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Conserv Genet (2012) 13:197–209 DOI 10.1007/s10592-011-0276-z

RESEARCH ARTICLE

Population structure and genetic diversity of Rana dalmatina in the Iberian Peninsula Vanessa Sarasola-Puente • Marı´a Jose´ Madeira • Alberto Gosa´ • Miguel Lizana • Benjamı´n Go´mez-Moliner

Received: 29 January 2011 / Accepted: 19 September 2011 / Published online: 15 October 2011 Ó Springer Science+Business Media B.V. 2011

Abstract The increasing fragmentation of natural habitats may strongly affect patterns of dispersal and gene flow among populations, and thus alter evolutionary dynamics. We examined genetic variation at twelve microsatellite loci in the Agile frog (Rana dalmatina) from 22 breeding ponds in the Iberian Peninsula, the southwest limit of its range, where populations of this species are severely fragmented and are of conservation concern. We investigated genetic diversity, structure and gene flow within and among populations. Diversity as observed heterozygosities ranged from 0.257 to 0.586. The mean number of alleles was 3.6. Just one population showed a significant FIS value. Four populations show evidence of recent bottlenecks. Strong pattern of structure was observed due to isolation by distance and to landscape structure. The average degree of genetic differentiation among populations was FST = 0.185. Three operational conservation units with metapopulation structure were identified. Additionally, there are some other isolated populations. The results reinforce the view that amphibian populations are highly structured even in small geographic areas. The knowledge of genetic structure pattern and gene flow is fundamental information

V. Sarasola-Puente  A. Gosa´ Observatory of Herpetology, Aranzadi Society of Sciences, Zorroagagaina 11, 20014 Donostia-San Sebastia´n, Spain V. Sarasola-Puente  M. J. Madeira (&)  B. Go´mez-Moliner Departamento de Zoologı´a y Biologı´a Celular Animal, Universidad del Paı´s Vasco, Paseo de la Universidad 7, 01006 Vitoria-Gasteiz, Spain e-mail: [email protected] V. Sarasola-Puente  M. Lizana Departamento de Biologı´a Animal, Universidad de Salamanca, Campus Miguel de Unamuno, 37071 Salamanca, Spain

for developing programmes for the preservation of R. dalmatina at the limits of its geographic distribution. Keywords Rana dalmatina  Microsatellites  Metapopulation  Conservation genetics  Iberian Peninsula

Introduction Biodiversity conservation and survival of species has become a priority for scientists, politicians and technical staff involved in the management of natural resources. Genetic diversity is recognized as a fundamental component of biodiversity (Noss 1990) and has become a recurrent objective for conservation biology because of its implications for understanding the biology of species and populations. The increasing concern of the significance of genetic variation is one of several currencies for biodiversity evaluation (Ehrlich and Wilson 1991; Reed and Frankham 2003), and stresses the need for a better understanding of the distribution of genetic variation within species. Peripheral populations tend to have lower diversity than interior populations owing to isolation, founder effects and smaller population size, but may be important for the survival and evolution of species and have high value for conservation (Lesica and Allendorf 1995). In this context, studies based on highly polymorphic DNA markers permit the identification of appropriate management units and the determination of the extent to which individual subpopulations should be protected as distinct genetics units or operational conservation units (Moritz 1994; Jarne and Lagoda 1996; Crandall et al. 2000; Shaffer et al. 2000). Recent research has focused on investigating genetic structure of different species in nature (Manel et al. 2003; Holderegger and Wagner 2008), and patterns of genetic

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and demographic connectivity among different groups of individuals within species (Newman and Squire 2001; Purrenhage et al. 2009; Wang 2009). This emphasize the importance of movement among populations as an integral component of population dynamics and sustainability (Hanski 1998). In systems where inter-pond dispersal is vital for population viability, fragmentation of terrestrial habitat will have a negative impact on populations (Hitchings and Beebee 1997; Cushman 2006). When connectivity is disrupted and populations become isolated, genetic and demographic rescue of threatened populations may become impracticable, increasing the risk of local extinction (Frankham 2005). Genetic studies are essential to infer gene flow between populations and the potential for re-colonization, which are two critical parameters in metapopulation models (Marsh and Trenham 2001). Besides, it is recognised that the amount of genetic variation can be correlated with fitness traits (Reed and Frankham 2003; Hitchings and Beebee 1997; Rowe et al. 1999; Ficetola et al. 2007). All these aspects are important for population conservation and for a better understanding of the ‘‘global amphibian population decline’’ (Stuart et al. 2004). Concerning amphibians, Beebee (2005) noted that this group exhibits a high degree of genetic population subdivision. It has been repeatedly suggested that amphibians exist in regional networks of subpopulations, or metapopulations (Marsh and Trenham 2001; Smith and Green 2005). The present study is focused on the Agile frog (Rana dalmatina), a relatively common species in the woods of Europe (Krone et al. 1997), which has its southwestern distribution limit in the Iberian Peninsula (Gosa´ 2002). In this peripheral region of its range, the species has a small range being restricted to the southwest of the Pyrenees, living only in the Basque country and Navarra. This species is of conservation concern in Spain, and particularly in the Basque Country and Navarra, being included in the national and regional red-list (Gosa´ 2002). As a result of its threatened status it has been the subject of a species recovery programme (Gosa´ and Sarasola 2009). Its habitat in the Iberian Peninsula is in oak woods of Quercus robur and Q. pyrenaica placed in valleys or at the bottom of mountain systems (500–600 m.a.s.l.), with humid meadows and networks of mature ponds. The disappearance of oak woods and/or wetlands has reduced its distribution during the last decades to a few isolated populations, becoming more and more separated by greater distances. Generally, these remaining populations are surrounded by a matrix of unsuitable habitats for the species devoid of wetlands (mainly intensive arable farms) which prevent the connection of the populations, resulting in their isolation (Gosa´ 2002). This species is a good example for addressing effects of forest fragmentation and disappearance of mature

