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ISSN 1566-0621, Volume 11, Number 1

RESEARCH ARTICLE

Genetic structure in peripheral populations of the natterjack toad,Bufo calamita, as revealed by AFLP

Bjorn Rogell • Hanna Thorngren • Stefan Palm •

Anssi Laurila • Jacob Hoglund

Received: 10 July 2009 / Accepted: 2 December 2009 / Published online: 24 December 2009

� Springer Science+Business Media B.V. 2009

Abstract Decreased fitness due to loss of genetic varia-

tion is a well recognised issue in conservation biology.

Along the Swedish west coast, the endangered natterjack

toad (Bufo calamita) occurs on, for the species, highly

unusual habitat of rocky islands. Although the toads inhabit

a restricted geographical area (maximum distance between

the populations is 71 km), the fragmented nature of the

landscape makes the genetic properties of the populations

of conservation interest. However, lack of genetic variation

found using conventional methods (microsatellites) has

impeded genetic studies within these peripheral popula-

tions so far. In this study we assess population structure and

genetic variation among seven of these fringe populations

using 105 polymorphic Amplified Fragment Length Poly-

morphism (AFLP) loci. We found a well-defined popula-

tion structure without evidence for isolation by distance,

implying restricted gene flow between populations. Addi-

tionally, the populations differed in their amount of genetic

variation, emphasizing the need to monitor genetically

impoverished populations for possible declines mediated

by inbreeding depression and reduced adaptive potential.

Conservation implications for these unique populations are

discussed in the light of our results.

Keywords AFLP � Genetic structure �Peripheral populations � Genetic diversity � Amphibians

Introduction

Small and isolated populations are expected to lose genetic

variation due to stronger genetic drift than in large popula-

tions and may thus suffer from decreased fitness due to

increased expression of deleterious alleles, i.e. inbreeding

depression, and loss of adaptive potential (Frankham et al.

2002; Hoglund 2009). Issues regarding fragmented habitat

are emphasised in organisms characterized by poor dispersal

abilities (Thomas 2000), such as amphibians (Beebee 2005).

Indeed, previous studies have found inbreeding depression

in the natterjack toad (Rowe and Beebee 2003) and in

two European Rana species (Ficetola et al. 2007; Johansson

et al. 2007).

In Sweden, the natterjack toad (Bufo calamita) lives at

the northern margin of its distribution (Beebee 1983).

During the last century it has decreased throughout its

northern range and the species is red-listed in many

countries including Sweden (Beebee 1983; Gardenfors

2005; Allentoft et al. 2009). The natterjack toad has two

separate distribution areas in Sweden: one in the south-

ernmost part of the country and the other one on approxi-

mately 30 small offshore islands in the Bohuslan

archipelago on the western coast (henceforth named

Bohuslan, Andren and Nilson 1985). During the late 20th

century, the species has disappeared from many previously

known localities both in the southern Sweden and

Bohuslan, although the decline seems to have been stronger

in the south (Andren and Nilson 1985). The Bohuslan

population is likely to exhibit spatial genetic structure since

open sea can be expected to be a solid barrier for

B. Rogell (&) � H. Thorngren � S. Palm � A. Laurila �J. Hoglund

Population Biology and Conservation Biology/Department of

Ecology and Evolution, Evolutionary Biology Centre, Uppsala

University, Norbyvagen 18D, 75236 Uppsala, Sweden

e-mail: Bjorn.Rogell@ebc.uu.se

H. Thorngren

Animal Ecology/Department of Ecology and Evolution,

Evolutionary Biology Centre, Uppsala University,

Norbyvagen 18D, 75236 Uppsala, Sweden

123

Conserv Genet (2010) 11:173–181

DOI 10.1007/s10592-009-0021-z Author's personal copy

amphibian dispersal (Rowe et al. 2000b; Rudh et al. 2007).

Since a well defined population structure may yield iso-

lated populations with low amount of genetic variation that

may be in need of genetic rescue in terms of migration, the

genetic structure of these populations is of interest for

putative conservation efforts.

The natterjack toad shows a negative correlation

between genetic variation and distance from its glacial

refuges, and the Swedish populations harbour little genetic

variation as compared to continental populations (Rowe

et al. 2006). This is a frequently encountered pattern in

northern hemisphere taxa (Merila et al. 1996; Zeisset and

Beebee 2001; Rowe et al. 2006). Thus, the geographical

pattern in genetic variation not only depends on recent

isolation but also on historical colonisation patterns, where

gradual dispersal combined with selection in novel envi-

ronments seems to have decreased the level of genetic

variation (Hewitt 1999).

