Radically different phylogeographies and patterns of genetic variation in two European brown frogs,...

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Radically different phylogeographies and patterns of genetic variationin two European brown frogs, genus Rana

Miguel Vences a,⇑, J. Susanne Hauswaldt a, Sebastian Steinfartz b, Oliver Rupp c, Alexander Goesmann d,Sven Künzel e, Pablo Orozco-terWengel f, David R. Vieites g,k, Sandra Nieto-Roman g, Sabrina Haas a,Clara Laugsch a, Marcelo Gehara a, Sebastian Bruchmann a, Maciej Pabijan a,h, Ann-Kathrin Ludewig a,Dirk Rudert a, Claudio Angelini a, Leo J. Borkin i, Pierre-André Crochet j, Angelica Crottini a,k, Alain Dubois l,Gentile Francesco Ficetola m, Pedro Galán n, Philippe Geniez j, Monika Hachtel o, Olga Jovanovic p,Spartak N. Litvinchuk i, Petros Lymberakis q, Annemarie Ohler l, Nazar A. Smirnov r

a Zoological Institute, Technische Universität Braunschweig, Mendelssohnstr. 4, 38106 Braunschweig, Germanyb University of Bielefeld, Department of Animal Behaviour, Unit Molecular Ecology, Morgenbreede 45, 33615 Bielefeld, Germanyc Computational Genomics, Center for Biotechnology (CeBiTec), and Cell Culture Technology, Bielefeld University, 33594 Bielefeld, Germanyd Computational Genomics, Center for Biotechnology (CeBiTec) and Bioinformatics Resource Facility, CeBiTec, Bielefeld University, 33501 Bielefeld, Germanye Max Planck Institute for Evolutionary Biology, 24306 Plön, Germanyf School of Biosciences, Cardiff University, Biomedical Science Building, Museum Avenue, Cardiff CF10 3AX, United Kingdomg Museo Nacional de Ciencias Naturales, MNCN–CSIC, C/José Gutierrez Abascal 2, 28006 Madrid, Spainh Department of Comparative Anatomy, Institute of Zoology, Jagiellonian University, ul. Gronostajowa 9, 30-387 Kraków, Polandi Department of Evolutionary Cytoecology, Institute of Cytology, Russian Academy of Sciences, Tikhoretsky Pr. 4, St. Petersburg 194064, Russiaj CNRS-UMR5175 CEFE, Centre d’Ecologie Fonctionnelle et Evolutive, 1919, route de Mende, 34293 Montpellier cedex 5, Francek CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Campus Agrário de Vairão, Rua Padre Armando Quintas, 4485-661 Vairão, Portugall UMR 7205 OSEB, Département de Systématique et Evolution, Muséum National d’Histoire Naturelle, 25 rue Cuvier, CP 30, 75005 Paris, Francem Department of Environmental Sciences, University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milano, Italyn Departamento de Bioloxía Animal, Bioloxía Vexetal e Ecoloxía, Facultade de Ciencias, Universidade da Coruña, 15071 A Coruña, Spaino Biologische Station Bonn, Auf dem Dransdorfer Berg 76, 53121 Bonn, Germanyp Croatian Herpetological Society – Hyla, Prva Breznicka 5a, 10000 Zagreb, Croatiaq Natural History Museum of Crete, University of Crete, Knossos Ave., 71409 Herakleio, Crete, Greecer Zoological Museum, National Museum of Natural History, Ukrainian Academy of Sciences, B. Khmelnitsky str. 15, 01601 Kyiv, Ukraine

a r t i c l e i n f o

Article history:Received 7 November 2012Revised 10 April 2013Accepted 16 April 2013Available online 28 April 2013

Keywords:AmphibiaRana temporariaRana dalmatinaGenetic variationPhylogeographyPalearctic

a b s t r a c t

We reconstruct range–wide phylogeographies of two widespread and largely co-occurring Western Pale-arctic frogs, Rana temporaria and R. dalmatina. Based on tissue or saliva samples of over 1000 individuals,we compare a variety of genetic marker systems, including mitochondrial DNA, single-copy protein-cod-ing nuclear genes, microsatellite loci, and single nucleotide polymorphisms (SNPs) of transcriptomes ofboth species. The two focal species differ radically in their phylogeographic structure, with R. temporariabeing strongly variable among and within populations, and R. dalmatina homogeneous across Europewith a single strongly differentiated population in southern Italy. These differences were observed acrossthe various markers studied, including microsatellites and SNP density, but especially in protein-codingnuclear genes where R. dalmatina had extremely low heterozygosity values across its range, includingpotential refugial areas. On the contrary, R. temporaria had comparably high range-wide values, includingmany areas of probable postglacial colonization. A phylogeny of R. temporaria based on various concate-nated mtDNA genes revealed that two haplotype clades endemic to Iberia form a paraphyletic group atthe base of the cladogram, and all other haplotypes form a monophyletic group, in agreement with anIberian origin of the species. Demographic analysis suggests that R. temporaria and R. dalmatina havegenealogies of roughly the same time to coalescence (TMRCA �3.5 mya for both species), but R. tempo-raria might have been characterized by larger ancestral and current effective population sizes than R. dal-matina. The high genetic variation in R. temporaria can therefore be explained by its early range expansionout of Iberia, with subsequent cycles of differentiation in cryptic glacial refugial areas followed by admix-ture, while the range expansion of R. dalmatina into central Europe is a probably more recent event.

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1055-7903/$ - see front matter � 2013 Elsevier Inc. All rights reserved.http://dx.doi.org/10.1016/j.ympev.2013.04.014

⇑ Corresponding author. Fax: +49 5318198.E-mail address: [email protected] (M. Vences).

Molecular Phylogenetics and Evolution 68 (2013) 657–670

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Molecular Phylogenetics and Evolution

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1. Introduction

Numerous phylogeographic studies on European animals andplants have contributed to understanding how past climaticchanges, with associated regional extinctions and recoloniza-tions, have shaped the distribution and genetic structure oforganisms (reviewed in Taberlet et al. (1998), Hewitt (2004),Waltari et al. (2007) and Weiss and Ferrand (2007)). The originof most species of animals and plants in this region predatesthe Pleistocene (e.g., Klicka and Zink, 1997; Avise et al., 1998;Willis and Niklas, 2004), but their major intraspecific phylogeo-graphic units have often diverged in refugial areas (Taberletet al., 1998; Hewitt, 2004; 2009) during the glacial episodes ofthe Quaternary, about 2 million years ago (mya) to present.Temperate-adapted species are generally thought to have re-treated during glaciations into one of the major Mediterraneanrefugial areas, i.e., the Iberian and Italian peninsulas and the Bal-kan region, while cold-adapted species retreated to northernrefugia during interglacials. Recent work has, however, revealeda more complex scenario in many species, featuring sporadicrefugia in otherwise uninhabitable areas (e.g., Provan and Ben-nett, 2008; Stewart et al., 2010). In fact, the existence of extra-Mediterranean refugial areas might be the rule and not theexception, and might also apply to many temperate-adaptedspecies (Schmitt and Varga, 2012), possibly favored by specificterrain characteristics (Dobrowski, 2011). As recently argued byRecuero and García-París (2011), geographical refugial areascan be furthermore subdivided into strict-sense refugia (i.e., pre-viously uncolonized areas into which a species retreatedduring climatic shifts) or sanctuaries (i.e., areas within aspecies range that remained climatically suitable during glacia-tions and to which the species was restricted during glacialperiods).

The phylogeographic history of a species will also have pro-found consequences on the genetic variation within species andpopulations. For markers predominantly shaped by historicaland neutral processes, the presence of deep genealogical lineagesand divergent haplotypes, often correlated with high values of ge-netic variation within populations, is suggested to characterizerefugial areas. On the other hand, only a few haplotypes and lim-ited genetic variation occur in recently (re)colonized areas be-cause range expansions with associated ‘‘allele surfing’’ lead tochanges in allele frequencies and an overall reduction of variation(Excoffier et al., 2009). Hence, in phylogeography, especially in itsnovel statistical form (Knowles, 2009), assessing variation is par-amount to tracing range expansions and understanding the loca-tion of refugial areas. On the other hand, genetic variation ofadaptive markers has a plethora of profound consequences forthe organisms involved, including population viability (e.g., re-viewed by Allentoft and O’Brien (2010) for amphibians) and thesuccess and speed of range expansion (Lockwood et al., 2005;Simberloff, 2009), and in general can influence the ecology ofpopulations, communities and ecosystems (Vellend, 2005; Vel-lend and Geber, 2005; Hughes et al., 2008). However, variationdoes not necessarily correlate between neutrally evolving mark-ers and those under selection (e.g., Reed and Frankham, 2001;Bekessy et al., 2003), and mitochondrial and nuclear variationare not necessarily correlated either (e.g., Jorde et al., 2000 vs.Bortoloni et al., 1998).

Cryptic refugial areas have also been found in various Euro-pean amphibians (e.g., Rana arvalis, R. temporaria, Bufo calamita,Pelobates fuscus: Babik et al., 2004; Rowe et al., 2006; Crottiniet al., 2007; Stefani et al., 2012). In general, phylogeographicstudies of widespread amphibian species, especially at northernlatitudes, have shown fast postglacial range expansions inferred

from genetic uniformity across large parts of their ranges (e.g.,Kuchta and Tan, 2005; Crottini et al., 2007; Babik et al., 2009;Makowsky et al., 2009). This suggests that some amphibian spe-cies are able to disperse over large distances in short time spans,although the phylogeographic pattern encountered in mostamphibians consists of deep gene trees with an allopatric distri-bution of major lineages, and often with private haplotypes inmost populations (category I of Avise, 2000).

The genus Rana, as currently understood (Frost et al., 2006), isa group of neobatrachian frogs largely restricted to the Palearctic.While the delimitation of the genus is not yet fully clarified withrespect to Asian species (e.g., Che et al., 2007), the systematics ofWestern Palearctic Rana is comparatively well understood (Veithet al., 2003). These comprise 10 species of medium to large-bod-ied (4–8 cm) brown-colored frogs, commonly called brown frogs.While some species have small ranges, e.g. on the southern slopeof the central Pyrenees (R. pyrenaica) or in Italy (northern Italy: R.latastei; along the Appennine chain: R. italica), others are wide-spread, occupying vast areas of Europe, as illustrated by R. arvalis,R. dalmatina and R. temporaria. Especially the latter, also calledthe European common frog, is renowned for occupying a diverseset of habitats and maintaining viable populations from sea–levelup to 2500–2800 m elevation in the Pyrenees and the Alps, andfrom subarctic to more temperate regions in northwestern Spain(Grossenbacher, 1997b; Vences et al., 2003; Tiberti and von Hard-enberg, 2012). Rana temporaria has been intensively studied inmany respects; by 2003 there were already more than 4200 sci-entific publications on this species available (Vences et al.,2003), and its evolution has since then been the focus of manyfurther studies (e.g., Hitchings and Beebee, 1997; Palo et al.,2004; Vieites et al., 2004; Veith et al., 2002, 2012; Schmellerand Merilä, 2007; Lesbarrères et al., 2007; Teacher et al., 2009;Phillimore et al., 2010; Richter-Boix et al., 2010; Zeisset and Bee-bee, 2010; Lind et al., 2011; Stefani et al., 2012). The Europeancommon frog contains deep mitochondrial lineages, which partlyare given the rank of subspecies (Veith et al., 2002, 2003, 2012;Palo et al., 2004; Teacher et al., 2009), but the exact geographicdistribution of these population lineages is unknown, as arange-wide comprehensive phylogeographic study for this speciesis missing so far. One recurrent theme in molecular studies of theEuropean common frog has been a relatively high amount of var-iation found among and within populations of this species. On thecontrary, preliminary data suggest low genetic variation in sev-eral other European brown frogs, such as the geographically re-stricted R. pyrenaica (Carranza and Arribas, 2008), R. latastei(Ficetola et al., 2007), and the northernmost populations of R. ita-lica (Canestrelli et al., 2008), but also the widespread agile frog, R.dalmatina (e.g., Petraccioli et al., 2010). This latter species co-oc-curs with R. temporaria over much of its range, although its distri-bution is located more to the south (Grossenbacher, 1997a), andits phylogeography is not well known.