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wetlands because of the strength of their relationship with suitable habitats at the edge of their range. Genetic diversity and structure of R. dalmatina, remains unknown in the majority of its distribution area. Previous to this work, the only study focused on genetic diversity and differentiation in Agile frog populations was made in France, but used allozyme markers (Lesbarre`res et al. 2003; Lesbarre`res et al. 2006). However, two microsatellite libraries have been published recently (Hauswaldt et al. 2008; Sarasola-Puente et al. 2010) and this genetic tool opens the possibility of further studies on this aspect. In the present work we used microsatellite markers to characterize the genetic population structure in the Iberian populations of R. dalmatina. We have included samples of the majority of the known populations on the Iberian Peninsula. This study was conducted in a landscape fragmented by urban enlargement and agricultural intensification. The main goals were to determine the effective population sizes of populations, the genetic diversity and differentiation between all subpopulations throughout this fragmented landscape, to examine regional population connectivity, to detect recent bottlenecks in populations, and to determine conservation units.

Materials and methods Sample collection and genotyping The material used in this study comprised samples from 22 populations (404 individuals) collected during the breeding season for 2003 (21 populations) and 2009 (one population), distributed over all the distribution area in the Iberian Peninsula (Fig. 1). Additionally, one population from France (22 individuals) and two populations from Germany (29 individuals) were analyzed to compare diversities and genetic distances. Samples included embryos or larvae, each population being sampled only once. Samples were stored in 95% ethanol until DNA extraction. In small populations all available clutches were sampled. In large populations around 24 embryos were collected (Table 2). We use the term population to refer to an individual pond. DNA was extracted using the DNeasy Tissue kit (Quiagen). Quality and quantity of DNA was estimated by running all samples on 1.5% agarose gels. Extracted DNA was used as templates in polymerase chain reactions (PCR) for twelve di-, tri- and tetranucleotide microsatellite loci (Treves et al. 1992; Hauswaldt et al. 2008; Sarasola-Puente et al. 2010; Table 1). Forward primers for each PCR were labelled with an end-fluorescent tag (6-FAM, NED, VIC or PET) for visualization. PCR was performed using 5 ll Qiagen Multiplex Mix (Qiagen), 2 ll template DNA and 2 lM of each primer. Conditions for all loci included an

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199

Fig. 1 The Iberian populations sampled in the present study

Table 1 Locus name; allele size range (SR), total number of alleles and average number of alleles per population in the Iberian peninsula; and publication Locus

Genbank accesions

Size range (bp)

Total n8 alleles

Average

Publication

Rdal_1

EU139058

121–133

4

1.9

Sarasola-Puente et al. (2010)

Rdal_5

EU139060

168–184

6

1.8

Sarasola-Puente et al. (2010)

Recalq

X64324

208–229

6

2.3

Treves et al. (1992)

Rdal_6

EU139061

124–144

7

2.5

Sarasola-Puente et al. (2010)

Radal_F5

EU364514

156–180

7

3.2

Hauswaldt et al. (2008)

Radal_G11

EU364515

350–374

7

3.8

Hauswaldt et al. (2008)

Radal_H1 Rdal_7

EU364517 EU139062

170–198 192–218

8 11

3.8 2.9

Hauswaldt et al. (2008) Sarasola-Puente et al. (2010)

Rdal_12

EU139063

207–231

12

4.4

Sarasola-Puente et al. (2010)

Radal_G12

EU364516

230–274

12

6.4

Hauswaldt et al. (2008)

Rdal_18

EU139064

122–156

13

4.6

Sarasola-Puente et al. (2010)

Radal_E8

EU364512

369–425

14

5.6

Hauswaldt et al. (2008)

initial denaturing at 94°C for 5 min; 35 cycles of denaturing at 94°C for 1 min, annealing at locus-specific 54°C for 1 min, and 72°C extension for 1 min; and a final 30 min extension at 72°C. All reactions included a negative control. PCR products were amplified on an ABI 3730 Genetic Analyzer (Applied Biosystems). Fragments were sized with LIZ 500 size standard and collected with GeneMapper (Applied Biosystems). The MICRO-CHECKER software (Van Oosterhout et al. 2004) was used to check for potential scoring errors, large allele dropout and the presence of null alleles. Pairwise linkage disequilibrium between loci was checked using the software GENETIX (Belkhir et al. 1996–2004).