From a conservation viewpoint, it is important to dis-

tinguish between loss of genetic variation due to recent

fragmentation or bottlenecks, and loss of genetic variation

due to historic colonisation patterns. For example, in their

study of the Italian agile frog Rana latastei, Ficetola et al.

(2007) found that while loss of genetic variation due to

recent fragmentation was associated with loss of fitness,

loss of genetic variation due to postglacial colonisation was

not. One possible explanation for this pattern may be that

purging of deleterious alleles is more likely to occur when

loss of genetic variation occurs during slow colonisation,

whereas purging is less likely during the short time frame

of recent fragmentation or local founder effects (Swindell

and Bouzat 2006). However, it seems likely that effects of

recent fragmentation may be more severe close to range

margins, since, due to the less frequent occurrence of

suitable habitats, many species are less abundant and

already occur as more fragmented populations in areas

close to their physiological limit (Hoffmann and Blows

1994). Thus, conservation issues regarding population

subdivision may become increasingly important as range

margins are approached.

When assessing population structure and levels of

genetic variation it is important to choose a genetic marker

system that works on an appropriate spatial and temporal

scale. In general, studies where populations have been

isolated for shorter periods of time require more poly-

morphic markers than do studies of populations fragmented

over long time periods. For example mitochondrial DNA

may be an appropriate choice when assessing broad-scale,

phylogeographic patterns across species distributions (e.g.

Avise 2000), while more polymorphic loci such as micro-

satellites can be a better choice when assessing population

structure at a fine-scale. Quantifications of fine-scaled

genetic structure are often limited by lack of statistical

power (Pemberton 2004), and the possibilities of assessing

population-wide levels of genetic variation within and

among peripheral populations, characterized by low levels

of genetic variation, may be particularly restricted (Zeisset

and Beebee 2001). Indeed, our previous attempts to

quantify genetic variation within and among the Bohuslan

natterjack populations using microsatellite markers were

impeded by lack of genetic variation yielding low statis-

tical power (Table 1), emphasising the need to develop

another methodology suitable for assessing genetic varia-

tion in these genetically impoverished populations.

Here we have examined the genetic population structure

and molecular genetic variation in a subset of the Bohuslan

natterjack toad populations using Amplified Fragment

Length Polymorphism (AFLP). Although dominant geno-

typic data is generated, a large number of polymorphic

markers can be derived with this methodology. Compared

to co-dominant marker systems such as microsatellites,

which typically are studied using fewer but more infor-

mative loci, AFLP may often provide equally robust esti-

mates of genetic variation when migration rates are low

(Mariette et al. 2002). In fact, this technique has proven to

have enough statistical power to pick up even fine-scale

differentiation (Bensch and Akesson 2005), and inferences

of population structure derived from co-dominant and

Table 1 Average levels of observed microsatellite-heterozygosity

(Ho) in Swedish (Rogell 2005) and continental natterjack toad

populations

Locus Ho Bohuslan pop. Ho Continental pop.

Bcall1 0.10 0.48A

Bcall2 0 0.31A

Bcall3 0.38 0.43A

Bcall4 0.37 0.46A

Bcall6 0 0.04A

Bcall7 0.05 0.29A

Bcall8 0.07 0.38A

Bcall9a – 0.81B

Bcall10 0.27 0.80B

Bcall11 0 0.70B

Buca1 0 0.63C

Buca 2 0 0.38C

Buca 3 0.21 0.63C

Buca 5 0 0.38C

All loci (without Bcall9a) 0.11 0.45

Ho for the Bohuslan populations is calculated on a total of 40

individualsA Rowe et al. (1997)B Rowe et al. (2000a)C Rogell (2005), Rogell et al. (2005)a The locus did not amplify well enough for objective scoring of

alleles

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dominant markers have generally been found to be con-

gruent (reviewed in Nybom 2004). The aims of this study

were to asses the population structure and relative amounts

of genetic variation at a subpopulation scale in the

Bohuslan natterjack toad. Due to the nature of the habitat

these populations inhabit, we expected to find a distinct

population structure over relatively small spatial scale.

