

3 5.1 – CA: Kern Co.,Tehachapi MountainPark

35.0673°N,118.4820°W

N81, N82 G01, G44, G45, G46,G47

1 5.1 – CA: Kern Co.,Woodford-TehachapiRd

35.1807°N,118.5150°W

N81 G45, G48

Total 116. Santa YnezMountains

3 6.1 – CA: SantaBarbara Co., ArroyoHondo Preserve

34.4855°N,120.1423°W

N64, N65, N66 –

2 6.1 – CA: SantaBarbara Co., ArroyoHondo Preserve

34.4855°N,120.1417°W

N7, N8 G07

1 6.2 – CA: SantaBarbara Co., SantaBarbara

34.47°N,119.73°W

N44 G14

5 6.3 – CA: Ventura Co.,LPNF, Murrietta Tr.

34.5009°N,119.3899°W

N50, N51, N52, N53,N54

G26, G05, G04, G27,G17, G28

1 6.4 – CA: Ventura Co.,Upper Ojai Valley

34.4504°N,119.1207°W

N42 G05, G22

3 6.5 – CA: Ventura Co.,Ventura

34.293°N,119.204°W

N28, N29, N30 G05, G17, G01, G18



APPENDIX Continued


Total 157. NorthwestTransverseRanges

1 7.1 – CA: SantaBarbara Co., UCSedgwick Reserve

34.6796°N,120.0387°W

N9 G04


34.6825°N,120.0445°W

N9


34.6842°N,120.0459°W

N11, N12 G04


34.6889°N,120.0428°W

N49 G24, G25


34.7127°N,120.0396°W

N35 G04, G20


34.7132°N,120.0395°W

N41 G04


34.7164°N,120.0396°W

N23 G04, G13


34.7197°N,120.0366°W

N31, N39 G04


34.7261°N,120.0483°W

N43 G20, G23


34.7269°N,120.0474°W

N9 G04, G08


34.7197°N,120.0415°W

– G04

1 7.2 – CA: SantaBarbara Co., LPNF,Sunset Valley

34.7538°N,119.9429°W

N3 G04

1 7.3 – CA: SantaBarbara Co., LPNF,Rancho Alegre

34.5410°N,119.9110°W

N24 G14, G15

2 7.4 – CA: SantaBarbara Co., LPNF,Big Pine Mountain

34.7021°N,119.6547°W

N4 G04

2 7.4 – CA: SantaBarbara Co., LPNF,Big Pine Mountain

34.7032°N,119.6526°W

N36, N38 G04

1 7.5 – CA: Ventura Co.,LPNF, north slopePine Mountain

34.6585°N,119.3800°W

N1 –

Total 208. CentralTransverseRanges

2 8.1 – CA: Kern Co.,LPNF, Tecuya Rd.

34.8484°N,119.0697°W

N94, N96 G01

4 8.1 – CA: Kern Co.,LPNF, Tecuya Trail

34.8300°N,119.0078°W

N94, N95 G01



APPENDIX Continued


Total 69. SierraPelona

7 9.1 – CA: Los AngelesCo., Angeles NF,Grass Mountain

34.6408°N,118.4148°W

N10, N18, N26, N37,N46, N47, N48

G01, G02, G03

Total 710. San Gabriel

Mountains7 10.1 – CA: Los

Angeles Co.,Placerita Cyn. Co.Pk.

34.3764°N,118.4403°W

N55, N56, N57, N58,N59, N60

G01

2 10.2 – CA: LosAngeles Co.,Angeles NF, AngelesCrest Hwy.

34.2717°N,118.0608°W

N25, N27 G16

3 10.2 – CA: LosAngeles Co.,Angeles NF, HwyN3

34.2964°N,118.1625°W

N25, N67, N68 G16, G29, G30

2 10.2 – CA: LosAngeles Co.,Angeles NF, HwyN3

34.2910°N,118.1695°W

N71, N85 G01, G50

2 10.3 – CA: LosAngeles Co.,Angeles NF, SanDimas Exp. Forest

34.2012°N,117.7736°W

N86, N87 G51, G52, G53, G54

Total 1611. San

BernardinoMountains

1 11.1 – CA: SanBernardino Co.,SBNF, Hwy 173

34.2955°N,117.2134°W

N84 G05, G31

1 11.2 – CA: SanBernardino Co.,SBNF, City Ck.

34.1864°N,117.1836°W

N45 G05, G21

1 11.3 – CA: SanBernardino Co.,SBNF, Deer Creek

34.1741°N,116.9844°W

N40 G06, G21

2 11.3 – CA: SanBernardino Co.,SBNF, W. of BartonFlats

34.1678°N,116.9143°W

N05, N06 G05, G06

4 11.3 – CA: SanBernardino Co.,SBNF, Santa Ana R.

34.1819°N,116.8884°W

N06, N70 G05, G06

2 11.3 – CA: SanBernardino Co.,SBNF, Camp CedarFalls

34.1646°N,116.9366°W

N69 G05

Total 11



APPENDIX Continued


12. San JacintoMountains

8 12.1 – CA: RiversideCo., UC JamesReserve

33.8092°N,116.7650°W

N16, N17, N19, N20,N21, N22

G05, G11

1 12.1 – CA: RiversideCo., UC JamesReserve

33.8081°N,116.7784°W

N83 G11

1 12.1 – CA: RiversideCo., SBNF, F.S.4S01

33.8448°N,116.7322°W

N19 G05

1 12.1 – CA: RiversideCo., SBNF, MarionMountain

33.7949°N,116.7214°W

N32 G05

Total 1113. Santa Cruz

Islands2 13.1 – CA: Santa

Barbara Co., SantaCruz Islands

34.0191°N,119.6878°W

N33, N34 G04, G04, G19

4 13.1 – CA: SantaBarbara Co.,Pelican Bay Tr.

34.0222°N,119.6906°W

N97, N98, N99,N100

G04, G19, G61, G62

Total 6

SUPPORTING INFORMATION

Additional Supporting Information may be found in the online version of this article:

Appendix S1. Haplotypes and GenBank accession numbers. ‘N’ haplotypes refer to the Nyctoporis carinata COIsequence; ‘G’ haplotypes refer to the Nyctoporis carinata GFT sequence.

Please note: Blackwell Publishing are not responsible for the content or functionality of any supportingmaterials supplied by the authors. Any queries (other than missing material) should be directed to thecorresponding author for the article.



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