Population variability Table 2 presents the population name, code, location, number of animals genotyped, genetic diversity for each sampled R. dalmatina breeding pond. Microsatellite genetic diversity within sample sites was quantified by the unbiased estimates of gene diversity: expected heterozygosity (HE), observed heterozygosity (HO) and allelic richness (NA) per locus and population. Deviations from Hardy–Weinberg equilibrium in each population and for each locus, as well as over all loci, were estimated using an exact probability value (Guo and Thompson 1992). These analyses were all performed with the program Genepop version 4.0.10 (Raymond and Rousset 1995). Furthermore significant differences of observed

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Table 2 Population name, code, location, number of animals genotyped (N), mean number of alleles per locus (Na), mean observed (Ho) and expected (HE) heterozygosities for the twelve microsatellite loci,

FIS for each sampled R. dalmatina breeding ponds, P value obtained under the TPM model of mutation in the program BOTTLENECK and effective population size (Ne) using the program COLONY

Population

Code

Coordinates

N

N

Ho

HE

FIS (P value)

TPM

Ne (CI 95%)

Cabredo

SOTA

428640 N 28420 W

23

3.917

0.586

0.531

-0.0811 (0.978)

0.04614

16 (9–34)

Kintana I

QI

428660 N 28470 W

23

3.917

0.533

0.512

-0.0187 (0.700)

0.15063

26 (14–49)

Kintana II

QII

428660 N 28470 W

24

4.167

0.545

0.517

-0.0342 (0.830)

0.39551

21 (11–42)

Urturi Lan˜o

URT ˜O LAN

428660 N 28500 W 428670 N 28640 W

24 24

4.083 3.833

0.448 0.543

0.461 0.505

0.0500 (0.120) -0.0522 (0.909)

0.57495 0.16016

19 (10–38) 17 (9–35)

Birgara

OLA

428740 N 28470 W

23

4.083

0.519

0.487

-0.0426 (0.845)

0.33862

20 (11–39)

Maeztu

MAE

428770 N 28430 W

24

4.167

0.576

0.519

-0.0888 (0.993)

0.25928

20 (12–38)

Loza

LOZA

428830 N 18710 W

23

3.250

0.500

0.457

-0.0715 (0.936)

0.08740

8 (4–24)

Berrioplano

BER

428850 N 18700 W

24

2.417

0.389

0.321

-0.1909 (1.00)

0.21289

11 (5–26)

Zuasti

ZUA

428860 N 18740 W

7

2.250

0.405

0.331

-0.1493 (0.953)

0.23047

18 (8–156)

Salburua

SAL

428860 N 28620 W

25

4.083

0.508

0.487

-0.0201 (0.686)

0.48291

14 (7–30)

0

0

Egiarreta

EGI

42891 N 1886 W

25

3.667

0.496

0.449

-0.0834 (0.979)

0.18750

21 (11–42)

Ostiz

OST

428920 N 18620 W

23

2.250

0.371

0.311

-0.1712 (0.997)

0.06445

11 (6–28)

Arbizu

ARB

428920 N 28020 W

12

2.667

0.468

0.382

-0.1816 (0.995)

0.12500

8 (4–23)

Eltso-Gerendiain

GOLF

428960 N 18670 W

15

2.250

0.364

0.301

-0.1724 (0.991)

0.38477

12 (6–28)

Altube

ALT

428970 N 28860 W

24

4.750

0.582

0.500

-0.1424 (1.00)

0.58447

24 (13–43)

0

0

Larraintzar

BAL

42899 N 1869 W

22

3.083

0.385

0.355

-0.0589 (0.871)

0.87988

14 (7–30)

Unza

UNZA

428990 N 28950 W

25

4.167

0.473

0.448

-0.0373 (0.822)

0.81250

20 (11–39)

Auza-Eltzaburu Eltzaburu

AUZ LAN

438000 N 18700 W 438000 N 18720 W

25 24

2.750 2.167

0.347 0.257

0.333 0.250

-0.0232 (0.681) -0.0084 (0.557)

0.68750 0.63281

17 (9–34) 13 (7–28)

Gorbea

GOR

438020 N 28770 W

23

5.667

0.501

0.534

0.0850 (0.012)

0.94507

32 (18–63) 8 (4–24)

0

0

Amurrio

AMU

43804 N 3800 W

32

3.833

0.517

0.512

0.0062 (0.427)

0.07568

Biarritz (France)

MOU

438470 N 18550 W

22

5.167

0.564

0.529

-0.0437 (0.883)

0.92432

Boimstorf (Germany)

I

528310 N 108770 E

14

4.250

0.518

0.505

0.0117 (0.015)

0.27832

Undeloh (Germany)

S

538190 N 98970 E

15

3.417

0.389

0.431

0.1324 (0.417)

0.81738

heterozygosities between populations were tested using the program FSTAT version 2.9.3.2 (Goudet 1995). Intra population subdivision coefficients (FIS) for all sampling sites were calculated using the method developed by Weir and Cockerham (1984) implemented in the program GENETIX version 4.05.2 (Belkhir et al. 1996–2004). Bottleneck detection Statistical methods have been developed to infer the demographic history of a population from a single genetic sample. The program BOTTLENECK version 1.2.02 (Cornuet and Luikart 1996; Piry et al. 1999) was applied to test for recent reductions in effective population size. During population bottlenecks, rare alleles are lost due to drift at faster rate than loss of heterozygosity. This disparity is used to detect past bottlenecks. The analyses were performed under the three microsatellite mutational models available. Following the recommendations of Cornuet and Luikart (1996) and Luikart et al. (1998), the Wilcoxon signed-rank test with a two phased model of mutation

