Are adaptive loci transferable across genomes of related species? Outlier and environmental...

Are adaptive loci transferable across genomes of relatedspecies? Outlier and environmental association analysesin Alpine Brassicaceae species

DEBORAH ZULLIGER, ELVIRA SCHNYDER and FELIX GUGERLI

WSL Swiss Federal Research Institute, Z€urcherstrasse 111, CH-8903 Birmensdorf, Switzerland

Abstract

Local adaptation is one possible response of organisms to survive in a changing environ-

ment. However, the genetic basis of adaptation is not well understood, especially in non-

model species. To infer recurrent patterns of local adaptation, we investigated whether

the same putative adaptive loci reoccur in related species. We performed genome scans

using amplified fragment length polymorphism (AFLP) markers on populations of five

Alpine Brassicaceae species sampled across a wide range of environmental conditions.

To identify markers potentially under directional selection, we performed outlier and

environmental association analyses using a set of topo-climatic variables available as

GIS layers. Several AFLP loci showed signatures of adaptation, of which one, found in

Cardamine resedifolia (Cre_P1_212.5), was associated with precipitation. We sequence-

characterized this candidate locus and genotyped single nucleotide polymorphisms

(SNPs) found within this locus for all species. Testing for environmental associations of

SNPs revealed the same association of this locus in Arabis alpina but not in other study

species. Cumulative statistical evidence indicates that locus Cre_P1_212.5 is environmen-

tally relevant or is linked to a gene under selection in our study range. Furthermore, the

locus shows an association to the same potentially selective factor in at least one other

related species. These findings help to identify trends in plant adaptation in Alpine

ecosystems in response to particular environmental parameters.

Keywords: amplified fragment length polymorphisms, Brassicaceae, environmental association

genetics, local adaptation, single nucleotide polymorphisms

Received 23 April 2012; revision received 22 November 2012; accepted 22 November 2012

Introduction

Comprehending the genetic basis of local adaptation

and identifying the underlying genomic regions is a

fundamental challenge facing molecular ecologists

(Reusch & Wood 2007). As the availability of molecular

markers and methods to infer selectively important

genetic polymorphism is advancing, the field of

evolutionary biology is increasingly detecting genetic

responses to environmental changes also in nonmodel

species (reviewed by Feder & Mitchell-Olds 2003;

Vasem€agi & Primmer 2005; Rako et al. 2007; Reusch &

Wood 2007; Hoffmann & Willi 2008; Kawecki 2008;

Mitchell-Olds et al. 2008; Lavergne et al. 2010). Nonethe-

less, a comprehensive theory on the mechanisms influ-

encing the evolution of species ranges and ecological

niches is still far from established (Kawecki 2008), par-

ticularly in natural populations of nonmodel species.

Plant species in alpine ecosystems are considered

particularly vulnerable to climate change, as small shifts

in key factors, such as duration of snow cover or tem-

perature, can influence the flowering phenology, repro-

duction and growth of these species within their

environmentally heterogeneous habitat (Arft et al. 1999;

Kudo & Hirao 2006). To model vegetation dynamics

under future climate conditions, it is therefore necessary

to identify which environmental factors are driving

local adaptation. Several studies have been carried out

with the objective to identify environmental factors or

habitat types and their association with loci under

directional selection or particular genotypes in alpineCorrespondence: Felix Gugerli, Fax: +41 44 739 2215;

E-mail: [email protected]

© 2013 Blackwell Publishing Ltd

Molecular Ecology (2013) 22, 1626–1639 doi: 10.1111/mec.12199

plant species (Byars et al. 2007; Parisod & Christin 2008;

Manel et al. 2010, 2012; Poncet et al. 2010). However,

few studies have gone as far as to characterize these

loci (Buehler et al. 2013) and even more so to test them

on related species.

For nonmodel species, it is therefore not known to

what extent adaptive loci are transferable in the sense

that signals of selection induced by particular environ-

mental factors are the same among related species.

Here, we therefore addressed the following question:

Are the same genomic regions responsible for the same

adaptations in different species? For this, we tested the

transferability of selective loci among five Alpine plant

species of the family Brassicaceae, which are related to

the fully sequenced model species Arabidopsis thaliana

(The Arabidopsis Genome Initiative 2000). Choosing

related species should facilitate the comparison of iden-

tified regions showing signatures of directional selection

among study species as well as with the whole-genome

sequence information of A. thaliana and should allow

the characterization of their genomic environment.

To avoid biases towards known genomic regions, as is

the case when testing candidate genes already identified

in model species, we applied the genome scan approach

(Storz 2005; Vasem€agi & Primmer 2005) using amplified

fragment length polymorphisms (AFLPs; Vos et al.

1995), a technique that genotypes dozens of markers for

each individual and is suitable for nonmodel species.

Two methods were then used to identify markers of

adaptive relevance: First, we used a population genomic

approach, which follows the principle that genetic

regions under selection, the so-called outlier loci, show

a higher genetic differentiation (FST) among populations

than neutral loci do (Beaumont & Nichols 1996; Vitalis

et al. 2001; Storz 2005). Second, we applied a landscape

genetic approach, which is based on finding associa-

tions between the occurrence or frequency of AFLP

markers and environmental gradients (Manel et al.

2012). On the basis of our results, we then characterized

one outlier locus associated with environmental vari-

ables and genotyped single nucleotide polymorphisms

(SNPs) in this locus in all five study species. Finally, we

performed an association analysis to test the relevance

of this locus in response to environmental variation

across related species.

Materials and methods

Study species and sampling

We sampled five diploid Alpine Brassicaceae species

(Arabis alpina, Arabis jacquinii, Cardamine resedifolia,

Draba aizoides and Thlaspi rotundifolium). A. alpina is a

common perennial arctic-alpine rosette herb (Bovet et al.

2006; Ehrich et al. 2007). It is widely distributed in the

European Alps and also has a large altitudinal distribu-

tion ranging from the montane region up to 3200 m

a.s.l. Arabis alpina grows on calcareous substrates in

open, moist and rocky habitats but also on nutrient-rich,

alkaline soils. Reproduction occurs sexually through

seeds or asexually via stoloniferous growth (Ansell et al.

