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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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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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
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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
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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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References Allentoft ME, O’Brien J (2010) Global amphibian declines, loss of genetic diversity and fitness: a review. Diversity 2:47–71 Allentoft ME, Siegismund HR, Briggs L, Andersen LW (2009) Microsatellite analysis of the natterjack toad (Bufo calamita) in Denmark: populations are islands in a fragmented landscape. Conserv Genet 10:15–28 Beebee TJC (2005) Conservation genetics of amphibians. Heredity 95:423–427 Beerli P (2004) Effect of unsampled populations on the estimation of population sizes and migration rates between sampled populations. Mol Ecol 13(4):827–836 Beerli P (2006) Comparison of Bayesian and maximum-likelihood inference of population genetic parameters. Bioinformatics 22(3): 341–345 Beerli P, Felsenstein J (2001) Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach. P Natl Acad Sci USA 98(8):4563–4568 Belkhir K, Borsa P, Chikhi L, Raufaste N, Bonhomme F (1996–2004) GENETIX 4.05, logiciel sous Windows TM pour la ge´ne´tique des populations. Laboratoire genome, populations, interactions, CNRS UMR 5171, Universite´ de Montpellier II, Montpellier Cornuet JM, Luikart G (1996) Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144(4):2001–2014 Crandall KA, Bininda-Emonds ORP, Mace GM, Wayne RK (2000) Considering evolutionary processes in conservation biology. Trends Ecol Evol 15:290–295 Cushman SA (2006) Effects of habitat loss and fragmentation on amphibians: a review and prospectus. Biol Conserv 128(2): 231–240 Ehrlich PR, Wilson EO (1991) Biodiversity studies: science and policy. Science 253(5021):758–762 Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the softwar STRUCTURE: a simulation study. Mol Ecol 14:2611–2620 Excoffier L, Laval G, Schneider S (2005) Arlequin (version 3.0): an integrated software package for population genetics data analysis. Evol Bioinform 1:47–50 Ficetola GF, Garner TWJ, De Bernardi F (2007) Genetic diversity, but not hatcing success, is jointly affected by postglacial colonization and isolation in the threatened frog, Rana latastei. Mol Ecol 16:1787–1797 Frankham R (2005) Genetics and extinction. Biol Conserv 126(2): 131–140 Gosa´ A (2002) Rana dalmatina Bonaparte, 1840. In: Pleguezuelos JM, Ma´rquez R, Lizana M (eds) Atlas y libro rojo de los anfibios y reptiles de Espan˜a. Direccio´n General de Conservacio´n de la Naturaleza-Asociacio´n Herpetolo´gica espan˜ola (2a impresio´n), Madrid, pp 120–122 Gosa´ A, Sarasola V (2009) Seguimiento y determinacio´n de la situacio´n de Rana dalmatina. Campan˜a 2009. Departamento de Desarrollo Rural y Medio Ambiente, Gobierno de Navarra, Ine´dito, p 23 Goudet J (1995) FSTAT (Version 1.2): a computer program to calculate F-statistics. J Hered 86(6):485–486 Guillot G, Mortier F, Estoup A (2005) GENELAND: a computer package for landscape genetics. Mol Ecol Notes 5(3):712–715 Guo SW, Thompson EA (1992) Performing the exact test of HardyWeinberg proportion for multiple alleles. Biometrics 48(2): 361–372 Hanski I (1998) Metapopulation dynamics. Nature 396(6706):41–49
123
Conserv Genet (2012) 13:197–209 Hauswaldt JS, Fuessel J, Guenther J, Steinfartz S (2008) Eight new tetranucleotide microsatellite loci for the Agile frog (Rana dalmatina). Mol Ecol Resour 8(6):1457–1459 Hitchings SP, Beebee TJC (1997) Genetic substructuring as a result of barriers to gene flow in urban Rana temporaria (common frog) populations: implications for biodiversity conservation. Heredity 79:117–127 Holderegger R, Wagner HH (2008) Landscape genetics. Bioscience 58:199–207 Jarne P, Lagoda PJL (1996) Microsatellites, from molecules to populations and back. Trends Ecol Evol 11(10):424–429 Johansson M, Primmer CR, Sahlsten J, Merila J (2005) The influence of landscape structure on occurrence, abundance and genetic diversity of the common frog, Rana temporaria. Global Change Biol 11:1664–1679 Knopp T, Merila¨ J (2009) Microsatellite variation and population structure of the moor frog (Rana arvalis) in Scandinavia. Mol Ecol 18:2996–3005 Krone A, Ku¨hnel KD, Berger H (1997) Der springfrosch-o¨kologie und bestandssituation. In: Krone A, Ku¨hnel KD, Berger H (eds) Sonderheft 2 der Rana, pp 309 Lesbarre`res D, Pagano A, Lode´ T (2003) Inbreeding and road effect zone in a Ranidae: the case of Agile frog, Rana dalmatina Bonaparte, 1840. CR Biol 326:68–72 Lesbarre`res D, Primmer CR, Lode´ T, Merila¨ J (2006) The effects of 20 years of highway presence on the genetic structure of Rana dalmatina populations. Ecoscience 13:531–538 Lesica