Multiple Pleistocene refugia and Holocene range expansion ... · Systematics, Palynology and Geobotany, Institute of Botany, University of Innsbruck, Sternwartestrasse 15, A-6020

Molecular Ecology (2010) 19, 3421–3443 doi: 10.1111/j.1365-294X.2010.04754.x

Multiple Pleistocene refugia and Holocene rangeexpansion of an abundant southwestern American desertplant species (Melampodium leucanthum, Asteraceae)

CAROLIN A. REBERNIG,* GERALD M. SCHNEEWEISS ,†1 KATHARINA E. BARDY,†* PETER

SCHONSWETTER,†‡ JOSE L. VILLASENOR,– RENATE OBERMAYER,* TOD F. STUESSY* and

HANNA WEISS-SCHNEEWEISS*

*Department of Systematic and Evolutionary Botany, University of Vienna, Rennweg 14, A-1030 Vienna, Austria; †Department

of Biogeography and Botanical Garden, University of Vienna, Rennweg 14, A-1030 Vienna, Austria; ‡Department of

Systematics, Palynology and Geobotany, Institute of Botany, University of Innsbruck, Sternwartestrasse 15, A-6020 Innsbruck,

Austria; –Instituto de Biologıa, Departamento de Botanica, Universidad Nacional Autonoma de Mexico, Tercer Circuito s ⁄ n,

Ciudad Universitaria, Delegacion Coyoacan, MX-04510 Mexico D. F., Mexico

Corresponde

E-mail: geral1Present Add

Maximilians-

Munich, Ger

� 2010 Black

Abstract

Pleistocene climatic fluctuations had major impacts on desert biota in southwestern

North America. During cooler and wetter periods, drought-adapted species were isolated

into refugia, in contrast to expansion of their ranges during the massive aridification in

the Holocene. Here, we use Melampodium leucanthum (Asteraceae), a species of the North

American desert and semi-desert regions, to investigate the impact of major aridification

in southwestern North America on phylogeography and evolution in a widespread and

abundant drought-adapted plant species. The evidence for three separate Pleistocene

refugia at different time levels suggests that this species responded to the Quaternary

climatic oscillations in a cyclic manner. In the Holocene, once differentiated lineages

came into secondary contact and intermixed, but these range expansions did not follow

the eastwardly progressing aridification, but instead occurred independently out of

separate Pleistocene refugia. As found in other desert biota, the Continental Divide has

acted as a major migration barrier for M. leucanthum since the Pleistocene. Despite being

geographically restricted to the eastern part of the species’ distribution, autotetraploids

in M. leucanthum originated multiple times and do not form a genetically cohesive

group.

Keywords: desert biota, Holocene aridification, Melampodium, phylogeography, polyploidy,

refugia

Received 24 March 2010; revision received 28 May 2010; accepted 5 June 2010

Introduction

The impact of Pleistocene climatic fluctuations on

directly affected areas, such as the Arctic or temperate

high mountain ranges, has been comparatively well

investigated phylogeographically in both plants and

animals (Hewitt 1996, 2001; Brunsfeld et al. 2001;

nce: Gerald M. Schneeweiss, Fax: +43 1 4277 9541;

[email protected]

ress: Systematic Botany and Mycology, Ludwig-

University Munich, Menzingerstrasse 67, D-80638

many.

well Publishing Ltd

Abbott & Brochmann 2003; Schonswetter et al. 2005).

The role of these climatic fluctuations in other regions,

however, remains less well understood. This is particu-

larly the case for arid regions in northern Mexico and

adjacent southwestern United States. Paleoclimatic and

paleovegetational evidence unambiguously suggests

that desert vegetation was strongly restricted during the

wetter and cooler pluvial periods (Wells 1966; Van

Devender & Spaulding 1979; Thompson & Anderson

2000) and confined to refugia in the west and south,

such as the lower Colorado River Basin, the plains

of Sonora, or the southern Chihuahuan Desert (Van

3422 C. A. REBERNIG ET AL.

Devender 1990; Thompson & Anderson 2000; Hunter

et al. 2001). Large-scale aridification of the whole region

started only after the end of the last glacial maximum

(Van Devender & Spaulding 1979; McClaran & Van

Devender 1995; Bousman 1998; Metcalfe et al. 2000;

Musgrove et al. 2001; Holmgren et al. 2007) and was

accompanied by a shift from xeric woodlands, abundant

until 8000 years BP (Van Devender 1977), to semidesert

grassland and eventually desert shrubland vegetation

(Neilson 1986). Consequently, drought-adapted species

are expected to have persisted in one or more distinct

refugia (Nason et al. 2002; Fehlberg & Ranker 2009),

from where they reached their current distribution after

range expansion within the last 10 000–6000 years (Van

Devender & Spaulding 1979; Spaulding 1990; Van

Devender 1990; Holmgren et al. 2007).

These range expansions into new arid regions are

expected to have had major impacts on population

structure and genetic diversity, for instance resulting in

loss of alleles because of bottlenecks and founder

events, or in secondary contact of genetic lineages dif-

ferentiated in allopatric refugia (Hewitt 2001, 2004). Pa-

leoclimatic modelling indicates that the aridification

progressed from the Sonoran Desert north- and east-

wards (Holmgren et al. 2007), and it can be expected

that range expansion of drought-adapted species fol-

lowed the same general direction (Fehlberg & Ranker

2009). Additionally, rapid expansion should also be

reflected in geographic patterns of genetic diversity,

which is expected to be lower in more recently colo-

nized areas because of founder effects (Hewitt 1996). A

longitudinal migration pattern may, however, be modi-

fied by the Continental Divide, whose establishment in

the late Tertiary is thought to have caused vicariant

diversification in a number of warm-desert animals

(Riddle & Hafner 2006; Castoe et al. 2007). Since then,

the divide has acted as a formidable migration barrier

for desert biota because of the lack of a spatially and

temporally continuous connection between the Sonoran

and the Chihuahuan Deserts, which currently come

closest at the Derning Plains near the border between

Arizona and New Mexico (Morafka 1977; Riddle & Haf-

ner 2006; Castoe et al. 2007). This barrier is expected to

enhance founder effects in the course of eastward

migration. Alternatively, if refugia of drought-adapted

species were also located east of the Continental Divide

(Hunter et al. 2001; Castoe et al. 2007), this region prob-

ably is the contact zone of western and eastern lineages

(Castoe et al. 2007). While several of these hypotheses

have been tested in a number of animal groups (Jaeger

et al. 2005; Riddle & Hafner 2006; Castoe et al. 2007;

Haenel 2007; Fontanella et al. 2008), comparable studies

in plants are lacking. The few studies from desert plants

either investigate species from only one side of the Con-

tinental Divide (Nason et al. 2002; Clark-Tapia & Moli-

na-Freaner 2003; Fehlberg & Ranker 2009; Garrick et al.

2009; Sosa et al. 2009) or they do not employ molecular

methods (Hunter et al. 2001; Holmgren et al. 2007).

By affecting the distribution of a species, environmen-

tal changes will also shape its evolution via, for

instance, enabling or interrupting gene flow in phases

of continuous distribution and range disruption, respec-

tively, or affecting the success of establishment of newly

formed polyploids (Husband 2004; Baack & Stanton

2005; Ramsey et al. 2008). The latter is of particular rele-

vance, because polyploidy is recognized as an impor-

tant mode of speciation in general and one of the more

likely means of sympatric speciation in particular (Otto

& Whitton 2000; Coyne & Orr 2004; Soltis et al. 2007).

While the role of allopolyploidy for speciation has long

been recognized (Ramsey & Schemske 1998, 2002; Le-

itch & Leitch 2008), the rapidly mounting evidence of a

high frequency of autopolyploids, often in mixed popu-

lations with their diploid progenitors (Husband 2004;

Suda et al. 2007), has led to a more positive view con-

cerning the evolutionary significance of autopolyploidi-

zation (Soltis et al. 2007). Despite several recent studies

dealing with the dynamics of diploid–autopolyploid

complexes (Baack & Stanton 2005; Schonswetter et al.

2007; Ramsey et al. 2008; Hulber et al. 2009), their evo-

lutionary significance and the factors involved in poly-

ploid cytotype formation and establishment are still

poorly understood (Baack & Stanton 2005).

Here we use Melampodium leucanthum (Asteraceae),

an abundant taxon of the North American desert and

semi-desert regions, to investigate the impact of the

major aridification in southern North America within

the last 10 000 years on phylogeography and evolution,

including cytotype differentiation, in a drought-

adapted plant species. This phylogenetically distinct

(Bloch et al. 2009) and morphologically and taxonomi-

cally homogeneous species is particularly well suited

to address these questions, because it is distributed

over several major arid and semi-arid biogeographic

regions ranging from the Sonoran and Chihuahuan

Deserts to the Tamaulipan Plain and Southern Plain

region (Stuessy 1972), and it comprises diploid and tet-

raploid cytotypes, the latter restricted to the eastern

part of the distribution area (Fig. 1, Table 1; Stuessy

et al. 2004). Our first aim is to analyse the phylogeo-

graphic patterns caused by post-Pleistocene aridifica-

tion and subsequent migration events. Specifically, we

want (i) to determine the locations of the refugia of

M. leucanthum and test whether these are congruent

with those suggested by paleoclimatic and phylogeo-

graphic data (Hunter et al. 2001; Castoe et al. 2007;

Holmgren et al. 2007; Fehlberg & Ranker 2009); (ii) to

infer the directionality of the range expansion, in

� 2010 Blackwell Publishing Ltd

(a)

(a)

(b)

(b)

Fig. 1 Physical map of the distribution of the analysed populations of Melampodium leucanthum. The collection area represents the

entire distribution range of the species (population numbers as in Table 1).

PHYLOGEO GRAPHY OF NORTH AMERICAN D ESERT PLANT 3423

particular, whether it was essentially unidirectional fol-

lowing the north- and eastwardly progressing aridifica-

tion (Holmgren et al. 2007); and (iii) to test whether

inferred range expansions fit the time frame predicted

by paleoclimatic data (Holmgren et al. 2007; Holliday

et al. 2008). A second aim is to infer origin and evolu-

tion of the polyploids. Specifically, we want to test (i)

whether the polyploids originated once, as suggested

by their compact and restricted distribution, or recur-

rently, as observed in many species including the clo-

sely related M. cinereum (Rebernig et al. 2010) and (ii)

whether they form a genetically cohesive group clearly

separated from the diploids, as has been found in

M. cinereum (Rebernig et al. 2010). To this end, we

generated amplified fragment length polymorphism

(AFLP) and cpDNA sequence data from several hun-

dred individuals, whose ploidy level was determined

flow cytometrically from 92 populations over the whole

distribution area. These data were analysed using,

among others, a coalescent-based Bayesian approach

for hypothesis testing and molecular dating, comple-

mented by ecological niche modelling for inferring

putative paleodistributions.


Materials and methods

Study species

Melampodium leucanthum (blackfoot daisy) is a drought-

tolerant, summer-flowering perennial subshrub grow-

ing on calcareous soils between 500 and 2590 m a.s.l.

and is abundant in its distribution area encompassing

northern Mexico and the southwestern United States

from Arizona to eastern Texas, northwards extending

into Oklahoma and Colorado (Fig. 1; Stuessy 1972).

Pollen ⁄ ovule ratios (Cruden 1977) indicate that

M. leucanthum is outcrossing (data not shown). No

population differentiation concerning morphology,

flowering time or breeding system has ever been

reported (Stuessy 1972). Melampodium leucanthum com-

prises diploid and tetraploid cytotypes (with occa-

sional triploid individuals in diploid populations;

Stuessy et al. 2004), which are morphologically indis-

tinguishable (Stuessy 1971, 1972). The two cytotypes

occur mostly parapatrically, and tetraploids occupy a

compact area in eastern Texas to the near exclusion of

diploids (Stuessy et al. 2004).