2008). Arabis jacquinii is a less common Middle and

South European Alpine herb. It grows on creek banks

and spring meadows from 1500 to 2600 m a.s.l. and

prefers wet, calcareous soils (Landolt 1992). It repro-

duces mainly through seed, while daughter rosettes

may disintegrate and reroot. Cardamine resedifolia is a

perennial herb and is common in the Middle and South

European Alps. Its altitudinal distribution ranges from

1500 to 3200 m a.s.l. Cardamine resedifolia occurs on

rocky rubble, in rock crevices and open meadows and

grows on lime-deficient soils and silicicolous bedrock,

where the species disperses by seed, including long-dis-

tance dispersal, but also by adventitious roots (Markg-

raf 1958). The species might be predominantly selfing

but outcrossing has not been ruled out (Lihova et al.

2009). Draba aizoides is a common Middle and South

European Alpine herb and occurs on scree, ridge and

open meadows from 1500 to 3000 m a.s.l. where it

grows on rocky, calcareous substrates (Landolt 1992).

Seed dispersal is presumably the main mode of repro-

duction (Markgraf 1958), but daughter rosettes poten-

tially separate and root after being translocated.

Thlaspi rotundifolium is a less common Alpine herb

growing between 1500 and 3000 m a.s.l. on piles of rub-

ble and scree. It exclusively occurs on calcareous sub-

strates (Landolt 1992) and shows high potential for

vegetative spread.

The five species were chosen in view of their wide

ecological amplitude and range/abundance. For molec-

ular genetic analysis, a total of 10–13 populations of

each species were sampled from July to September of

2009 in the Swiss Alps and the Jura Mountains

(Table S1 and Fig. S1, Supporting information). Twenty

individuals per population were collected allowing for

frequency-based outlier analyses. Sampling sites were

chosen to represent a wide range of variety in

environmental variables, such as temperature, evapo-

transpiration and precipitation.

DNA extraction and AFLPs

DNA was extracted from 10-mg leaf tissue, dried on

silica gel immediately after sampling, using a Qiagen

DNeasy Plant Mini Kit following the manufacturer’s

instructions (Qiagen). Within each species, samples

were arranged randomly on PCR plates including three

replicates within and three replicates among plates.


ADAPTIVE LOCI IN ALPINE BRASSICACEAE 1627

These two types of duplicates served for rigorously

testing the reliability of the genotyping procedure

during AFLP marker selection. Moreover, we added

10–20% blind samples to calculate mismatch error rates

in the set of AFLP markers retained for analysis.

We produced AFLP data (Vos et al. 1995) using the

standard protocol described in Gugerli et al. (2008) in

all five species. First, 5 lL of approximately 18 ng/lLof genomic DNA was digested at 37 °C for 2 h using 2

U MseI (New England Biolabs) and 5 U PstI restriction

enzyme (New England Biolabs) in a 20-lL reaction mix.

MseI and PstI adaptors were then ligated to the restric-

tion fragments, and preselective and selective PCRs

were performed following the standard protocol in

Gugerli et al. (2008). Preselective PCRs were run on a

PCT-100 thermo cycler (MJ Research) using MseI-C and

PstI-A primers. Selective PCRs were run on a Veriti

thermo cycler (Applied Biosystems) using three primer

pair combinations: P1—MseI-CA and PstI-AAG; P3—

MseI-CT and PstI-AAC; and P9—MseI-CA and

PstI-AAC as in Herrmann et al. (2010). Using the same

primer/enzyme combinations in all study species, we

aimed at increasing the chance to obtain homologous

markers to facilitate among-species comparisons. Prod-

ucts of selective PCRs were analysed on an ABI 3730

automated sequencer (Applied Biosystems). Markers

were scored using the software GENEMAPPER 3.7 (Applied

Biosystems), and automatically set bins were adjusted

manually. We ran the script scanAFLP v. 1.0 (Herrmann

et al. 2010) in R (R Development Core Team 2010) to

evaluate markers for their reproducibility. To keep a

marker in the data set, the number of errors among

duplicated samples was set to 1, among replicated sam-

ples to 0 and among all negative controls to 1. Finally,

we only considered markers with at least three poly-

morphisms per species. Matrices of the three primer/

enzyme combinations were joined to generate one

matrix per species. Mismatch error rates were calcu-

lated as suggested by Bonin et al. (2004) using replicate

samples after discarding markers with high error rates

from the matrix.

Outlier analysis

We tested for the presence of significant association

between pairs of loci based on an exact test for linkage

disequilibrium (LD) using the program ARLEQUIN v. 3.5.

(Excoffier et al. 2005). For each species, we calculated

the percentage of loci with possible LD.

We used the program BAYESCAN v. 2.1. (Foll &

Gaggiotti 2008) to find markers subject to directional

selection in each species. The method implemented in

this program has been adapted to dominant loci, such

as AFLP markers, and identifies outlier loci as loci that

show significantly higher (or lower) FST coefficients

than can be expected under the neutral theory. Such

outlier loci are assumed to be under directional (or bal-

ancing) selection. We defined the significance threshold

as ‘decisive’ according to Jeffrey’s (1961) interpretation

(P(selected) = 99%, log10(Bayesfactor) � 2).

Environmental association analysis

To identify loci correlated with environmental variables,

we used an approach similar to the one described by

Manel et al. (2012). We extracted environmental vari-

ables by intersecting the sampling locations with GIS

data of 19 topo-climatic variables collected in 1961–1990

(25-m resolution, Zimmermann & Kienast 1999;

Table S2, Supporting information). We performed a

principal component analysis (PCA) in R (R Develop-

ment Core Team 2010) to identify those variables that

explain the most variation and to remove redundancies

among correlated variables. Groups of variables were

formed based on a correlation of |r| � 0.8 correspond-

ing to the method of Manel et al. (2012), and within the

first few principal component axes, which explained

most of the variation, we kept at the most two variables

for each axis, choosing only the main contributor of no

more than two groups of variables.

For outlier loci identified by BAYESCAN, we calculated

population-wise frequencies of AFLP band presences

within each species. Multiple linear regressions were

then performed, using the statistical software package

SPSS v. 17.0 (SPSS, Inc., Chicago IL, USA), between the

band frequencies as dependent variables and the

selected environmental variables as independent vari-

ables (see Table S1 for values of environmental vari-

ables, Supporting information). Multiple linear

regressions were also performed using second-degree

polynomials of all variables, except for topography

(TOPO) and topographic wetness index (TWI), which

are ‘index’ values, to account for nonlinear relationships

between AFLP band presence and environmental vari-

ables (Legendre & Legendre 1998). Loci with an

R2adj > 0.5 were considered associated with at least one

environmental variable (Manel et al. 2012), with the pre-

mise that the adjusted coefficient of determination gives

unbiased estimates of how much of the total variation

is accounted for by a response variable in a linear

model (Ohtani 2000). Simple linear regressions were

then performed for each variable separately to assess

their significance.