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(TPM) was chosen. The parameters chosen for TPM were: variance 30%, probability for SMM 80% and estimations were based on 2000 iterations. Effective population size estimation To estimate effective population sizes of R. dalmatina Iberian populations, we performed the sibship assignment method implemented in COLONY version 2.0 (Wang 2009). This method first determines the probabilities of all pairs of samples from a population being full-sibs and half-sibs. These assignment probabilities are then used to determine Ne based on a predictive equation that relates the probability of drawing these assignments from a randomly sampled single cohort to the number of effective breeding adults. Testing panmixia and estimating the number of populations The program FSTAT version 2.9.3.2 (Goudet 1995) was applied to obtain the pairwise FST values between all pairs

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201

Gene flow detection

of populations and the correct P values for tests of population differentiation among pairs of populations. The program STRUCTURE, version 2.3.3 (Pritchard et al. 2000) was applied to analyse population structure in the microsatellite dataset. This software uses a Bayesian clustering algorithm and assigns individuals to clusters without relying on a priori delineation of populations. We used an admixture model with correlated allele frequencies for K (number of clusters) values from one (panmixia) to 10, and performed 10 runs for each K value with one million Markov chain Monte Carlo (MCMC) iterations and a ‘‘burn-in’’ period of 100.000 iterations. The inference for the best value of K was based on the estimated log probability of data for each value of K and its geographical agreement, bearing in mind the distances between ponds and the different habitats presents. The inference based on DK (Evanno et al. 2005) was not clear. We performed an analysis of molecular variance (AMOVA) based on STRUCTURE results and grouping populations in ARLEQUIN version 3.0 (Excoffier et al. 2005). Statistical significance was based on 10.000 permutations.

We investigated pairwise migration in some fine scale clusters and among the clusters defined by STRUCTURE software using the MIGRATE program version 3.0.3 (Beerli and Felsenstein 2001; Beerli 2006). This software estimates the number of migrants (4 Nm) exchanged between populations per generation using an expansion of the coalescent theory in a Bayesian approach. Following the recommendations of Beerli (2004), we did an initial run with the default values using FST (implemented in the program) to find the start parameters and we used the output of the initial run as the start parameters of our second run. Because there were only minor differences between the outputs from the first and the second runs, we stopped and presented output from the second run in the results below. We have used the geographical distances between populations, but not between clusters. When this is used, the migration rates are not only scaled by the mutation rate but also by this geographical distance.

Landscape genetic analysis Results An analysis of isolation by distance was performed using a Mantel test (Mantel 1967) implemented in the program FSTAT (Goudet 1995). The geographical distances between populations were calculated from the population coordinates, so these are Euclidean distances. Population genetic structure was investigated using the software GENELAND (Guillot et al. 2005), which uses a Bayesian approach with Markov Chain Mote Carlo algorithms to identify genetic spatial discontinuities. The most probable number of clusters (K) was found using eight replicates with 1 9 105 MCMC iterations and using Dirichlet as the selected model for allele frequencies. For each run the maximum rate of the Poisson–Voronoi tessellation was fixed at 3,000 to better reflect the number of individuals in the data set. The European populations were not included in this analysis.

ULTZAMA

Genetic diversity All loci were polymorphic across individuals from the sampled ponds. The number of alleles observed at each locus ranged from four to fourteen with a mean of 8.9 alleles per locus. Of 6,540 samples (twelve loci across 545 individuals), 90 (1.4%) missing values were found. There were no signs of linkage disequilibrium between any pair of loci when applying a Bonferroni correction for the probability levels (P [ 0.00076). Different loci in five populations indicated signs of a null allele (Radal_H1 in AMU, Rcalc in AUZA and Rdal_12 in OST, QI and URT). We did not exclude any of them from the analyses. When tested for Hardy–Weinberg equilibrium within populations (HWE), just one Iberian population (GOR)

KANPEZU

PAMPLONA

LLANADA

ALTONERVIÓN

1.00 0.80 0.60 0.40 0.20 0.00 AUZ

BAL GOLF

LAN

OST

LAÑO

MAE

OLA

QI

QII

SOTA

URT

BER LOZA ZUA ALT

AMU

UNZA GOR

ARB

EGI

SAL

Fig. 2 Results of Bayesian clustering and individual assignment analysis as implemented in structure 2.3.3, for K = 5 clusters. Vertical bars delimit sampling locations

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Table 3 Pairwise FST and geographical distances between the 25 R. dalmatina populations sampled Population