Plant material

Plant material was collected from 92 populations of

M. leucanthum covering the entire distribution of the

species (Fig. 1). Samples were dried and stored in silica

gel until DNA isolation. Herbarium vouchers are

deposited in the herbarium of the University of Vienna

(WU; voucher numbers given in Table 1).

Ploidy level determination and molecular methods

Measurements of DNA ploidy levels (Suda et al. 2006)

were conducted as described in Rebernig et al. (2010).

Correct interpretation of DNA ploidy levels was con-

firmed by chromosome numbers determined for

selected individuals using standard Feulgen staining as

described by Weiss-Schneeweiss et al. (2007).

Total genomic DNA was extracted as described in Re-

bernig et al. (2010). AFLP fingerprint profiles were gen-

erated for 1–5 individuals per population totalling 377

individuals (Table 1) following the protocol described

in Dixon et al. (2008). Two negative controls were

included in each PCR, and 6.25% of the samples were

replicated. After initial screening of 33 selective primer

combinations with three to four selective nucleotides,

the following five primer combinations were selected

for the final analyses (fluorescent dyes in parentheses):

EcoRI-ACA ⁄ MseI-CAT (FAM), EcoRI-ACG ⁄ MseI-CAA

(VIC), EcoRI-ACC ⁄ MseI-CAG (NED), EcoRI-ACT ⁄ MseI-CAC (FAM), EcoRI-AGG ⁄ MseI-CAA (VIC). Purification

of selective PCR products, their electrophoretic separa-

tion and subsequent alignment as well as their scoring

(bands in the size range of 100–500 bp) were carried

out as described in Rebernig et al. (2010). Nonreproduc-

ible bands identified by comparisons among replicated

individuals were excluded from further analyses.

The following three noncoding chloroplast DNA

spacer regions were amplified and sequenced as

described in Rebernig et al. (2010) for one to three indi-

viduals per population (Table 1), totalling 228 individu-

als: psbA-trnH, rpl32-trnL, and ndhF-rpl32. Sequences

were assembled using SEQMAN II 5.05 (DNAStar, Madi-

son, WI, USA) and manually aligned using BIOEDIT 7.0

(Hall 1999). Sequences are deposited in GenBank

(Table 1).

Data analyses

AFLP. AFLP data descriptors include the total number

of fragments (Fragtot), the percentage of polymorphic

fragments (Fragpoly), the number of private fragments

(Fragpriv) and the index of average differences within

populations (AWD; Kosman 2003) calculated for popu-

lations with at least three individuals in ARLEQUIN 3.10

(Excoffier et al. 2005). Geographic patterns in AFLP de-

scriptors were tested using multiple linear regressions

in Excel 2007 (Microsoft, Redmond, CA, USA). Principal

coordinate analysis (PCO) was conducted using

NTSYSPC 2.20e (Rohlf 2007) with the default settings

both on the whole data set as well as on a reduced data

set after exclusion of the genetically distinct western

populations (pops. 1–17; see Results). Neighbour-nets of

the same two data sets were constructed with SPLITSTREE

4.8 (Huson & Bryant 2006) using Nei-Li distances (Nei

& Li 1979) calculated with FAMD 1.108 (Schluter &

Harris 2006). Three-level hierarchical analyses of molec-

ular variance (AMOVA) using the groups suggested by

PCO (see Results) were conducted on both data sets

with ARLEQUIN 3.10 (Excoffier et al. 2005), estimating the

significance of variance components from 10 000 per-

mutations.

Genetically homogeneous groups of diploid individu-

als were identified using genetic mixture analysis

implemented in STRUCTURE 2.2 (Pritchard et al. 2000; Fa-

lush et al. 2007) as described in Rebernig et al. (2010)

with minor modifications (see Supporting materials).

Tetraploid individuals were excluded, because the mod-

els implemented in STRUCTURE are not suited for analy-

sing polyploids (Pritchard et al. 2000).

Assignment tests to determine the most likely source

populations for the tetraploid individuals were per-

formed using AFLPOP 1.1 (Duchesne & Bernatchez

2002) with the default settings. All diploid populations

were considered as potential source populations, and

allocation was tested using two levels (0 or 2) of mini-

mal log-likelihood differences (as recommended by

Duchesne & Bernatchez 2002) with frequency values of

zero replaced by 1 ⁄ (sample size + 1).

cpDNA. Prior to all analyses, inversions in the plastid

sequence data were re-inverted to avoid introducing

substitutional mutations, which in fact are the result of

structural mutations (Lohne & Borsch 2005). This data

set was used only for the BEAST analysis, and for the

other analyses, indels longer than 1 base pair and inver-

sions were additionally recoded as single characters,

and mononucleotide repeats were removed because of

their high degree of homoplasy at larger geographic

scales (Ingvarsson et al. 2003).

Group and population differentiation was assessed

via a spatial analysis of molecular variance (SAMOVA),

which allows defining population groups that are genet-

ically differentiated from each other and occur in a

geographically homogeneous area (Dupanloup et al.

2002). This analysis was conducted with the program

SAMOVA 1.0 (available from http://web.unife.it/progetti/


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J846

434,

FJ8

4643

5

21N

32.2

63,

W10

7.23

3(2

0000

)3

⁄223

318

.15

00.

117

±0.

254

2xF

J846

229,

FJ8

4623

0,F

J846

027;

FJ8

4602

8,F

J846

431,

FJ8

4643

2

22N

32.3

98,

W10

6.61

4(2

0046

)5

⁄225

118

.60

00.

090

±0.

196

2xF

J846

287,

FJ8

4628

8,F

J846

085;

FJ8

4608

6,F

J846

489,

FJ8

4649

0

23N

32.9

53,

W10

7.49

0(2

0007

)5

⁄226

229

.02

10.

125

±0.

214

2xF

J846

238,

FJ8

4623

9,F

J846

036;

FJ8

4603

7,F

J846

440,

FJ8

4644

1

24N

33.2

75,

W10

7.28

2(2

0010

)5

⁄227

227

.98

00.

133

±0.

225

2xF

J846

240,

FJ8

4624

1,F

J846

038;

FJ8

4603

9,F

J846

442,

FJ8

4644

3

25N

34.1

46,

W10

6.90

8(2

0011

)5

⁄226

926

.93

00.

129

±0.

223

2xF

J846

242,

FJ8

4624

3,F

J846

040;

FJ8

4604

1,F

J846

444,

FJ8

4644

5

26N

33.7

94,

W10

6.27

4(2

0014

)4

⁄228

228

.72

00.

152

±0.

247

2xF

J846

244,

FJ8

4624

5,F

J846

042;

FJ8

4604

3,F

J846

446,

FJ8

4644

7

27N

34.0

10,

W10

5.94

2(2

0016

)5

⁄227

627

.23

00.

128

±0.

220

2xF

J846

246,

FJ8

4624

7,F

J846

044;

FJ8

4604

5,F

J846

448,

FJ8

4644

9

28N

34.9

46,

W10

6.19

1(2

0017

)5

⁄327

628

.57

10.

135

±0.

224

2xF

J846

248,

FJ8

4624

9,F

J846

250;

FJ8

4604

6,F

J846

047,

FJ8

4604

8;

FJ8

4645

0,F

J846

451,

FJ8

4645

2

29N

35.2

88,

W10

6.21

6(2

0021

)5

⁄326

325

.00

00.

119

±0.

216

2xF

J846

251,

FJ8

4625

2,F

J846

253;

FJ8

4604

9,F

J846

050,

FJ8

4605

1;

FJ8

4645

3,F

J846

454,

FJ8

4645

5

30N

34.0

36,

W10

4.74

7(2

0032

)5

⁄228

928

.57

00.

135

±0.

224

2xF

J846

261,

FJ8

4626

2,F

J845

059;

FJ8

4506

0,F

J846

463,

FJ8

4646

4

31N

34.8

98,

W10

4.71

8(2

0031

)5

⁄226

924

.40

00.

115

±0.

212

2xF

J846

259,

FJ8

4626

0,F

J845

057;

FJ8

4505

8,F

J846

461,

FJ8

4646

2

32N

35.3

96,

W10

4.18

0(2

0030

)4

⁄228

727

.08

00.

142

±0.

242

2xF

J846

257,

FJ8

4625

8,F

J845

055;

FJ8

4505

6,F

J846

459,

FJ8

4646

0

33N

35.2

30,

W10

3.76

7(2

0029

)4

⁄228

025

.30

00.

133

±0.

236

2xF

J846

254,

FJ8

4625

5,F

J846

256

FJ8

4505

2,F

J845

053,

FJ8

4505

4;

FJ8

4645

6,F

J846

457,

FJ8

4645

8

34N

31.3

16,

W10

6.07

8(2

0045

)5

⁄228

126

.39

00.

125

±0.

215

2xF

J846

285,

FJ8

4628

6,F

J846

083;

FJ8

4608

4,F

J846

487,

FJ8

4648

8

35N

31.0

05,

W10

4.82

5(1

8726

)3

⁄338

220

.09

00.

130

±0.

264

2xF

J846

120,

FJ8

4612

1,F

J846

122;

FJ8

4591

8,F

J845

919,

FJ8

4592

0;

FJ8

4632

2,F

J846

323,

FJ8

4632

4



Ta

ble

1(C

on

tin

ued

)

po

p

nr.

Lo

cati

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(vo

uch

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Sam

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N

AF

LP

⁄cp

DN

AF

rag

tot*

Fra

gp

oly%

†F

rag

pri

v‡

AW

D±

Std

Dev

Plo

idy

§

Gen

Ban

k

acce

ssio

nn

um

ber

s–

36N

31.0

04,

W10

4.82

5(2

0043

)4

⁄230

028

.42

00.

147

±0.

242

2xF

J846

280,

FJ8

4628

1,F

J846

078;

FJ8

4607

9,F

J846

482,

FJ8

4648

3

37N

30.7

89,

W10

4.03

3(1

8725

)5

⁄233

833

.33

10.

159

±0.

237

2xF

J846

118,

FJ8

4611

9,F

J845

916;

FJ8

4591

7,F

J846

329,

FJ8

4632

1

38N

30.9

99,

W10

3.75

6(1

8727

)5

⁄233

331

.99

00.

152

±0.

233

2x*

FJ8

4612

3,F

J846

124,

FJ8

4592

1;

FJ8

4592

2,F

J846

325,

FJ8

4632

6

39N

31.3

52,

W10

3.57

9(1

8729

)2

⁄225

513

.99

00.

135

±0.

342

2xF

J846

125,

FJ8

4612

6,F

J845

923;

FJ8

4592

4,F

J846

327,

FJ8

4632

8

40N

31.6

11,

W10

4.85

7(2

0044

)5

⁄331

434

.97

10.

163

±0.

234

2xF

J846

282,

FJ8

4628

3,F

J846

284;

FJ8

4608

0,F

J846

081,

FJ8

4608

2;

FJ8

4648

4,F

J846

485,

FJ8

4648

6

41N

32.4

90,

W10

4.34

8(2

0033

)5

⁄326

225

.74

30.

112

±0.

199

2xF

J846

263,

FJ8

4626

4,F

J846

265;

FJ8

4506

1,F

J845

062,

FJ8

4506

3;

FJ8

4646

5,F

J846

466,

FJ8

4646

7

42N

32.5

29,

W10

3.80

2(2

0034

)3

⁄325

921

.28

00.

137

±0.