Materials and methods

In May 2006 tissue samples were taken from 30 adult

natterjack toads from each of six small islands (Fig. 1);

Altarholmen (henceforth AL, 58�10N, 11�260E), Buskar

(BU, 58�210N, 11�110E), Fagelskar (FA, 58�60N, 11�200E),

Hyppeln (HY, 57�450N, 11�360E), Oxskar (OX, 58�70N,

11�220E) and Pater Noster (PA, 57�530N, 11�270E). A

biopsy sample was collected from the adults by removing a

small piece of skin from the swim web with a dermal

punch. Approximately 3 9 2 mm piece of skin was taken

and stored in 70% of EtOH for later analyses. The adult

animals were released immediately after sampling. In

addition, 20 individuals were collected as embryos (one per

full sib family) at the island Maseskar (MA, 58�50N,

11�190E) and stored in 70% EtOH. The pair-wise distances

between the sampled islands range between 1 and 71 km.

AFLP-screening

DNA was extracted using standard high salt purification

(Rudh et al. 2007; slightly modified from Miller et al.

1988). We used the AFLP protocol of Bensch et al. (2002),

modified from Vos et al. (1995), with some further modi-

fications: 10 ll of the extracted DNA was cut in the fol-

lowing mix: 6.9 ll of ddH2O, 2 ll of TA buffer 109

(100 mM Tris–acidic pH 7.9, 100 mM MgAc, 500 mM

KaC, 10 mM DTT), 1 ll of BSA (1 lg/ll), 0.05 ll of

EcoRI (50 U/ll) and 0.05 ll of Tru1 (50 U/ll). The

restriction mix was incubated for 1 h at 37�C. To ligate

adaptors, 5 ll of a mix containing 4.15 ll of ddH2O, 0.5 ll

of T4 ligase buffer 109, 0.025 ll of E-adaptor (100 lM),

0.25 ll of M-adaptor (100 lM) and 0.1 ll of T4 ligase

(5 U/ll) was added to the cut DNA and incubated for

additional 3 h at 37�C. The E-adaptor and the M-adaptor

was build of the primers 50-CTCGTAGACTGCGTACC-30,50-AATTGGTACGCAGTC-30 and 50-GACGATGAGTCC

TGAG-30, 50-TACTCAGGACTCAT-30.To improve the ligation capability of the adaptors these

were denatured in 94�C for 3 min and then cooled down in

room temperature before being added the to the ligation

mixture. After completed ligation, samples were diluted ten

times with ddH2O and amplified in a non-selective PCR. In

the PCR, 10 ll of the diluted, cut and ligated DNA was

amplified with the mix of 1 ll of ddH2O, 2 ll of MgCl2(25 mM), 2 ll of Fermentas Taq buffer (109), 4 ll of

dNTP (1 mM), 0.06 ll of E-primer (100 lM), 0.06 ll of

M-primer (100 lM), 0.08 ll of Fermentas Taq polymerase

and 0.8 ll of BSA (1 lg/ll). The reactions were incubated

with the following temperature profile: (94�C 2 min); (94�C

30 s, 56�C 30 s, 72�C 60 s) 9 20 cycles; (72�C 10 min).

E-primer and M-primer were 50-GACTGCGTACCAAT

TCN-30and 50-GATGAGTCCTGAGTAAN-30, where N is

an arbitrary chosen base.

After completion of the PCR reactions, the products were

diluted 10 times with ddH2O. The non-selective amplifica-

tion was followed by a selective amplification where 7.5 ll

of the diluted product was mixed with 2.9 ll of ddH2O, 1 ll

of MgCl2 (25 mM), 1 ll of Fermentas Taq buffer (109),

2 ll of dNTP (1 mM), 0.06 ll of E-primer (100 lM),

0.06 ll of M-primer (100 lM), 0.08 ll of Fermentas Taq

polymerase and 0.4 ll of BSA (1 lg/ll). The mix was run in

a touchdown PCR-reaction with the following temperature

profile: (94�C 2 min); [94�C 30 s, (65–0.7�C/cycle) 30 s,

72�C 60 s] 9 12 cycles; (94�C 30 s, 56�C 30 s, 72�C

60 s) 9 23 cycles; (72�C 10 min). E-primer and M-primer

were 50-GACTGCGTACCAATTCNNN-30and 50-GATGA

GTCCTGAGTAANNN-30, where N are arbitrary chosen

bases, the first N the same as in the non-selective

amplification.