We refrained from correcting for multiple testing under

the premise that we wanted to identify loci that primarily

show high explanatory power by respective environmen-

tal factors. While taking the risk of identifying false posi-

tives, our analysis in turn included several steps aimed


1628 D. ZULLIGER, E . SCHNYDER and F . GUGERLI

at reducing the rate of false positives, such as only

considering outliers that show an R2adj > 0.5 for the

multiple linear regression with all environmental

variables (both linear and polynomial) plus have at

least one environmental variable (both linear and poly-

nomial) with a significant association. In order not to be

too conservative and risk type II errors, we chose this

approach over correcting for false positives using, for

example, Bonferroni or false discovery rates.

Spatial variation

Ecological data are often to some degree spatially auto-

correlated, as the value at one site may be influenced

by the values at neighbouring sites. Autocorrelation can

therefore violate the assumption of independence and

introduce a bias in standard statistical inference meth-

ods (Legendre 1993; Dray et al. 2006). On the other

hand, spatial autocorrelation can also be used as a

proxy for important environmental clines that were not

included in the set of environmental variables used in a

study (Manel et al. 2010, 2012). To assess the degree of

spatial autocorrelation within our data, we chose an

approach based on Moran’s eigenvector maps (MEMs;

Dray et al. 2006). This method uses Moran’s I statistic to

identify the principal coordinate axes that reflect

significant spatial autocorrelation and allows to model

complex spatial patterns at different spatial scales (Dray

et al. 2006). MEMs were generated for each species

using the R package spacemakeR. Multiple linear regres-

sions were then performed in SPSS between AFLP band

frequencies of outlier loci as dependent variables and

MEMs as independent variables.

To assess the effects of population structure, we

applied the program INSTRUCT (Gao et al. 2007), which

infers population structure while considering inbreed-

ing by estimating selfing rates within populations, with

K ranging from 1 to n + 1, with n = number of popula-

tions for a given species, for 200 000 generations

(burn-in = 50 000).

Sequencing of outlier locus Cre_P1_212.5

Of the outlier loci consistently detected across all analyses

(see Results section), we selected one locus, Cre_P1_212.5,

for sequence-characterizing. To do this, we produced

AFLP genotypes using the procedures described above

for eight individuals: four samples showing the candidate

AFLP band and four lacking the band. Selective PCR was

performed using the primer combination P1—MseI-CA

and PstI-AAG. Locus Cre_P1_212.5 was then isolated

using the procedures of Roden et al. (2009). To electropho-

rese the AFLP bands, 20 lL of the selective PCR products

was loaded on Spreadex� EL 400 mini gels (Elchrom Sci-

entific) in 30 mM Tris-acetate EDTA (TAE) buffer, using

M3Marker (Elchrom Scientific) as size standard. The elec-

trophoresis chamber and gel were preheated at 55 °C, andsamples were run at 120 V for 180 min. We stained the gel

for 30 min using SYBR GOLD (Clare Chemical Research)

and viewed the bands in an Epi Chemi II Darkroom (UVP

Laboratory Products). Each amplified Cre_P1_212.5 band

was picked twice using a cylinder of 1 mm diameter. As a

control, one larger and one shorter band were also picked.

Gel cylinders were then eluted in 25 lL deionized water

at 4 °C for 24 h. We amplified the excised DNA bands in a

PCR reaction using primers designed to anneal to the

adaptor sequences (MseI + 0 and PstI + 0) as described in

Roden et al. (2009). As the PCR product still included

other bands besides Cre_P1_212.5, we purified the band

by repeating electrophoresis, band picking and cylinder

elution. The re-excised bands were then re-amplified

using the preselective PstI-A primer and the selective

MseI-CA primer. To purify the re-amplified bands, we

added 2 lL of ExoSAP IT (GE Healthcare) to 5 lL of

each PCR product and incubated the samples at 37 °Cfor 15 min and at 80 °C for 15 min. Bands were

sequenced both forward and reverse using BigDye�

Terminator Cycle Sequencing Kit v3.1 (Applied Biosys-

tems) on an ABI 3130 Genetic Analyzer (Applied

Biosystems). We aligned the sequences using the

software DNADynamo (BlueTraktorSoftware) and

BLAST-searched (Altschul et al. 1990) the obtained

consensus sequence against the nucleotide database of

GenBank for Arabidopsis thaliana.

Sequencing of flanking regions of Cre_P1_212.5 anddetecting SNPs for SNaPshots

To characterize the distinct mutation(s) underlying the

AFLP band polymorphism of locus Cre_P1_212.5, that

is, to find the mutation(s) in the restriction sites and/

or in the selective bases, we aimed at sequencing the

flanking regions of the excised band. As there was

only one putative homologue sequence detected in

the BLAST search, we searched for conserved regions

upstream and downstream of locus Cre_P1_212.5 and

designed three forward primers and three reverse

primers (Table 1) using PRIMER3 (Rozen & Skaletsky

2000). We applied standard PCR conditions for nine

primer pair combinations (Table 2) to test for the

combination(s) that amplified the region best in seven

individuals of C. resedifolia and four individuals each

of A. alpina, A. jacquinii, D. aizoides and T. rotundifolium.

Using primer combination P9 for A. alpina, A. jacquinii

and C. resedifolia and combination P7 for D. aizoides

and T. rotundifolium (Table 2), we sequenced the amp-

lified fragments and aligned the sequences as descri-

bed above. Within each species, the sequences were



compared to find SNPs for genotyping. The coding

regions of the obtained consensus sequences of each

species were translated into protein sequences and

BLASTed against the protein database of GenBank. For

each species, we checked which SNPs were synony-

mous mutations and which ones induced a change in

the protein sequence, and we compared the variation

in protein sequences within and among species.

SNaPshots

A SNP was detected in the MseI restriction site in

C. resedifolia (see Results section) presumably leading

to the observed AFLP band presence/absence pattern.

To confirm whether this polymorphism corresponded

to the observed AFLP band presence/absence pattern

in C. resedifolia, we genotyped this SNP plus two

additional SNPs identified in the seven sequenced

C. resedifolia individuals. We designed primers follow-

ing the recommendations in the SNaPshot protocol

(Applied Biosystems; Table 3) and used the software

OLIGO EXPLORER v.1.5 (Gene Linke 2010) to check for

primer dimers and secondary primer structures.