270

2xF

J846

266,

FJ8

4626

7,F

J846

268;

FJ8

4506

4,F

J845

065,

FJ8

4506

6;

FJ8

4646

8,F

J846

469,

FJ8

4647

0

43N

32.5

07,

W10

3.12

7(2

0035

)4

⁄325

924

.12

00.

130

±0.

239

2xF

J846

269,

FJ8

4627

0,F

J846

271;

FJ8

4506

7,F

J845

068,

FJ8

4506

9;

FJ8

4647

1,F

J846

472,

FJ8

4647

3

44N

32.2

88,

W10

2.61

1(1

8737

)3

⁄228

921

.28

00.

137

±0.

270

2xF

J846

139,

FJ8

4614

0,F

J845

937;

FJ8

4593

8,F

J846

341,

FJ8

4634

2

45N

31.8

52,

W10

3.11

4(1

8738

)3

⁄229

620

.04

00.

159

±0.

365

2xF

J846

141,

FJ8

4614

2,F

J845

939;

FJ8

4594

0,F

J846

343,

FJ8

4634

4

46N

31.6

40,

W10

2.63

4(1

8730

)5

⁄231

630

.95

00.

147

±0.

231

2xF

J846

127,

FJ8

4612

8,F

J845

925;

FJ8

4592

6,F

J846

329,

FJ8

4633

0

47N

31.6

98,

W10

2.57

2(1

8731

)5

⁄231

729

.02

00.

134

±0.

220

2x⁄3

xF

J846

129,

FJ8

4613

0,F

J845

927;

FJ8

4592

8,F

J846

331,

FJ8

4633

2

48N

30.7

48,

W10

2.90

7(1

8722

)5

⁄236

940

.92

00.

188

±0.

240

2xF

J846

116,

FJ8

4611

7,F

J845

914;

FJ8

4591

5,F

J846

318,

FJ8

4631

9

49N

30.2

39,

W10

3.38

0(2

0038

)5

⁄231

532

.59

00.

149

±0.

226

2xF

J846

272,

FJ8

4627

3,F

J846

070;

FJ8

4607

1,F

J846

474,

FJ8

4647

5

50N

29.7

85,

W10

3.17

7(2

0039

)5

⁄327

624

.70

00.

119

±0.

218

2xF

J846

274,

FJ8

4627

5,F

J846

276;

FJ8

4607

2,F

J846

073,

FJ8

4607

4;

FJ8

4647

6,F

J846

477,

FJ8

4647

8

51N

29.5

16,

W10

3.40

3(2

0040

)3

⁄325

318

.30

10.

118

±0.

255

2xF

J846

277,

FJ8

4627

8,F

J846

279;

FJ8

4607

5,F

J846

076,

FJ8

4607

7;

FJ8

4647

9,F

J846

480,

FJ8

4648

1

52N

31.9

34,

W10

1.86

6(1

8732

)1

⁄120

60

0⁄

2xF

J846

131,

FJ8

4592

9,F

J846

333



Ta

ble

1(C

on

tin

ued

)

po

p

nr.

Lo

cati

on

(vo

uch

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r.)

Sam

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N

AF

LP

⁄cp

DN

AF

rag

tot*

Fra

gp

oly%

†F

rag

pri

v‡

AW

D±

Std

Dev

Plo

idy

§

Gen

Ban

k

acce

ssio

nn

um

ber

s–

53N

31.8

71,

W10

1.64

6(1

8733

)5

⁄233

332

.39

00.

156

±0.

238

2xF

J846

132,

FJ8

4613

3,F

J845

930;

FJ8

4593

1,F

J846

334,

FJ8

4633

5

54N

32.9

64,

W10

2.01

2(1

8735

)5

⁄233

233

.33

10.

156

±0.

233

2xF

J846

137,

FJ8

4613

8,F

J845

935;

FJ8

4593

6,F

J846

339,

FJ8

4634

0

55N

35.3

26,

W10

2.37

0(1

8753

)3

⁄229

423

.51

00.

152

±0.

280

2xF

J846

143,

FJ8

4614

4,F

J845

941;

FJ8

4594

2,F

J846

345,

FJ8

4634

6

56N

35.0

03,

W10

1.91

9(1

8756

)4

⁄230

328

.27

00.

149

±0.

246

2xF

J846

145,

FJ8

4614

6,F

J845

943;

FJ8

4594

4,F

J846

347,

FJ8

4634

8

57N

34.9

86,

W10

1.71

7(1

8757

)3

⁄228

623

.51

00.

152

±0.

280

2xF

J846

147,

FJ8

4614

8,F

J845

945;

FJ8

4594

6,F

J846

349,

FJ8

4635

0

58N

36.2

21,

W10

1.33

4(1

8758

)5

⁄232

020

.21

20.

140

±0.

225

2xF

J846

149,

FJ8

4615

0,F

J845

947;

FJ8

4594

8,F

J846

351,

FJ8

4635

2

59N

36.4

49,

W10

0.37

2(1

8760

)4

⁄329

826

.04

00.

138

±0.

240

2xF

J846

151,

FJ8

4615

2,F

J846

153;

FJ8

4594

9,F

J845

950,

FJ8

4595

1;

FJ8

4635

3,F

J846

354,

FJ8

4635

5

60N

36.4

27,

W99

.883

(187

61)

4⁄2

298

20.8

70

0.15

2±

0.24

82x

⁄3x

FJ8

4615

4,F

J846

155,

FJ8

4595

2;

FJ8

4595

3,F

J846

356,

FJ8

4635

7

61N

35.8

45,

W10

0.39

7(1

8762

)3

⁄325

718

.90

00.

122

±0.

278

2xF

J846

156,

FJ8

4615

7,F

J846

158;

FJ8

4595

4,F

J845

955,

FJ8

4595

6;

FJ8

4635

8,F

J846

359,

FJ8

4636

0

62N

35.4

32,

W10

0.77

0(1

8764

)3

⁄227

319

.64

00.

127

±0.

262

2xF

J846

159,

FJ8

4616

0,F

J845

957;

FJ8

4595

8,F

J846

361,

FJ8

4636

2

63N

35.0

09,

W10

0.89

6(1

8772

)4

⁄228

420

.98

10.

111

±0.

222

2x⁄3

xF

J846

168,

FJ8

4616

9,F

J845

966;

FJ8

4596

7,F

J846

370,

FJ8

4637

1

64N

34.7

88,

W10

0.89

8(1

8771

)1

⁄120

40

0⁄

2xF

J846

167,

FJ8

4596

5,F

J846

369

65N

34.3

80,

W10

1.11

1(1

8770

)4

⁄229

333

.44

00.

152

±0.

231

2xF

J846

165,

FJ8

4616

6,F

J845

963;

FJ8

4596

4,F

J846

367,

FJ8

4636

8

66N

34.2

19,

W10

0.88

8(1

8769

)3

⁄130

929

.02

00.

187

±0.

230

2xF

J846

163,

FJ8

4616

4,F

J845

961;

FJ8

4596

2,F

J846

365,

FJ8

4636

6

67N

33.8

60,

W10

0.85

2(1

8768

)3

⁄227

023

.10

00.

149

±0.

278

2xF

J846

161,

FJ8

4616

2,F

J845

959;

FJ8

4596

0,F

J846

363,

FJ8

4636

4

68N

31.9

00,

W10

0.71

7(1

8734

)5

⁄ 331

326

.79

00.

127

±0.

221

2xF

J846

134,

FJ8

4613

5,F

J846

136;

FJ8

4593

2,F

J845

933,

FJ8

4593

4;

FJ8

4633

6,F

J846

337,

FJ8

4633

8

69N

29.7

18,

W10

1.36

1(1

8720

)4

⁄329

829

.32

10.

159

±0.

256

4xF

J846

113,

FJ8

4611

4,F

J846

115;

FJ8

4591

1,F

J845

912,

FJ8

4591

3;

FJ8

4631

5,F

J846

316,

FJ8

4631

7

70N

28.2

28,

W10

1.05

6(1

9056

)3

⁄322

918

.75

00.

121

±0.

267

2xF

J846

226,

FJ8

4622

7,F

J846

228;

FJ8

4502

4,F

J845

025,

FJ8

4502

6;

FJ8

4642

8,F

J846

429,

FJ8

4643

0



Ta

ble

1(C

on

tin

ued

)

po

p

nr.

Lo

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(vo

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r.)

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N

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LP

⁄cp

DN

AF

rag

tot*

Fra

gp

oly%

†F

rag

pri

v‡

AW

D±

Std

Dev

Plo

idy

§

Gen

Ban

k

acce

ssio

nn

um

ber

s–

71N

31.2

26,

W99

.757

(187

74)

5⁄2

300

25.1

50

0.11

9±

0.21

52x

⁄3x

FJ8

4617

0,F

J846

171,

FJ8

4596

8;

FJ8

4596

9,F

J846

372,

FJ8

4637

3

72N

31.7

81,

W99

.779

(187

76)

4⁄2

278

17.4

10

0.08

9±

0.20

64x

FJ8

4617

2,F

J846

173,

FJ8

4597

0;

FJ8

4597

1,F

J846

374,

FJ8

4637

5

73N

31.7

61,

W98

.899

(187

78)

4⁄2

281

17.8

60

0.09

5±

0.21

04x

FJ8

4617

4,F

J846

175,

FJ8

4597

2;

FJ8

4597

3,F

J846

376,

FJ8

4637

7

74N

31.6

89,

W98

.814

(187

79)

4⁄2

292

23.1

70

0.12

6±

0.23

44x

FJ8

4617

6,F

J846

168,

FJ8

4597

4;

FJ8

4597

5,F

J846

378,

FJ8

4637

9

75N

31.7

93,

W98

.228

(187

87)

4⁄2

252

12.2

81

0.11

4±

0.22

64x

FJ8

4618

7,F

J846

188,

FJ8

4598

5;

FJ8

4598

6,F

J846

389,

FJ8

4639

0

76N

31.5

75,

W97

.834

(187

86)

4⁄2

248

20.9

80

0.11

2±

0.22

44x

FJ8

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FJ8

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3;

FJ8

4598

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8

77N

31.3

58,

W98

.129

(187

81)

5⁄2

255

24.5

50

0.11

8±

0.21

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8,F

J846

179,

FJ8

4597

6;

FJ8

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J846

380,

FJ8

4638

1

78N

31.1

70,

W98

.183

(187

82)

5⁄2

237

22.1

70

0.10

3±

0.20

14x

FJ8

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0,F

J846

181,

FJ8

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FJ8

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J846

382,

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4638

3

79N

30.9

28,

W98

.002

(187

83)

1⁄1

171

00

⁄4x

FJ8

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J845

980,

FJ8

4638

4

80N

31.0

64,

W97

.572

(187

85)

4⁄2

246

20.0

90

0.10

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24x

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J846

385,

FJ8

4638

6

81N

30.6

16,

W97

.860

(187

09)

5⁄2

248

20.3

90

0.09

5±

0.19

74x

*F

J846

103,

FJ8

4610

4,F

J845

901;

FJ8

4590

2,F

J846

305,

FJ8

4630

6

82N

30.6

94,

W97

.981

(187

10)

3⁄2

224

15.3

30

0.09

9±

0.23

74x

FJ8

4610

5,F

J846

106,

FJ8

4580

3;

FJ8

4580

4,F

J846

307,

FJ8

4630

8

83N

30.6

96,

W98

.254

(187

11)

5⁄2

252

21.5

80

0.10

1±

0.20

24x

FJ8

4610

7,F

J846

108,

FJ8

4590

5;

FJ8

4590

6,F

J846

309,

FJ8

4631

0

84N

30.6

71,

W98

.256

(187

12)

5⁄2

248

21.4

30

0.10

2±

0.20

54x

FJ8

4610

9,F

J846

110,

FJ8

4590

7;

FJ8

4590

8,F

J846

311,

FJ8

4631

2

85N

30.2

80,

W98

.907

(186

81)

5⁄2

306

33.7

80

0.15

±0.