Here, we focus on two European brown frogs that share largeparts of their distributions, and compare their range-wide phylog-eographic patterns and genetic variation in a wide array ofmarker systems, ranging from mitochondrial and nuclear single-copy genes to microsatellite loci and sequence reads from thetranscriptome. Our target species are the ecologically and mor-phologically diversified European common frog, Rana temporaria,and the more specialized agile frog, R. dalmatina. Our goal is touse these species to understand (i) if the previously reported dif-ferences in genetic variation among these two species are arange-wide attribute observable across different genetic markers,and (ii) if these species are characterized by different demo-graphic and phylogeographic histories that could explain thesepatterns.

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2. Materials and methods

2.1. Sampling and DNA isolation for DNA sequencing andmicrosatellite analyses

Materials for DNA sequencing and microsatellite analyses werechosen to cover the entire ranges of both species, Rana temporariaand R. dalmatina, and also included some populations of most otherWestern Palearctic brown frog species for comparison. We hereinuse the term sample to refer to the material used to obtain DNAof an individual frog, consisting of either muscle tissue, toe or finclips of metamorphosed or larval specimens, embryos in earlydevelopmental stages, or saliva swabs, typically preserved in pureethanol but sometimes dried or frozen. Where larvae or embryoswere sampled, these came either from different ponds or clutchesor were in distinctly different developmental stages to make sureno siblings were sampled. We used populations with >6 specimens(mostly >10) for most calculations of intra-populational variation.For some Spanish and French populations we used samples fromprevious projects in which voucher specimens were collected;see Veith et al. (2002, 2012) for voucher catalog numbers. Totalgenomic DNA from all samples was isolated using a standard saltextraction protocol (Bruford et al., 1992) and used for subsequentDNA sequencing and microsatellite loci genotyping.

2.2. DNA sequencing and sequence analysis

For all available samples, we sequenced a fragment of the mito-chondrial cytochrome b gene (COB) and for a large subset of sam-ples, a fragment of the nuclear recombination-activating gene 1(RAG1). To resolve the phylogenetic relationships among majormitochondrial lineages of R. temporaria we sequenced fragmentsof various additional mitochondrial genes: 12S rRNA and 16S rRNA(12S and 16S), cytochrome oxidase subunit 1 (COX1), NADH dehy-drogenase subunits 1 and 2 (ND1 and ND2), and short stretches ofintervening tRNAs for single individuals representing all of the ma-jor lineages. In order to extend the results obtained for RAG1, wesequenced from representative populations of the two target spe-cies fragments of three additional protein-coding nuclear genes:brain-derived neurotrophic factor (BDNF), pro-opiomelanocortin(POMC), and tyrosinase exon 1 (TYR). All DNA fragments were di-rectly amplified from genomic DNA via polymerase chain reaction(PCR), using primers specifically developed for the target species(COB, RAG1) or universal amphibian primers (all other fragments).Primers and thermocycling protocols are summarized in Table S1.

PCR products were purified by Exonuclease I (NEB) and ShrimpAlkaline Phosphatase (Promega) digestion and sequenced usingBigDye v3.1 cycle sequencing chemistry on an ABI 3130xl capillarysequencer (Applied Biosystems). All nuclear genes were sequencedin both directions, while mitochondrial genes were typically se-quenced with the forward primer only. Chromatograms were qual-ity-checked by eye and sequences were corrected where necessaryusing CodonCode Aligner (v. 3.7.1, Codon Code Corporation), espe-cially to identify heterozygous positions in the nuclear genes. All2241 newly determined sequences were deposited in GenBank(accession numbers KC798478–KC800339). For a detailed over-view of the specimens and populations analyzed for each marker,see Appendix, in particular Tables S3–S6.

Sequences were aligned using the Clustal algorithm in MEGA,version 5 (Tamura et al., 2011). Only very few indels needed tobe added to the rRNA and tRNA fragments, and those sites were ex-cluded from further analyses; all other genes had no insertions ordeletions. Haplotypes of nuclear gene sequences were inferredusing the PHASE algorithm implemented in DNASP software (Ver-sion 5.10.3; Librado and Rozas, 2009), and the same program was

used to calculate values of genetic diversity per population. Insome cases of highly divergent and rare sequences, haplotypereconstruction was not unambiguously possible; we neverthelessdecided to include these haplotypes (the pair with the highestscore for each individual) in the network analysis because awrongly inferred haplotype would only slightly alter the numberof haplotypes and their placement in the haplotype network, butnot the relevant values of genetic diversity such as the numberof haplotypes and nucleotide diversity. Haplotype network recon-structions were performed under statistical parsimony (Templetonet al., 1992) using the software TCS, version 1.21 (Clement et al.,2000). A partitioned multi-gene phylogenetic analysis of R. tempo-raria mitochondrial lineages was performed in MrBayes 3.1.2.(Ronquist and Huelsenbeck, 2003), after determining the appropri-ate substitution models for each partition by applying the Akaikeinformation criterion in MRMODELTEST v2.3 (Nylander, 2004). Theanalysis consisted of two runs, each with four MCMC chains run-ning for 20 million generations, and sampling every 1000th gener-ation. We conservatively discarded the trees corresponding to thefirst 10 million generations after assessing appropriate mixingand stationarity of chains using the software AWTY (Nylanderet al., 2008). The runs were repeated under different partitioningschemes (unpartitioned, by gene, and with four partitions:rRNA + tRNA genes, and 1st, 2nd, and 3rd positions of all protein-coding genes merged). All runs led to exactly the same tree topol-ogy and similar posterior probabilities.

2.3. Microsatellite loci genotyping and analysis

For each target species eight microsatellite loci (seven tetranu-cleotide and one dinucleotide locus) were amplified. Primers forRana temporaria were from Matsuba and Merilä (2009). For Ranadalmatina primers for seven loci were from Hauswaldt et al.(2008) with one exception: for locus Radal-G11 a modified forwardprimer was used (Radal-G11X-F: 50-GCCTATGAGGTTTCTCCAAT-GAC-30). One previously unpublished dinucleotide locusRadal-B10 with (AC) motif was included: Radal-B10-F 50-ACC-CCGGTATCTTTATTTACAGC-30 and Radal-B10-R 50-AGCTGTGAAAA-TGTGGAACAGAC-30. Altogether, five multiplex and one single PCRwere performed using the Type-it Microsatellite PCR Kit (Qiagen).All reactions were performed in volumes of 5 ll with 1.45 llType-It Multiplex Kit and 0.8 ll of DNA. Every forward primerwas fluorescently labeled. For dye, multiplex primer concentrationand combination, and for thermocycling protocols, see Table S2.Fragment sizes were determined on an ABI 3130XL automated se-quencer using LIZ 600 (Applied Biosystems) as an internal sizestandard. GENEMAPPER software (Applied Biosystems) was usedfor allele calling.

We tested for the presence of null alleles, as well as large alleledropout and scoring errors due to stutter peaks with MICRO-CHECKER

(van Oosterhout et al., 2004) and tested the loci for departuresfrom linkage equilibrium using GENEPOP 4.0.10 (Rousset, 2008) with104 permutations. Estimates of allelic diversity and expected heter-ozygosity (HO and HE) were made using GENALEX 6.1 (Peakall andSmouse, 2006). Allelic richness was calculated for each speciesbased on a minimum of seven individuals using FSTAT 2.9.3.2 (Gou-det, 2001).

2.4. Analyses of population structure and demography

Microsatellite data and mitochondrial (COB) sequences weresubjected to various analyses to quantify phylogeographic struc-ture and to infer bottleneck and expansion events.

The microsatellite data sets were pruned to contain only popu-lations with an initial minimum of 9 individuals, and from these,all individuals with 10% or more missing data were removed. The

M. Vences et al. / Molecular Phylogenetics and Evolution 68 (2013) 657–670 659


software MSA (Dieringer and Schlötterer, 2003) was used to calcu-late the distances between all pairs of populations for the propor-tion of shared alleles (Bowcock et al., 1994). The distance data wereused to calculate a neighbor-joining tree with 100 bootstrap repli-cates using PHYLIP 3.69 (Felsenstein, 1993). We furthermore per-formed group-based Bayesian analyses of population structureusing BAPS (Corander and Marttinen, 2006) to determine the mostlikely clustering solution of the populations within each speciesand the level of admixture between clusters. The BAPS analyseswere performed assuming a prior upper-bound vector of the num-ber of clusters equal to the number of sampled populations, andwere repeated three times to assess convergence of the results.We used the software BOTTLENECK (Piry et al., 1999) to detect excessheterozygosity with respect to the expectation under the StepwiseMutation Model and the Two Phased Stepwise Mutation Model.Signatures of population expansion were identified by applying gtests (Goldstein et al., 1999), which measure the variance acrossloci of the variance in repeat number and compare it to that ex-pected under demographic expansion.

For the mtDNA data, analyses of demographic change were esti-mated using mismatch distributions based on COB variation inARLEQUIN 3.5 (Excoffier and Lischer, 2010), with 1000 simulationsunder a scenario of no recombination, for different clusters and lin-eages of each species (see Supplementary material SM). We deter-mined the fit of the observations with the raggedness index (RI)and the sum of square deviations (SSD) between the observedand expected curves, as well as Tajima’s D and Fu’s FS (see SM).Analyses of Molecular Variance (AMOVAs) were computed inARLEQUIN. To estimate the change in population size through time,and the time to the most recent common ancestor (tMRCA) of eachspecies, we performed a Bayesian Skyline Plot analysis (BSP)(Drummond et al., 2005) implemented in Beast 1.7.4 (Drummondet al., 2012). Two independent analyses were performed with allCOB sequences available for each species. In both cases we useda Tamura-Nei substitution model with gamma distribution as sug-gested by the Akaike Information Criterion (AIC) (Akaike, 1974) injModelTest (Posada, 2008). The analysis was run using a piecewise-constant Bayesian Skyline prior, with four groups, a strict clockmodel and a standard COB substitution rate of 1%/site/MYA as cal-ibration. An additional analysis with ten groups was also run toverify the influence of number of groups on the posterior esti-mates. The increase of number of groups did not change the re-sults, and we therefore favored a four-group setting in the finalanalysis to avoid overparametrizing the model. We ran a MCMCof 300 � 106 steps, sampling every 20 � 103 steps. The analysiswas repeated three times with different seed numbers to checkfor convergence. Effective sample size (ESS), mixing and conver-gence were checked using TRACER 1.5 (Rambaut and Drummond,2009).