SNaPshot reactions were performed following the pro-

tocol for the ABI Prism SNaPshot� Multiplex Kit

(Applied Biosystems) but using a reduced reaction

volume (6 lL) as described in Buehler et al. (2013).

Electrophoresis of SNaPshot fragments was conducted

on an ABI 3130 Genetic Analyzer (Applied Biosys-

tems), and the results were viewed on GENEMAPPER 3.7

(Applied Biosystems).

Further, we wanted to test whether locus Cre_P1_212.5

is of adaptive relevance or is linked to a genomic region

under selection also in other species related to C. resedifo-

lia. Thus, we genotyped nucleotide variation in the

remaining study species, that is, three SNPs in D. aizoides

and four in T. rotundifolium. Only one SNP was found

and genotyped in A. alpina in spite of sequencing an

additional 34 individuals, and no SNPs were detected in

A. jacquinii after sequencing another ten individuals.

SNaPshot genotyping was performed as described above

(see Table 3 for SNaPshot primers).

Table 1 Primers tested for sequencing of the fragment Cre_P1_212.5

andflanking regions in fiveAlpine Brassicaceae species

Primer Sequence 5′-3′

Cre212_1a_R (F) GCA TGT CGA AAT GAC ATC CA

Cre212_1b_R (F) GCC AAA AGG CAA GAA TGG T

Cre212_2_R (F) GTC GAA ATG ACA TCC ACA GG

Cre212_1c_R (R) TGA CTT TGC CCA ATG TTT CA

Cre212_2b_R (R) AGC CAA CTG CCT TAA ACG AA

Cre212_3_R (R) TCC CAG TAA CCG GAT CCA TA

Table 2 Primer combinations tested for sequencing of the

fragment Cre_P1_212.5 with flanking regions in five Alpine

Brassicaceae species

Primer

combination

Forward

primer

Reverse

primer

Anticipated

fragment

length (bp)

P1 Cre212_1a_R Cre212_1c_R 587

P2 Cre212_1a_R Cre212_2b_R 560

P3 Cre212_1a_R Cre212_3_R 463

P4 Cre212_1b_R Cre212_1c_R 620

P5 Cre212_1b_R Cre212_2b_R 593

P6 Cre212_1b_R Cre212_3_R 495

P7 Cre212_2_R Cre212_1c_R 584

P8 Cre212_2_R Cre212_2b_R 557

P9 Cre212_2_R Cre212_3_R 459

Primer combinations in bold were used for further fragment

amplifications.

Table 3 SNaPshot primers used for single nucleotide polymorphism (SNP) genotyping of sequence-characterized markers identified

from a genome scan using amplified fragment length polymorphisms (AFLPs) in five Alpine Brassicaceae species

Species SNaPshot primer Sequence 5′-3′

Cardamine resedifolia Cre_SNP40 (GACT)2* TGC TCC TTT CAT GGC AGT GTA CAA

Cre_SNP239 (GACT)4 GAT ATC AAC AAA TCT GAG ATG TTG

Cre_SNP253_R (GACT)6 TTG AAG ATT ATC TGA TTC CAA TAT

Draba aizoides Dai_SNP353 (GACT)2 TCG TTA GCT ATT TGA ATC TCA TGC

Dai_SNP433 (GACT)4 TAA AAC TGA TGG ATC CGATTA CCG

Dai_SNP434_R (GACT)6 GTT GAT GCA GAT ATC ACA AGA AAT

Thlaspi rotundifolium Tro_SNP106 (GACT)2 ACT AAT GGC GCA GCT AGA GAA TCT

Tro_SNP222_R (GACT)4 AGC TTT AAT AAC ATG TCA GCT TTG

Tro_SNP307 (GACT)6 GCA GGT ACA ATA AAT GCT TTC TTT

Tro_SNP342 (GACT)8 ATG TTG ATT AGG TAC TTA CTA CTT

Arabis alpina Aal_SNP208 (GACT)2 CAT TGA TGT TTG CCT TGC AAT TGA

*(GACT)n was used to separate fragment lengths in multiplex PCRs.



Population-wise SNP allele frequencies were calcu-

lated for each species, and a multiple linear regression

analysis was performed in SPSS between the allele

frequencies (Table S4, Supporting information) and the

same seven selected environmental variables as previ-

ously used with the AFLP markers. Allele frequencies

were further utilized to perform multiple linear regres-

sions with MEMs as well as second-degree polynomials

of the environmental variables.

Results

AFLP data and outlier analyses

We obtained AFLP markers for 197 (C. resedifolia) to 260

(A. alpina) individuals per species (Table 4). AFLP geno-

typing, scoring and screening for reproducible markers

with at least three polymorphisms resulted in a total of

405 reliable AFLP loci (53–124 loci per species; Table 4).

Mismatch error rates ranged from 2.4% to 4.0% per spe-

cies.

Of the pairwise tests for LD between loci, the average

percentage of loci showing significant linkage over all

populations within species ranged from 7.3% in T. rot-

undifolium to 14.8% in C. resedifolia. In the other species,

percentages of loci in LD were 11.9% in A. alpina,

14.2% in A. jacquinii and 10.1% in D. aizoides. Locus

Cre_P1_212.5, which was chosen for sequence-charac-

terizing, showed 23.8% LD in the pairwise comparisons

between loci in C. resedifolia.

Using BAYESCAN, 43 loci (10.6%) were identified as

outliers over all species and were distributed among all

species with the most in A. alpina (14) and the least in

D. aizoides (3).

PCA and association analyses

The PCA of 19 environmental variables exhibited 73%

of the variation on the first two axes. Including the

third axis, 96% of the variation was covered, and with a

fourth axis, this value increased only by 2%. The vari-

ables TWI (abbreviations given only for those variables

selected for further analysis), site water balance, slope

(SLP25) and annual number of frost days during grow-

ing season (SFROYY) showed below-threshold correla-

tions (|r| < 0.8), whereas the remaining variables were

correlated within the following groups: (i) aspect value

and annual radiation (SRADYY); (ii) degree days above

0 °C, degree days above 3 °C, degree days above 5.56 °C,mean average annual temperature, mean average sum-

mer temperature (TAVESU), and mean average winter

temperature; (iii) annual precipitation (PRECYY), pre-

cipitation in summer, precipitation days in summer,

average yearly moisture index and summer average

moisture index; (iv) topographical variables (TOPO)

and smoothed topographic variables (for abbreviations

see Table S2, Supporting information). We chose one to

two variables of the first four PC axes, and in the case

of correlated variables, only the representative of each

group with the highest variability. This left the follow-

ing seven variables for further calculations: PRECYY,

SFROYY, SLP25, SRADYY, TAVESU, TOPO and TWI.