229

2xF

J846

093,

FJ8

4609

4,F

J845

891;

FJ8

4589

2,F

J846

295,

FJ8

4629

6

86N

30.1

70,

W98

.849

(186

87)

3⁄2

246

23.8

10

0.13

4±

0.28

14x

FJ8

4610

1,F

J846

102,

FJ8

4589

9;

FJ8

4590

0,F

J846

303,

FJ8

4630

4

87N

30.1

70,

W98

.907

(186

82)

5⁄2

289

29.0

21

0.13

7±

0.22

52x

⁄3x

FJ8

4609

5,F

J846

096,

FJ8

4589

3;

FJ8

4589

4,F

J846

297,

FJ8

4629

8

88N

29.6

16,

W98

.757

(186

86)

3⁄2

237

18.7

50

0.12

1±

0.25

74x

FJ8

4609

9,F

J846

100,

FJ8

4589

7;

FJ8

4589

8,F

J846

301,

FJ8

4630

2

89N

29.8

91,

W98

.408

(186

83)

5⁄2

295

32.8

90

0.16

0±

0.24

14x

FJ8

4609

7,F

J846

098,

FJ8

4589

5;

FJ8

4589

6,F

J846

299,

FJ8

4630

0

90N

30.1

94,

W98

.478

(186

76)

0⁄2

⁄⁄

⁄⁄

2xF

J846

087,

FJ8

4608

8,F

J846

289;

FJ8

4629

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J845

885,

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92N

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79)

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93N

30.3

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14)

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319

25.6

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genetica/isabelle/samova.html) employing 500 repli-

cates and testing K = 2–8, the final number of groups

being chosen based on the highest FCT value, which

describes the proportion of total genetic variance

because of differences between groups of populations

(Dupanloup et al. 2002). A haplotype network was con-

structed using statistical parsimony as implemented in

TCS 1.21 (Clement et al. 2000). The recoded gaps were

treated as a fifth character state, and the connection level

was set to 95%.

Demographic histories, especially population expan-

sions, were tested in several ways. We used Tajima’s D

(1996) and Fu’s FS (1997), where negative values indi-

cate population expansion, and the R2 statistic, where

small values indicate population expansion (Ramos-On-

sins & Rozas 2002). All three statistics and their signifi-

cance, assessed using 10 000 samples simulated under a

model of constant population size, were calculated with

DNASP 5.10 (Rozas et al. 2003). Values for FS were con-

sidered significant at P £ 0.02 (Fu 1997). As an alterna-

tive approach, we used mismatch distribution (the

distribution of pairwise differences among individuals),

where a unimodal distribution indicates population

expansion (Rogers & Harpending 1992), as described in

Rebernig et al. (2010) with minor modifications (see

Supporting Material). All these analyses were con-

ducted for the whole data set as well as for the three

population groups identified by the haplotype network

and by the SAMOVA (see Results), applying in case of

multiple comparisons P-value correction via sequential

Bonferroni correction (Rice 1989). As another way to

test for population expansions, we used the method

implemented in BEAST 1.4.8 (Drummond & Rambaut

2007), which allows divergence times to be estimated

simultaneously, as described in Rebernig et al. (2010)

with some modifications (see Supporting Material).

Migration directionality was tested using BEAST 1.4.8

employing the best demographic history identified in

the previous step. Each of the three main haplotype

groups (see Results) was considered in return as repre-

senting the source for range expansion of the whole

species. Using BEAST, these hypotheses can only be

implemented via topological constraints, specifically by

constraining the nonrefugial populations to be mono-

phyletic. By doing so, we have to assume that gene lin-

eages within the nonrefugial populations coalesced

before they coalesced with those from the refugial pop-

ulations. Besides, we also tested whether polyploid

populations of M. leucanthum originated once (consti-

tute a monophyletic group) as suggested by the com-

pact distribution area parapatric to that of the diploids.

All hypotheses testing in BEAST employed Bayes fac-

tors (BF; Suchard et al. 2001, 2005). Marginal likelihoods

(including their Monte Carlo error: Suchard et al. 2003;



Redelings & Suchard 2005) and BFs were calculated

with Tracer 1.4 (available from http://evolve.zoo.ox.a-

c.uk/). As test statistic, we used the widely applied

2 · lnBF, considering 2 · lnBFmodel 1 vs. model 2 > 10 as

strong support for model 1. The BEAST input files

(xml-files) are available as Supporting online material.

As a different approach for assessing geographic loca-

tions of ancestors and migration directionalities of con-

tinuously distributed species, we used the method

implemented in PHYLOMAPPER 1.b1 (Lemmon & Lemmon

2008). Briefly, using a spatial random walk model of

migration, it calculates the likelihood of geographic coor-

dinates of clade ancestors (i.e., specified internal nodes)

and the mean per-generation dispersal distance (which

may be treated as a nuisance parameter), given the geo-

graphic coordinates of the sampled individuals (i.e., tree

terminals) and assuming tree topology and branch

lengths to be known without error (Lemmon & Lemmon

2008). We took the maximum clade posterior probability

tree (determined with the TreeAnnotator module of

BEAST) from the BEAST analysis with the best sup-

ported demographic model (see Results), complemented

with dummy outgroup sequences (required for defini-

tion of clades). If populations included individuals with

identical haplotypes, only one individual was retained.

We estimated likelihood surfaces for the parameter of

interest, i.e., geographic location of the ancestor, using 1�steps on a geographic grid with a longitudinal extension

from 116 to 95� E and a latitudinal extension from 14 to

40� N (resulting in 594 grid points), thus safely covering

the current as well as the putative paleodistribution of

M. leucanthum (see Results). These analyses were per-

formed on the whole data set as well as on the three

main haplotype groups (see Results). As the western

haplotype group is not mono- but paraphyletic (see

Results), we pruned sequences of the other haplotype

groups prior to the analysis. All analyses included 1000

optimization iterations.

Ecological niche modelling

To model the ecological niche and geographic distribu-

tion of Melampodium leucanthum, spatially interpolated

climate data on grids with a resolution of 2.5 arc-min

were obtained from the WorldClim database (Hijmans

et al. 2005; available from http://www.worldclim.org/),

which is based on data from the PMIP2-project (http://

pmip2.lsce.ipsl.fr) and consists of 19 bioclimatic vari-

ables (Table S1). For reconstruction of the geographic

distribution during the last glacial maximum (LGM;

c. 21 000 years BP), two coupled climate models, which

have been successfully used in the past (e.g., Carstens

& Richards 2007; Waltari et al. 2007; Cordellier &

Pfenninger 2009), were used: the Community Climate


System Model version 3 (CCSM3: Collins et al. 2006)

and the Model for Interdisciplinary Research on Cli-

mate version 3.2 (MIROC3.2: Hasumi & Emori 2004).

The list of localities of M. leucanthum (Table 1) was

augmented with data from our own collections and pre-

viously published data (Stuessy et al. 2004), resulting in

a final data set of 164 entries. Distribution modelling

was performed using MAXENT 3.2.19 (available from

http://www.cs.princeton.edu/~schapire/maxent/),

which uses the maximum entropy method. Using pres-

ence-only data, it estimates a target probability distribu-

tion by finding the maximum entropy probability

distribution with the constraint that the expected value

of each feature should match its empirical average

(Phillips et al. 2006). The model for the current distribu-

tion was calculated using all 19 bioclimatic variables

and was in the following applied to the bioclimatic

variables of CCSM and MIROC, respectively. Perfor-

mance of this model was evaluated by the area under

the receiver operating characteristic (ROC) curve

(AUC), which ranges from 0.5 (random prediction) to 1

(maximum prediction), and a binomial test of omission

with the default convergence threshold (10)5) and the

maximum number of iterations set to 500, using 25% of

localities for model training (Phillips et al. 2006). The

relative contribution of each variable was assessed via

the increase in gain (a measure of model fit) of the

model for a given environmental variable in the train-

ing set. An alternative test for determining which vari-

ables are the most important ones employs a jackknife

procedure and compares models with single variables

(assessing the model gain from one variable) and mod-

els with all variables except one to the full model

(assessing the decrease of model gain when not consid-

ering one variable), again using the training set. Model

predictions were visualized in ARCMAP 9.3 (ESRI, Red-

lands, CA, USA).

Results

DNA ploidy level

DNA ploidy level analyses with flow cytometry of all

molecularly investigated individuals showed the pres-

ence of diploid and tetraploid cytotypes (Table 1), their

ploidy levels being confirmed by chromosome counts of

selected populations (Weiss-Schneeweiss et al. 2009; data

not shown). In only three diploid populations, intrapop-

ulational cytotype mixture with triploids was found.

AFLP

The five AFLP primer combinations chosen for the anal-

ysis generated 691 unambiguously scorable fragments:


EcoRI-ACA ⁄ MseI-CAT (FAM), 162; EcoRI-ACG ⁄ MseI-CAA (VIC), 141; EcoRI-ACC ⁄ MseI-CAG (NED), 84;

EcoRI-ACT ⁄ MseI-CAC (FAM), 155; EcoRI-AGG ⁄ MseI-CAA (VIC), 149. All 377 individuals investigated had a

unique AFLP profile. The error rate based on pheno-

typic comparisons among replicated individuals (Bonin

et al. 2004) amounted to 4%.

The total number of AFLP fragments per population

ranged from 171 to 382 (mean ± SD 273.7 ± 35.1), with

0–40.9% (mean ± SD 23.4 ± 7.1) being polymorphic

and 0–3 (median 0) private bands. The distribution of

the genetic diversity estimated with AWD (mean ± SD)

ranged from 0.077 ± 0.192 (population 15) to

0.188 ± 0.240 (population 48; Table 1). None of these

descriptors suggested any significant geographic pat-

(a) (b

(c) (d)

Fig. 2 Genetic structure of Melampodium leucanthum inferred from A

data set and (b) of one excluding the western populations; (c) Neighb

a data set excluding the tetraploid populations and those of unknow

the western (red), central (blue), eastern (green) and tetraploid gro

lighter green in case of the eastern group). Inserts in (d) as in Figure

tern (P-values >0.05) with the exception of AWD,

which showed a weak yet significant positive relation-

ship with longitude (slope 0.0016, P = 0.0068), i.e.,

AWD values were higher in the east than in the west.

PCO conducted on the whole data set resulted in a

clear separation of a western group comprising the

populations west of the Continental Divide (pops. 1–

17) from the remaining ones (Fig. 2a). After exclusion

of these western populations, a group of several, yet

not all, tetraploid populations (pops. 75–84) was sepa-

rated from the rest (Fig. 2b). The remaining tetraploid

individuals grouped together with diploids in one big

group, which was weakly differentiated into a central

and an eastern subgroup (Fig. 2b). Results from the

neighbour-net network (Fig. 2c) are highly congruent

)

FLP data. (a) Principal Coordinate Analysis (PCO) of the whole

our-net analysis of the whole data set; (d) STRUCTURE analysis of

n ploidy level (indicated by small black dots). Colours refer to

up (grey). In (c), tetraploids are indicated by thick lines (and

1.



with those from the PCO. Specifically, the western

populations are clearly distinct from the remaining

populations, and those tetraploids, which were sepa-

rated in the second PCO analysis, constitute two well-

separated subgroups. All triploid individuals included

in the analysis fall within their diploid source popula-

tions. The second analysis conducted on the reduced

data set did not reveal any new structure (data not

shown). In an AMOVA on the whole data set with four

groups identified in the PCO, 22.37% of the variance

was accounted for by variation among groups, 23.27%

among populations within the groups and 54.36%

within populations (P < 0.001 in all cases). Excluding

the western populations (using only three groups)

gave similar results with 17.37% of the variance

accounting for among-group variation, 25.59% for vari-

ation among populations within a group and 57.04%

for variation within populations (P < 0.001 in all

cases).