2.4.1. RNA isolation, cDNA synthesis and sequencing of transcriptomesWe sampled tadpoles of Rana dalmatina and R. temporaria from

different locations in Germany in 2011. Rana dalmatina was sam-pled from four distinct locations in western and central Germany(Juntersdorf, Kottenforst and Königsdorf near Bonn/Cologne, andElm near Braunschweig), and R. temporaria was sampled at twodistinct locations (Kleiwiesen near Braunschweig and Kottenforstnear Bonn). Tadpoles were caught by dip netting, and completeindividuals were directly put into liquid nitrogen and subsequentlystored at �80 �C in the laboratory. Total RNA was extracted fromindividual tissue samples (5–10 mg weight) by homogenizationwith TRIzol (Invitrogen) using one steel bead in a bead mill (Qia-gen): 30 Hz for 30 min. Crude total RNA was further cleaned usinglithium chloride precipitation. One volume of 5 M LiCl was addedto the extracted RNA and precipitated for 1 h at -20 �C. Afterwards,samples were centrifuged at 16,000 g for 30 min at 4 �C and

washed twice with 1 ml 70% EtOH (centrifuged 5 min at10,000 g). At the end of the procedure, the RNA pellet was brieflyair-dried and resuspended in 50 ll DEPC-water.

Full-length complementary DNA was synthesised from eachpolyA mRNA sample following the SMART™ PCR cDNA synthesisProtocol (Clontech). Obtained cDNA was purified with AmpureBeads (Agentcourt) following the instructions in the manual. AllcDNA samples were PCR amplified using the Advantage 2 PCR Kit(Clontech) with PCR conditions as follows: initial denaturationfor 1 min at 95 �C, followed by 24 cycles (1 cycle: 30 s at 95 �C,30 s at 65 �C, 6 min at 68 �C). After amplification, all samples werequantified using a Nanodrop spectrophotometer. Afterwards, thesamples were processed according to the cDNA Rapid Library Prep-aration Method Manual of the manufacturer (Roche) starting withstep 3.3. Multiplex Identifier (MID) Adaptors for Rapid Library(Roche) were ligated to the DNA fragments. The DNA fragmentswere cleaned and quantified using the Agilent 2100 Bioanalyzer.As a last step, the individual samples were combined to cDNA li-brary pools. An aliquot of each library was run on an Agilent2100 Bioanalyzer prior to emulsion PCR and sequencing as recom-mended by Roche. The libraries were subsequently sequenced on a454 GS-FLX using Titanium sequencing chemistry.

2.4.2. Analysis of single nucleotide polymorphism (SNP) frequencyThe standard flow gram files (.sff) with raw data for all the reads

for each individual were converted into FASTA and quality filesusing the sffinfo tool (GS De Novo Assembler, Roche). MID-tagsand adapter sequences were automatically removed from thesequences.

In order to compute individual-based SNP frequencies, weassembled the reads for each individual using the program CAP3(Huang and Madan, 1999) with default parameters and the result-ing contigs were screened for ‘‘high-quality’’ regions. These weredefined as regions with at least 50 bp length and a minimal con-sensus quality of phred 50 for each nucleotide. Additionally, suchregions should have a coverage of at least 10 sequence reads witha minimal quality of phred 20 for each nucleotide. In order to min-imize assembly errors, sequence reads with longer stretches ofbases deviating from the consensus base were discarded from fur-ther analysis. The resulting set of ‘‘high quality’’ regions werescreened for the occurrence of single nucleotide polymorphisms(SNPs). A SNP was scored if at least 3 sequence reads indicated adiffering base. For each assembly, the final SNP frequency wascomputed as the total number of SNPs divided by the total numberof underlying bases in the respective ‘‘high quality’’ regions multi-plied by 100. To address the difference in read numbers for eachindividual, we performed a random re-sampling analysis. For eachindividual 100 re-sampled datasets were created. Each datasetconsisted of 10,000 randomly drawn sequence reads from the ori-ginal pool of reads of each individual. The same analysis as de-scribed above was performed on each dataset.

2.5. Enviromental niche modeling

In order to identify potential Pleistocene refugial areas for thetarget species, we performed environmental niche modeling(ENM) analyses using MaxEnt 3.3.3e (Phillips et al., 2006) undercurrent and Last Glacial Maximum (LGM) climatic conditions. Wedid not include other potential predictive variables (e.g., habitat)as there is no information for such variables from the LGM. MaxEntfinds the distribution of maximum entropy subject to constraintsimposed both by the observed distribution of the species and theenvironmental conditions across the defined study area, and esti-mates the likelihood of a species being present. It computes a prob-ability distribution across the defined study area for which itrequires presence and background absence data. To obtain back-



ground pseudo-absence data, we randomly selected 3000 datapoints across the Western Palearctic outside the distributionranges of each species. Occurrence data points were assembledfrom different sources including HerpNet (www.herpnet.org) theGlobal Biodiversity Information Facility (GBIF), several herpetolog-ical atlas compilations, and other literature sources. We discardedlocalities limiting the data set to only one point for each10 � 10 km UTM square to avoid duplications, although for manyareas only 50 � 50 km UTM grid cell data from the European Atlasof Amphibians and Reptiles (Grossenbacher, 1997a, 1997b) wereavailable. In total, we included 13,159 points for Rana temporariaand 2230 for R. dalmatina. We are aware that some of the includeddistribution points are controversial, such as far-eastern records ofR. temporaria and East Anatolian records of R. dalmatina, but nopublished data exist at present that support the exclusion of thesesites, which in any case would not have altered our results in anyrelevant aspect.

We evaluated nineteen climatic variables from the WorldClimdatabase version 1.4 (Hijmans et al., 2005) as predictors for theenvironmental niche models. The Worldclim dataset was createdby interpolation of observed world weather station data, using athin-plate smoothing spline and longitude, latitude and elevationas independent variables. In order to assess if these variables areautocorrelated, we randomly generated a dataset of points acrossthe Western Palearctic, extracted the climatic values for these loca-tions and performed partial correlations among variables usingSPSS version 21. These analyses showed that all variables are cor-related with bilateral p < 0.0001, with the exception of eight cross-comparisons (not shown). Hence, a principal component analysiswas performed in order to reduce the number of variables for anal-yses. The first four principal components (PC) explained 90.2% ofthe variance, the two most important ones being defined by tem-perature and precipitation variables, respectively (see Vieiteset al., 2009). In the third PC, the variables that contributed the mostwere a mix of precipitation and temperature variables, and themean temperature of the wettest quarter was the variable thatcontributed most to the fourth component. Among these variables,we selected one from each component, and we produced datasetswith permutations of selected variables to assess whether somecombinations performed better in predicting the actual distribu-tion for each species. After these analyses we selected the annualmean temperature from PC1, annual precipitation from PC2, pre-cipitation of the driest month from PC3, and the mean temperatureof the wettest quarter from PC4.

LGM climate data included the same variables, which weredrawn from two general circulation-model simulations from a gen-eral Model for Interdisciplinary Research on Climate (MIROC, ver-sion 3.2, available at http://www.pmip2.cnrs-gif.fr) and theCommunity Climate System Model (CCSM, version 3, Otto-Bliesneret al., 2006). These variables were statistically downscaled andsubtracted to current Worldclim data including a sea-level de-crease of 200 m (Waltari et al., 2007). In order to evaluate the mod-els we used the AUC index (Fielding and Bell, 1997), and inparticular the ‘‘test AUC’’ (see Sardà-Palomera and Vieites, 2011)using 30% of the occurrences as test points to obtain the AUC.

3. Results

3.1. Range-wide phylogeographic patterns

The analysis of COB and RAG1 sequences revealed radically dif-ferent phylogeographic patterns in the two target species: highlystructured and variable in Rana temporaria and largely homoge-nous and invariable in R. dalmatina. The COB haplotype networkof R. temporaria (Fig. 1) based on 640 specimens from 115 popula-

tions contains 47 distinct haplotypes arranged in five main mito-chondrial clades (based on a 330 bp fragment; see Table S3 for afull list of populations and haplotypes). Three of the main clades(T1–T3) had very restricted ranges and were endemic to Spain(Fig. 2). T4 was widespread ranging from the Pyrenees across Wes-tern Europe into the Balkans and Greece. The fifth clade T5 had thewidest distribution, occurring in the northeastern portion of therange of the species, including the easternmost localities in Russiaand Ukraine. Phylogenetic analysis of a mitochondrial multi-genedata set (4413 bp) revealed highly supported sister-group relation-ships of T1 with T2, and of T4 with T5 (Fig. 2). Clade T3 was the sis-ter group to a clade comprising the remaining haplotypes but thisplacement was not supported significantly. Further geographicstructure was found within the widespread T4, with groups of hap-lotypes differing from each other by only single or few substitu-tional steps being restricted to the Pyrenees, Italy, or to theBalkans and Greece. T5 had very limited variability, with a centralhaplotype occurring in most populations across its entire range.Specimens belonging to different mitochondrial clades occurredsyntopically in various populations. In the Benasque valley, all pop-ulations having T3 also contained specimens with a mitochondrialgenome belonging to T4. In various populations of southern Ger-many, T4 and T5 occured syntopically. The RAG1 sequences(505 bp) of 305 specimens from 69 populations of R. temporariacontained 47 haplotypes. These were not arranged in distinct geo-graphical clusters, but some correspondence with geography couldbe observed, with specific RAG1 haplotypes corresponding to thepopulations of the mitochondrial clades T1 and, to a lesser degree,T5.

In R. dalmatina, the 408 specimens from 63 populations se-quenced for COB (396 bp) were grouped in 33 haplotypes. By farthe largest number of individuals had a single haplotype that oc-curred across the entire range, from northern Spain to Turkey. Afurther 18 haplotypes differed by only a single substitution fromthis central haplotype, and several haplotypes—found only in Italy,the Balkans and Greece—had a few more substitutions (up to five).Only the population DA_COS from Cosenza (Calabria, southernItaly) corresponded to a strongly divergent mitochondrial lineage,differing by 28–32 substitutions from the most common haplotypein the COB fragment studied. This pattern of low genetic structureamong populations was even more strongly expressed in the RAG1sequences (482 bp), where almost all of the 165 sequenced indi-viduals from 36 populations were homozygous for a single haplo-type (see also Fig. 3), and only three individuals wereheterozygous, each having besides the most common haplotype asingleton differing by one or two substitutions.

3.2. Pleistocene refugial areas

Paleoclimatic envelope modeling of the two species yieldedcontrasting distribution patterns during the LGM. Models wereconstructed for both species using the complete set of variables,and using a partial set with four variables. Both variable sets wereused for modeling with two available LGM global circulation mod-els, MIROC3 and CCSM3. We partitioned each dataset using 70% ofthe data for training the model and 30% for testing, resulting invery high (>97%) AUC values each. Of the four models per species,we chose the MIROC models based on a partial set of variables tobe represented in Fig. 4 but include details of all four models perspecies in Supplementary Materials, Fig. S11.