Eleven selective primer combinations were used based

on the E-primers: -TGC, -TAG, -TCT and M-primers:

-CAC, -CGT, -CTA, -CAG. Finally 1 ll of the PCR product

was mixed with 9.75 ll ddH2O and 0.25 ll of MegaBACE

ET400-R size standard. The mix was run in MegaBACE

1000 (Amersham Biosciences) and the results were analysedFig. 1 Map of the study area. Study populations: 1, BU; 2, OX; 3,

FA; 4, MA; 5, AL; 6, PA; 7, HY

Conserv Genet (2010) 11:173–181 175

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using MegaBACE Fragment profiler version 1.2 (Amer-

sham Biosciences). All individuals were scored for pres-

ence (1) or absence (0) of fragments in the region between

50 and 400 bases. In order to avoid bias from different PCR

runs, samples were randomised before the AFLP procedure

and scored blindly with respect to sampling origin.

Statistical analyses

The amount of global genetic differentiation between all

populations (i.e. island samples) was estimated with FST

(Weir and Cockerham 1984) using the program TFPGA

(www.marksgeneticsoftware.net), where estimation of

allele frequencies for dominant loci is performed using

Taylor expansion (Lynch and Milligan 1994). Estimates of

FST across loci were bootstrapped with 1,000 iterations in

order to generate 95% confidence intervals. Pairwise esti-

mates of between population differentiation were calcu-

lated using the FST analogue UPT, in GenAlEx version 6

(Peakall and Smouse 2006). Here UPT is calculated as the

proportion of variance explained by population origin,

i.e. VP/VP ? VW, where VP is the among-population variance

and VW is the within-population variance. Corresponding

probability values, testing for pairwise differentiation, were

based on 999 permutations.

PHYLIP (Felsenstein 2004) was used to construct an

unrooted neighbor-joining dendrogram based on the pair-

wise UPT matrix. In order to test for isolation by distance,

we used the software ISOLDE, as implemented in

GENEPOP (Raymond and Rousset 1995). Here a Mantel

test is used to examine the association between a popula-

tion differentiation matrix (linearised pairwise UPT values

i.e. UPT/1 - UPT) and a matrix of pairwise Euclidean

distances between localities (natural logarithm of the dis-

tance in kilometres, Rousset 1997). We also calculated the

percentage of polymorphic loci within populations and

tested for differences in genetic variation among popula-

tions. Confidence limits for these estimates were obtained

by bootstrapping with 2,000 replicates (Crawley 2007).

The analyses were performed using the ‘‘base’’ and ‘‘boot’’

packages implemented in R (Canty 2006; R Development

Core Team 2006).

To examine the most likely number of distinct genetic

clusters (K) in our dataset, we used the Bayesian clustering

method implemented in STRUCTURE (Pritchard et al.

2000) adjusted for dominant markers (Falush et al. 2007).

Since toad dispersal can be expected to be strongly limited

over the matrix of sea, the models were run assuming no

admixture as suggested by Pritchard et al. (2000). In

addition, for each potential value of K in the relevant range

between 1 and 12 (Evanno et al. 2005), 20 MCMC chains

were run drawing 10,000 samples after a burn-in of

100,000 iterations. The measures maximum Ln(K) (i.e. the

log posterior probability for the data given K genetic

clusters; Pritchard et al. 2000) and DK [the mean L00(K): the

second order of change of L(K), averaged over 20 simu-

lations, divided by the standard deviation; Evanno et al.

2005] were used to infer the number of biologically rele-

vant clusters. In simulations, DK has been shown to be

better at identifying the true number of clusters than max

Ln(K) (Evanno et al. 2005).

Since the assignments of individual genotypes to clus-

ters with STRUCTURE to some extent appeared rather

arbitrary (see below), we also compared our empirical

results to those for a simulated data set that roughly

resembled the real one with respect to levels of genetic

differentiation. To this end, we used EASYPOP (Balloux

2001) and simulated drift and migration (no mutation)

within a hierarchically structured finite island model with a

total of seven ideal populations, all of the same limited

effective size (Ne = 30). The simulated system consisted

of two hierarchical ‘‘groups’’, with two and five local

populations each. Migration rates were set to 0.05 and

0.005 within and between the two groups, respectively. The

Ne:s and m-values in the model were arbitrarily chosen to

result in a population structure that mimicked our empirical

one with respect to levels of genetic differentiation (i.e.,

global FST and pairwise UPT0s). Since migration rates (gene

flow) and population sizes (genetic drift) have interacting

effects on levels of differentiation (e.g. Frankham et al.