Nonhierarchical clustering of only diploid individuals

using STRUCTURE suggested K = 3 clusters as the optimal

solution (Fig. S1), irrespective of whether an admixture

or a no-admixture model was used. The geographic dis-

tribution of these three groups, which are concordant

with the ones found in the PCO (excluding the tetra-

ploid populations), is shown in Fig. 2d. Whereas popu-

lations of the western cluster showed no or negligible

admixture, populations in the southern part of the con-

tact zone between the other two clusters showed clear

signs of admixture (Fig. 2d).

Table 2 Number of individuals of tetraploid populations (in rows; nu

as in Table 1) or remaining unassigned (columns) using a cut-off leve

48 49 71

69 4 (4)

72 5 (5)

73 3 (4)

74 4 (4)

75 1 (1) 2 (2)

76 2 (2) 0 (1)

77 2 (5)

78 1 (3) 1 (2)

79

80 0 (3)

81 1 (2) 1 (2)

82 1 (3)

83 1 (5)

84 3 (3)

86

88

89

93 1 (4)

Sum 15 (27) 6 (15) 12 (13)


Genetic assignment tests congruently suggested six

populations (pops. 48, 49, 71, 85, 87, 92) as the most

likely sources for the majority of tetraploid individuals.

Qualitatively similar results were obtained when using

the more stringent cut-off level of two, which led to a

higher proportion of individuals remaining unassigned

(Table 2). Four of the potential source populations

(pops. 71, 85, 87, 92) are in close proximity to the tetra-

ploid populations, whereas two (pops. 48, 49) are

located further to the west (Fig. 1). The genetic differ-

entiation of the tetraploid populations (Fig. 2b, c) is not

reflected in their assignments to diploid putative source

populations.

cpDNA data

Combining psbA-trnH (359–427 bps), rpl32-trnL (679–

1057 bp) and ndhF-rpl32 (795–1020 bp) resulted in an

aligned data matrix of 2584 characters, of which, after

conversion of microinversions, 44 were variable. After

recoding indels and inversions as single characters and

removal of mononucleotide repeats, the alignment of

2000 bp included 42 variable characters, of which 30

were parsimony-informative.

The SAMOVA suggested K = 6 groups, whose distribu-

tion is shown in Fig. 3a. Using these six groups,

75.97% of the genetic variation is found among groups,

12.33% among populations within groups and 11.70%

within populations (all P < 0.0001). A similar apportion-

ment is obtained with K = 3 groups (Fig. 3a), i.e., the

mbers as in Table 1) assigned to diploid populations (numbers

l of two or, in parentheses, of 0

85 87 92 Unassigned

0

0

1 (0)

0

0 (1) 1 (0)

1 (1) 1 (0)

3 (0)

3 (0)

1 (1) 4 (4) 0

1 (1) 3 (0)

0 (1) 3 (0)

2 (0)

4 (0)

0 (1) 1 (1) 1 (0)

1 (2) 0 (1) 2 (0)

2 (2) 0 (1) 1 (0)

1 (3) 1 (2) 3 (0)

0 (1) 4 (0)

7 (11) 1 (4) 5 (8) 32 (0)

(a)

(b)

(c)

Fig. 3 Genetic structure of Melampodium leucanthum inferred from plastid sequence data. (a) spatial analysis of molecular variance

(SAMOVA); (b) statistical parsimony network (unsampled haplotypes indicated by ticks); (c) relaxed clock Bayesian analysis with a

demographic model of constant population size (node heights correspond to median ages; clades have posterior probabilities of 0.99

or more unless noted otherwise, their size being proportional to the height of the triangles; terminals are populations numbered as in

Table 1; scale bar with increments of 0.25 million years). The three main groups are indicated by colours. In (a), additionally the cir-

cumscription of the groups with K = 6, which has the highest FCT value, is indicated by different shades of red (western group) and

green (eastern group). Populations harbouring haplotypes belonging to two different groups are highlighted in (a) by an outline col-

our corresponding to the second group involved or in (c) by a larger font of the population numbers. Inserts in (a) as in Figure 1.


number of groups identified with network and tree

methods, with 72.91% of the genetic variation among

groups, 16.03% among populations within groups and

11.03% within populations (all P < 0.0001).

Using statistical parsimony, all 78 haplotypes are

joined in a single network (Fig. 3b). Of those, 38 were

found in more than one individual, whereas the

remaining ones are singletons (Fig. S2). The haplotype

network falls into three haplotype groups, which corre-

spond to the ones identified in a SAMOVA with K = 3, dif-

ferences concerning those populations, which harbour

haplotypes from two different haplotype groups. These

three groups are separated from each other by at least

four mutational steps (Fig. 3b) and are geographically

separated along a longitudinal gradient (Fig. 3a). Nota-

bly, the western haplotype group crosses the Continen-

tal Divide and extends east of the upper Rio Grande.

The majority of populations are monomorphic, whereas

one-third of populations comprise haplotypes separated

by single steps only or haplotypes separated by several

mutational steps but still belonging to the same haplo-

type groups (Fig. 3a). Three populations possess haplo-

types belonging to two different haplotype groups

(pops. 40, 47, 56; Fig. 3a).

For population expansion tests, we used the whole

data set, the three haplotype groups suggested by the

statistical parsimony network, and the haplotype

groups delimited by a SAMOVA with K = 3, because the


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only slightly better solution of K = 6 resulted in three

small and genetically homogeneous groups not amena-

ble to mismatch distribution analysis. Population expan-

sion in the whole data set is supported by Fu’s FS and

by the sum of squared differences test of the mismatch

distribution (Table 3), although the plot of the observed

pairwise differences was multimodal (Fig. S3), as

expected for a structured population (Schneider & Ex-

coffier 1999). Whereas there was congruent strong evi-

dence for population expansion in the central group, no

population expansion was inferred for the western

group (Table 3). Evidence for population expansion in

the eastern group is ambiguous and supported only by

Fu’s FS and a unimodal plot of the observed pairwise

differences (Fig. S3), but not by the sum of squared dif-

ferences test (Table 3). Estimates for the time of expan-

sion varied considerably depending on the generation

time used, ranging from 77 to 193 kyr for the whole

data set (confidence limits 9–581 kyr). If the central and

eastern subgroups are considered separately, their

expansion times are largely congruent, ranging from 32

to 34.5 kyr with long generation time to c. 75–86 kyr

with short generation time, again with wide confidence

intervals from 0.5 to 173 kyr (Table 3).

Bayesian skyline plots with different group intervals

(m = 20, 30, 40) gave similar results (the absolute value

of 2 · lnBF <2.6) with no obvious indications for popu-

lation size changes through time and were indeed

rejected in favour of the simpler model of constant pop-

ulation size through time (2 · lnBF <)11). Likewise, a

model of different constant population sizes for each

haplotype group was rejected in favour of a model of

one constant population size (2 · lnBF -6.8). Under a

model of constant population size, the diversification

age of the whole species (given as mean ⁄ median and,

in parentheses, its 95% highest posterior density inter-

val) is estimated to be 2.22 ⁄ 1.43 (0.24–7.11) million years

ago (that is more than twice as old as inferred under

the Bayesian Skyline Plot model; data not shown). The

maximum clade posterior probability tree as well as a

50% majority rule consensus tree revealed the same

three groups as the TCS network with eastern and cen-

tral groups as monophyletic clades (mean ⁄ median ages

of 0.64 ⁄ 0.37 and 0.65 ⁄ 0.37, respectively, with 95% high-

est posterior density intervals of 0.05–2.12 and 0.03–

2.21, respectively) and the western group as a paraphy-

letic grade (Fig. 3c). Explicit testing the locations of the

source for the whole species range expansions provides

negligible to positive evidence for a central refugium

compared with an eastern (2 · lnBF 1.822) or a western

refugium (2 · lnBF 5.098). Monophyly of the polyploids

is clearly rejected (2 · lnBF -64.942).

Results of the maximum likelihood approach imple-

mented in PHYLOMAPPER for inferring ancestral locations



were unstable, and different runs of 1000 optimizations

on the same tree resulted in sometimes largely different

geographic coordinates (data not shown). This behav-

iour was not restricted to the maximum clade posterior

probability tree but also occurred in other posterior

trees tested (data not shown). Ancestral location likeli-

hood surfaces were flat over large parts of the covered

geographic range (Fig. S4) and only small areas (0.5%

of grid points for the whole species, 4.7% to 8.6% of

grid points for the three haplotypes groups) could be

rejected as ancestral locations using a doubled likeli-

hood difference of two or more. In the whole species

data set, 72.4% of grid points had better likelihood

scores (up to 0.173 log units) than the maximum likeli-

hood locality identified after optimizations and used to

obtain the dispersal parameter values. Similar values

were obtained for the western and the central haplotype

group (24.9% and 35.2% of grid points with likelihood

scores better up to 0.011 and 0.053 log units, respec-

tively). Consequently, inference of the ancestral loca-

tions for these three data sets was not sensibly possible.

For the eastern haplotype group, only 1.7% of grid

points had better likelihood scores (up to only 0.004 log

units), and a region of unlikely ancestral location was

inferred for an area between 25–34� N and 101–108� W,

thus covering major parts of the Chihuahuan Desert

(Fig. 4).
Fig. 4 Contour graph of the likelihood surface of ancestral
locations for the eastern haplotype group of Melampodium leu-

canthum. Darker colours indicate higher doubled log-likelihood

differences, i.e., less likely ancestral locations. Pixels represent

1�·1� cells centred at the sampled points, which cover an area

between 116–95� E and 14–40� N. Coastline shown for the last

glacial maximum.

Ecological niche modelling

Model predictive performance of the bioclimatic model

was high with AUC values of 0.98 for the training data

and 0.96 for the test data and a highly significant bino-

mial test of omission (P < 0.001) and a consequently

high fit of the modelled and the actually observed cur-

rent distribution (Fig. 5a). The most important environ-

mental variables, assessed with the heuristic estimates

of relative contributions, were mean temperature of

wettest quarter (bio08), the mean temperature of coldest

quarter (bio11) and the precipitation of warmest quarter

(bio18; 20.9 20.8 and 20.6, respectively). Jackknife tests

of variable importance on different sets and test statis-

tics congruently suggested the precipitation of warmest

quarter (bio18), the mean temperature of coldest quarter

(bio11) and the precipitation of wettest quarter (bio16)

as the most important variables (Fig. S5).

Both climatic models used, CCSM3 and MIROC3.2,

indicate two distinct areas of environmental suitability

(Fig. 5b, c), but these differ in their extent and their

precise locations. Whereas in the western area this

mostly affects the latitudinal extent with otherwise simi-

lar distribution of highly suitable regions in the north-

ern Sonora, in the eastern area it affects both latitudinal

extension and longitudinal position. Specifically, under

the CCSM3 model, regions with highest predicted spe-

cies occurrence are found east of 102�W, thus mostly

falling into the Tamaulipan Plains, while under the MI-

ROC3.2 model, the less compact region of highest pre-

dicted species occurrence is found mostly west of

101�W, thus being essentially restricted to the current

Chihuahuan Desert (Fig. 5b, c).