P, Allendorf FW (1995) When are peripheral populations valuable for conservation? Cons Biol 9:753–760 Luikart G, Allendorf FW, Cornuet JM, Sherwin WB (1998) Distortion of allele frequency distributions provides a test for recent population bottlenecks. J Hered 89(3):238–247 Manel S, Schwartz MK, Luikart G, Taberlet P (2003) Landscape genetics: combining landscape ecology and population genetics. Trends Ecol Evol 18:189–197 Mantel N (1967) Detection of disease clustering and a generalized regression approach. Cancer Res 27:209–220 Marsh DM, Trenham PC (2001) Metapopulation dynamics and amphibian conservation. Conserv Biol 15(1):40–49 Monsen KJ, Blouin MS (2004) Extreme isolation by distance in a montane frog Rana cascadae. Conserv Genet 5(6):827– 835 Moritz C (1994) Applications of mitochondrial-DNA analysis in conservation–a critical review. Mol Ecol 3(4):401–411 Newman RA, Squire T (2001) Microsatellite variation and fine-scale population structure in the wood frog (Rana sylvatica). Mol Ecol 10(5):1087–1100 Noe¨l S, Ouellet M, Galois P, Lapointe FJ (2007) Impact of urban fragmentation on the genetic structure of the eastern red-backed salamander. Conserv Genet 8:599–606 Noss RF (1990) Can we maintain biological and ecological integrity. Conserv Biol 4(3):241–243 Palo JU, Schmeller DS, Laurila A, Primmer CR, Kuzmin SL, Merila J (2004) High degree of population subdivision in a widespread amphibian. Mol Ecol 13(9):2631–2644 Piry S, Luikart G, Cornuet JM (1999) BOTTLENECK: a computer program for detecting recent reductions in the effective population size using allele frequency data. J Hered 90(4):502–503 Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155(2): 945–959 Purrenhage JL, Niewiarowski PH, Moore FBG (2009) Population structure of spotted salamanders (Ambystoma maculatum) in a fragmented landscape. Mol Ecol 18(2):235–247
Conserv Genet (2012) 13:197–209 Raymond M, Rousset F (1995) GENEPOP (version-1.2): populationgenetics software for exact tests and ecumenicism. J Hered 86(3):248–249 Reed DH, Frankham R (2003) Correlation between fitness and genetic diversity. Conserv Biol 17(1):230–237 Rowe G, Beebee TJC, Burke T (1999) Microsatellite heterozygosity, fitness and demography in natterjack toads Bufo calamita. Anim Conserv 2(2):85–92 Sarasola-Puente V, Beebee TJC, Gosa´ A, Go´mez-Moliner BJ, Lizana M and Madeira MJ (2010) Characterization of ten polymorphic microsatellite loci in Rana dalmatina from enriched genomic libraries. In: Permanent genetic resources added to molecular ecology resources database 1 April 2010–31 May 2010. Mol Ecol Resour 10:1098–1105 Shaffer G, Fellers GM, Magee A, Voss R (2000) The genetics of amphibian declines: population substructure and molecular differentiation in the Yosemite Toad, Bufo canorus (Anura, Bufonidae) based on single-strand conformation polymorphism analysis (SSCP) and mitochondrial DNA sequence data. Mol Ecol 9(3):245–257 Sjo¨gren P (1991) Genetic variation in relation to demography of peripheral pool frog populations (Rana lessonae). Evol Ecol 5:248–271 Smith MA, Green DM (2005) Dispersal and the metapopulation paradigm in amphibian ecology and conservation: Are all amphibian populations metapopulations? Ecography 28(1): 110–128 Spear SF, Peterson CR, Matocq MD, Storfer A (2005) Landscape genetics of the blotched tiger salamander (Ambystoma tigrinum melanostictum). Mol Ecol 14(8):2553–2564 Stuart SN, Chanson JS, Cox NA, Young BE, Rodrigues ASL, Fischman DL, Waller RW (2004) Status and trends of amphibian
209 declines and extinctions worldwide. Science 306(5702): 1783–1786 Templeton AR, Shaw K, Routman E, Davis SK (1990) The genetic consequences of habitat fragmentation. Ann Mo Bot Garden 77(1):13–27 Treves S, Vilsen B, Chiozzi P, Andersen JP, Zorzato F (1992) Molecular-cloning, functional expression and tissue distribution of the cDNA-encoding frog skeletal-muscle calsequestrin. Biochem J 283:767–772 Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004) MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes 4(3):535–538 Vos CC, Antonisse-De Jong AG, Goedhart PW, Smulders MJM (2001) Genetic similarity as a measure for connectivity between fragmented populations of the moor frog (Rana arvalis). Heredity 86:598–608 Wang IJ (2009) Fine-scale population structure in a desert amphibian: landscape genetics of the black toad (Bufo exsul). Mol Ecol 18(18):3847–3856 Waples RS, Gaggiotti O (2006) What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity. Mol Ecol 15(6):1419–1439 Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population-structure. Evolution 38(6):1358–1370 Wright S (1978) Evolution and the genetics of populations: variability within and among natural populations, vol 4. University of Chicago Press, Chicago Zeisset I, Beebee TJC (2001) Determination of biogeographical range: an application of molecular phylogeography to the European pool frog Rana lessonae. Proc R Soc B 268:933–938
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