2002), the exact magnitude of the simulated parameters

should thus not be interpreted as being biologically rele-

vant for the natterjack toad system (true Ne:s could be

larger, migration rates lower, etc.).

The simulation was run for 100 generations which was

found to be enough for the system to reach pseudo-equi-

librium between drift and migration (i.e. stable FST values)

but to avoid fixations at individual loci. A total of 105

di-allelic loci were simulated (same number as for our real

natterjack data), and the codominant genotypes were sub-

sequently recoded into dominance by randomly choosing

one of the two alleles at each locus to represent the dom-

inant one.

Results

The 11 primer combinations yielded a total of 105 poly-

morphic loci. We found a well-defined population structure

with a global FST of 0.157 (lower and upper 95% bootstrap

confidence intervals 0.139 and 0.176). Pairwise UPT values

between localities ranged between 0.056 and 0.267, all

values being significantly different from zero (Table 2). No

evidence for isolation by distance among the seven study

populations could be seen [b = 0.000037 ± 0.02 (SE),

P = 0.57]. Rather, the neighbor-joining dendrogram

176 Conserv Genet (2010) 11:173–181

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indicated presence of a hierarchical population structure,

with the two neighbouring populations MA and FA (cf.

Fig. 1) being somewhat more genetically similar than

compared to the other populations (Fig. 2).

The populations differed significantly in the amount of

genetic variation with percentages of polymorphic AFLP

bands varying between 91.4 and 42.8% with largely non-

overlapping confidence intervals (Table 2). AL and OX

had highest genetic variation, while HY and MA displayed

the lowest. Additionally, the genetic diversity found in the

two genetically least variable populations represented a

subset of the genetic variation found in the more variable

populations, as they did not have any private alleles.

The clustering method implemented in STRUCTURE

yielded the highest Ln(K) for twelve genetic clusters

(Fig. 3a), whereas DK gave most support for two clusters

(DK = 2579, Fig. 3b). However, the curve for DK also

indicated another local optimum located at K = 7

(DK = 187, Fig. 3b) being equal to the number of sampled

localities. When STRUCTURE assigned the proportion of

membership of individual genotypes in the pre-defined

populations into seven clusters, two populations (FA and

MA) were assigned with high probabilities to the same

genetic cluster, whereas the individuals from the other five

pre-defined populations seemed to be assigned largely

randomly to the remaining clusters (Fig. 4a).

Under a model with K = 2, the individuals from FA and

MA were rather strongly assigned to the same cluster (on

average with a 0.90 and 0.87 probability, respectively;

Fig. 4b), and for K = 7 the assigned probabilities remained

almost as high (0.89 and 0.87 to the same cluster, respec-

tively; Fig. 4a). In contrast, the individuals from the other

five pre-defined populations were assigned to the remaining

clusters with lower probabilities; at K = 2 the maximum

assignment proportion for those individuals varied in the

range 0.67–0.55, indicating that the members of the pop-

ulations were scattered more or less randomly over the two

clusters (Fig. 4b). For K = 7 their maximum assignment

proportions were even lower, varying between 0.43 and

0.26 and only with a weak association to the pre-defined

populations (Fig. 4a).

The assignment results based on our simulated data set

(true K = 7) were rather congruent with those for the

natterjack toad (Fig. 4c), indicating that for the present

population structure and number of AFLPs, the ability of

STRUCTURE to correctly assign individuals to popula-

tions appears rather limited. In accordance with the real

data, populations 1 and 2 belonging to the smaller hierar-

chical subset (i.e. the simulated populations with highest

average FST against the other populations: 0.15 and 0.19 vs.

0.11–0.13; Weir and Cockerham 1984) were assigned to

the same cluster (to be compared with the two real popu-

lations MA and FA in Fig. 4a). In comparison, the

remaining individuals were assigned much more randomly

in relation to their pre-defined (true) populations, though

not as pronounced as for the real dataset (cf. Fig. 4a, c).

