low genetic variation but high germination in glacial ... - Springer Link

Biodivers Conserv (2013) 22:1301–1316 DOI 10.1007/s10531-013-0471-y ORIGINAL PAPER

Vital survivors: low genetic variation but high germination in glacial relict populations of the typical rock plant Draba aizoides Frank Vogler • Christoph Reisch

Received: 17 August 2012 / Accepted: 14 March 2013 / Published online: 11 April 2013 Ó Springer Science+Business Media Dordrecht 2013

Abstract Glacial relict populations are isolated remnants of arctic-alpine species resulting from shifts of the distribution range during glaciations. Recently, the conservation value of relict populations has been emphasized, since they are adapted to stressful ecological conditions, which may be important for future distribution range shifts due to climate change. However, glacial relict populations have strongly been affected by historical fragmentation processes. Limited genetic variation and reduced reproduction can, therefore, be postulated for glacial relict populations. In our study we tested these assumptions. We investigated central European populations of the typical rock plant Draba aizoides from the Alps (considered as a core distribution area) and from the Swabian Alb, the Southern and Northern Franconian Jura (where its populations are considered glacial relict populations). We analysed genetic variation using molecular markers AFLPs and studied the reproduction of the populations in germination experiments. Glacial relict populations were genetically less variable and strongly differentiated, but they exhibited higher germination than populations from the Alps. From our results it can be concluded that glacial relict populations may have limited genetic variation, but they do not necessarily exhibit a limited reproductive capacity. Glacial relict populations are, therefore, vital survivors of the Pleistocene, which deserve full conservation attention, especially against the background of future climate change. Keywords

Draba aizoides Glacial relict AFLP Reproduction Population size

Introduction The present day distribution of plant species has been shaped tremendously by environmental changes in preceding eras. Especially the climatic oscillations within the quaternary Electronic supplementary material The online version of this article (doi:10.1007/s10531-013-0471-y) contains supplementary material, which is available to authorized users. F. Vogler C. Reisch (&) Institute of Botany, University of Regensburg, 93040 Regensburg, Germany e-mail: [email protected]

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paved the way for today’s plant distribution (Webb and Bartlein 1992; Willis and Whittaker 2000). Rapid fluctuations of the environmental conditions shifted the borders of the colonized areas (Hewitt 1996) and caused the actually fragmented and disjunct distribution areas of many plant species (Hulteń and Fries 1986). Alpine plant species survived glaciations in several refugia along the borders of the Alps (Scho¨nswetter et al. 2005; Tribsch 2004; Tribsch and Scho¨nswetter 2003). However, survival in the prealpine region cannot fully be excluded. Clear evidence exists for Minuartia biflora (Scho¨nswetter et al. 2006), but several other species were also supposed to have survived in central Europe (Bauert et al. 1998; Holderegger et al. 2002; Reisch 2008; Reisch et al. 2003; Scho¨nswetter et al. 2004). Arctic-alpine species immigrated postglacially from their refugia into the Alps and to Scandinavia. Simultaneously, forest species immigrated from south eastern and south western refugia to central Europe and the lower regions of the Alps displacing alpine species in these regions. However, on some naturally ‘forest-free islands’, such as cliffs, populations of less competitive alpine species survived postglacial reforestation. They exhibit a highly fragmented, disjunct distribution, with isolated populations mostly at the edge of the distribution range (Hampe and Jump 2011). Being remnants of a formerly different or more widespread distribution, these populations are considered as glacial relict populations (Crawford 1989; Wilmanns 2005). The fragmentation processes occurring during glaciations have, in all likelihood, affected the characteristics of glacial relict populations. First, geographic isolation of glacial relict populations due to climatic oscillations may have changed the structure of genetic variation. In postglacial colonized parts of the distribution area, low levels of genetic variation within and between populations have been observed due to founder events and long-distance dispersal (Comes and Kadereit 1998; Hewitt 1996, 1999, 2000). In contrast, glacial relict populations often exhibit low levels of variation within populations but high levels of variation between populations due to consecutive bottlenecks and long-term isolation (Hensen et al. 2010; Reisch et al. 2002, 2003). Second, limited size and decreased genetic variation could have affected reproductive traits. Recent studies demonstrated a clear positive relationship between genetic variation and reproductive success such as seed set or seedling survival (Fischer and Matthies 1998; Kolb 2005; Leimu et al. 2006). Long-term isolation and decreased genetic variation could, therefore, have resulted in reduced reproductive capacity of glacial relict populations. Recently, the conservation value of relict populations has been emphasized (Habel et al. 2010a; Hampe and Petit 2005), mainly due to their evolutionary significance: relict populations have existed successfully under stressful ecological conditions for long periods of time (Ronikier et al. 2012). Furthermore, they are exposed to strong directional selection processes and therefore specifically adapted to their environment, which is mostly located at the edge of the distribution range. For this reason, relict species may be relevant to challenge future climatic changes (Habel et al. 2010b). Studies dealing explicitly with glacial relict populations are, however, rare. While the genetic variation of glacial relict populations has been analysed at least in few studies (Hensen et al. 2010; Reisch et al. 2002, 2003; Bauert et al. 2007; Sˇmidova´ et al. 2011; Rusterholz et al. 2012), actually no data are specifically available about the reproduction of glacial relict populations. In our study we tried to bridge this gap. Therefore, we selected the yellow whitlow grass (Draba aizoides), a typical rock species which is mainly distributed in the Alps but also occurs in central Europe with isolated glacial relict populations. In a comparative approach we studied the impact of pleistocene glaciations on genetic variation and seed germination capacity of these relict populations.