SOTA

QI

URT

QII

˜O LAN

MAE

OLA

LOZ

BER

SAL

ZUA

EGI

OST 0.28

SOTA

0

0.008

0.015

0.015

0.005

0.023

0.007

0.217

0.318

0.107

0.232

0.168

QI

4.8

0

0.025

0.03

0.02

0.026

0.008

0.222

0.314

0.106

0.231

0.16

0.275

URT

6.8

2

0

0.021

0.018

0.03

0.012

0.206

0.314

0.108

0.206

0.14

0.248

QII ˜O LAN

4.8

0.8

2.4

0

0.04

0.029

0.011

0.245

0.354

0.109

0.239

0.189

0.301

17.8

13.3

11.3

13.7

0

0.02

0.014

0.214

0.307

0.086

0.232

0.158

0.268

MAE

10.9

9.3

9.7

8.5

18.1

0

0.013

0.223

0.323

0.079

0.233

0.153

0.291

OLA

12.6

9.7

9.5

9.1

15.9

3.3

0

0.238

0.34

0.085

0.236

0.172

0.288

LOZ

62.2

65.5

67.3

64.9

77.9

60.2

63

0

0.071

0.263

0.112

0.085

0.136

BER

63.2

66.5

68.2

65.9

78.8

61

63.8

1.6

0

0.35

0.172

0.152

0.228

SAL

29.3

25.3

24

24.9

21

20.5

17.5

74.5

74.9

0

0.267

0.186

0.322

ZUA

61.1

64.2

65.9

63.6

76.4

58.5

61.3

3.3

2.9

72.2

0

0.074

0.201

EGI

55.2

57.7

59.2

57

69

50.9

53.4

15.1

14.7

62.4

11.9

0

0.175

OST

72.7

75.7

77.3

75.1

87.7

69.7

72.3

11.9

10.4

82.2

11.8

19.8

0

ARB

45.4

47.2

48.4

46.4

57.4

39.4

41.5

27.6

27.6

49.2

24.7

13.6

33.3

GOLF GOR

71.5 51.4

74.3 46.8

75.8 45.2

73.6 46.7

85.7 38.2

67.6 43.4

70.1 40.3

14.6 94.9

13 95.2

78.9 23.1

13.1 92.4

16.8 81.7

6.2 101.3

ALT

51.4

46.9

45.2

46.7

38.2

43.4

40.3

94.9

95.2

23.1

92.3

81.6

101.3

UNZA

57.8

53.2

51.5

53.1

43.6

50.3

47.2

102.2

102.5

30.2

99.7

88.9

108.5

BAL

72.1

74.5

76

73.8

85.6

67.5

69.9

18

16.5

77.8

16

16.8

10.4

AUZA

71.5

73.9

75.3

73.2

84.9

66.8

69.2

18.2

16.7

76.9

16.1

16.3

11.1

LAN

70.2

72.6

74

71.8

83.5

65.4

67.7

18.5

17

75.3

16.1

15.1

12.4

AMU

64.9

60.3

58.6

60.1

50.8

57.1

54

107.3

107.5

36.7

104.7

93.6

113.1

MOU

116.8

117.7

118.6

117

125.6

109

110.2

72.2

70.7

110.6

70.3

67.1

62.1

S

1,078

1,077

1,076

1,076

1,077

1,068

1,067

1,054

1,052

1,056

1,051

1,045

1,044

I

1,174

1,172

1,171

1,171

1,171

1,163

1,162

1,152

1,150

1,150

1,149

1,143

1,143

Population

ARB

GOLF

S

I

GOR

ALT

UNZA

BAL

AUZA

LAN

AMU

MOU

SOTA

0.227

0.267

0.118

0.123

0.204

0.253

0.272

0.316

0.176

0.11

0.188

0.202

QI

0.242

0.273

0.116

0.125

0.193

0.255

0.273

0.32

0.167

0.105

0.187

0.192

URT

0.216

0.254

0.137

0.115

0.187

0.245

0.256

0.303

0.185

0.103

0.181

0.209

QII

0.226

0.302

0.144

0.147

0.212

0.288

0.308

0.354

0.207

0.122

0.196

0.204

˜O LAN

0.223

0.241

0.107

0.108

0.192

0.232

0.244

0.278

0.17

0.101

0.166

0.182

MAE

0.23

0.269

0.125

0.119

0.191

0.254

0.28

0.316

0.176

0.097

0.182

0.17

OLA

0.24

0.285

0.117

0.114

0.198

0.264

0.287

0.329

0.179

0.092

0.179

0.184

LOZ

0.189

0.135

0.161

0.137

0.166

0.135

0.131

0.211

0.143

0.18

0.22

0.286

BER

0.262

0.2

0.256

0.216

0.245

0.205

0.174

0.262

0.172

0.243

0.291

0.367

SAL

0.218

0.303

0.099

0.121

0.179

0.289

0.302

0.328

0.177

0.109

0.164

0.161

ZUA

0.196

0.233

0.195

0.16

0.19

0.198

0.182

0.299

0.122

0.154

0.221

0.265

EGI OST

0.112 0.336

0.156 0.122

0.138 0.256

0.099 0.22

0.128 0.264

0.152 0.129

0.152 0.145

0.214 0.226

0.098 0.208

0.111 0.212

0.172 0.319

0.216 0.362

ARB

0

0.287

0.192

0.151

0.162

0.28

0.276

0.356

0.167

0.17

0.229

0.238

GOLF

29.7

0

0.222

0.193

0.254

0.012

0.041

0.068

0.168

0.206

0.329

0.352

GOR

68.1

97.3

0

0.027

0.082

0.203

0.21

0.256

0.067

0.103

0.162

0.183

ALT

68.1

97.2

0.1

0

0.047

0.177

0.177

0.226

0.054

0.076

0.157

0.18

UNZA

75.3

104.4

7.4

7.4

0

0.245

0.241

0.31

0.079

0.158

0.216

0.243

BAL

28.8

4.2

95.6

95.5

102.6

0

0.016

0.044

0.157

0.189

0.31

0.321

AUZA

28

4.9

94.7

94.6

101.7

0.9

0

0.035

0.158

0.203

0.303

0.337

LAN

26.4

6.2

93

92.9

100

2.7

1.8

0

0.205

0.238

0.365

0.387

123

Conserv Genet (2012) 13:197–209

203

Table 3 continued Population

ARB

GOLF

GOR

ALT

UNZA

BAL

AUZA

LAN

AMU

MOU

S

I

AMU

80.1

108.8

13.7

13.7

7.2

106.7

105.8

104.1

0

0.094

0.192

0.192

MOU

72.2

57.7

119.9

119.8

125.3

54.3

54.2

54.3

126.6

0

0.143

0.123

S

1,045

1,039

1,045

1,045

1,044

1,036

1,035

1,035

1,038

982

0

0.150

I

1,141

1,138

1,138

1,138

1,136

1,134

1,133

1,133

1,131

1,081

113

0

Pairwise FST estimates were averaged across all loci (upper diagonal). Values statistically no differents appears in bold. Geographical distances are represented in km (below diagonal)