Multiple linear regressions between outlier loci as

dependent and the seven environmental variables as

independent variables resulted in six loci with an

R2adj > 0.5, whereas using second-degree polynomials of

the environmental variables resulted in nine loci with

R2adj > 0.5 (Table 4). When performing linear regressions

using each environmental variable separately, 11 loci

were significantly (P < 0.05) associated with an environ-

mental variable and 10 when using second-degree poly-

nomials (Table 4). Loci were predominantly associated

with PRECYY, TOPO and TWI. In summary, four loci

(Aja_P1_433.7, Cre_P1_212.5, Dai_P1_121.3 and

Dai_P1_191.1) were detected as outliers by BAYESCAN,

showed an R2adj > 0.5 in the multiple linear regressions

with environmental variables (linear and second-degree

polynomials) and were significantly associated with at

least one environmental variable (linear and second-

degree polynomials) in the simple linear regressions.

Spatial variation

Multiple linear regressions between outlier loci and

MEMs resulted in an R2adj > 0.5 for 11 loci (Table 4),

indicating that these loci show some signs of spatial

autocorrelation or that there are other environmental

variables that are relevant for these loci but were not

accounted for in the association analysis.

Deviance information criteria (DICs) obtained from

the program INSTRUCT for each K were lowest for high

Ks close or equal to the number of sampled populations

(Fig. 1). Posterior distributions of selfing rates for

inferred clusters were high in all species ranging from

0.65 to 0.96 and therefore justified the use of INSTRUCT.

The results of these analyses suggest that population

structure can be considered as well represented by the

sampled locations.

Sequencing and characterization of outlier locus

Of the four loci most probably being of ecological rel-

evance, we chose locus Cre_P1_212.5 for exemplary

sequence-characterizing. Due to its high band peaks

in the electropherograms and the lack of other peaks

with similar fragment sizes, this marker appeared

suitable to be excised from the gel from a technical

point of view.



After amplifying and sequencing the excised

Cre_P1_212.5 band, we obtained a 214-bp fragment

including the two primers, that is, fragment length was

in the expected size range. The BLAST search showed

that this sequence was 96% congruent with a DNA

sequence assigned to the nucleotidyltransferase family

protein in A. thaliana located on chromosome 2 (Gen-

Bank accession no. NC_003071.7). After sequencing the

Table 4 Results of outlier and environmental association analyses of amplified fragment length polymorphism (AFLP) loci in five

Alpine Brassicaceae species

Species n AFLP loci

n of

outliers

(BAYESCAN) Outlier ID MEMs (R2adj)

Environmental

variables (R2adj)

Significant

environmental

variables*

Arabis alpina 260 88 14 Aal_P1_91 �0.067 0.466 TOPO

Aal_P1_248 �0.696 0.460 —

Aal_P1_303.4 �0.464 0.552 TAVESU, SFRO

Aal_P1_326.5 �0.458 0.250 —

Aal_P1_331.6 0.362 0.272† —Aal_P1_383 �0.038 0.156 —

Aal_P1_433.7 �0.267 0.727† TWI

Aal_P3_322 �0.027 0.316 —

Aal_P3_325.5 0.070 �0.354 —

Aal_P9_179.4 0.241 �0.640 —Aal_P9_181.4 �0.922 �0.310 —

Aal_P9_210.7 0.739 0.009 —Aal_P9_368.4 �0.170 �0.017 —

Aal_P9_467.8 �0.670 0.253 —Arabis jacquinii 235 59 10 Aja_P1_113.4 �1.064 0.433 –

Aja_P1_129.3 0.117 �0.454 TOPO

Aja_P1_130.4 0.197 �0.474 TOPO

Aja_P3_148.3 0.082 �0.217 PRECYY

Aja_P3_151 �0.758 �0.109 —

Aja_P3_239.2 �0.548 0.244 —Aja_P9_175 0.409 0.376† PRECYY, SRADYY

Aja_P9_176.7 �0.042 0.237† PRECYY

Aja_P9_259.3 0.152 0.242† PRECYY

Aja_P9_350.9 0.271 �0.111 —Cardamine resedifolia 197 53 12 Cre_P1_179.5 �0.106 0.606 —

Cre_P1_212.5 0.827 0.624† PRECYY

Cre_P1_214.2 0.822 �1.260 —

Cre_P1_216.6 0.847 �0.991 —Cre_P3_217.3 0.821 �1.057 —

Cre_P9_93.3 0.949 �1.118 —Cre_P9_148.5 0.435 0.414 —

Cre_P9_191.3 0.970 �0.854 —Cre_P9_401.2 0.917 �1.268 —

Cre_P9_413.4 �0.570 �0.258 —Cre_P9_419.6 �0.574 �0.183 —

Cre_P9_426.8 �0.118 0.620 —Draba aizoides 200 81 3 Dai_P1_121.3 0.088 0.992† PRECYY, TOPO, TWI

Dai_P1_185.9 0.459 �1.123

Dai_P1_191.1 �0.325 0.791† PRECYY, TWI

Thlaspi rotundifolium 198 124 4 Tro_P1_206 0.218 0.143 —Tro_P9_111.1 0.006 0.686† —

Tro_P9_268.3 �0.249 �0.166 —

Tro_P9_312.5 0.789 �0.951 —

P1, P3 and P9 refer to the primer/enzyme combinations detailed in the Materials and Methods section.

Numbers in bold: R2adj > 0.5 for linear environmental variables.

*Simple linear regression. Variables in italics are significant even though R2adj � 0.5 in multiple linear regression.

†R2adj > 0.5 for second-degree polynomials of environmental variables.



flanking regions in C. resedifolia using primer combina-

tion P9 (Table 2), we obtained a 420-bp fragment

including the Cre_P1_212.5 region (GenBank accession

no. JN982363). The first 310 bp of this region were

highly conserved compared with the sequence of A. tha-

liana and were thus considered a putative protein-cod-

ing region with mostly synonymous mutations.

SNP genotyping

In the seven sequences of C. resedifolia, four SNPs were

found, of which one (Cre239) was located in the MseI

restriction site (Fig. 2) and one was in the noncoding

region of the sequence. Using the SNaPshot protocol,

we genotyped the three SNPs located in the putative

coding region of this sequence. For 100% of all C. resedi-

folia samples, the presence of the mutation in the MseI

restriction site in both alleles corresponded to the

absence of the Cre_P1_212.5 band in the AFLP band

profile. In the other species, DNA sequences of locus

Cre_P1_212.5 showed exactly the same length of 214 bp

as in C. resedifolia. It can therefore be assumed that

homologous bands in the AFLP electropherograms are

equivalent to locus Cre_P1_212.5. In A. jacquinii and

D. aizoides, this band was always present, as there were

no mutations in the restriction sites or selective bases.