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Methods Species description Draba aizoides is a perennial evergreen chamaephyte that grows in dense cushions in crevices and on ledges of calcareous cliffs (Do¨rr and Lippert 2001; Kay and Harrison 1970). Flowering takes place from March to August (Seybold 2006). Pollination is mainly entomophilous but selfing is possible (Kay and Harrison 1970; Sebald et al. 1998). Seed production is abundant (Wilmanns and Rupp 1966), but vegetative propagation by detached rosettes is also possible (Sebald et al. 1998). D. aizoides is mainly distributed throughout the Alps, the Pyrenees and the Carpathians (Hegi 1986; Kay and Harrison 1970; Seybold 2006). However, in Germany the species also occurs in the mountain regions of the Northern and Southern Franconian Jura and the Swabian Alb (Haeupler and Scho¨nfelder 1988). These populations are considered as glacial relict populations (Sebald et al. 1998; Widmer and Baltisberger 1999). In south eastern central Europe the species is endangered (Scheuerer and Ahlmer 2003), and the populations are affected by rock climbing (Vogler and Reisch 2011). Study design and sampled populations Covering the distribution range of D. aizoides in Germany, for this study we selected populations on five cliffs from the Alps (AL) and, with increasing distance to the Alps, on each five cliffs from the Swabian Alb (SA), the Southern Jumbo (SJ) and the Northern Franconian Jura (NJ) (Table 1; Fig. 1). The selected cliffs were single outcrops, which were more or less completely inhabited by D. aizoides. The spatial distance to the next adjacent population was minimum 500 m. Individuals from each cliff were, therefore, considered as one population and population size was determined at each locality by estimating cliff size, which differed clearly among the four regions (Table 2). The largest populations occurred in the NJ (2,362 ± 840 m2), followed by those from the SA (1,817 ± 650 m2) and the Southern Franconian Jura (399 ± 130 m2). The smallest populations have been found in the Alps (318 ± 70 m2). Genetic variation was assessed through genome-wide genotyping with AFLPs, amplified fragment length polymorphisms (Vos et al. 1995). For this study, young leaf rosettes were collected randomly from ten individuals, covering the whole range of each population and dried over silica gel. Reproduction of glacial relict populations was analysed in germination experiments. For these experiments seed material was collected from June to August from at least 20 individuals per population and placed in paper bags. Seed material was air dried, cleaned and stored few weeks at 4 °C until the start of the germination experiment. Stratification was not necessary, since seeds germinated very well in preexperiments and exhibit no dormancy following literature (Kay and Harrison 1970). Molecular analyses DNA for AFLPs was extracted from dried leaf rosettes following the CTAB protocol from Rogers and Bendich (1994) in an adaptation by Reisch (2007). Solutions were diluted with water to 7.8 ng/lL and used for AFLPs, which were conducted in accordance with the protocol from Beckmann Coulter as described before (Bylebyl et al. 2008; Reisch 2008). DNA adapters were prepared by adding equal volumes of both single strands of EcoRI and