In R. temporaria, with the MIROC climate model we found onlylimited differences among models generated by the full vs. partialsets of variables. The hindcast to LGM conditions shows wide suit-able areas in Western Europe, from western Ireland to the north-western Iberian Peninsula, including most of France, continuingin the south to the Italian Peninsula and the Balkans. The eastern



part of Europe contains few patches of climatically suitable areas,mainly on the south around the Black Sea. Many of the areas ofhigh suitability during the LGM are also occupied today, suggestingcontinuous presence of the species. The models for R. temporariawith the CCSM climatic dataset show a much constrained distribu-tion using all variables, and a wider one with the partial dataset(Fig. S11).

The paleoclimatic envelope models for Rana dalmatina show amore fragmented distribution of suitable climatic conditions dur-ing the LGM. The MIROC model with all variables shows patchesof suitable climatic areas north of the Alps in eastern France andwestern and southern Germany, some areas south of the Alps, Ibe-

ria, some areas in southern Italy disconnected to those south of theAlps, and the Balkans and the Black Sea. Other areas in westernFrance or northern Iberia where suitable climatic conditions ex-isted during the LGM are now mostly submerged because ofpost-glacial sea-level rise. The MIROC model with a partial setof variables shows a similar pattern but more connectivity northof the Alps, Italy being isolated from the rest of European popula-tions as well as the southern part of Italy from the rest (Fig. 4). Aswith R. temporaria, the CCSM models provide much more restricteddistributions. The CCSM model based on all variables suggests avery fragmented distribution for the species, with isolated popula-tions in Iberia, northern and southern Italy, the Balkans and the

Fig. 1. Haplotype networks and geographic distribution of haplotypes of fragments of the mitochondrial COB and the nuclear RAG1 genes in Rana temporaria and R. dalmatina.Colors were chosen to mark major haplotype clades and to maximize geographic information in the graphs. Sizes of circles represent the frequency of a haplotype. SeeSupplementary Figs. S1–S4 for more detailed graphs showing numbers and distribution of each haplotype and Fig. 2 for a detailed mtDNA phylogeny of R. temporaria. (Forinterpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)



Black Sea. Based on the partial set of variables the pattern is simi-lar, but a wider potential distribution area is predicted in westernFrance and in the northern and eastern Iberian Peninsula.

3.3. DNA sequence variation within populations

The different patterns of phylogeographic structure observed inthe two target species were mirrored by distinct differences in in-tra-populational genetic variation (Fig. 3, Table S4), here quantifiedby haplotype diversity (H) and nucleotide diversity (p) per popula-tion, for those populations with >6 (mostly >10) individuals se-quenced. In the COB data set, genetic diversity was higher in R.temporaria, but the difference was not significant (Mann–WhitneyU-test, P = 0.056; Fig. 3), while for RAG1, the difference was highlysignificant (P < 0.0001; Fig. 3). Similarly strong and statistically sig-nificant differences between the two species occurred for threeother nuclear genes (POMC: 372 bp; TYR: 483 bp; BDNF: 520 bp),as summarized in Fig. 3 (also Table S6).

Areas in southern Europe, i.e., Italy and Balkans south of theAlps, and the Iberian Peninsula south of the Pyrenees, may haveserved as glacial refugia and sanctuaries for amphibians, includingthe two target species (Fig. 4). In general, the low genetic variationof R. dalmatina is also observed within the southernmost parts ofits distribution, where the species has likely persisted during glaci-ations, especially regarding the nuclear genes. In the DA_COS pop-ulation, characterized by a highly divergent mitochondrial DNAsequence, only a single RAG1 haplotype and only 2 COB haplotypeswere found. We compared genetic diversity values among (1) pop-ulations within or (2) outside of these potential refugial areas and,for R. temporaria only, (3) high-mountain areas (> 1500 above sealevel) close to refugial areas, i.e., the Pyrenees and high mountainsin central Italy. We found a statistically significant difference in R.dalmatina COB variation, with p and H values being highest in po-tential refugial areas, while they were consistently zero in thenorthern populations (U-test; Z = 2.872; P = 0.004). In R. temporar-ia, COB values did not significantly differ among geographical re-gions. Comparable results were obtained for RAG1 but withoutstatistical support: in R. dalmatina p and H were higher in potentialrefugial areas, and montane (Pyrenean) R. temporaria had lower

diversity values than those from potential refugial and non-refu-gial areas.

Within R. temporaria, the observed nucleotide substitutionstranslated into amino acid (aa) substitutions at 5 out of 110 aminoacid sites in COB, four of which were observed only in 1–2 individ-uals, and one was a polymorphism typical for specimens of cladesT1 and T2. In RAG1, 10 out of 167 aa positions were variable, withdifferent alleles often occurring in numerous individuals. In R. dal-matina, 11 out of 131 aa sites in COB had substitutions, several ofthese in multiple individuals; all four RAG1 substitutions observedin R. dalmatina led to one aa substitution (4 out of 160 aa sites).

3.4. Variation in microsatellite loci

We genotyped a total of 255 specimens from 20 populations ofR. temporaria and 239 specimens from 25 populations of R. dalma-tina (Table 1). The values of observed heterozygosity and allelicrichness in R. temporaria were consistently higher than those inR. dalmatina, i.e., 0.69 (0.51–0.85) vs. 0.61 (0.38–0.84), and 6.6(3.8–9.3) vs. 4.7 (2.4–7.4), and these differences were statisticallysignificant (P = 0.01 and P = 0.015; Fig. 3). The markers used had16–43 alleles in R. temporaria and 15–34 alleles in R. dalmatina(see Table S7 for more details). Out of the eight loci genotypedper species, seven had 20 or more alleles in R. temporaria, whereasthis was true for only five of the R. dalmatina markers. In R. dalma-tina, both heterozygosity and allelic richness were significantlyhigher in populations occurring in potential refugial areas com-pared to central and northern European populations (U-tests;Z = 2.013, P = 0.044; and Z = 2.582, P = 0.01), while in R. temporariano significant correlation between geographic location and geneticdiversity was observed.

3.5. Transcriptome characteristics and SNP variability

We analyzed transcriptome data for a total of seven individualsof Rana dalmatina from four localities and six individuals of R.temporaria from two localities in Germany (see Table 2). The num-ber of used sequence reads ranged from 7763 to 40,868 bp in R.dalmatina, and 25,538–63,759 in R. temporaria. The number of

Fig. 2. Phylogenetic tree (50% majority rule consensus tree) derived from a Bayesian Inference analysis of 4413 bp of mitochondrial DNA sequences (genes: 12S, 16S, COB,COX1, ND1, ND2, and stretches of intervening tRNAs) of representative individuals of all major mitochondrial clades identified within Rana temporaria. Colors follow thoseused in the haplotype network of R. temporaria COB sequences. T1–T5 denotes major mitochondrial clades as discussed in the text. A double asterisk shows posteriorprobabilities of 1.0; all other nodes received PP values <0.95. The inset tree shows the same analysis including hierarchical outgroups, with R. temporaria lineages collapsed; R.nigromaculata was used as outgroup and is not shown for graphical reasons. R. pyrenaica is resolved as the sister species to R. temporaria. (For interpretation of the referencesto colour in this figure legend, the reader is referred to the web version of this article.)



reads and the total number of bases were consistently lower in R.dalmatina. Altogether, 247,254 analyzed reads corresponded to R.temporaria (66.7%) and 123,507 to R. dalmatina (33.3%). The num-ber of SNPs per 100 bp of high-quality regions was consistentlylower in R. dalmatina (Table 2). To exclude the possibility that thisdifference was causally related to the overall lower number ofreads in R. dalmatina, we performed a resampling procedure of10,000 reads randomly drawn and analyzed 100 times from eachindividual transcriptome of both species. The results confirmed aconsistently lower number of SNPs per 100 bp of high-quality iso-tigs in Rana dalmatina (Table 2; Fig. 3).

3.6. Population structure and demographics

The Bayesian analysis of microsatellite population structurerevealed nine clusters within R. dalmatina (posterior probabilityof 0.93) and eleven clusters within R. temporaria (posterior prob-ability of 0.99). The proportion of significantly admixed individ-uals was very low: 2.8% in R. dalmatina (5 individuals) and 1.4%in R. temporaria (3 individuals). With a few exceptions, popula-tions belonging to different mitochondrial lineages were alsoplaced into separate clusters, but the microsatellite-basedclustering and the trees based on the microsatellite variation(see Figs. S7–S10) revealed additional substructure within COB

lineages. We did not find a consistent signature of bottlenecksin any population of the two target species (Table S9), with afew more tests indicating a bottleneck in single populationsand clusters of R. dalmatina. Equally, no signature for populationexpansion was found in the microsatellite-based data(Table S10).

Mismatch-distribution analysis of COB sequences provided evi-dence for recent population expansions in two populations of R.dalmatina and three populations and two population clusters ofR. temporaria (Tables S8). At the level of species and main mito-chondrial lineages, we found no significant indication of popula-tion expansion in R. temporaria. The main mtDNA clade in R.temporaria (T4) showed no evidence of an expansion, but the east-ern clade T5 presented a negative value of Tajima’s D (�1.38;P = 0.009). For R. dalmatina after exclusion of the divergent DA_COSpopulation we found a negative D (�1.87; P < 0.0001) and Fu’s Fs(�8.87; P < 0.0001). AMOVA confirmed the strongly different pat-terns of COB variation among the two species: In R. temporaria,61% of the variation occurred among the main mtDNA clades,27% among populations, and only 12% within populations, in R. dal-matina most of the variation (63–64%) occurred within populations(Tables S11–S14).

The Bayesian skyline plots suggest that the Ne of both speciesincreased from approximately 10 kya to the present (Fig. 5). De-

Fig. 3. Summary of genetic diversity values for the different markers in populations of Rana dalmatina (grey boxes) and R. temporaria (black): nucleotide diversity infragments of one mitochondrial (COB) and four nuclear genes, allelic richness and observed heterozygosity in 8 microsatellite loci per species, and number of SNPs per 100 bpas observed in standardized transcriptome data (see Section 2.4.2). The graphs show minimum and maximum values, standard error (boxes) and mean values. Adjusted Z andP values refer to Mann–Whitney U-tests performed separately for each gene. Values under each plot are the number of analyzed populations and number of individualssummarized over all populations. Original data used for analysis are summarized in Supplementary Tables S3–S6.



spite the large confidence interval (commonly observed in thiskind of analysis; Ho and Shapiro, 2011), the median Ne shows astronger and slightly later increase in R. dalmatina compared toR. temporaria. On the other hand, the ancestral and the current Ne

of R. temporaria were estimated as being respectively 2.5 and 1.5times larger than the Ne of R. dalmatina. The two species presentvery similar tMRCA values, with largely overlapping posteriordensities [R. temporaria – tMRCA: 3.46 MYA (95%HPD = 2.31–5.05 mya); R. dalmatina – tMRCA: 3.33 mya (95%HPD = 2.05–4.92mya)].