In A. alpina and T. rotundifolium, the band was always

absent (Fig. 2). In A. alpina, the absence was due to the

same mutation as found in C. resedifolia in the MseI

restriction site. In T. rotundifolium, the band was absent

because of a mutation in the selective bases of the MseI

(pre-)selective primers (Fig. 2).

In A. alpina, we genotyped one SNP located within

the coding region, whereas three SNPs, all located in

the noncoding region, were genotyped in D. aizoides. In

T. rotundifolium, we genotyped four SNPs, of which two

were located in the coding and two in the noncoding

region (GenBank accession nos. JN982363–JN982367).

No ambiguities in SNP genotyping were found in any

of the species, as replicated samples were to 100%

compliant. Population-wise allele frequencies for each

genotyped SNP are listed in Table S4 (Supporting

information).

SNP association analyses

Multiple linear regressions using allele frequencies of

SNPs and the seven environmental variables showed an

R2adj > 0.5 in the A. alpina SNP, in two out of three SNPs

in C. resedifolia, in one out of three SNPs in D. aizoides

and in two out of four SNPs in T. rotundifolium

(Table 5). Significant associations to environmental vari-

ables were found in most of these SNPs, whereas the

same association with PRECYY as originally found in

C. resedifolia was only confirmed in A. alpina (Table 5;

Fig. 3a,b). This association was even stronger in A. alp-

ina than in C. resedifolia, and, opposed to C. resedifolia,

no significant spatial autocorrelation was found in

A. alpina in the multiple linear regressions with MEMs.

Using second-degree polynomials of environmental

variables in multiple linear regressions produced very

similar results for all species (data not shown).

Protein sequences

The comparison of protein sequences within species

revealed only few minor changes (Fig. 4). In A. alpina

and C. resedifolia, there was one change each from

aspartic acid (D) to glutamic acid (E), which are both

negatively charged amino acids. In T. rotundifolium,

there was a change from asparagine (N) to serine (S).

These amino acids are both polar and uncharged.

Among species, changes were less conservative as

several mutations involved changes between different

0

2000

4000

6000

8000

10 000

12 000

14 000

16 000

DIC

0 5 10 15

K

Arabis alpina

A. jacquinii

Cardamine resedifolia

Draba aizoides

Thlaspi rotundifolium

Fig. 1 Deviance information criteria (DIC) vs. K as inferred by

the program INSTRUCT (Gao et al. 2007) for five Alpine species

of Brassicaceae. Full symbols represent the K values with the

lowest DIC for each species.

Band presence 5‘ PstI-RS SB MseI RS 3‘

Cardamine resedifolia 1 CTGCAG AG ... ... TG TTAA 0 CTGCAG AG ... ... TG CTAA

Arabis alpina 0 CTGCAG AG ... ... TG CTAAArabis jacquinii 1 CTGCAG AG ... ... TG TTAADraba aizoides 1 CTGCAG AG ... ... TG TTAAThlaspi rotundifolium 0 CTGCAG AG ... ... TA TTAA

SB

Fig. 2 Restriction sites (RS), selective bases (SB) and single

nucleotide polymorphisms (SNPs) for locus Cre_P1_212.5 in

five Alpine Brassicaceae species. Rectangles highlight mutation

sites and small triangles indicate where the restriction enzymes

cut.



‘categories’ of amino acids, for example, between posi-

tively charged and hydrophobic ones (Fig. 4).

Discussion

In the present study, we identified loci showing signals

of directional selection in Alpine Brassicaceae species at

the molecular level and characterized a new candidate

locus (Cre_P1_212.5). By genotyping SNPs within the

coding region of this locus, we could confirm an associ-

ation with mean annual precipitation (PRECYY) in the

two species C. resedifolia and A. alpina. Although the

marker was originally identified in C. resedifolia, as this

was the only species in which it was polymorphic, this

association was even stronger in the only SNP identi-

fied in A. alpina, reinforcing the initially presumed

adaptive relevance of this locus. In two other species,

D. aizoides and T. rotundifolium, this locus showed asso-

ciations with other environmental variables, that is,

average annual number of frost days during growing

season (SFROYY) and to TWI, respectively. In a fifth

species, A. jacquinii, we could not test for associations

as we did not find any SNPs for genotyping in the

sequences obtained.

Multiple linear regressions between outlier loci/SNPs

and environmental variables were generally not charac-

terized by a strong statistical power because there were

limitations to the number of sampled populations. As

we carried out sampling for five species, and we were

interested in frequency-based association analysis,

certain confinements had to be accepted, which also

concerned the number of independent variables that

could be included in the regression analysis.

Nevertheless, cumulative evidence from different

methods used implied that this marker is an outlier

locus showing association with annual precipitation in

C. resedifolia. Moreover, and functioning as a type of

replicate, we have confirmation of the same association

in A. alpina, even though this species was sampled in

different locations than C. resedifolia. We are therefore

confident that this genetic region is either linked to a

locus or is itself under directional selection in at least

these two plant species. We acknowledge, however, that

an alternative factor may as well underlie this environ-

mental association, as we removed those factors from

the analysis which showed the highest correlations with

annual precipitation (PRECYY). Nevertheless, the

respective factors that showed the highest correlations,

namely precipitation in summer, precipitation days in

summer, average yearly moisture index and summer

average moisture index, were all related to precipita-

tion. This finding supports our interpretation that the

adaptive signature detected most likely relates to water

availability.

Previous studies have already identified precipitation

as a major environmental factor driving plant adapta-

tion (Richardson et al. 2009; Manel et al. 2010, 2012;

Poncet et al. 2010). Additional predominant factors are

temperature (Hamilton et al. 2002; St Clair et al. 2005;

Richardson et al. 2009; Manel et al. 2010, 2012; Poncet

et al. 2010) and unknown variables recognized by

accounting for spatial autocorrelation among sampling

sites using the approach of MEMs (Dray et al. 2006).