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Table 1 Code, names, and geographic location of the analysed populations in the four study regions Northern Franconian Jura (NJ), Southern Franconian Jura (SJ), Swabian Alb (SA) and Alps (AL) with their altitude (in m above sea level) and size (PS in m2) Code

Population

NJ01

Kreuz-Berg

Northern Franconian Jura

402

80

NJ02

Hohe Leite


528

64

NJ03

Pfarrfelsen Du¨sselbacher Wand


445

2,000


456

3,600


457

1,200

SJ01

Zanklstein Lo¨cherwand

Southern Franconian Jura

382

240

SJ02

Teufelsfelsen


522

320

SJ03

Lintlberg


366

350

SJ04

Schulerloch


396

500

SJ05

Drabafels


398

300

SA01

Swabian Alb

724

3,500

SA02

Stuhlfels ¨ schlesfels O

Swabian Alb

917

1,000

SA03

Schwarzlochfels

Swabian Alb

603

1,500

SA04

Wiesfels

Swabian Alb

766

1,200

SA05

Kreuzfels

Swabian Alb

606

3,200

AL01

Geißhorn

Alps

2,316

600

AL02

Alps

2,243

150

AL03

Kreuzeck Gru¨nten

Alps

1,674

400

AL04

Karkopf

Alps

1,404

400

AL05

Hochgern

Alps

1,660

250

NJ04 NJ05

Region

Altitude

PS

MseI adaptors (MWG Biotech) following a 5 min heating at 95 °C with a final 10 min step at 25 °C. DNA restriction and adapter ligation were performed in one step by adding a 3.6 lL mixture containing 2.5 U EcoRI (MBI Fermentas), 2.5 U MseI (MWG Biotech), 0.1 lM EcoRI and 1 lM MseI adapter pair, 0.5 U T4 Ligase with its corresponding buffer (MBI Fermentas), 0.05 M NaCl and 0.5 lg BSA (New England BioLabs) to 6.4 lL of genomic DNA in a concentration of 7.8 ng/lL. Following an incubation at 37 °C for 2 h with a final enzyme denaturation step at 70 °C for 15 min, the restriction-ligation products were diluted tenfold with 19 TE buffer for DNA (20 mM Tris–HCl, pH 8.0; 0.1 mM EDTA, pH 8.0). For preselective DNA amplification, 1 lL diluted DNA restriction-ligation product, preselective EcoRI and MseI primers (MWG Biotech) were added to an AFLP core mix (PeqLab, Germany) containing 19 buffer S, 0.2 mM dNTPs and 1.25 U taq-polymerase. In a 5 lL reaction volume PCR was performed on at 94 °C for 2 min then 30 cycles of 20 s denaturation at 94 °C, 30 s annealing at 56 °C and 2 min elongation at 72 °C and a final 2 min 72 °C and 30 min 60 °C step for complete extension ending with a final cool down to 4 °C. After PCR, products were diluted 20-fold with 19 TE buffer for DNA. Three primer combinations were chosen for a subsequent selective PCR reaction after a wider-scale screening (M-CAC/D2-E-AGC, M-CTT/D3-E-AAG, M-CAC/D4-E-ACT, Beckman Coulter). Therefore, PCR was carried out in a total reaction volume of 5 lL containing an AFLP Core Mix (19 buffer S, 0.2 mM dNTP’s, 1.25 U taq-polymerase,

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NJ01,02 NJ03 NJ05 NJ04 SJ01-04

SA04

SA05

SA02

SJ05

SA03

SA01 AL04

AL05

AL03 AL02 AL01

Fig. 1 Geographic position of the sampled populations in the four study regions: Northern Franconian Jura (NJ), Southern Franconian Jura (SJ), Swabian Alb (SA) and Alps (AL). Squares indicate the analysed population

Peqlab, Germany), 0.05 lM selective EcoRI (Proligo, France), 0.25 lM MseI (MWG Biotech) primers and 0.75 lL diluted preselecive amplification product. For detection, EcoRI primers labelled with different fluorescent dyes were used. PCR parameters used