Table 2 Proportions of polymorphic AFLP loci per population with

95% confidence intervals (first column) and pairwise UPT values

among the seven sampled natterjack toad localities (below the

diagonal) with associated Bonferroni corrected probability values

based on 999 permutations (above the diagonal)

%P AL BU FA HY MA OX PA

AL 91.4 (82.9; 95.2)% 0.021 0.021 0.021 0.021 0.021 0.021

BU 71.4 (61.0; 78.1)% 0.065 0.021 0.021 0.021 0.042 0.021

FA 57.1 (46.7; 65.7)% 0.153 0.224 0.021 0.021 0.021 0.021

HY 49.5 (39.1; 58.1)% 0.063 0.094 0.165 0.021 0.021 0.021

MA 42.9 (32.4; 51.4)% 0.170 0.297 0.122 0.160 0.021 0.021

OX 77.1 (66.7; 83.8)% 0.056 0.088 0.150 0.071 0.193 0.021

PA 53.3 (42.3; 61.2)% 0.096 0.180 0.267 0.106 0.206 0.108

0.02

HY

BU

PA

OX

AL

FA

MA

Fig. 2 Unrooted neighbor-joining dendrogram based on pairwise

UPT values in Table 2

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Discussion

We found a well defined genetic structure among the local

natterjack toad populations in the archipelago of Bohuslan

with intermediate to high pairwise UPT values and a global

FST of 0.157. Though the structure analysis was not

congruent between the DK and the ln(K) estimators used to

assess the number of independent genetic clusters, the

increase of standard deviation of ln(K) at K = 8 (Fig. 3a)

seems to supports our interpretation that each of the seven

sampled islands represent a local population (cf. Evanno

et al. 2005).

In addition, we found no isolation by distance which,

combined with the distinct population structure, implies

that even a short distance over the sea acts as an impene-

trable dispersal barrier and emphasises the importance of

genetic drift over gene flow in this system (cf. Hutchison

and Templeton 1999). The dendrogram in Fig. 2 rather

suggests a hierarchical structure, with the islands FA and

MA constituting one ‘‘genetic group’’, while the rest of the

populations constitute another. The reason behind this

pattern is unclear, but we note that FA and MA are closely

located (ca. 1 km), and it is possible that their relative

genetic similarity could be explained by some recent

(otherwise unusual) immigration or colonisation event.

Our results are in line with a previous study showing

that the natterjack toad, despite it’s for amphibians rare

capacity to inhabit estuarine habitats (Beebee 1983;

Gomez-Mestre and Tejedo 2003), is a poor disperser over

sea (Rowe et al. 2000b). In contrast, a previous allozyme

study on the common toad B. bufo in a similar skerry

habitat, but in the less saline Baltic sea, reported a much

lower FST value of 0.019 (Seppa and Laurila 1999),

emphasising the importance of saline ocean as a dispersal

barrier for anurans.

We also found strong differences in the genetic variation

among the populations, which together with the variation in

pairwise UPT values suggests large variation in local

2 4 6 8 10 12

-600

0-5

500

-500

0-4

500

K

Ln(K

)

a

2 4 6 8 10

050

010

0015

0020

0025

00

K

∆K

bFig. 3 a The logarithmic

posterior probability of the data

(ln(K)) given K (a putative

number of populations) plotted

against K in the natterjack toad

dataset. b DK, the mean ln0 0(K)

averaged over 20 simulations

and divided by the standard

deviation, plotted against K

Fig. 4 STRUCTURE-assignments of proportion of membership of

the pre-defined populations into suggested clusters. a Natterjack toad,

seven assumed clusters. b Natterjack toad, two assumed clusters.

c Simulated data set, seven assumed clusters. The simulation

consisted of seven true populations sub-structured into two hierar-

chical ‘‘groups’’ (pre-defined populations 1–2 and 3–7, respectively).

See text for further details

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effective population sizes and (or) different demographic

histories such as founder effects during colonisation. Lack

of historical demographic records and estimates of current

effective sizes makes it difficult to settle between these

alternatives. The cumulative effect of genetic drift is

dependent on two factors—time and effective population

size. Though we do not have any direct estimates of pop-

ulation sizes in Bohuslan, the present total sizes of the

study populations must be considered to be ‘‘large’’, as it is

possible to encounter hundreds of adult individuals on each

island (B. Rogell, personal observations). The effective

population sizes are expected to be much smaller, however.