showed a deviation from HWE due to heterozygote deficiency at three loci (Radal_G11, Rdal_12 and Rcalc). But, all the loci showed no deviations from Hardy–Weinberg equilibrium when applying Bonferroni correction (P [ 0.00015). The same population (GOR) was the only one showing significant FIS values (Table 2). Levels of observed heterozygosity ranged from 0.257 to 0.586, and allelic richness from 2.167 to 3.917 (Table 2). The lowest genetic diversities were found in the populations from Navarra, placed in the south-eastern limit of distribution for this species in the Iberian Peninsula. Genetic diversity results showed a significant differentiation between the populations belonging to the clusters obtained from structure software (P = 0.011 after 1000 permutations). When applying Bonferroni correction, no population showed any signs of a recent bottleneck under the TPM model selected (one-tailed test for heterozygote excess, P \ 0.002). Applying bottleneck tests to the clusters obtained from STRUCTURE software (Fig. 2) also showed no signals of bottleneck. The sibship assignment method (Wang 2009) indicated that effective population sizes were small in each sample site, varying from 8 to 32 individuals. The correlation between observed heterozygosities and effective population sizes was not significant (r = 0.389; P = 0.074). Population structure Population differentiation (FST) between Iberian and European populations ranged from 0.076 (MOU-ALT) to 0.387 (I-LAN), all the values being statistically significant (P \ 0.05). Population differentiation (FST) across the ˜ Owhole Iberian study area ranged from 0.0054 (LAN SOTA) to 0.356 (LAN-ARB), showing values statistically significant (P \ 0.05) in 231 of 254 pairwise comparisons (Table 3). The following clusters of adjacent or closely located sampling ponds were not significantly differentiated from each other: AUZA-BAL-GOLF, ALT-GOR ˜ O-MAE-OLA-KI-KII-SOTA-URT. These results and LAN correlated with the results obtained using Bayesian methods. In the ten independent simulations of the

Bayesian clustering of individuals method implemented in STRUCTURE (Pritchard et al. 2000), K = 5 was selected as the most probable number of clusters across the study area. K = 5 had an average ln P(X|K) = -11173. From K [ 5 the value of ln P(X|K) decreased and variance between the independent runs was larger. The five clusters were largely associated with isolated landscape patches. These clusters corresponded to: Ultzama (AUZ, BAL, ˜ O, MAE, OLA, QI, GOLF, LAN and OST), Kanpezu (LAN QII, SOTA and URT), Pamplona (BER, LOZA and ZUA), Llanada (SAL, EGI and ARB) and Alto Nervio´n (ALT, AMU, UNZA and GOR; Fig. 2). There were two Iberian populations (EGI and GOR) with a mixed structure between two clusters. We examined the partitioning for increasing K (1–20) to determine the number of clusters (Evanno et al. 2005), but the result was not clear. In the same way, six population clusters (K) were consistently identified by GENELAND. Each population had a posterior probability [0.90, of belonging to their regional group (white area surrounded by the 0.90 isocline; Fig. 3), which provided support for the clustering result. These groups are similar to the previous, and were named as follows: IP-1 ˜ O, MAE, OLA, QI, ‘‘Kanpezu-Montan˜a alavesa’’ (LAN QII, SOTA and URT), IP-2 ‘‘Cuenca de PamplonaSakana’’ (BER, LOZA, ZUA and EGI), IP-3 ‘‘Alto Nervio´n’’ (ALT, AMU, UNZA and GOR), IP-4 ‘‘Ultzama-Odieta’’ (AUZ, BAL, GOLF, LAN and OST), IP-5 ‘‘Salburua’’ (SAL) and IP-6 ‘‘Arbizu’’ (ARB). Each group included geographically proximate ponds. The a posteriori AMOVA tests revealed that the clusters resulting from Bayesian results, obtained from STRUCTURE software, explained 14.4% of the molecular variance, the 5.9% was explained by between population variation and 79.6% by within population variation. All of them were statistically significant (Table 4). Strong isolation by distance was apparent for the entire set of ponds. Results of Mantel test examining the correlation between geographic and genetic distances for the Iberian populations, indicated a significant positive correlation (r = 0.59; P = 0.01; Fig. 4). However, within each of the main clusters defined, this correlation was not so clear. The results of Mantel test was significant in the cluster

123

204

Conserv Genet (2012) 13:197–209

Fig. 3 continued

‘‘Ultzama’’ (r = 0.91; P = 0.000), but not significant in the clusters ‘‘Kanpezu’’ and ‘‘Alto Nervio´n’’ (r = 0.133; P = 0.56 and r = 0.468; P = 0.35, respectively). The studied populations showed substantial migration at small geographic distances (Table 5). Nm values ranged from 2.6 to 45.3 in the three studied clusters with distances between 0.1 and 18.1 km (Tables 2, 5). Among clusters may also be observed some migration, although the maximum value of Nm was 13.3.