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Table 2 Molecular variance within and among populations from the four study regions calculated in different analyses of molecular variance (AMOVA) Level of variation

df

SS

MS

%

18.08

UPT

NJ–SJ–SA–AL Among regions

3

778.08

259.36

Among populations

16

1330.03

83.13

38.16

Within populations

165

1516.54

9.19

43.76

0.562***

NJ Among populations

4

303.24

75.81

48.44

Within populations

45

328.20

7.29

51.56

0.484***

SJ Among populations

4

272.71

68.18

44.63

Within populations

43

335.69

7.81

55.37

0.446***

SA Among populations

4

332.45

83.11

46.18

Within populations

37

376.86

10.19

53.82

0.462***

AL Among populations

4

421.63

105.41

46.72

Within populations

40

475.76

11.86

53.38

0.467***

SS indicates the sum of squares, MS the mean squares, % the proportion of genetic variability. Levels of significance are based on 999 iteration steps and are indicated by three * (p \ 0.001). Regions: Northern Franconian Jura (NJ), Southern Franconian Jura (SJ), Swabian Alb (SA) and Alps (AL)

were 2 min at 94 °C, 10 cycles 20 s denaturation at 95 °C, annealing 30 s at 66 °C and 2 min elongation at 72 °C, where annealing temperature was reduced every subsequent step by 1 °C, additional 25 cycles of 20 s denaturation at 94 °C, 30 s annealing at 56 °C and 2 min elongation at 72 °C completed by a following 30 min step at 60 °C and a cool down to 4 °C. Selective PCR products were diluted fivefold (D2) and tenfold (D4) with 19 TE buffer for DNA. D3 products were used without a further dilution. After pooling 5 lL of each selective PCR product of a given sample and adding them to a mixture of 2 lL sodium acetate (3 M, pH 5.2), 2 lL Na2EDTA (100 mM, pH 8) and 1 lL glycogen (Roche), DNA was precipitated in a 1.5 mL tube by adding 60 lL of 96 % ethanol (-20 °C) and an immediate shaking. DNA was pelleted by 20 min centrifugation at 14,0009g at 4 °C, the supernatant was poured off and the pellet was washed once by adding 200 lL 76 % ethanol (-20 °C) and centrifugation at the latter conditions and was subsequently vacuum dried in a concentrator. After redissolving the pelleted DNA in a mixture of 24.8 lL sample loading solution (SLS, Beckman Coulter) and 0.2 lL CEQ size standard 400 (Beckman Coulter), selective PCR products were separated by capillary gel electrophoresis on an automated sequencer (CEQ 8000, Beckmann Coulter). Results were examined using the CEQ 8000 software (Beckman Coulter) and analyzed using the software Bionumerics 6.6 (Applied Maths, Kortrijk, Belgium). From the computed gels only those fragments were taken into account for further analyses that showed