Discussion

Southwestern North America faced dramatic changes in

the Holocene with a shift from woodlands to the cur-

rently widespread desert vegetation, and this was

accompanied by range expansions of drought-adapted

species from their refugia into the newly forming habi-

tats (Van Devender 1977; Van Devender & Spaulding

1979; Hunter et al. 2001; Jaeger et al. 2005; Haenel

2007). Based on fossil and paleoclimatic data as well

as on phylogeographic inferences, refugia have been


(a)

(b)

(c)

Fig. 5 Ecological niche modelling of Melampodium leucanthum

for (a) the present and (b, c) the last glacial maximum (c.

21 000 years BP). The paleodistribution was modelled using (b)

the CCSM3 and (c) the MIROC3.2 climatic models (see text for

details). White dots represent the 164 localities of M. leucant-

hum used for ecological niche modelling and cover the entire

range of the species. Darker colours indicate higher climatic

suitability. In (b) and (c), the coastline is that of the last glacial

maximum.



suggested in the western and southern parts of the cur-

rent deserts, such as the lower Colorado Basin, the east-

ern Sonoran or the southern Chihuahuan Desert (Van

Devender 1977; Van Devender & Spaulding 1979; Hun-

ter et al. 2001; Riddle & Hafner 2006; Castoe et al. 2007;

Holmgren et al. 2007; Fehlberg & Ranker 2009; Sosa

et al. 2009).

The presence of three genetically clearly separated

(more than 70% of genetic variance are explained by

among-group variation) and longitudinally arranged

haplotype groups in M. leucanthum (Fig. 3), which lar-

gely correspond to three major AFLP groups identified

by PCO and STRUCTURE analyses (Fig. 2), suggests three

separate refugia. Ecological niche modelling supports

two major geographically separate refugia west and east

of the Continental Divide (Fig. 5), but these were prob-

ably not homogeneous, but rather consisted of multiple

refugia at least east of the Continental Divide (Castoe

et al. 2007; Haenel 2007). As suggested by the location

of ecologically suitable areas at the LGM (Fig. 5b, c),

the refugium for the western haplotype group is proba-

bly tied to the lower Colorado basin refugium identified

previously (Hunter et al. 2001; Jaeger et al. 2005; Castoe

et al. 2007; Fehlberg & Ranker 2009). Inferences for

M. leucanthum east of the Continental Divide are more

difficult, because the circumscription of ecologically

suitable areas at the LGM differs considerably between

the two climatic models used (Fig. 5b, c). Both models

congruently support a refugium in the central to south-

ern Chihuahuan Desert, which has been repeatedly

identified for desert plants and animals (Hunter et al.

2001; Riddle & Hafner 2006; Castoe et al. 2007) and

probably harboured the refugium for the central haplo-

type group. Although the eastern haplotype group

might also be connected to the Chihuahuan refugium, a

more easterly refugium around the Tamaulipan Plains

(Castoe et al. 2007; Rebernig et al. 2010) remains plausi-

ble and finds support from the CCSM3 climatic model

(Fig. 5b). A refugium east of the Chihuahuan Desert is

also supported by the ancestral location inferred with

PHYLOMAPPER (Fig. 4). For the other groups, this method

gave, however, noninterpretable results (Fig. S4). Rea-

sons for this weak performance may include insufficient

signal in our data or deficiencies in the underlying

models, such as the single spatially and temporarily


constant dispersal parameter. A more detailed assess-

ment of these issues is, however, beyond the scope of

this paper and will require more extensive simulation

studies. In summary, the refugia inferred for M. leu-

canthum are congruent with those previously suggested

by paleoclimatic and phylogeographic data for both

plants and animals (Hunter et al. 2001; Jaeger et al.

2005; Riddle & Hafner 2006; Castoe et al. 2007; Haenel

2007; Holmgren et al. 2007; Fehlberg & Ranker 2009).

Plastid and AFLP data are congruent with respect to

number and location of refugia, but because the fast-

evolving AFLPs probably trace more recent events

(Kropf et al. 2009) than plastid sequences, they probably

show the signal of different time levels. Although no

direct age estimates can be obtained from AFLP data

(the presence of an AFLP clock suggested by Kropf

et al. 2009 is still contentious: Ehrich et al. 2009), their

signal probably reflects Late Quaternary differentiation

possibly as recent as the last glacial maximum. In con-

trast, lineage differentiation identified in the plastid

data is deeply within the Pleistocene and might date

back to the Tertiary (Fig. 3c). Unless M. leucanthum

remained restricted to these refugia over an extended

period of time, the evidence for refugia at different time

levels suggests that this species responded to the Qua-

ternary climatic oscillations in a cyclic manner (Stewart

et al. 2010).

Paleoclimatic data suggest that the Holocene aridifica-

tion progressed from the west to the east (Holmgren

et al. 2007) with desert species occurring earlier in the

Sonoran Desert (Van Devender 1990; McAuliffe & Van

Devender 1998). Thus, it may be expected that range

expansion of drought-adapted species would follow the

same general direction. For M. leucanthum, this hypoth-

esis finds, however, no support from our data.

Although the topology from the Bayesian analysis is in

line with the hypothesis of an eastward migration start-

ing from an ancestral western lineage (Fig. 3c), explicit

hypothesis testing provides evidence for a central or

eastern origin instead. Furthermore, there is no evi-

dence for eastwardly decreasing times of range expan-

sion (Table 3), a loss of average within-population

diversities inferred from AFLP data or the number of

polymorphic or private AFLP fragments (Table 1), as

would be expected if colonization progressed east-

wards. Instead, both plastid and AFLP data (Figs. 2, 3)

indicate that independent range shifts occurred out of

several Pleistocene refugia, a widespread pattern in

southern North American desert biota (Hunter et al.

2001; Jaeger et al. 2005; Castoe et al. 2007; Fehlberg &

Ranker 2009; Garrick et al. 2009; Rebernig et al. 2010).

A major obstacle for longitudinal migration is the

Continental Divide. Since its initial formation because

of the uplift of the Colorado Plateaus and the Sierra

Madre Occidental in the late Miocene to early Pliocene

(Morafka 1977), it was an effective barrier for exchange

between desert biota in this region (Morafka 1977; Mor-

afka et al. 1992; Hunter et al. 2001; Jaeger et al. 2005;

Riddle & Hafner 2006; Castoe et al. 2007). The effective-

ness of this barrier explains the strong phylogeographic

split between populations on both sides of the Conti-

nental Divide seen in the AFLP data (Fig. 2). It has

been suggested that Pleistocene climatic fluctuations in

combination with a relatively broad ecological ampli-

tude allowed migrations between western and eastern

deserts (Jaeger et al. 2005; Riddle & Hafner 2006; Castoe

et al. 2007). This appears also to be the case for M. leu-

canthum, whose migration across the Continental

Divide, albeit only in an easterly direction, is evident

from the extension of the western haplotype group

across this barrier (Fig. 3a). The discrepancy between

plastid and AFLP data is likely due to the rapid homog-

enization of AFLPs (because of repeated backcrossing of

hybrids between resident and immigrant genotypes

with the resident ones, Zhou et al. 2005), which, in con-

trast to plastid data, will quickly erase traces of gene

flow across the Continental Divide.

Reduced migration possibilities across the Continental

Divide probably contribute to the contrasting range

dynamics of populations west and east of it. In the

west, ecologically suitable areas at the LGM were geo-

graphically close and of comparable extent to the cur-

rent distribution area in this region (Fig. 5). Supported

by the lack of signal for range expansion (Table 3), this

indicates that in this region postglacial range shifts and

population size changes were of limited magnitude

(Castoe et al. 2007). In contrast, east of the Continental

Divide, the presumptive refugial areas were much

smaller than the current distribution area (Fig. 5),

implying major postglacial range expansion. This is

supported by signals for range expansion in both the

central and the eastern haplotype group (Table 3). The

stronger signal for range expansion in the central haplo-

type group, which is also reflected in the rather star-like

structure in the haplotype network (Fig. 3b), might be

because of a more rapid colonization or more strongly

reduced population sizes in a smaller refugium

(Fig. 5b, c), but further data are necessary to distin-

guish among these hypotheses.

In the course of range expansions from separate refu-

gia, lineages, which differentiated in isolation, came

into secondary contact and started to intermix. This is

evident from the co-occurrence of plastid haplotypes of

different haplotype groups within the same populations

(Fig. 3) as well as the strong signal for genetic admix-

ture (Fig. 2) in some populations east of the Continental

Divide as the result of extensive interpopulational

gene flow (M. leucanthum is an obligate outcrosser).



Secondary contact zones are well known from other

regions, where Pleistocene climatic fluctuations caused

major range shifts (Taberlet et al. 1998; Soltis et al. 2006;

Castoe et al. 2007; Fehlberg & Ranker 2009). The

secondary contact of differentiated lineages evidenced

by the AFLP data is probably connected to range

expansions during the Holocene aridification (Van

Devender 1977; Webb 1977; Van Devender & Spaulding

1979; Holmgren et al. 2007; Holliday et al. 2008), but

this cannot be directly tested with the AFLP data.

Range expansions for the central and eastern haplotype

groups (there is no evidence for range expansion in the

western haplotype group) are dated to the late Pleisto-

cene (Table 3), but these estimates are burdened with

wide confidence intervals partly extending to the Holo-

cene, although only under an assumed generation time

of 5 years. These time estimates might be biased

towards older ages because of the time dependency of

molecular rates (Ho et al. 2005, 2007), although its effect

appears to be of smaller magnitude than initially antici-

pated (Debruyne & Poinar 2009). Consequently, it

remains uncertain whether the range expansions

inferred from the plastid data are connected to the

Holocene aridification or to earlier phases of warmer

and drier climate (Allen & Anderson 2000).

The distribution pattern of tetraploids in M. leucant-

hum is conspicuous, because this cytotype is found

exclusively in the eastern part of range of the species

(Stuessy et al. 2004; Fig. 2d). Despite this geographic

distinctness, tetraploids do not form a genetically cohe-

sive group (Fig. 2), which contrasts with the pattern

observed in the closely related M. cinereum (Rebernig

et al. 2010). Instead, a monophyletic origin of polyp-

loids is clearly rejected by the plastid data (2 · lnBF

<)64.942). Furthermore, AFLP data detect both distinct

polyploid lineages as well as polyploids that usually

intermix with those diploid populations they were

assigned to (Table 2), thus supporting the hypothesis of

multiple origins. The presence of triploid individuals in

diploid populations (Table S2; Stuessy et al. 2004; R.

Obermayer et al. unpublished) suggests that the polyp-

loids formed via unreduced gametes. Despite their

reduced viability (triploids show a higher amount of

aborted pollen, data not shown), the number of resul-

tant triploids could be sufficient to act as triploid bridge

(Ramsey & Schemske 1998, 2002; Leitch & Leitch 2008).

The alternative, not mutually exclusive, hypothesis for

explaining the genetic heterogeneity of tetraploids is

gene flow between cytotypes (Gauthier et al. 1998),

which may be facilitated by the lack of any ecological

or breeding system differentiation among cytotypes but

is counteracted by their geographic cohesiveness. The

restriction of tetraploids to the eastern edge of the dis-

tribution of the species might be attributed to establish-


ment during geographic isolation in phases of climatic

deterioration, as suggested for M. cinereum (Rebernig

et al. 2010), but further data on the actual dynamics at

the contact zone are necessary to test this or alternative

hypotheses.