4. Discussion

4.1. Phylogeographic structure and demography

Phylogeographic analysis is based on the assumption that thedistribution of alleles across populations of a species at least partlyreflects its demographic history. Rana dalmatina contains a diver-gent mitochondrial lineage in southern Italy, in agreement withthis being a refugial area for other taxa (e.g., Canestrelli et al.,2012; Colangelo et al., 2012; Hauswaldt et al., 2011; see also Tab-

Fig. 4. Differences in genetic variation between populations of Rana dalmatina (grey bars) and R. temporaria (black bars) occurring in glacial refugial areas vs. areas ofpostglacial range expansion. The maps represent the distribution of the two species at present (top) and climate envelope models for the last glacial maximum, with a partialset of climatic variables and using the MIROC global circulation climate model (bottom). The potential distributions vary from yellow (zero probability of occurrence) to darkbrown (probability of 1). Light blue is the extent of permanent ice during the Last Glacial Maximum (LGM). Ref: populations from sites within glacial refugial areas as inferredfrom the paleomodeling, or from nearby such areas. Exp: populations from postglacial latitudinal range expansion areas. Exp-M: populations from mountains into whichaltitudinal postglacial range expansions took place (Pyrenees and central Italian massif). Mann–Whitney U-tests were carried out only among intraspecific groups; differencesfor COB and microsatellites in R. dalmatina were significant.



erlet et al., 1998), but the remaining range of this species is occu-pied by a single mtDNA lineage with clear indications for a recentrange expansion. On the contrary, R. temporaria contains variousdeep mtDNA clades, of which some (T1, T4) are clearly geographi-cally structured (with a proportion of variation explained by inter-populational differences in AMOVA) and at least one (T5) shows aclear signature of recent range expansion.

Geographic areas harboring phylogenetically disparate haplo-types and high genetic variation are usually interpreted as centersof origin of intra-specific diversity, whereas more shallow and de-rived lineages and low variation indicate areas of recent rangeexpansion. Our analysis detects the phylogenetic component ofthis pattern in both target species, with a divergent mtDNA lineageof R. dalmatina in southern Italy, and clades separated at basalnodes characterizing Spanish R. temporaria. In R. temporaria thetwo deepest intraspecific mtDNA clades (T1 and T2 + T3) occur inthe Iberian Peninsula and all non-Iberian populations belong to aclade (T4 + T5) grouped with one of these Iberian-endemic clades(in our analysis, T2 + T3, but without support; Fig. 2). Such a phy-

logeographic pattern is in agreement with an early out-of-Iberiaorigin of R. temporaria, followed by subsequent diversification. AnIberian origin of R. temporaria is also suggested by its inferred sisterspecies, R. pyrenaica, an Iberian endemic (Veith et al., 2003). In R.dalmatina, the deepest mtDNA split separates the Calabrian haplo-types from all others, but this simple dichotomous pattern does notallow for a conclusive statement on the geographic origin of thisspecies.

In R. dalmatina, a range expansion from southern refugia isalso supported by the fact that mtDNA and nucDNA sequences,as well as microsatellite loci, showed higher variability in poten-tial refugial areas (Fig. 3; Tables S4–S5). On the contrary, in R.temporaria, only a weak indication was found for lower variationin montane compared to lowland populations, thus deviatingfrom the expected pattern and suggesting that this species mighthave been more tolerant to cooler conditions during glacial cyclesand persisted in more extensive refugial areas (see also Veithet al., 2012; Stefani et al., 2012). This hypothesis is confirmedby the paleoclimatic models (Figs. 3 and S13). All models agree

Table 1Microsatellite-based genetic variation of populations of Rana temporaria and R. dalmatina based on 8 loci in each species. The table shows the number of genotyped individuals(N), observed (Ho) and expected (He) heterozygosity, and allelic richness (AR) as calculated for 7 individuals. Values marked by an asterisk indicate the average number of allelesacross loci (AL). The column ‘‘Refugial area’’ denotes whether a population occurs in a region flagged as containing a possible glacial refugial area (refugium or sanctuary) by thepaleomodeling analysis for the respective species: 0, outside refugial area: 1, inside refugial area; 2, montane regions within potential refugial areas. The last column shows inwhich cluster the respective population was placed by Bayesian analysis (see Figs. S7 and S8).

Population Country Refugial area N HO HE AR/AL⁄ Cluster

Rana temporariaTE_LOK Croatia 1 9 0.769 0.818 7.52 4TE_JYK Finland 0 3 0.667 0.535 2.87 5TE_PII Finland 0 7 0.75 0.733 5.75 6TE_GAV France 1 19 0.718 0.841 7.3 4TE_VIG France 1 16 0.705 0.804 6.51 11TE_DIT Germany 0 10 0.588 0.738 5.82 10TE_BRE Germany 0 20 0.713 0.799 6.76 10TE_MAI Germany 0 11 0.743 0.795 6.9 10TE_PFA Germany 0 15 0.725 0.79 7.24 10TE_MAS Germany 0 13 0.511 0.69 5.64 10TE_GLA Ireland 1 20 0.761 0.735 5.77 7TE_LAG Italy 1 14 0.539 0.553 3.77 8TE_BOB Italy 1 12 0.604 0.656 5.11 9TE_SOK Poland 0 22 0.797 0.811 6.85 5TE_CAR Spain 1 9 0.823 0.87 9.34 1TE_RES Spain 2 19 0.57 0.77 5.83 2TE_ETO Spain 2 5 0.725 0.718 5.25� 3TE_TUR Spain 2 5 0.625 0.665 5 3TE_CAL Spain 2 17 0.632 0.811 6.6 3TE_SHY Ukraine 0 9 0.847 0.859 8.82 4

Rana dalmatinaDA_MOT Croatia 1 11 0.591 0.748 5.57 5DA_MAL Croatia 1 13 0.729 0.778 6.12 5DA_LUM France 0 5 0.725 0.645 4.63� 4DA_ESR Germany 0 8 0.449 0.468 3.62 2DA_MAS Germany 0 9 0.433 0.516 2.96 2DA_RAE Germany 0 10 0.596 0.633 4.33 2DA_LEL Germany 0 20 0.543 0.636 4.18 2DA_THI Germany 0 9 0.612 0.605 3.75� 3DA_UND Germany 0 5 0.719 0.588 3.13� 3DA_WIL Germany 0 4 0.490 0.440 2.63� 3DA_VIL Germany 0 9 0.656 0.652 4.27 4DA_KOT Germany 0 8 0.536 0.678 5.25� 5DA_ALB Germany 0 14 0.321 0.389 2.39 6DA_SIL Germany 0 5 0.700 0.681 3.88� 4DA_SNE Italy 1 7 0.679 0.710 5.5 4DA_MAC Italy 1 19 0.737 0.760 6.05 4DA_FOG Italy 1 9 0.694 0.831 7.39 9DA_LAT Italy 1 15 0.842 0.828 7.12 9DA_COS Italy 1 6 0.700 0.608 3.88� –DA_SWB Sweden 0 24 0.510 0.581 3.53 1DA_BOS Switzerland 0 4 0.594 0.710 4.63� 7DA_FON Switzerland 0 10 0.662 0.815 6.67 4DA_PIA Switzerland 0 5 0.581 0.663 4.63� 4DA_OVA Turkey 1 6 0.500 0.550 3.63� 8



in predicting a more restricted and scattered potential distribu-tion of R. dalmatina in the LGM compared to R. temporaria, whilethe latter species, especially in the MIROC models, had a largecontinuous potential range extending well into ice-free areas ofcentral Europe. The models also suggest small refugial areas forboth species at northern locations, at the latitude of Scandinavia,which might be seen as an indication for overprediction. Exceptfor this aspect, the MIROC models agreed rather well with thefossil history of R. temporaria during the Pleistocene, which in-cludes records from Poland and Hungary in the East to the IberianPeninsula in the West (Holman, 1998). The Pleistocene fossil re-cord of R. dalmatina is scarce (e.g. Holman, 1998), but tentativerecords exist from the Pleistocene of Croatia, France, Germany,Hungary, Italy and Spain, in agreement with the MIROC models.Hence, in R. temporaria, the predicted paleodistribution suggestsan uninterrupted presence through time in larger and more con-tinuous areas, in agreement with its wider climatic tolerance. Inboth species, mountain areas during the LGM were not occupiedaccording to the models, suggesting recent colonization of highelevations.

Interpretation of the demographic analysis needs to take intoaccount the uncertainty arising from the largely overlapping confi-dence intervals of the results for each species (Fig. 5). If only themedian values are considered, the results suggest coalescence ofmtDNA of the two species at roughly the same time (around3.5 mya), which fits remarkably well with the estimate of3.2 mya for the splitting of R. temporaria from its closest relatives(i.e., R. arvalis and R. pyrenaica) by Veith et al. (2003). If the recon-structed differences correctly reflect the evolutionary and demo-graphic trajectories of the two species, then ancestral and currenteffective population sizes were much larger in R. temporaria, andpossibly increased more strongly and later in R. dalmatina. If therather young age of R. temporaria inferred by Veith et al. (2003)is correct, then Miocene fossils assigned to Rana temporaria (e.g.,Böhme, 2003) might rather belong to a species ancestral to the R.temporaria-arvalis-pyrenaica clade than to R. temporaria as cur-rently understood.

4.2. Variation across marker systems

The observed differences in phylogeographic structure and ge-netic variation observed between the two target species were con-gruent among the marker systems studied, although some of thesemight be more suitable than others as indicators of overall geno-

mic variation. Especially, the analysis of DNA sequences of the nu-clear protein-coding genes is unambiguous in identifying a lack ofvariation in R. dalmatina. Also our measure of variation in the tran-scriptome data clearly followed the pattern observed in the single-copy nuclear genes sequenced directly from the genome (Fig. 3),without overlap between the low values of R. dalmatina and thecomparably higher values of R. temporaria. The differences be-tween the two target species were only weakly discernable inmtDNA, probably because variation in mtDNA can be reducedmore quickly than in nuclear loci during fast range expansionsdue to its fourfold lower effective population size (Meiklejohnet al., 2007; Galtier et al., 2009), and possibly because it may be af-fected regionally by selective sweeps (Ballard and Whitlock, 2004;Bazin et al., 2006; but see Karl et al., 2012). The microsatellite dataalso supported lower variation in R. dalmatina compared to R.temporaria; however, this comparison is based on different setsof microsatellite loci specifically developed for either R. temporariaor R. dalmatina, and these sets might differ in average mutationrates, also hampering comparison with published microsatellitedata (Palo et al., 2004; Lesbarrères et al., 2007; Zeisset and Beebee,2010; Lind et al., 2011; Hauswaldt et al., 2008; Sarasola-Puenteet al., 2012; discussed in more detail in the Appendix). Other geno-mic methods such as RAD sequencing (Rowe et al., 2011) might bemore suitable to assess genome-wide variation but comparisonsbetween species separated by long and independent evolutionaryhistories (and thus, having large genomic differences), such asmany amphibians, may prove difficult.

4.3. Patterns of genome-wide variation in brown frogs

In the two target species the phylogeographic structure (the de-gree of genetic subdivision between populations across space) is ingeneral agreement with patterns of genetic variation within popu-lations, i.e., R. dalmatina is weakly structured phylogeographicallyand has low levels of variation, whereas R. temporaria is highlystructured especially in mtDNA and exhibits higher variation. Thispattern is stable across the large distributions of both species,including those areas that were likely to serve as refugial sanctuar-ies (Recuero and García-París, 2011) during glacial periods. In agroup of tropical frogs from Madagascar, Pabijan et al. (2012)found the amount of genetic variation within 40 species correlatedbetween two rather distant geographic sites. Species with high-ge-netic variation at one site had similarly high values at the secondone, whereas other species had low values at both sites.