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intense and articulate bands. Samples yielding no clear banding pattern or obviously representing PCR artefacts were first repeated. However, due to weak and ambiguous banding patterns, 15 specimens were finally excluded from the analyses. Reproducibility of molecular analyses was investigated by means of estimating the genotyping error rate (Bonin et al. 2004). We replicated 10 % of all analysed samples (18 individuals) using the same DNA extracts and replicated the whole AFLP procedure separately. We scored fragments and calculated the percentage of fragments where differences between original and replicate occurred. Following this procedure we determined a genotyping error rate of 2.3 %. Germination Germination was studied in experiments at three standard temperature treatments (06/14, 14 and 14/22 °C) with a 14 h light period in germination chambers (RUMED, Rubarth Apparate GmbH, Germany) representative for the temperature regimes in the natural habitat (Baskin et al. 2006). For the experiment each 15 seeds were placed in Petri dishes with a double layer of filter paper (Sartorius-Stedim 3hw) moistened with distilled water. Each germination treatment was analysed using 8 replicates per population. The experiments ran over a period of 5 weeks and Petri dishes were controlled twice a week. When the radicle was at least 1 mm long seeds were considered as germinated and removed from the Petri dishes. We used a Tetrazolium test (Lakon 1942) to check viability of seeds, but observed no significant differences between seeds from the different regions. Statistical analysis With the AFLP fragment data, a binary matrix was created. Using this matrix, a hierarchical AMOVA based on pairwise Euclidian distances between samples was performed applying GenAlEx 6.3 (Peakall and Smouse 2006) to analyse the genetic relationships within and between populations. A Mantel test was used to analyse whether genetic and geographic distances between populations were correlated (Mantel 1967). Additionally, a Bayesian cluster analysis using 10,000 markov chain monte carlo (MCMC) simulations was calculated with 20 iterations per K = 2–21 and a burning period of 10,000 with the software Structure 2.2 (Pritchard et al. 2000). From the values of the groups K thus obtained and the average of the log likelihood at each step of the MCMC subtracted with half their variance LnP(D), DK was computed as the slope of the absolute value for the second order rate of the likelihood function LnP(D) divided by the standard deviation sd of LnP(D): K = (m|LnP(D)00 |)/(sd[LnP(D)]) (Evanno et al. 2005). Genetic variation within populations was determined applying the program PopGene 1.32 (Yeh et al. 1997) as percentage of polymorphic bands PB, Nei’s Gene Diversity H = 1 - R(pi)2 and shannon’s information index SI = R(pi)ln(pi), where pi represents the allele frequency. Data on genetic variation within populations and germination exhibited normal distribution and homoscedasticity of variances (after arcsine-square root transformation of germination percentages). Linear mixed models were computed using R software version 2.15.2 (R-Core-Team 2012) and were simplified via backward selection of the least significant variables until the final minimal adequate model contained significant terms (p value \ 0.05). Improved models were verified by computing ANOVAs (Crawley 2007). Models were calculated using (a) germination percentages or (b) genetic variation indices as depending and altitude, population size, region (NJ, SJ, SA, AL) and (a) genetic variability indices or (b) germination percentages as explaining variable.

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Correlation analyses of germination and time point of seed collection as well as seed viability were based on Spearman’s rank correlation coefficient and were conducted with PASW Statistics version 17 (SPSS, Illinois).

Results Genetic variation AFLP genotyping of 185 individuals yielded 214 clear and distinct fragments (D2 and D3: 75, D4: 64) of which 90.19 % were polymorphic. In the Bayesian cluster analysis the dataset was clearly structured into three distinct groups (DK = 22.08). An evaluation of the cluster data unravelled these groups to be arranged in 70 % of the iteration cases in a group of individuals from the Northern and in a group of individuals from the Southern Franconian Jura and in a third group comprising individuals from the Swabian Alb and the Alps (Fig. 2). In 30 % of the iteration cases, a group of individuals from the Northern Franconian Jura was opposed to a group of individuals from the Southern Franconian Jura and the Swabian Alb and to a third group of individuals from the Alps. In a three level AMOVA, 43.76 % of the total variance was observed within populations, 38.16 between populations within regions and 18.08 % among the different regions with UPT of 0.56 (Table 2). Mean pairwise genetic distances UPT were highest between populations from the Northern Franconian Jura and populations from the Southern Franconian Jura and lowest between populations from the Swabian Alb and the Alps (data not

Fig. 2 Bar plot generated from the data of the Bayesian cluster analysis which based on 20 iterations and a burning period of 10,000. Distinct groups are the Northern Franconian Jura (bright grey), the Southern Franconian Jura (dark grey) and the Swabian Alb-Alps group (grey)

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shown). In separate analyses, genetic variation among populations within the study regions varied only marginally (UPT = 0.44–48). The Mantel test revealed a weak but significant correlation of genetic distances (UPT) and geographic distances (km) between populations (r = 0.263, p \ 0.005). Genetic variation measured as percentage of polymorphic bands (PB), Nei’s Gene Diversity (H) and shannon’s information index (SI) was largest in the alpine populations and declined with increasing distance to the Alps (Table 3). In simplified linear mixed models only the factor region was able to explain significant differences in genetic variability (Table 4). Differences were most significant between Jurassic and alpine populations. Notably, the highest genetic variation was obtained within the smallest alpine populations. Germination From a total of 7,200 seeds, 61.00 % germinated already within the first two weeks. Germination varied between 39.17 and 100.00 %. Independent from the treatment,