The Holocene aridification in southwestern North

America undoubtedly had a major impact on the phy-

logeography and population history of drought-adapted

species (Van Devender & Spaulding 1979; Spaulding

1990; Van Devender 1990; Castoe et al. 2007; Holmgren

et al. 2007). In M. leucanthum, phases of restriction to

multiple refugia, which enhanced lineage differentiation

and possibly also polyploid establishment, alternated

with phases of range expansions and secondary contact,

the Continental Divide currently being the only major

migration barrier. These dynamics resulted in a com-

plex phylogeographic history in this seemingly homoge-

neously distributed species.

Acknowledgements

The authors thank Michael Lenko (University of Vienna, Aus-

tria), Enrique Ortiz (UNAM, Mexico), Monique Reed and Hugh

Wilson (Texas A&M University, U.S.A.), and Donovan Bailey

and Patrick Alexander (New Mexico State University, U.S.A.)

for help with material collection. We thank Gudrun Kohl (Uni-

versity of Vienna, Austria) for technical assistance and Sabine

Jakob (IPK Gatersleben, Germany) for help with the bioclimatic

data. We thank two anonymous reviewers for helpful criti-

cisms. The study was financially supported by Austrian Science

Fund (FWF) grants no. P18201-B03 (to TFS) and Hertha-Firn-

berg postdoctoral fellowship T-218 (to HWS).

References

Abbott RJ, Brochmann C (2003) History and evolution of the

arctic flora: in the footsteps of Eric Hulten. Molecular Ecology,

12, 299–313.

Allen BD, Anderson RY (2000) A continuous, high-resolution

record of late Pleistocene climate variability from the

Estancia basin, New Mexico. The Geological Society of America

Bulletin, 112, 1444–1458.

Baack EJ, Stanton ML (2005) Ecological factors influencing

tetraploid speciation in snow buttercups (Ranunculus

adoneus): niche determination and tetraploid establishment.

Evolution, 59, 1936–1944.

Bloch C, Weiss-Schneeweiss H, Schneeweiss GM et al. (2009)

Molecular phylogenetic analyses of nuclear and plastid DNA

sequences support dysploid and polyploid chromosome

number changes and reticulate evolution in the

diversification of Melampodium (Millerieae, Asteraceae).

Molecular Phylogenetics and Evolution, 53, 220–233.

Bonin A, Bellemain E, Eidesen PB et al. (2004) How to track

and assess genotyping errors in population genetics studies.

Molecular Ecology, 13, 3261–3273.

Bousman BC (1998) Paleoenvironmental change in central

Texas: the palynological evidence. Plains Anthropologist, 43,

201–219.


Brunsfeld SJ, Sullivan J, Soltis DE, Soltis PS (2001) Comparative

phylogeography of northwestern North America: a

synthesis. In: Integrating Ecology and Evolution in a Spatial

Context (eds Silverton J, Antonovics J). pp. 319–339,

Blackwell Science, Oxford.

Carstens BC, Richards CL (2007) Integrating coalescent and

ecological niche modeling in comparative phylogeography.

Evolution, 61, 1439–1454.

Castoe TA, Spencer CL, Parkinson CL (2007) Phylogeographic

structure and historical demography of the western

diamondback rattlesnake (Crotalus atrox): a perspective on

North American desert biogeography. Molecular Phylogenetics

and Evolution, 42, 193–212.

Clark-Tapia R, Molina-Freaner F (2003) The genetic structure of

a columnar cactus with a disjunct distribution: Stenocereus

gummosus in the Sonoran desert. Heredity, 90, 443–450.

Clement M, Posada D, Crandall KA (2000) TCS: a computer

program to estimate gene genealogies. Molecular Ecology, 9,

1657–1660.

Collins WD, Bitz CM, Blackmon ML et al. (2006) The

Community Climate System Model Version 3 (CCSM3).

Journal of Climate, 19, 2122–2143.

Cordellier M, Pfenninger M (2009) Inferring the past to predict

the future: climate modelling predictions and

phylogeography for the freshwater gastropod Radix balthica

(Pulmonata, Basommatophora). Molecular Ecology, 18, 534–

544.

Coyne JA, Orr HA (2004) Speciation, Sinauer, Sunderland, MA.

Cruden RW (1977) Pollen-ovule ratios: a conservative indicator

of breeding systems in flowering plants. Evolution, 31, 32–46.

Debruyne R, Poinar HN (2009) Time dependency of molecular

rates in ancient DNA data sets, a sampling artifact?

Systematic Biology, 58, 348–359.

Dixon CJ, Schonswetter P, Schneeweiss GM (2008) Morpho-

logical and geographical evidence are misleading with

respect to the phylogenetic position and origin of the narrow

endemic polyploid Androsace cantabrica (Primulaceae).

Systematic Botany, 33, 384–389.

Drummond AJ, Rambaut A (2007) BEAST: Bayesian

evolutionary analysis by sampling trees. BMC Evolutionary

Biology, 7, 214 (doi:10.1186/1471-2148-7-214).

Duchesne P, Bernatchez L (2002) AFLPOP: a computer

program for simulated and real population allocation, based

on AFLP data. Molecular Ecology Notes, 2, 380–383.

Dupanloup I, Schneider S, Excoffier L (2002) A simulated

annealing approach to define the genetic structure of

populations. Molecular Ecology, 11, 2571–2581.

Ehrich D, Eidesen PB, Alsos IG, Brochmann C (2009) An AFLP

clock for absolute dating of shallow-time evolutionary

history – too good to be true? Molecular Ecology, 22, 4526–

4532.

Excoffier L, Laval LG, Schneider S (2005) Arlequin (version

3.0): an integrated software package for population genetics

data analysis. Evolutionary Bioinformatics Online, 1, 47–50.

Falush D, Stephens M, Pritchard JK (2007) Inference of

population structure using multilocus genotype data:

dominant markers and null alleles. Molecular Ecology Notes,

7, 574–578.

Fehlberg SD, Ranker TA (2009) Evolutionary history and

phylogeography of Encelia farinosa (Asteraceae) from the

Sonoran, Mojave, and Peninsular Deserts. Molecular

Phylogenetics and Evolution, 50, 326–335.

Fontanella FM, Feldman CR, Siddall ME, Burbrink FT (2008)

Phylogeography of Diadophis punctatus: extensive lineage

diversity and repeated patterns of historical demography in

a trans-continental snake. Molecular Phylogenetics and

Evolution, 46, 1049–1070.

Fu XY (1997) Statistical tests of neutrality of mutations against

population growth, hitchhiking and background selection.

Genetics, 147, 915–925.

Garrick RC, Nason JD, Meadows CA, Dyer RJ (2009) Not just

vicariance: phylogeography of a Sonoran Desert euphorb

indicates a major role of range expansion along the Baja

peninsula. Molecular Ecology, 18, 1916–1931.

Gauthier P, Lumaret R, Bedecarrats A (1998) Genetic variation

and gene flow in alpine diploid and tetraploid populations

of Lotus (L. alpinus (D.C.) Schleicher ⁄ L. corniculatus L.). I.

Insights from morphological and allozyme markers. Heredity,

80, 683–693.

Haenel GJ (2007) Phylogeography of the tree lizard Urosaurus

ornatus: responses of populations to past climate change.

Molecular Ecology, 16, 4321–4334.

Hall TA (1999) BioEdit: a user-friendly biological sequence

alignment editor and analysis program for Windows

95 ⁄ 98 ⁄ NT. Nucleic Acids Symposium Series, 41, 95–98.

Hasumi H, Emori S (2004) K-1 coupled GCM (MIROC)

description. Technical Report 1, Center for Climate System

Research, University of Tokyo (http://www.ccsr.u-

tokyo.ac.jp/kyosei/hasumi/MIROC/tech-repo.pdf).

Hewitt GM (1996) Some genetic consequences of ice ages, and

their role in divergence and speciation. Biological Journal of

the Linnean Society, 58, 247–276.

Hewitt GM (2001) Speciation, hybrid zones and phylogeo-

graphy – or seeing genes in space and time. Molecular

Ecology, 10, 537–549.

Hewitt GM (2004) Genetic consequences of climatic oscillations

in the Quaternary. Philosophical Transactions of the Royal

Society London B Biological Sciences, 359, 183–195.

Hijmans RJ, Cameron SS, Parra JL, Jones PG, Jarvis A (2005)

Very high resolution interpolated climate surfaces for global

land areas. International Journal of Climatology, 25, 1965–1978.

Ho SYW, Phillips MJ, Cooper A, Drummond AJ (2005) Time

dependency of molecular rate estimates and systematic

overestimation of recent divergence times. Molecular Biology


Ho SYW, Shapiro B, Phillips MJ, Cooper A, Drummond AJ

(2007) Evidence for time dependency of molecular rate

estimates. Systematic Biology, 56, 515–522.

Holliday VT, Mayer JH, Fredlund GG (2008) Late Quaternary

sedimentology and geochronology of small playas on the

Southern High Plains, Texas and New Mexico, USA.

Quaternary Research, 70, 11–25.

Holmgren CA, Norris J, Betancourt JL (2007) Inference about

winter temperature and summer rains from the late

Quaternary record of C4 perennial grasses and C3 desert

shrubs in the northern Chihuahuan Desert. Journal of

Quaternary Science, 22, 141–161.

Hulber K, Sonnleitner M, Flatscher R et al. (2009) Ecological

segregation drives fine-scale cytotype distribution of Senecio

carniolicus in the Eastern Alps. Preslia, 81, 309–319.



Hunter KL, Betancourt JL, Riddle BR, Van Devender TR, Cole

KL, Spaulding WG (2001) Ploidy race distributions since

the Last Glacial Maximum in the North American desert

shrub, Larrea tridentata. Global Ecology and Biogeography, 10,

521–533.

Husband B (2004) Polyploidy and plant adaptation: a

framework for future research. In: Plant Adaptation: Molecular

Genetics and Ecology (eds Cronk QCB, Whitton J, Ree RH,

Taylor IEP). pp. 119–126, NRC Research Press, Ottawa.

Huson DH, Bryant D (2006) Application of phylogenetic

networks in evolutionary studies. Molecular Biology and

Evolution, 23, 254–267.

Ingvarsson PK, Ribstein S, Taylor DR (2003) Molecular

evolution of insertions and deletion in the chloroplast

genome of Silene. Molecular Biology and Evolution, 20, 1737–

1740.

Jaeger JR, Riddle BR, Bradford DF (2005) Cryptic Neogene

vicariance and Quaternary dispersal of the red-spotted

toad (Bufo punctatus): insights on the evolution of North

American warm desert biotas. Molecular Ecology, 14, 3033–

3048.

Kosman E (2003) Nei’s gene diversity and the index of average

differences are identical measures of diversity within

populations. Plant Pathology, 52, 533–535.

Kropf M, Comes HP, Kadereit JW (2009) An AFLP clock for

the absolute dating of shallow-time evolutionary history

based on the intraspecific divergence of southwestern

European alpine plant species. Molecular Ecology, 18, 697–708.

Leitch AR, Leitch IJ (2008) Genome plasticity and diversity of

polyploid plants. Science, 320, 481–483.

Lemmon AR, Lemmon EM (2008) A likelihood framework for

estimating phylogeographic history on a continuous

landscape. Systematic Biology, 57, 544–561.

Lohne C, Borsch T (2005) Molecular evolution and

phylogenetic utility of the petD group II intron: a case study

in basal angiosperms. Molecular Biology and Evolution, 22,

317–332.

McAuliffe JR, Van Devender TR (1998) A 22,000-year record of

vegetation change in the north-central Sonoran Desert.

Palaeogeography, Palaeoclimatology and Palaeoecology, 141, 253–

275.

McClaran MP, Van Devender TR (1995) The Desert Grassland,

University of Arizona Press, Tucson, AZ.

Metcalfe SE, O¢Hara SL, Caballero M, Davies SJ (2000) Records

of late Pleistocene-Holocene climatic change in Mexico – a

review. Quaternary Science Reviews, 19, 699–721.