Table 2Analysis of transcriptome data of seven individuals of Rana dalmatina and six of R. temporaria from various German populations. The table shows the total length of all high-quality regions from each transcriptome and the total number of SNPs identified therein. SNP frequency is given for the overall data set, and for those transcriptomes of >9000reads; results from a resampling analysis with 10,000 reads randomly drawn and analyzed 100 times for each transcriptome (see Section 2).

Species/sample

Numberof reads

Total no of bpin ‘‘high-quality’’regions

Number of‘‘high-quality’’regions

Numberof SNPs

SNPs/100 bp

SNPs/100 bp –mean ofresampling

SNPs/100 bp –median ofresampling

SNPs/100 bp – SDof resampling

Rana dalmatinaJuntersdorf 1 19,547 14,148 120 43 0.3039 0.1796 0.1811 0.0463Juntersdorf 2 7763 4143 40 8 0.1931 – – –Juntersdorf 3 8027 6441 58 15 0.2328 – – –Königsdorf 1 16,118 11,027 105 47 0.4262 0.2983 0.2934 0.0642Elm 1 14,308 14,269 140 62 0.4345 0.2961 0.2931 0.0406Elm 2 16,880 12,380 114 52 0.42 0.3069 0.3029 0.0497Elm 3 40,868 31,055 253 116 0.3735 0.3041 0.304 0.0538

Rana temporariaKottenforst (NT5) 7 45,014 33,585 295 139 0.4138 0.3602 0.3534 0.0618Kottenforst (NT5) 3 38,966 31,175 281 143 0.4587 0.4135 0.4096 0.0609Kottenforst (BT7) 1 32,710 22,674 211 119 0.5248 0.3769 0.3687 0.0717Kleiwiesen 1 25,538 14,879 147 72 0.4839 0.3809 0.3818 0.0783Kleiwiesen 2 40,537 33,635 323 172 0.5113 0.374 0.3666 0.0626Kleiwiesen 3 63,759 39,076 326 144 0.3685 0.3654 0.3695 0.0807



The northeastern T5 clade of R. temporaria haplotypes most clo-sely resembled R. dalmatina in that the same haplotype occurs overa wide geographic range with limited mitochondrial variability,and in showing a clear signature of population expansion. In fact,the three populations of T5 sampled sufficiently to calculate COBsummary statistics, TE_SOK, TE_AHR and TE_FLE, presented nucle-otide variabilities between 0.0007–0.0017, thus clearly at the low-er end of the range of values found in R. temporaria, and lower thanthe highest R. dalmatina values found in southern European popu-lations. However, in RAG1, population TE_SHY, which occurs withinthe range of T5, had four distinct RAG1 haplotypes and a nucleotidevariability of 0.0013, which is rather low compared to other R.temporaria populations but still higher than values for all R. dalma-tina populations. Hence, at least for RAG1, our data suggest that R.temporaria is more variable throughout its entire range comparedwith any population of R. dalmatina.

The low amounts of diversity in R. dalmatina agree with those ofbrown frog species that are restricted to small ranges and typicallyspecialized to particular habitats in southern Europe, while thevariation found in R. temporaria widely exceeds that of all otherbrown frog species studied (see Tables S3–S6). This again indicatesthat the pattern in this species is unusual and warrants an alterna-tive explanation. The demographic analysis suggests a scenario inwhich R. temporaria was already rather widespread in its Iberianarea of origin from which it expanded early on into other areas,

and then was able to persist in multiple cryptic and non-crypticrefugial areas, with episodes of differentiation among refugia andsubsequent admixture. In contrast, R. dalmatina might have per-sisted over most of the Pleistocene in small ranges within one ortwo of the Mediterranean refugial areas, expanded more recentlyand rather fast into areas of central Europe, and only during thisexpansion increased its effective population size. It is worthemphasizing that R. temporaria, despite its apparent persistenceoutside of the Mediterranean refugial areas during glaciationsand tolerance of cold climate, does not qualify for the category of‘‘Siberian’’ or arctic species of De Lattin (1957), but according tothe mtDNA phylogeography originated in temperate western Eur-ope. The demographical results combined with the phylogeograph-ical pattern and the paleomodeling characterize R. temporaria as atemperate-adapted species that has been able to persist in crypticrefugia outside of the Mediterranean, probably similar to othersuch species (Schmitt and Varga, 2012).

We conclude that variation in at least certain markers and incomparisons of some species – exemplified by R. dalmatina andR. temporaria herein – can be a species-specific attribute. In theexample studied here, these drastic differences can largely be ex-plained by different demographic and phylogeographic histories.It is unclear whether these differences also extend to parts of thegenome involved in adaptive processes, but R. temporaria is knownto show high morphological variability and local adaptation (e.g.,Brand and Grossenbacher, 1979; Phillimore et al., 2010; Lindet al., 2011). As a speculative but testable hypothesis, populationsof R. temporaria might not only have a higher genetic variation thanthose of R. dalmatina but also a higher (adaptive) variability in eco-logical, morphological, physiological, behavioral and possiblyphysiological traits which might in turn have favored its persis-tence during climatic shifts.

Acknowledgments

We are grateful to a large number of friends and colleagues whoprovided crucial tissue samples or helped during collection of sam-ples in the field, in particular to Paolo Eusebio Bergò, AlexandreBoissinot, Zbyszek Boratynski, Jean-Claude Bracconier, HeleneBracconier, Giacomo Bruni, Bruno Cari, Riccardo Cari, Thibaut Cou-turier, Philippe Evrard, Tiziano Fiorenza, Kåre Fog, Michael Franzen,Frank Glaw, Pierre Grillet, Kurt Grossenbacher, Julia Günther, Me-lissa Guerret, Nathan Hugo, Marija Kuljeric, Chiara Minuzzo, Dani-ele Nembi, Dario Pellegrini, Gilles Pottier, Pauline Priol and theAssociation ‘‘Cistude Nature’’, Emilio Sperone, Alexandre Teyniéand the association Alcide, Burkhard Thiesmeier, Andrea Tiberi,Sandro Tripepi, and Jean-Claude Vignes. Meike Kondermann, GabiKeunecke, Eva Saxinger, Steffen Berthold, Jose Borrero, Jurij Petersand Torsten Steinbach helped with labwork. Diethard Tautz madetranscriptome sequencing facilities in his lab available. Boyke Bunkprovided software help to handle the transcriptome data. D.R.V.was supported by a Spanish Ministry of Environment 206/2010.S.N. was supported by an Angeles Alvariño grant of the Xunta deGalicia. A.C. was supported by the Zukunftsfonds of TU Braun-schweig, and during the final stages of preparation of this manu-script by a postdoctoral grant from the Portuguese Fundação paraa Ciência e Tecnologia (SFRH/BPD/72908/2010). M.P. was sup-ported by a postdoctoral fellowship of the Alexander von Hum-boldt Foundation.

Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.ympev.2013.04.014.

A

B

Fig. 5. Bayesian Skyline plots showing changes in effective population size throughtime for Rana temporaria (black lines) and R. dalmatina (gray lines), based on COBsequences. X-axis shows time before present in years, time goes backwards fromleft to right. Y-axis shows the effective population size. (A) graph showing themedian of the posterior distribution of the effective population size through timefor each species. (B) Graph showing the 95% high posterior density of the effectivepopulation size through time for each species (R. temporaria: black and dashed; R.dalmatina: gray and stippled).



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1

Radically different phylogeographies and patterns of genetic variation in 1

two European brown frogs, genus Rana 2

3 Supplementary data 4 5 6 Expanded Methods and Results 7 8 Genetic variation of other Western Palearctic brown frog species 9 Data on COB (395 bp) and RAG1 variation in five other species of Western Palearctic brown 10 frog (1–3 populations each) are summarized in Tables S4 and S5. Most of these species show 11 values of nucleotide variability that are similar or even lower than those found in R. 12 dalmatina, and almost entirely are distinctly lower than those found in R. temporaria. This is 13 particularly obvious in species with small ranges, i.e., R. pyrenaica, R. italica and R. latastei. 14 These had the lowest π values of all species, ranging from 0 to 0.0005, whereas the RAG1 the 15 pattern was more mixed, with very low values of 0 and 0.0001 in R. italica and R. latastei and 16 a higher value of 0.001 in R. pyrenaica. For the latter species, however, only a small sample 17 size (N = 4) was available. Rana arvalis, a widespread species occurring mostly in 18 northeastern Europe, also showed very low diversity values for COB and RAG1 (0.001 and 0) 19 but our sampling did not include populations from the southern part of its range. Rana 20 iberica, which has a moderately wide distribution throughout Spain and Portugal, had 21 comparatively higher values (0.003 and 0.0008). Comparing diversity values between COB 22 and RAG1 for all populations of all species, we found no significant correlation for H or π 23 values. 24 25 Comparison with published microsatellite and allozyme genetic variation data 26 Most of the published average values of allozyme heterozygosity are lower in populations of 27 R. temporaria (0.17, 0.06, 0.097; Reh and Seitz, 1990; Hitchings and Beebee, 1997; Veith et 28 al.,, 2002) than those published for R. dalmatina (0.28 and 0.41; Lesbarrères et al.,, 2003; 29 Lodé and Lesbarrères, 2004), but in the latter studies the authors specifically selected the most 30 variable allozyme systems to detect multiple paternity and differences among populations. 31 The same is true for microsatellite studies, with average gene diversities of 0.64, 0.58, 0.71, 32 and 0.51 reported for populations of Rana temporaria from various localities across its range 33 (Palo et al.,, 2004; Lesbarrères et al.,, 2007; Zeisset and Beebee, 2010; Lind et al.,, 2011), 34 which are lower than those values we found in this study and not conspicuously higher than 35 the values of 0.72 and 0.49 reported for R. dalmatina in previous studies (Hauswaldt et al.,, 36 2008; Sarasola–Puente et al.,, 2012). 37 38 Population Structure and Demographics 39 The pruned microsatellite dataset used for demographic and population structure analyses 40 presents a significantly lower expected heterozygosity in R. dalmatina (average He across 41 populations: 0.67) than in R. temporaria (average He across populations: 0.79; P–value of t–42 test assuming unequal variances: 0.0015). Nevertheless, the results for these reduced datasets 43 do not differ from those obtained for the complete datasets. As past demographic changes 44 may dramatically change the genetic make up of populations, we attempted to detect historic 45 bottlenecks and/or population expansions in these two species that could explain the observed 46 differences in expected heterozygosity. 47

2

The signature left by a recent bottleneck is characterized by a transient excess of 48 heterozygosity with respect to what is expected for the observed set of alleles under mutation–49 drift equilibrium. This pattern arises because low frequency variants are rapidly lost during a 50 bottleneck, therefore reducing the allelic diversity while the heterozygosity takes longer to 51 decrease. We used the software BOTTLENECK (Piry et al.,, 1999) to detect this excess of 52 heterozygosity under the Stepwise Mutation Model (Kimura and Ohta, 1978) and the Two–53 phased Stepwise Mutation Model (Di Rienzo et al.,, 1994; Slatkin, 1995). We did not find a 54 consistent signature of recent bottlenecks in either species (Table A.9). While some 55 populations showed a significant excess of heterozygosity, for some tests none presented 56 significant results for all tests. However, the R. dalmatina populations of DA_SWB and 57 DA_FOG showed for both the standardized difference test (T2) (Cornuet and Luikart, 1996) 58 and the Wilcoxon test an excess of heterozygotes. Furthermore, pooling the samples into the 59 clusters found by BAPS (thus, increasing the sample size and the statistical power) did not 60 improve the results and the bottleneck signature remained spurious at best. Nevertheless, it 61 should be noted that overall more tests in R. dalmatina indicated bottlenecks. 62