Table 3 Genetic variation measured as percentage of polymorphic bands (PB), Nei’s Gene Diversity (H) and Shannon’s Information Index (SI) as well as the results of the germination at the three studied treatments (06/14, 14/14, 14/22 °C; in %) of the analysed populations from the four study regions Northern Franconian Jura (NJ), Southern Franconian Jura (SJ), Swabian Alb (SA) and Alps (AL) Code

PB

H

SI

06/14 °C

14/14 °C

14/22 °C

NJ01

18.6

0.07

0.10

95.8

86.5

95.8

NJ02

18.2

0.06

0.09

98.3

99.1

98.3

NJ03

23.3

0.08

0.12

96.6

90.0

64.1

NJ04

22.9

0.08

0.12

94.1

93.3

93.3

NJ05

16.8

0.06

0.09

77.5

93.3

85.0

Mean

20.0 ± 1.32

0.07 ± 0.005

0.10 ± 0.007

92.46 ± 3.80

92.44 ± 2.09

87.30 ± 6.22

SJ01

20.5

0.07

0.11

92.5

95.0

71.4

SJ02

24.3

0.10

0.14

93.3

98.3

55.0

SJ03

14.9

0.05

0.07

79.1

66.6

67.5

SJ04

23.3

0.08

0.12

64.1

70.9

44.1

SJ05

15.4

0.05

0.08

100

97.5

96.6

Mean

19.7 ± 1.95

0.07 ± 0.009

0.10 ± 0.012

85.80 ± 6.40

85.66 ± 6.96

66.92 ± 8.84

SA01

30.3

0.11

0.16

95.0

95.8

79.1

SA02

24.3

0.08

0.12

61.6

55.0

40.0

SA03

24.3

0.09

0.13

78.3

89.1

63.3

SA04

20.0

0.08

0.11

60.0

67.5

46.6

SA05

19.6

0.08

0.11

86.6

94.1

82.5

Mean

23.7 ± 1.93

0.08 ± 0.007

0.13 ± 0.010

76.30 ± 6.86

80.30 ± 8.10

62.30 ± 8.47

AL01

33.6

0.10

0.16

75.8

78.3

47.5

AL02

26.6

0.09

0.13

50.0

65.0

42.5

AL03

25.7

0.09

0.13

60.0

79.1

47.5

AL04

32.7

0.12

0.18

66.6

86.6

89.1

AL05

28.5

0.10

0.15

39.1

65.8

49.1

Mean

29.4 ± 1.60

0.10 ± 0.006

0.15 ± 0.008

58.30 ± 6.39

74.96 ± 4.16

55.14 ± 8.56

Standard errors are given for mean values

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Table 4 Summary statistics of the linear mixed models for genetic variation measured as the percentage of polymorphic bands (PB), Nei’s Gene Diversity Index (H), and Shannon‘s Information Index (SI) Variable

Estimate

Std. error

t value

p value

\0.001***

PB Intercept

29.420

1.720

17.11

Region NJ

-9.460

2.432

-3.89

0.001**

Region SJ

-9.740

2.432

-4.00

0.001**

Region SA

-5.720

2.432

-2.35

0.032*

\0.001***

H Intercept

0.100

0.007

15.16

Region NJ

-0.030

0.009

-3.21

0.005**

Region SJ

-0.030

0.009

-3.22

0.005**

Region SA

-0.012

0.009

-1.29

0.217

SI Intercept

\0.001***

0.150

0.086

15.23

Region NJ

-0.046

0.122

-3.30

0.005**

Region SJ

-0.046

0.122

-3.30

0.005**

Region SA

-0.024

0.122

-1.72

0.104

ANOVA results for PB: R2: 0.566, F3,16 = 6.966, p = 0.0033**, for H: R2: 0.482, F3,16 = 4.966, p = 0.0127* and for SI: R2: 0.482, F3,16 = 4.962, p = 0.0127* *** p \ 0.001, ** p \ 0.01, * p \ 0.05