MEMs were the best predictors of allele distribution in

the previous studies on A. alpina (Manel et al. 2010,

2012), suggesting either that the outlier character of the

Table 5 Results of environmental association analyses for single nucleotide polymorphism (SNP) in five Alpine Brassicaceae species

Species SNP locus Allele

R2adj > 0.5

All

environmental

variables

Single environmental variables

MEMsPRECYY SFROYY SLP25 SRADYY TAVESU TOPO TWI

Arabis alpina Aal208 A/T 0.80* 0.69***

Cardamine

resedifolia

Cre40 G/A

Cre239 C/T 0.55 0.40* 0.84*

Cre253 C/A 0.51 0.40* 0.87*

Draba aizoides Dai353† C/T

Dai433† G/A 0.87 0.50* 0.55

Dai434† C/T

Thlaspi

rotundifolium

Tro106 A/C

Tro222 T/C

Tro307† C/G 0.83

Tro342† C/T 0.91 0.44* 0.75

*P < 0.05; ***P < 0.001.†SNPs not in putative coding regions.



markers used in this study is mostly influenced by

spatial autocorrelation or that there are other environ-

mental factors driving local adaptation that were not

accounted for in these studies.

On the other hand, eigenvector methods, such as the

MEM approach, suffer the problem of inflating the vari-

ation explained by a given causal process (Gilbert &

Bennett 2010). This can result in R2adj values that are up

to 0.5 higher than actual R2adj values and is to a certain

extent due to eigenvector axes accounting for random

noise. For this reason, we did not put too much weight

on R2adj values >0.5 as exclusion criterion for sequence-

characterizing. Nevertheless, we included this informa-

tion, as there are a number of desirable aspects of

eigenvector methods (Borcard et al. 2004), such as the

decomposition of spatial scales for analyzing scale-

dependent community structuring. Moreover, it is

possible that outlier detection resulted in false positives,

for example, because of population structure (Excoffier

et al. 2009). To compensate for such type II error,

post hoc environmental association analyses substantiated

our candidate loci showing signatures of local adaptation.

Our marker Cre_P1_212.5 also showed a significant

R2adj for MEMs in C. resedifolia, implying that there

might be autocorrelation between the sampling sites for

this locus, or another relevant, co-varying environmen-

tal variable that we did not consider is driving local

adaptation besides precipitation. For the genotyped

SNP in A. alpina, however, values of R2adj for MEMs

were not significant or even above 0.5. This outcome

substantiates that precipitation is indeed the major pre-

dictor of genetic variation at this locus or a genomic

region in LD in A. alpina. It has to be noted though that

in several studies (Manel et al. 2010, 2012; Poncet et al.

2010), and also including the present one, the same set

of topo-climatic variables were used. Thus, a certain

bias towards the same variables as predictors cannot be

ruled out. Moreover, the climate data used are interpo-

lated from meteorological recordings (Zimmermann &

Kienast 1999). In future studies, it would therefore be

interesting to include additional variables, such as, for

example, microclimatic characteristics, soil composition

0

1

2

3

4

5

0

5000

10 000

15 000

20 000

25 000

0 0.2 0.4 0.6 0.8 1

PR

EC

YY

pol

yn. (

x 10

8 )P

RE

CY

Y p

olyn

. (x

108 )

PR

EC

YY

Cre_SNP239_C allele frequency

PRECYY PRECYY polyn.

(a)

(b)

PR

EC

YY

Aal_SNP208_A allele frequency

0

2

3

5

7

8

0

5000

10 000

15 000

20 000

25 000

30 000

0 0.2 0.4 0.6 0.8 1

4

1

6

Fig. 3 Single nucleotide polymorphism (SNP) allele frequencies

in locus Cre_P1_212.5 in two Alpine Brassicaceae species vs.

annual precipitation (PRECYY; sum of 1/10 mm/month) and

second-degree polynomials of PRECYY (PRECYY polyn.). (a)

SNP locus Cre_SNP239_C in Cardamine resedifolia, (b) SNP

locus Aal_SNP208_A in Arabis alpina.

Cre IHRHDAPFMAVYKSLIPAEEELEKQKQLMAQLENLVAKEWPHAKLYLYGSCANSFGFPKSDIDVCLAIEDDDINKSEMLLKLADILESDNLQNVQVKI H M Y K A H D SE E ES K

Aal I Y M Y R A H E D SE D QS L I Y M Y R A H D D SE D QS L

Aja ? Y M Y R G Q D SE D QS D

Dai H Y I F R A H D SE D QS Q

Tro I Y M Y K A H E N AD E EA Q I Y M Y K A H E S AD E EA Q

Fig. 4 Protein sequences of locus Cre_P1_212.5 in the five Brassicaceae species studied, indicating the changes of amino acids within

and among species. Changes within species are highlighted in grey. Cre—Cardamine resedifolia; Aal—Arabis alpina; Aja—A. jaquinii;

Dai—Draba aizoides; Tro—Thlaspi rotundifolium.



and nutrient availability, as well as data collected at the

sampling sites, although these probably comprise

shorter time spans than the 30-year records used here.

The linearity of the relationship between allele fre-

quencies vs. environmental gradients is occasionally

debated. In fact, many (if not most) environmental

association studies published in the literature assume

and test for a linear relationship between environmen-

tal variables and allele frequencies at potentially selec-

tive loci, particularly so if the underlying response is

considered quantitative (Coop et al. 2010; Manel et al.

2010, 2012; Poncet et al. 2010). There is theoretical as

well as empirical support for a linear relationship

between environmental factors along ecological gradi-

ents and allele frequencies (Schmidt et al. 2008). In a

recent article, Kooyers & Olsen (2012) used linear

regressions to correlate allele frequencies with environ-

mental factors for one of the classical textbook exam-

ples of plant adaptation to the environment, namely

cyanogenesis in Trifolium repens. The association of

allele frequencies at cyanogenesis genes and minimum

winter temperature in T. repens was found to be linear.

Even though it remains speculative what type of

response curve other than linear is ecologically most

appropriate (e.g. exponential, logarithmic), we are con-

vinced that the assumption of a linear association

between allele frequencies at loci of adaptive relevance

and environmental factors is well justified and reason-

ably assumed in whole-genome searches for environ-

mental associations of unknown type.