Morafka DJ (1977) A biogeographic analysis of the Chihuahuan

desert through its herpetofauna. Biogeographica, 9, 1–313.

Morafka DJ, Adest GA, Reyes LM (1992) Differentiation of

North American deserts: a phylogenetic evaluation of a

vicariance model. In: Biogeography of Mesoamerica (eds

Darwin SP, Welden AL). pp. 195–226, Tulane Studies in

Zoology and Botany Special Publication, New Orleans, LA.

Musgrove M, Banner JL, Mack LE et al. (2001) Geochronology

of late Pleistocene to Holocene speleothems from central

Texas: implications for regional paleoclimate. Geological

Society of America Bulletin, 113, 1532–1543.

Nason JD, Hamrick JL, Fleming TH (2002) Historical vicariance

and postglacial colonization effects on the evolution of

genetic structure in Lophocereus, a Sonoran Desert columnar

cactus. Evolution, 56, 2214–2226.


Nei M, Li WH (1979) Mathematical model for studying genetic

variation in terms of restriction endonucleases. Proceedings of

the National Academy of Sciences of the United States of America,

76, 5269–5273.

Neilson RP (1986) High-resolution climatic analysis and

southwest biogeography. Science, 232, 27–34.

Otto SP, Whitton J (2000) Polyploidy incidence and evolution.

Annual Review of Genetics, 34, 401–437.

Phillips SJ, Anderson RP, Schapire RE (2006) Maximum

entropy modeling of species geographic distributions.

Ecological Modelling, 190, 231–259.

Pritchard JK, Stephens M, Donnelly PJ (2000) Inference of

population structure using multilocus genotype data.

Genetics, 155, 945–959.

Ramos-Onsins SE, Rozas J (2002) Statistical properties of new

neutrality tests against population growth. Molecular Biology


Ramsey J, Schemske DW (1998) Pathways, mechanisms, and

rates of polyploid formation in flowering plants. Annual

Review of Ecology and Systematics, 29, 467–501.

Ramsey J, Schemske DW (2002) Neopolyploidy in flowering

plants. Annual Review of Ecology and Systematics, 33, 589–639.

Ramsey J, Robertson A, Husband B (2008) Rapid adaptive

divergence in New World Achillea, an autopolyploid

complex of ecological races. Evolution, 62, 639–653.

Rebernig CA, Weiss-Schneeweiss H, Schneeweiss GM et al.

(2010) Quaternary range dynamics and polyploid evolution

in an arid brushland plant species (Melampodium cinereum,

Asteraceae). Molecular Phylogenetics and Evolution, 54, 594–

606.

Redelings BD, Suchard MA (2005) Joint Bayesian estimation of

alignment and phylogeny. Systematic Biology, 54, 401–418.

Rice WR (1989) Analyzing tables of statistical tests. Evolution,

43, 223–225.

Riddle BR, Hafner DJ (2006) A step-wise approach to

integrating phylogeographic and phylogenetic biogeographic

perspectives on the history of a core North American warm

desert biota. Journal of Arid Environments, 66, 435–461.

Rogers AR, Harpending HC (1992) Population growth makes

waves in the distribution of pairwise genetic differences.

Molecular Biology and Evolution, 9, 552–569.

Rohlf FJ (2007) NTSYSpc: Numerical Taxonomy System, ver. 2.20,

Exeter Publishing, Setauket, NY.

Rozas J, Sanchez-DelBarrio JC, Messeguer X, Rozas R (2003)

DnaSP, DNA polymorphism analyses by the coalescent and

other methods. Bioinformatics, 19, 2496–2497.

Schluter PM, Harris SA (2006) Analysis of multilocus

fingerprint data sets containing missing data. Molecular

Ecology Notes, 6, 569–572.

Schneider S, Excoffier L (1999) Estimation of past demographic

parameters from the distribution of pairwise differences

when the mutation rates vary among sites; application to

human mitochondrial DNA. Genetics, 152, 1079–1089.

Schonswetter P, Stehlik I, Holderegger R, Tribsch A (2005)

Molecular evidence for glacial refugia of mountain plants in

the European Alps. Molecular Ecology, 14, 3547–3555.

Schonswetter P, Lachmayer M, Lettner C et al. (2007)

Sympatric diploid and hexaploid cytotypes of Senecio

carniolicus (Asteraceae) in the Eastern Alps are separated

along an altitudinal gradient. Journal of Plant Research, 120,

721–725.


Soltis DE, Morris AB, McLachlan JS, Manos PS, Soltis PS (2006)

Comparative phylogeography of unglaciated eastern North

America. Molecular Ecology, 15, 4261–4293.

Soltis DE, Soltis PS, Schemske DW et al. (2007) Autopolyploidy

and sympatric speciation in angiosperms: have we grossly

underestimated the number of species? Taxon, 56, 13–30.

Sosa V, Ruiz-Sanchez E, Rodriguez-Gomez FC (2009) Hidden

phylogeographic complexity in the Sierra Madre Oriental:

the case of the Mexican tulip poppy Hunnemannia fumariifolia

(Papaveraceae). Journal of Biogeography, 36, 18–27.

Spaulding WG (1990) Vegetational and climatic development

of the Mojave Desert: the last glacial maximum to the

present. In: Packrat Middens: The Last 40,000 years of Biotic

Change (eds Betancourt JL, Van Devender TR, Martin PS).

pp. 167–199, University of Arizona Press, Tucson, AZ.

Stewart JR, Lister AM, Barnes I, Dalen L (2010) Refugia

revisited: individualistic responses of species in space and

time. Proceedings of the Royal Society London Series B Biological

Sciences, 277, 661–671.

Stuessy TF (1971) Systematic relationships in the white-rayed

species of Melampodium (Compositae). Brittonia, 23, 177–190.

Stuessy TF (1972) Revision of the genus Melampodium

(Compositae: Heliantheae). Rhodora, 74, 1–70, 161–219.

Stuessy TF, Weiss-Schneeweiss H, Keil DJ (2004) Diploid and

polyploid cytotype distribution in Melampodium cinereum and

M. leucanthum (Asteraceae, Heliantheae). American Journal of

Botany, 91, 889–898.

Suchard MA, Weiss RE, Sinsheimer JS (2001) Bayesian

selection of continuous-time Markov chain evolutionary

models. Molecular Biology and Evolution, 18, 1001–1013.

Suchard MA, Weiss RE, Dorman KS, Sinsheimer JS (2003)

Inferring spatial phylogenetic variation along nucleotide

sequences: a multiple change-point model. Journal of the

American Statistical Association, 98, 427–437.

Suchard MA, Weiss RE, Sinsheimer JS (2005) Models for

estimating Bayes factors with applications to phylogeny and

tests of monophyly. Biometrics, 61, 665–673.

Suda J, Krahulcova A, Travnıcek P, Krahulec F (2006) Ploidy

level versus DNA ploidy level: an appeal for consistent

terminology. Taxon, 55, 447–450.

Suda J, Weiss-Schneeweiss H, Tribsch A, Schneeweiss GM,

Travnıcek P, Schonswetter P (2007) Complex distribution

patterns of di-, tetra- and hexaploid cytotypes in the

European high mountain plant Senecio carniolicus Willd.

(Asteraceae). American Journal of Botany, 94, 1391–1401.

Taberlet P, Fumagalli L, Wust-Saucy AG, Cosson JF (1998)

Comparative phylogeography and postglacial colonization

routes in Europe. Molecular Ecology, 7, 453–464.

Tajima F (1996) The amount of DNA polymorphism

maintained in a finite population when the neutral mutation

rate varies among sites. Genetics, 143, 1457–1465.

Thompson JN, Anderson KH (2000) Biomes of western North

America at 18 000, 6000 and 0 14C yr BP reconstructed from

pollen and packrat midden data. Journal of Biogeography, 27,

555–584.

Van Devender TR (1977) Holocene woodlands in the

Southwestern deserts. Science, 198, 189–192.

Van Devender TR (1990) Late Quaternary vegetation and

climate of the Chihuahuan Desert, United States and Mexico.

In: Packrat Middens: The Last 40 000 years of Biotic Change (eds

Betancourt JL, Van Devender TR, Martin PS). pp. 104–133,

University of Arizona Press, Tuscon, AZ.

Van Devender TR, Spaulding WG (1979) Development of

vegetation and climate in the Southwestern United States.

Science, 204, 701–710.

Waltari E, Hijmans RJ, Peterson AT, Nyari A, Perkins SL,

Guralnick RP (2007) Locating Pleistocene refugia: comparing

phylogeographic and ecological niche model predictions.

PLoS ONE, 2, e563. (doi:10.1371/journal.pone.0000563).

Webb SD (1977) A history of savannah vertebrates in the New

World. Part I: North America. Annual Reviews in Ecology and

Systematics, 8, 355–380.

Weiss-Schneeweiss H, Schneeweiss GM, Stuessy TF et al.

(2007) Chromosomal stasis in diploids contrasts with

genome restructuring in auto- and allopolyploid taxa of

Hepatica (Ranunculaceae). New Phytologist, 174, 669–682.

Weiss-Schneeweiss H, Stuessy TF, Villasenor JL (2009)

Chromosome numbers, karyotypes, and evolution in

Melampodium (Asteraceae). International Journal of Plant

Sciences, 170, 1168–1182.

Wells PV (1966) Late Pleistocene vegetation and degree of

pluvial climatic change in the Chihuahuan Desert. Science,

153, 970–975.

Zhou R, Shi S, Wu CI (2005) Molecular criteria for determining

new hybrid species – an application to the Sonneratia

hybrids. Molecular Phylogenetics and Evolution, 35, 595–601.

This study is part of the PhD thesis of C.A.R., dedicated to the

evolution of the southern North American xerophytic species

of series Leucantha of Melampodium (Asteraceae). This work

was supervised by T.F.S. and H.W.-S., both interested in plant

evolution (including chromosomal and genome evolution) and

speciation. G.M.S. is interested in different aspects of plant

evolution, including genome evolution of parasitic plants, phy-

logeography, polyploid evolution and speciation. K.E.B. is

interested in phylogeography, polyploid evolution and hybrid-

ization of plants on the Balkan Peninsula. P.S. is interested in

polyploid evolution and in the spatio-temporal diversification

of European alpine plants. J.L.V. works on the flora of Mexico

(especially Asteraceae). R.O. is interested in genome size evolu-

tion.

Supporting information

Additional supporting information may be found in the online

version of this article.

Table S1 Bioclimatic variables used to calculate the probability

of geographic distribution of Melampodium leucanthum.

Table S2 Relative fluorescent intensity and derived DNA

ploidy level of all investigated individuals of Melampodium leu-

canthum.

Fig. S1 Summary of the STRUCTURE analysis of AFLP data of

diploid individuals of Melampodium leucanthum.

Fig. S2 Distribution of plastid haplotypes sampled in Melampo-

dium leucanthum.



Fig. S3 Mismatch distributions for all populations as well as

each haplotype group found in Melampodium leucanthum.

Fig. S4 Likelihood surfaces for ancestral locations estimated

with PHYLOMAPPER in Melampodium leucanthum.

Fig. S5 Jackknife tests of variable importance in ecological

niche modelling in Melampodium leucanthum.

Supporting File 1 Details of data analyses.


Supporting File 2–8 BEAST input files, their names indicating

the used demographic model (files 1–3) or topological hypothe-

sis tested (files 4–7; see text for details).

Please note: Wiley-Blackwell are not responsible for the content

or functionality of any supporting information supplied by the

authors. Any queries (other than missing material) should be

directed to the corresponding author for the article.

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