germination was highest in populations from the Northern Franconian Jura, followed by those from the Southern Franconian Jura and the Swabian Albs. The lowest germination was observed in populations from the Alps. Mean germination calculated over all treatments was 76.88 ± 5.03 % for populations from the Northern Franconian Jura, 59.63 ± 5.80 % for the Southern Franconian Jura, 52.44 ± 5.48 % for the Swabian Alb and 48.78 ± 4.98 % for the Alps (Table 3). In simplified linear mixed models based on the germination percentages, only the factor region was able to explain significant differences in germination rates (Table 5). These differences were significant in the lowest (06/14 °C) temperature regime treatment and just marginally not significant for the 14/22 °C treatment, and always prevailed between germination rates of populations from the Northern Franconian Jura and the Alps. Germination at all three treatments was not correlated with the time point of seed collection or with seed viability (Spearman’s rank correlation p [ 0.05).

Discussion Genetic variation AFLP analysis revealed substantial genetic variation both between populations within the four studied regions and between these regions. Such a pattern of strong genetic differentiation is typical for glacial relict populations and has been observed in previous studies

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Table 5 Summary statistics of the linear mixed models for germination the three studied treatments 06/14, 14 constant and 14/22 °C Treatment

Estimate

Std. error

t value

p value

Intercept

0.872

0.083

10.47

\0.001***

Region NJ

0.447

0.118

3.79

0.002**

Region SJ

0.368

0.118

3.12

0.007**

Region SA

0.212

0.118

1.80

0.091

Intercept

1.052

0.081

12.95

\0.001***

Region NJ

0.255

0.115

2.22

0.041*

Region SJ

0.178

0.115

1.55

0.141

Region SA

0.092

0.115

0.80

0.434

Intercept

0.848

0.098

8.68

\0.001***

Region NJ

0.395

0.138

2.86

0.011*

Region SJ

0.135

0.138

0.98

0.342

Region SA

0.070

0.138

0.51

0.620

06/14 °C

14/14 °C

14/22 °C

ANOVA results for 06/14 °C: R2: 0.512, F3,16 = 5.594, p = 0.0081**, for 14/14 °C: R2: 0.255, F3,16 = 1.830, p = 0.1824 and for 14/22 °C: R2: 0.368, F3,16 = 3.106, p = 0.0561 *** p \ 0.001, ** p \ 0.01, * p \ 0.05

for species such as Ranunculus pygmaeus, Saxifraga cernua or S. paniculata (Bauert et al. 1998; Reisch 2008; Reisch et al. 2003). Climatic warming since the end of the last glaciation has resulted in a fragmented distribution of these species, followed by subsequent isolation and genetic drift, which enhances genetic differentiation processes (Le Corre et al. 1997; Tremblay and Schoen 1999). The present day occurrence of D. aizoides can, therefore, be regarded as remainders of a more wide-spread distribution during the last glacial period (Wilmanns 2005; Wilmanns and Rupp 1966), and the related fragmentation process obviously increased isolation and genetic variation between relict populations. Glacial relict populations from the Swabian Alb were more similar to populations from the Alps, than the other relict populations, which may indicate that these two regions are linked by a common postglacial history. However, further phylogeographic analyses would be necessary to resolve this question finally. Comparing the four regions, genetic differentiation between populations from the Alps was not weaker than between relict populations from the Swabian and Franconian Alb. This may be due to the specific habitat of D. aizoides. Rocky outcrops are always strongly isolated habitats, which results in considerable genetic differentiation as observed for many other rock populating plant species such as Erinus alpinus or Eritrichum nanum (Stehlik et al. 2002a, b). Genetic variation within populations continuously decreased with distance to the Alps and was lowest in the most distant relict populations from the Northern Franconian Jura. This observation supports the results of a previous study on D. aizoides, where also declining levels of nucleotide diversity have been found in relict populations from the Swiss Jura Mountains compared to populations from the Alps (Widmer and Baltisberger