Currently, we cannot conclusively explain why the

association of Cre_P1_212.5 to annual precipitation is

transferable from C. resedifolia to A. alpina but not to

the other species. In our study, the same sequencing

primers were suitable for A. alpina, A. jacquinii and

C. resedifolia, but alternative primers had to be devel-

oped for D. aizoides and T. rotundifolium, indicating

molecular differences between the species in this par-

ticular genomic region. This circumstance, however,

contrasts with the most recently accepted phylogeny

of Brassicaceae (Franzke et al. 2011), which suggests

that the tribes Thlaspideae (T. rotundifolium) and Ara-

bideae (A. alpina, A. jacquinii) are more closely related

to each other than they are to Cardamineae (C. resedi-

folia). Another contradiction is that species of the

genus Draba are also considered to belong to the tribe

Arabideae. One possibility is that we did not detect

and genotype all relevant SNPs or that the association

with some environmental variables is not reasonably

comparable because sampling among species was not

identical. Finally, these five Brassicaceae species all

have different ecological preferences and niches. For

example, of the two species for which we found a

common locus of ecological relevance, C. resedifolia is

restricted to silicicolous substrate, whereas A. alpina is

largely confined to calcareous soils. It is therefore

likely that even in related species the same genes

react differently to the various environmental factors.

Moreover, we lack functional proof for the SNPs asso-

ciated with environmental variation, so that the

genetic variation actually relevant for the signature of

selection may stem for a genomic region linked to the

SNPs studied here. Linkage must be given due dili-

gence considering that the average percentage of LD

estimated between pairs of loci was quite high in all

five Brassicaceae species, possibly a consequence of

substantial inbreeding, and particularly so for locus

Cre_P1_212.5. Accordingly, we cannot exclude that a

genomic region in LD with Cre_P1_212.5 is in fact

responsible for the association found in C. resedifolia

and A. alpina.

Within species, differences in the DNA sequences at

locus Cre_P1_212.5 lead to only one mutation each in

the protein sequences of A. alpina, C. resedifolia and

T. rotundifolium (Fig. 4). These are only minor changes

among amino acids with similar characteristics, that

is, with both negatively charged side chains (aspartic

and glutamic acid) or with both polar and uncharged

side chains (serine and asparagine). Such small differ-

ences most probably do not affect the folding domain

of the protein (Chakraborty et al. 2000); however, they

can nonetheless alter its functional specificity (Smoo-

ker et al. 2000; He et al. 2011). Locus Cre_P1_212.5

might therefore not only be linked to a locus under

directional selection but could itself have some adap-

tive relevance. Whether this gene is at all expressed

and under which environmental conditions could be

examined in a future study by sampling over an

environmental gradient and performing expression

studies, for example, by real-time PCRs on cDNA or

microarray technology. This way we could verify the

correlation between the environmental variables and

the amount of the expressed gene. Finally, selection

experiments, such as common garden trials and reci-

procal transplantations, are an indispensable tool for

testing and predicting evolutionary responses to

projected future conditions (Reusch & Wood 2007;

Holderegger et al. 2010).

The approach presented in this study is simple and,

even though AFLP-based, allows for subsequent

sequence-characterizing and SNP genotyping; it is

therefore suitable for any species and can be performed

by small laboratories. Although more up-to-date meth-

ods, such as next generation sequencing, exist, they cur-

rently remain very costly and therefore restrict the

number of populations and genomes sampled as well

as the target species, that is, those for which ample

genomic information is at hand (cf. Turner et al. 2010).



Conclusions

Identifying and characterizing key markers under direc-

tional selection is a first step towards exploring the

underlying mechanisms of local adaptation. Here, we

applied different methods to reliably detect genomic

regions with adaptive relevance in natural populations

of Alpine plant species. In a second step, we character-

ized and genotyped one of these candidate loci to test

whether the same association can be found in different

species. To the best of our knowledge, this is the first

study investigating the transferability of a new candi-

date locus derived from a nonmodel Alpine plant

species to another, closely related species. Detecting

genomic patterns of environmental adaptation among

species is considered useful for modelling the evolu-

tionary potential of Alpine plants under predicted

future climate conditions and sheds light on ecological

niche evolution. While it has been shown that precipita-

tion is a key environmental factor associated with signa-

tures of selection in a broad spectrum of Alpine plants

(Manel et al. 2012), we here go one step further and

show that respective outlier loci can be transferred

among closely related taxa, that is, that environmental

associations may be the same. Even though we lack

experimental proof of the functionality of the adaptive

signature detected here, which is common place for

studies using genome scans, the results of this study

deepen our understanding and represent a valuable

contribution to model scenarios of the fate of Alpine

species in a changing environment.

Acknowledgments

We thank Sabine Brodbeck, Linda Feichtinger, Christoph

Schw€orer and Jolanda Zimmermann for assistance in the labora-

tory and for sampling, St�ephanie Manel and Thomas Sattler for

useful advice on statistical analysis, Niklaus Zimmermann for

providing the GIS data and Gy€orgy Sipos for his help with inter-

preting the protein sequences. For valuable comments improving

the manuscript, we thank Daniela Csencsics, Silke Werth and the

anonymous referees. AFLP data analysed in this study were

generated at the Genetic Diversity Center (GDC) of the ETH Zur-

ich. For his involvement in the conception of this study, including

the valuable hint regarding the issue of linear vs. nonlinear

responses of allele frequencies to environmental factors, we

thank Rolf Holderegger. Funding was provided by the EC-sup-

ported Integrated Project EcoChange (FP6-036866) and by the

Swiss National Science Foundation (SNF) Project Sinergia-AVE

(CRSI33_127155), which were both spoken to Rolf Holderegger.

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During her postdoc, D.Z. genotyped and scored the the

AFLP data for all five study species, performed the out-

lier and association analyses and the spatial variation

analysis. D.Z. also sequenced and characterized the out-

lier and developed SNPs for SNaPshots and produced

SNP data for three out of four study species. D.Z. ana-

lyzed the data and compiled the manuscript. E. S. In

his research group, F.G. studies population processes

like adaptation in the context of environmental varia-

tion or landscape configuration.

Data accessibility

DNA sequences: GenBank accession nos JN982363–

JN982367.

Sampling locations and environmental data: Table S1

(Supporting information).

AFLP and SNP data sets: DRYAD entry doi:10.5061/

dryad.mm155.

Supporting information

Additional supporting information may be found in the online

version of this article.

Table S1 Sampling locations and respective values of seven

uncorrelated environmental variables for five Alpine Brassica-

ceae species.

Table S2 Nineteen topo-climatic variables available as GIS

layers (Zimmermann & Kienast 1999) used for the principal

component analysis (PCA).

Table S3 Mismatch error rates and number of loci for ampli-

fied fragment length polymorphisms in five Alpine Brassica-

ceae species.

Table S4 Population-wise allele frequencies of single nucleo-

tide polymorphisms (SNPs) in five Alpine Brassicaceae species.

Fig. S1 Sampling locations of five Brassicaceae species sampled

in Switzerland.



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