The footprint of a demographic expansion in the gene genealogies of a population is 63 characterized by leaving long branches that rapidly coalesce close to the base of the tree at the 64 time of the expansion. The g test (Goldstein et al.,, 1999; Reich et al.,, 1999) takes advantage 65 of this pattern by measuring the variance across loci of the variance in repeat number in a 66 sample and comparing it to that expected under a demographic expansion. All the 67 populations/clusters tested showed a g value larger than that expected under a demographic 68 expansion, thus rejecting it. However, it is worth mentioning that among the tests performed, 69 R. dalmatina showed on average 27% of the populations/cluster with a g value no larger than 70 3x the expected value, and with a median absolute difference between the expected and 71 observed value of only 0.634. Contrastingly, R. temporaria shows on average 41% of the tests 72 smaller than 3x the expected g value and with a median of the absolute difference between 73 expected and observed g of 0.71. As pointed out by Reich et al (1996), it is possible that 74 inflated g values that lead to a rejection of the expansion model are the result of mutation rate 75 heterogeneity across loci. Consequently, while we fail to find statistically significant evidence 76 of demographic changes in our microsatellite dataset, our results indicate that some more 77 accurate statistical modeling (e.g. MSVAR: Beaumont, 1999, or approximate Bayesian 78 computation: Beaumont et al.,, 2002) coupled with larger sample sizes (e.g. more loci: Reich 79 et al.,, 1999) may infer that a large proportion of the R. temporaria populations had passed 80 through demographic expansions in the recent past, while the majority of the R. dalmatina 81 populations have been demographically stable. 82

The microsatellite analyses were complemented with the estimation of mismatch 83 distributions (Rogers and Harpending, 1992) on the mitochondrial COB, and the estimation of 84 population genetic statistics originally intended for the detection of selection but that are also 85 sensitive to demographic changes. The rationale behind these tests is that a demographically 86 stable population presents a multimodal (i.e. ragged) distribution (Slatkin and Hudson, 1991), 87 which will largely deviate from the smooth unimodal distribution found after a demographic 88 expansion. Consequently, both the RI and SSD will be large under a stable scenario, which 89 significantly differs from the null expectation. Additionally, we also calculated Tajima’s D 90 (Tajima, 1989) and Fu’s Fs (Fu, 1997). After a demographic expansion, these statistics expect 91 the site frequency spectrum to be distorted presenting an excess of low frequency variants, 92 while under demographic stability the proportion of rare mutations does not contribute more 93 to the population’s GV than the alleles at higher frequencies. 94 A significantly positive D could be evidence of a population bottleneck or balancing 95 selection, whereas a significantly negative D could be evidence of positive selection or a 96 recent range expansion (because of a large number of recent mutations relative to segregating 97 sites assuming neutrality). A nonsignificant D is consistent with a population at drift–98

3

mutation equilibrium. Negative Fs values reflect an excess of rare polymorphisms in a 99 population, which is consistent with either positive selection or an increase in population size. 100 Positive Fs values indicate an excess of intermediate-frequency alleles in a population and 101 can result from balancing selection, population bottlenecks or the presence of population 102 structure. 103 The mismatch distribution analysis (Table A.8, Fig. A.11) on the R. dalmatina clusters gave 104 significant values for one or several indicators of recent populations expansion in two clusters 105 three populations DA_MAC, DA_SNE, and DNA_LAT, as well as in the full species data set, 106 especially after excluding the divergent DA_COS population. In R. temporaria, four out of 107 eleven clusters present either unimodal mismatch distributions or negative Tajima’s D and 108 Fu’s Fs. Six populations show a slight indication, and three populations a consistent signature 109 of a population expansion, namely TE_CAR (D: -1.31, Fs:-0.99), TE_LOK (D: -1.11, Fs:-110 0.34) and TE_RES (D: -0.97, Fs:-1.31). 111 112 113 References for Expanded Methods and Results 114 115 Beaumont MA (1999) Detecting population expansion and decline using microsatellites. Genetics, 153, 2013–116

2029. 117 Beaumont MA, Zhang W, Balding DJ. (2002). Approximate Bayesian computation in population genetics. 118

Genetics, 162, 2025–2035. 119 Cornuet JM, Luikart G (1996) Description and power analysis of two tests for detecting recent population 120

bottlenecks from allele frequency data. Genetics 144, 2001–2014. 121 Di Rienzo A, Peterson AC, Garza JC, Valdes AM, Slatkin M, Freimer NB (1994) Mutational processes of 122

simple–sequence repeat loci in human populations. Proceedings of the National Academy of Sciences, 91, 123 3166–3170. 124

Fu YX (1997) Statistical tests of neutrality of mutations against population growth, hitchhiking and background 125 selection. Genetics, 147, 915–925. 126

Goldstein DB, Roemer GW, Smith DA, Reich DE, Bergman A, Wayne RK (1999) The use of microsatellite 127 variation to infer population structure and demographic history in a natural model system. Genetics, 151, 128 797–801. 129

Hauswaldt JS, Fuessel J, Guenther J, Steinfartz S (2008) Eight new tetranucleotide microsatellite loci for the 130 agile frog (Rana dalmatina). Molecular Ecology Resources, 8, 1457–1459. 131

Hitchings SP, Beebee TJ (1997) Genetic substructuring as a result of barriers to gene flow in urban Rana 132 temporaria (common frog) populations, implications for biodiversity conservation. Heredity, 79, 117–127. 133

Kimura M, Ohta T (1978) Stepwise mutation model and distribution of allelic frequencies in a finite population. 134 Proceedings of the National Academy of Sciences, 75, 2868–2872. 135

Lesbarrères D, Pagano A, Lodé T (2003) Inbreeding and road effect zone in a Ranidae, the case of Agile frog, 136 Rana dalmatina Bonaparte, 1840. Comptes Rendus Biologies, 326, S68–S72. 137

Lesbarrères D, Schmeller DS, Primmer CR, Merilä J (2007) Genetic variability predicts common frog (Rana 138 temporaria) size at metamorphosis in the wild. Heredity, 99, 41–46. 139

Lind MI, Ingvarsson PK, Johansson H, Hall D, Johansson F (2011) Gene flow and selection on phenotypic 140 plasticity in an island system of Rana temporaria. Evolution, 65, 684–697. 141

Lodé T, Lesbarrères D (2004) Multiple paternity in Rana dalmatina, a monogamous territorial breeding anuran. 142 Naturwissenschaften, 91, 44–47. 143

Palo JU, Schmeller DS, Laurila A, Primmer CR, Kuzmin SL, Merilä J (2004) High degree of population 144 subdivision in a widespread amphibian. Molecular Ecology, 13, 2631–2644. 145

Piry S, Luikart G, Cornuet J–M (1999) BOTTLENECK, a computer program for detecting recent reductions in 146 the effective population size using allele frequency data. Journal of Heredity, 90, 502–503. 147

Reh W, Seitz A (1990) The influence of land use on the genetic structure of populations of the common frog 148 Rana temporaria. Biological Conservation, 54, 239–250. 149

Reich DE, Feldman MW, Goldstein DB (1999) Statistical properties of two tests that use multilocus data sets to 150 detect population expansions. Molecular Biology and Evolution, 16, 453–466. 151

Rogers AR, Harpending H (1992).Population growth makes waves in the distribution of pairwise genetic 152 differences. Molecular Biology and Evolution, 9, 552–569. 153

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Sarasola–Puente V, Madeira MJ, Gosá A, Lizana M, Gómez–Moliner B (2012) Population structure and genetic 154 diversity of Rana dalmatina in the Iberian Peninsula. Conservation Genetics, 13, 197–209. 155

Slatkin M. 1995. A measure of population subdivision based on microsatellite allele frequencies. Genetics, 139, 156 457–462. 157

Slatkin M, Hudson RR (1991) Pairwise comparisons of mitochondrial DNA sequences in stable and 158 exponentially growing populations. Genetics, 129, 555–562. 159

Tajima F (1989) Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics, 160 123, 585–595. 161

Veith M, Vences M, Vieites DR, Nieto–Roman S, Palanca A (2002) Genetic differentiation and population 162 structure within the Spanish common frogs (Rana temporaria complex; Ranidae, Amphibia). Folia 163 Zoologica, 51, 307–318. 164

Zeisset I, Beebee TJ (2010) Larval fitness, microsatellite diversity and MHC class II diversity in common frog 165 (Rana temporaria) populations. Heredity, 104, 423–430. 166

167

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168 Table S1. Primers and thermocycling protocols used for amplification of mitochondrial and 169 nuclear DNA fragments. 170 171 Gene Primer name and sequence (5’ – 3’) Primer

source Thermocycling protocol

COB Rana-Cytb-F2: TTAGTAATAGCCACAGCTTTTGTAGGC

Rana-Cytb-R2: AGGGAACGAAGTTTGGAGGTGTGG Newly developed

94(90), [94(30), 53(45), 72(90) x 35], 72(600)

12S 12SAL: AAACTGGGATTAGATACCCCACTAT

16SR3: TTTCATCTTTCCCTTGCGGTAC Palumbi et al.,, (1991)

94(90), [94(45), 55 (45), 72(90) x 33], 72(300)

16S 16SL3: AGCAAAGAHYWWACCTCGTACCTTTTGCAT

16SAH: ATGTTTTTGATAAACAGGCG Vences et al.,, (2003)

94(90), [94(45), 55 (45), 72(90) x 33], 72(300)

16S 16SAr-L: CGCCTGTTTATCAAAAACAT

16SBr-H: CCGGTCTGAACTCAGATCACGT Palumbi et al.,, (1991)

94(90), [94(45), 55 (45), 72(90) x 33], 72(300)

COX1 LCO_1490: GGTCAACAAATCATAAAGATATTGG

HCO_2198: TAAACTTCAGGGTGACCAAAAAATCA Folmer et al.,, (1994)

94(90), [94(30), 49 (45), 72(90) x 33], 72(600)

ND1 L3914: GCCCCATTTGACCTCACAGAAGG

H4419: GGTATGGGCCCAAAAGCTT Macey et al.,, (1998b)

94(90) [94(35), 50(35),70(152) x 30] 70(600)

ND2 / COXI

L4437: AAGCTTTCGGGCCCATACC H6564: GGGTCTCCTCCTCCAGCTGGGTC

Macey et al.,, (1997) / Macey et al.,, (1998a)

94(90) [94(35), 50(35),70(152) x 30] 70(600)

RAG1 SH_RAG1_RANA_F1:

ATAACTGGTCGTCAGATCTTTCAGCC SH_RAG1_RANA_R1: CTTGGACTGACGAGCATTCAT

Newly developed

94(120) [94(20), 58(50), 72(180) x40] 72(600)

POMC POMC_DRV_F1:

ATATGTCATGASCCAYTTYCGCTGGAA POMC_DRV_R1: GGCRTTYTTGAAWAGAGTCATTAGWGG

Vieites et al.,, (2007)

95(120) [95(60), 53(60), 72(90) x 38] 72(600)