For example, Scribner et al. (1997) studied populations of

common toad around ponds in the UK and estimated the

effective number of breeders per year (Nb) to be 55–230

times smaller than the corresponding estimates of total

population size.

Implications for conservation

The fact that some of the populations had considerable

lower genetic variation than the others raises concerns for

the future and conservation of the Bohuslan natterjack

toads. Loss of fitness due to inbreeding depression is a well

documented threat to the persistence of genetically

impoverished populations (Frankham et al. 2002), that may

apply to some of the Bohuslan populations. This is further

emphasized by previous studies showing inbreeding

depression in other natterjack toad populations (Rowe and

Beebee 2003). In addition to the immediate problem of

inbreeding depression, loss of genetic variation may also

affect the adaptive potential of populations (Lande 1995).

Though not as well examined as inbreeding depression in

natural populations, this may be a specific concern to the

Bohuslan populations since they inhabit a highly specific

habitat where selection pressures are likely to differ from

those in other northern populations (Beebee 1983; Andren

and Nilson 1985). Indeed, larval life history traits in the

Bohuslan natterjack toads are locally adapted to the high

desiccation risk of their specific habitat (Rogell et al. 2009).

Congruent with our present results, other studies

examining population structure of peripheral natterjack

toad populations have found more well defined population

structures (Rowe et al. 2000a, b; Allentoft et al. 2009) as

when compared to the studies of more centrally located

populations (Gomez-Mestre and Tejedo 2004). This may

be either an auto-correlative pattern since genetic variation

decreases in a similar manner (Rowe et al. 2006) and may

statistically affect differentiation indexes, or a result of

increasing population divergence due to increasing popu-

lation fragmentation and isolation towards the edge of the

species range (Hoffmann and Blows 1994; Petit et al.

2001). Since natterjack toad populations are known to be

more isolated in peripheral regions of the distribution area

(Beebee 1983), the latter alternative (increased population

isolation) seems likely to explain the observed patterns.

From a conservation point of view, this suggests that

peripheral populations of the natterjack toad could be

particularly vulnerable to declines mediated by processes

occurring within isolated subpopulations.

It is widely accepted that low genetic variation has

negative effects on the viability of a population and is

expected to limit its adaptability (Lande 1995; Frankham

et al. 2002). Hence, differences in genetic variation should

be taken into account when planning conservation strate-

gies for the Swedish natterjack toad populations. For

example, support releases could be considered if geneti-

cally impoverished populations decline in size (Madsen

et al. 1999). A possible problem with support releases

may be the presence of local adaptations amongst popu-

lations that may decrease fitness if expressed in the wrong

environment (i.e. outbreeding depression, Tallmon et al.

2004). Though the Bohuslan natterjack toads are locally

adapted to their specific insular habitat in larval life his-

tory traits (Rogell et al. 2009), the same traits show low

variation amongst populations within the archipelago,

suggesting uniform selection patterns (Rogell 2009). Thus,

in the case of Bohuslan natterjacks, local populations with

high levels of genetic variation within the same archi-

pelago would be the most immediate choice for source

populations.

Additionally, since extinction risk may increase due to

interactions between inbreeding depression and environ-

mental stress (Liao and Reed 2009), a more complete

assessment of threats to the Bohuslan natterjack popula-

tions is needed, including both genetic and ecological

information. For example, complementary studies focusing

on estimation of current local effective population sizes

from genetic and demographic data would be informative

for the future conservation of these populations (e.g. Jorde

and Ryman 1995). Finally, conventional population genetic

markers such as microsatellites may be impeded by low

statistical power when assessing genetic structure and

variation in systems of populations characterised by gen-

erally low genetic variation. Although AFLP is a method

with certain limitations (yielding dominant, not locus

specific data that, for example, may perform poorly in

assignment tests), our results show that the use of AFLP

may nevertheless facilitate population genetic studies of

peripheral populations.

Acknowledgements We thank Sara Bergek and Andreas Rudh for

comments on earlier versions of this manuscript, and Maarten

Hofman and Jonas Andersson for help with the map. The project was

funded by Formas (to J. Hoglund) and Zoologiska stiftelsen

(B. Rogell). The study was conducted under permissions from the

Conserv Genet (2010) 11:173–181 179

123

Author's personal copy

Ethical Committee for Animal Experiments in Uppsala County and

the county board in Vastra Gotaland.

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