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1999). Decreased levels of genetic variation within relict populations have also been reported for R. pygmaeus, Stipa capillata, S. cernua or S. paniculata (Bauert et al. 2007; Hensen et al. 2010; Reisch 2008; Reisch et al. 2003). Since genetic variation within populations of D. aizoides was not correlated generally with population size, although this relationship has been reported in many studies e.g. (Fischer and Matthies 1998; Galeuchet et al. 2005; Hensen et al. 2005; Reisch et al. 2003), differences can most likely be ascribed to historical events. Thus it can be assumed that glacial relict populations of D. aizoides suffered from reduced gene flow and bottlenecks, what consequently reduced genetic variation (Hewitt 2000). Germination Draba aizoides seeds germinated very well, which corresponds to the results of previous studies, where also high germination has been observed (Kay and Harrison 1970; Wilmanns and Rupp 1966). However, we observed clear differences in germination between seeds of populations from the different regions. At all treatments, seeds of populations from the Northern Franconian Jura germinated best, while worst germination was observed for seeds of populations from the Alps. This is in contrast to the widespread assumption that isolated populations, especially at the edge of a species range, exhibit a reduced fecundity (Jump and Woodward 2003; Pfeifer et al. 2009; Sagarin et al. 2006). However, in only few studies germination of seeds from small and isolated populations as a reproductive trait has been studied and if so, germination failed (Tsaliki and Diekmann 2009) or revealed no difference compared to other populations (Lammi et al. 1999). Different reasons may serve as explanation for the observed germination pattern. On the one hand, climatic differences between seeds of populations from different regions could have caused the observed variation in germination. Previous studies already revealed differences in germination due to variation in climatic conditions (Milberg and Andersson 1998; Shimono and Kudo 2005; Wagner and Simons 2008). Since climatic conditions on the Swabian Alb and in the Southern and Northern Franconian Jura are quite similar, specifically the decreased germination of seeds from alpine populations could be explained in this way. In previous studies it was already reported that germination of seeds from harsher environments at higher altitudes may be lowered (Schu¨tz and Milberg 1997). This may be due to the fact that adaptation to specific environmental conditions could have resulted in the development of ecotypes with varying germination ecology. Differences in germination were shown to persist even under similar environmental conditions in a common garden experiment (Wagner and Simons 2008). Another reason for this observation could, however, be that seeds from milder climates are less dormant than seeds from alpine conditions (Wagner and Simons 2008). The fact that after-ripening of D. aizoides seeds during winter (Hegi 1986) has been reported supports this assumption, which could then result in the observed decrease in germination rate of seeds from the Alps. On the other hand, it has been shown that population size may affect germination (Buza et al. 2000; Menges 1991), although this is not always the case (Oostermeijer et al. 1994). In this study, no general correlation of germination and population size was found, mainly due to the drastically increased germination of seeds from the Northern Franconian Jura and the small populations of the Southern Franconian Jura. The analysis of a larger number of populations and a more specific method to determine population size would perhaps reveal a significant relationship between both parameters. However, the smallest populations from the Alps showed the lowest germination and the largest populations from the Northern Franconian Jura exhibited the highest germination. The possible relationship

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between population size and germination should, therefore, not fully be excluded. The relationship between population size and fitness is well known (Leimu et al. 2006). Therefore, it is quite well conceivable, that larger glacial relict populations also exhibit a higher fitness, which manifests in higher germination rates. Conclusions with respect to conservation In contrast to previous assumptions, glacial relict populations are not necessarily small, genetically impoverished and exhibit a limited reproductive capacity. In our analysis glacial relict populations even proved to be larger and more reproductive than alpine populations, although genetic variation was decreased. Relict populations can be assumed, as many other isolated range margin populations, to occur under ecologically more stressful conditions (Hoffmann and Blows 1994). As a consequence, directional selection pressures are expected to promote, in association with genetic drift due to isolation, local adaptation and genetic divergence (van Rossum et al. 2003). Selection favors alleles that increase fitness under local conditions (Samis and Eckert 2007), which may be a reason for the observed strong reproduction in the glacial relict populations. This assumption is corroborated by the results of a study on Lychnis viscaria, where germination was also not decreased in isolated populations (Lammi et al. 1999). Previous authors already stated that the value of relict populations has remained largely unperceived by conservation biologists (Hampe and Petit 2005). From the results of our study it can be clearly concluded that these vital survivors indeed deserve specific attention in conservation. Acknowledgments The authors thank P. Gerstberger, J. Wagenknecht and the Verein zur Erforschung der Flora des Regnitzgebietes for their help with plant localities, M. Bernhardt- Roemermann for his support in statistical analysis and P. Poschlod for his generous support.

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