Discussion Patterns of genetic diversity

Fig. 3 Maps of population clusters (K) identified by GENELAND based on posterior probabilities of population membership. Each cluster had a posterior probability [0.90 (white area surrounded by the 0.90 isocline)

123

Values of genetic variability (observed heterozygosities) were significant different among sites, although the sample is homogenous (CV \ 30%), and were consistent with known population histories. Populations with highest ˜ O) are diversity values (SOTA, ALT, MAE, QII, and LAN situated in areas where the habitat is well preserved and no particular threats are affecting them. On the other hand, populations with the lowest diversity values (LAN, AUZ, OST, GOLF, and BAL) are situated in a region where forests are well preserved, but the number of ponds has been steadily depleted. In the latter case, current populations depend on the remaining ponds, which are largely

Conserv Genet (2012) 13:197–209 Table 4 Analysis of molecular variance (AMOVA) assessing the genetic structure of Iberian R. dalmatina populations

205

df

% variation

P

5

475.129

0.48102

14.41

\0.001

213.187

0.19766

5.92

\0.001

Within populations

1,065

2831.277

2.65848

79.66

\0.001

Total

1,089

3519.594

3.33715

In addition, there is no substantial difference between the analyzed European and Iberian population diversities (Table 2), showing a fairly high genetic variability in the majority of the Iberian populations studied. In peripheral populations it is common to find low genetic diversity values (Sjo¨gren 1991; Rowe et al. 1999; Zeisset and Beebee 2001), so these are exceptional values of diversity. These peripheral populations may be important because of the adaptive potential, the possibility to drift and adapt to novel environments. High genetic variability may correlate significantly with fitness (Reed and Frankham 2003; Allentoft and O’Brien 2010) and is an important parameter for the conservation of these populations placed in the limits of distribution of this species.

Mantel r = 0.592; p = 0.01

0.30

Genetic distance (Fst)

Variance components

19

Among clusters Among populations within clusters

The probabilities were assessed with 10,000 permutations

Sum of squares

0.20

0.10

0.00 0.00

25.00

50.00

75.00

100.00

Geographical distance (km)

Fig. 4 Isolation by distance analyses. There was a positive correlation between geographical distance and genetic distance

disconnected and, as a consequence, migration between ponds is very limited. Thus, isolation has resulted in loss of genetic diversity, increasing inbreeding processes within populations. The low genetic diversity values obtained for these populations indicated that the isolation process is ancient enough to be detected. These results are in accordance with population depletion observed in these populations of R. dalmatina based on habitat destruction (Gosa´ 2002). Recovery plans have been under way since 1999 mainly focused in habitat regeneration (Gosa´ and Sarasola, 2009). Those populations which are completely isolated from each other showed an intermediate situation, with relatively high genetic diversity values (SAL, LOZA, EGI, ARB, and ZUA). Fragmentation processes by habitat loss contributed to the isolation of these populations over the last 50 years, from a previously almost continuous distribution. These recent processes of isolation and the population sizes could explain the relatively high levels of genetic diversity that persist at the present time.

Regional population structure The results show that Iberian Agile frog populations are highly structured. On the one hand, populations show a clear pattern of isolation by distance over the entire study area covering nearly its whole range within the Iberian Peninsula (Fig. 4). This effect has been previously reported in some other studies of amphibian populations (Vos et al. 2001; Monsen and Blouin 2004; Palo et al. 2004; Spear et al. 2005; Knopp and Merila¨ 2009) but not in others (Allentoft et al. 2009; Purrenhage et al. 2009). When the analysis is applied to the three main clusters, this pattern is not so clear. The hypothesis that the greater geographic distance, the higher genetic differences between populations, is not fulfilled. Therefore, geographic distance does not seem to be the main cause of the observed genetic inter-population differentiation. Our results indicate that habitat configuration seems to be a primary cause of the genetic structuration. Estimates of FST and ‘‘genetic clusters’’ (STRUCTURE and GENELAND) were consistent with each other, and they indicated high levels of admixture between some groups of populations. For instance the clusters: ‘‘Kanpezu’’, ‘‘Ultzama’’ and ‘‘Alto Nervio´n’’ defined by STRUCTURE analysis (Fig. 2), fully agree with the results obtained by GENELAND: IP-1, IP-4 and IP-3 (Fig. 3). The populations included in each cluster had not significant values of pairwise FST estimates (Table 3), and

123

123

– 8 16.1 10 18.6 16.9 17.9

7.9 – 2.8 2.6 10.2 18 15.6

QI

10.9 2.7 – 4.3 20.5 14.8 22.9

URT 6.8 2.6 3.9 – 14 20.4 15.6

QII 19.5 33.5 27.4 9.6 – 18.4 17.9

˜O LAN 23.7 26.5 13 13.6 10 – 4.9

MAE 14.7 23.6 6.4 12.1 17.6 3.1 –

OLA

– 23.1 28.2 28.1 26.3

OST

22.6 – 27.4 27.5 18

GOLF

Ultzama

20.9 26.1 – 5 15

BAL

24.5 26.7 11.4 – 17.2

AUZA

Migration is bi-directional, in horizontal are donor populations and in vertical are receiving populations

SOTA QI URT QII ˜O LAN MAE OLA OST GOLF BAL AUZA LAN GOR ALT UNZA AMU Alto Nervio´n Ultzama Pamplona Kanpezu Llanada

SOTA

Kanpezu

23.8 22.5 22.6 18 –

LAN

– 2.6 15.9 29.9

GOR

2.7 – 13.8 33.9

ALT

Alto Nervio´n

16.3 21.9 – 20.2

UNZA

45.3 39.3 23.5 –

AMU

– 2.9 2.9 2.7 4.9

Alto Nervio´n

6.9 – 3.3 3.1 7.6

Ultzama

3.4 2.6 – 2.6 3

Pamplona

12.2 3.7 6.5 – 13.3

Kanpezu

3.1 2.5 2.7 2.6 –

Llanada

Table 5 Number of migrants among pairwise population in the clusters of: ‘‘Kanpezu’’, ‘‘Ultzama’’ and ‘‘Alto Nervio´n’’, and among the clusters obtained from STRUCTURE software

206 Conserv Genet (2012) 13:197–209

Conserv Genet (2012) 13:197–209

showed a distinct pattern of effective movement of individuals between populations (gene flow) (Table 5). All these three clusters are situated in areas where the optimum habitat of the species is relatively well preserved. A continuous population does no exist, thus there is not possibility of migration of specimens between the clusters because this species is characterised by movements following a stepping stone model. As individuals move into adjacent ponds and breed, the genetic structure is diluted by recombination. Alleles move to new populations over several generations and Nm is the number of migrants (genetic individuals) per generation, each carrying a small portion of the alleles from an ancestor in the distant ponds (Purrenhage et al. 2009). It is worth highlighting, that the destruction of wetlands in some areas like ‘‘Ultzama’’ causes the fragmentation of R. dalmatina populations even when forests are well preserved, and this is the most probable cause for the absence of the species in areas where the habitat seems to be suitable for it. The low values of genetic variability obtained in Ultzama populations (Table 2) may reflect the isolation of some populations. Gene flow values obtained may be indicative of past rather than current migration of individuals. On the other hand, Wright (1978) emphasized that genetic differentiation is ‘‘by no means negligible’’ if FST is as small as 0.05. This value implies that Nm \ 5, and is an intermediate value of the possible population criterion (quantitative criteria to determine when groups of individuals are different enough to be considered populations) defined by Waples and Gaggiotti (2006). This criterion was largely fulfilled by the populations included in each of the three defined clusters (Tables 3, 5), suggesting we can define each one of these three clusters (Kanpezu, Ultzama and Alto Nervio´n) as metapopulations (Hanski 1998; Marsh and Trenham 2001). These three metapopulations show significant genetic differentiation and they should require proper management actions. Thus, they should be considered as three different operational conservation units (Moritz 1994; Marsh and Trenham 2001). The last two clusters defined by the software STRUCTURE (‘‘Pamplona’’, ‘‘Llanada alavesa’’) did not fully coincide with those defined by GENELAND (IP2, IP5, IP6). The main incongruence comes from the population of ARB. Nevertheless, taking into account the small number of samples studied from this population (Table 2) and the important role that the geographic parameter play in the GENELAND software, we consider that this difference is irrelevant. FST values were significant between these populations so we consider each of them as one isolated population. In this area all known populations were sampled. There are few ponds separated by long distances, and isolated by a matrix of inhabitable habitats for the Agile frog, like grain farms or urbanizations. The negative effects

207

of intensive cultures and urbanizations on population size and genetic diversity in anurans have been previously reported (Johansson et al. 2005; Noe¨l et al. 2007). Furthermore, there are highly developed infrastructures (roads and railroads) whose negatives effects on R. dalmatina population connectivity are known (Lesbarre`res et al. 2003, 2006). These barriers to gene flow, together with habitat reduction and fragmentation are causing isolated microevolutionary processes in R. dalmatina. Conservation measures should be focused to habitat restoration in order to increase the number and size of frog populations in the area, and to maintain the contact among them. These results are consistent with the literature suggesting that genetic structure is very sensitive to habitat fragmentation isolating more and more the subpopulations (Templeton et al. 1990; Hitchings and Beebee 1997; Wang 2009). Given the enormous impact that fragmentation can have on genetic structure, ecological and evolutionary dynamics, and therefore, population persistence, all these effects should be considered when designing strategies for conservation management. Based on the genetic structure obtained, three operational conservation units can be defined with a clear gene flow pattern within each one of them (Moritz 1994; Crandall et al. 2000). Understanding patterns of gene flow between subpopulations is important for effective conservation because species and population survival may depend on dispersal between patches of suitable habitat (Monsen and Blouin 2004).

Conclusions Population genetic analyses based on microsatellite data of Agile frog populations of the Iberian Peninsula showed that there are considerably high levels of genetic diversity between populations of R. dalmatina in its southernmost European limit of distribution. Results allowed the definition of three metapopulations in this area with a clear gene flow pattern. They allowed also the identification of some isolated populations which may need immediate implement conservation actions. These data have implications for understanding R. dalmatina population dynamics, and conservation efforts should take all this information into account. Acknowledgments The authors are very grateful to Xabier Rubio for his essential assistance. Our research was supported by a project from the Basque Government. The authors acknowledge the permissions and financial assistance of the Department of Rural Development and the Environment from Navarra and the Basque Country. Very many thanks to Julia Gu¨nther and Heike Proehl who kindly sent us the European samples. Elisabeth Anderson revised the English version of the manuscript. Comments by two anonymous reviewers on an earlier draft greatly improved the manuscript.

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