TYR Tyr1a: AGGTCCTCTTRAGCAAGGAATG

Tyr-1g: TGCTGGCRTCTCTCCARTCCCA Bossuyt and Milinkovitch (2000)

95(120) [95(60), 53(60), 72(90) x 38] 72(600)

BDNF BDNF_F1: ACCATCCTTTTCCTKACTATGG

BDNF_R1: CTATCTTCCCCTTTTAATGGTC Vieites et al.,, (2007)

94(120), [94(20), 57(45), 72(120) x 39], 72(600)

172 173 Primer references 174 175 Bossuyt F, Milinkovitch MC (2000) Convergent adaptive radiations in Madagascan and Asian ranid frogs reveal 176

covariation between larval and adult traits. Proceedings of the National Academy of Sciences, 97, 6585–177 6590. 178

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Folmer O, Black M, Hoeh W, Lutz R, Vrijenhoek R (1994) DNA primers for amplification of mitochondrial 179 cytochrome c oxidase subunit I from diverse metazoan invertebrates. Molecular Marine Biology and 180 Biotechnology, 3, 294–299. 181

Macey JR., Larson A, Ananjeva NB, Fang Z, Papenfuss TJ (1997) Two novel gene orders and the role of light–182 strand replication in rearrangement of the vertebrate mitochondrial genome. Molecular Biology and 183 Evolution, 14, 91–104. 184

Macey JR, Schulte JA II, Ananjeva NB, Larson A, Rastegar–Pouyani N, Shammakov SM, Papenfuss TJ (1998a) 185 Phylogenetic relationships among agamid lizards of the Laudakia caucasia species group: Testing 186 hypotheses of biogeographic fragmentation and an area cladogram for the Iranian Plateau. Molecular 187 Phylogenetics and Evolution, 10, 118–131. 188

Macey JR, Schulte JA. II, Larson A, Fang Z, Wang Y, Tuniyev BS, Papenfuss TJ (1998b) Phylogenetic 189 relationships of toads in the Bufo bufo species group from the eastern escarpment of the Tibetan Plateau: A 190 case of vicariance and dispersal. Molecular Phylogenetics and Evolution, 9, 80–87. 191

Palumbi S, Martin A, Romano S, McMillan WO, Stice L, Grabowski G (1991 )The simple fool’s guide to PCR. 192 Version 2. Honululu, Hawai. 193

Vences M, Kosuch J, Glaw F, Böhme W, Veith M (2003) Molecular phylogeny of hyperoliid treefrogs: 194 biogeographic origin of Malagasy and Seychellean taxa and re–analysis of familial paraphyly. Journal of 195 Zoological Systematics and Evolutionary Research, 41, 205–215. 196

Vieites DR, Min MS, Wake DB (2007) Rapid diversification and dispersal during periods of global warming by 197 plethodontid salamanders. Proceedings of the National Academy of Sciences, 104, 19903–19907. 198

199 200 201 202 203 204 205

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206 Table S2. Primers and thermocycling protocols used for amplification of microsatellites (see 207 Materials and Methods for primer references): (A) multiplex mix, loci used, fluorescent labels 208 of forward primers and concentration (µM) of each primer per reaction. (B) PCR conditions 209 for microsatellite amplification; each amplification consisted of 35 cycles. Values in 210 parentheses are for Rana dalmatina. 211 212 A 213 214

Rana temporaria Rana dalmatina MPLX Locus Label Conc. MPLX Locus Label Conc.

BFG-151 FAM 0.90 1 Radal-H1 FAM 0.5 BFG-250 CY3 0.72 Radal-G11 HEX 0.45 BFG-237 HEX 0.44 Radal-B5 FAM 0.40 1

BFG-161 FAM 0.40 Radal-B10 HEX 0.5 BFG-090 FAM 0.60 2 Radal-F5 FAM 0.3 BFG-129 CY3 0.74 Radal-F3 FAM 0.45 2 BFG-242 HEX 0.60 Radal-G12 HEX 0.40

3 BFG-134 FAM 0.46 3

Radal-E8 FAM 0.40 215 216 B 217 218 Temp. °C Time Initial denaturation 95 5 min Denaturation 95 30 sec Annealing 55 (60) 90 sec Elongation 72 40 (30)secFinal elongation 60 30 min 219

8

Table S3. Geographical coordinates of all collecting localities of brown frogs (genus Rana), 220 total number of samples analyzed for COB and RAG1 from each locality, as well as 221 haplotypes identified in Rana dalmatina and R. temporaria (corresponding to haplotype 222 numbers in Supporting figures S1–S4). The number of individuals tested for the eight 223 microsatellite loci is also provided. N refers to the number of individuals genotypes for 224 microsatellites (MS), Cytochrome b (COB) and RAG1 analysis. 225 226 227 228 Locality Code

Locality Country Latitude Longitude N MS

N COB

COB Haplotypes

N RAG1

RAG1 Haplotypes

R. arvalis

AR_BRA Riddagshausen Germany 52.273022 10.579787 — 1 AR_CHL Chelm Poland 51.136544 23.473635 — 1 AR_DRO Drömling Germany 52.532775 10.929251 — 18 18 AR_KOP Kopanina Poland 52.5567 16.5375 — 1 AR_SOK Sokolniki Poland 52.52398 16.50156 — 3 R. dalmatina

DA_ALB Albertshofen Germany 49.7556 10.1840 14 4 H1 (8) DA_ALT Puerto de

Altube Spain 42.972349 –2.861677 5 H1 (5)

DA_ARB Piano di Arbigo Switzerland 46.173968 8.744234 4 H1 (8) DA_BCA Brughiera di Ca

del Re Italy 45.617 9.098 6 H1 (6)

DA_BOR Borgofranco d'Ivrea

Italy 45.5139259 7.8592407 1 H1 (1)

DA_BOS Bosco Penz Switzerland 45.833133 9.009891 4 1 H1 (1) 2 H1 (4) DA_BRI Parco Brughiera

Briantea Italy 45.70294 9.15659 1 H1 (1)

DA_BSR Bessières, forêt de Buzet

France 43.76 1.57 3 H1 (3)

DA_CHI Chernivtsi Ukraine 48.288 25.989 3 H2 (3) DA_CLA Ceriano

Laghetto Italy 45.639 9.092 8 H1 (7),

H24 (1)

DA_COS Cosenza Italy 39.355472 16.227108 6 9 H32 (5), H33 (4)

8 H1 (16)

DA_CPA Candia Canavese

Italy 45.335946 7.885423 3 H1 (3)

DA_DEL Đelekovec Croatia 46.233524 16.850279 11 H1 (7), H10 (1), H11 (1), H12 (1), H13 (1)

DA_DID Didymoteicho Greece 41.353181 26.4897912 1 H1 (1) DA_DKF Svendborg Denmark 55.107097 10.662546 10 H1 (10) DA_DKS Køge Denmark 55.439428 12.148762 6 H1 (6) DA_DKT Thurø

Østerskov Denmark 55.047197 10.6917 8 H1 (8)

DA_DRH Drover Heide Germany 50.742063 6.514585 1 H1 (1) DA_END Lago d'Endine Italy 45.780901 9.939694 15 H1 (15) 4 H1 (8) DA_ERE Erenli Turkey 40.9272 37.9523 2 H1 (1),

H14 (1) 2 H1 (4)

DA_ESR Eschenrode Germany 52.2904 11.1320 8 3 H1 (3) 3 H1 (6) DA_ESY Espiye Turkey 40.969 38.762 1 H1 (1) DA_FOG Nettuno, Rome Italy 41.467624 12.720434 9 9 H1 (1),

H21 (1), H22 (1), H29 (2),

8 H1 (16)

9

H30 (4) DA_FON Fornace, Arzo Switzerland 45.881014 8.95041 10 3 H1 (6) DA_FRU Fruška Gora Serbia 45.178617 19.695511 2 H1 (2) 3 H1 (6) DA_GRM Grmošćica Croatia 45.8156 15.9192 16 H1 (16) DA_IVR Ivrea-Morena Italy 45.386258 7.840712 11 H1 (9), H3

(2) 7 H1 (13), H4 (1)

DA_KAB Kabadüz Turkey 40.9085 37.8797 1 H1 (2) DA_KAM Kam'iana Ukraine 48.222 25.818 5 H2 (5) DA_KOR Kopački Rit Croatia 45.591515 18.853369 9 H1 (8), H9

(1) 5 H1 (10)

DA_KOT Kottenforst Germany 50.669687 7.030449 8 11 H1 (11) DA_KOU Koufovouno Greece 41.352818 26.4534742 1 H1 (1) 1 H1 (2) DA_KUC Malyi Kuchuriv Ukraine 48.453 25.934 5 H2 (5) DA_LAB South of

Roviano Italy 42.014381 13.000019 4 H28 (4)

DA_LAR West of Roviano

Italy 42.021131 12.980583 8 H28 (8)

DA_LAT Tagliente Primo Italy 41.765859 12.794026 15 15 H1 (2), H20 (4), H21 (8), H31 (1)

3 H1 (6)

DA_LEL Lelm Germany 52.2135 10.8313 20 17 H1 (17) 6 H1 (12) DA_LUM Lumbin France 45.2958 5.9274 5 1 H15 (1) 5 H1 (10) DA_MAC Chiaverano Italy 45.4911 7.9122 19 21 H1 (17),

H4 (2), H8 (1), H25 (1)

12 H1 (24)

DA_MAL Malinska (Čepić)

Croatia 45.122247 14.538124 13 19 H1 (13), H23 (6)

20 H1 (39), H3 (1)

DA_MAM Morna river dam, Pierias

Greece 40.1968 22.2122 1 H1 (1)

DA_MAS Mascherode Germany 52.2313 10.5713 9 7 H1 (7) 7 H1 (14) DA_MAV Vermio Mt. Greece 40.6747 21.9691 1 H26 (1) DA_MIC Monte San

Michele –Greve in Chianti

Italy 43.554702 11.388159 13 H1 (13)

DA_MOT Motovunska Croatia 45.543543 18.717869 11 12 H1 (11), H23 (1)

9 H1 (18)

DA_MUR Dekanovec, Muršcak

Croatia 46.46162 16.61340 2 H1 (2)

DA_NEO Neo Chimonio Greece 41.4612026 26.5648471 1 H4 (1) 1 H1 (2) DA_NES Nestos River Greece 40.9433487 24.7513342 1 H1 (1) DA_NEU Neusiedler See Austria 47.854853 16.757469 3 H1 (3) DA_OLY Olympos Mt. Greece 40.1373911 22.3045421 2 H26 (2) 2 H1 (4) DA_OSS Ossa Mt. Greece 39.7545809 22.6799923 1 H27 (1) 1 H1 (2) DA_OVA Çarşamba Ovası Turkey 41.273678 36.829233 6 3 H1 (3) 5 H1 (9), H2 (1) DA_PER Pertouli Greece 39.5659035 21.5082854 1 H26 (1) 1 H1 (2) DA_PIA Piazzogna Switzerland 46.136147 8.820555 5 2 H1 (4) DA_POI Poirino Italy 44.915035 7.789965 1 H16 (1) 1 H1 (2) DA_QUA Quattroventi Italy 46.160303 13.140793 13 H1 (12),

H7 (1)

DA_RAE Räblke Germany 52.1823 10.8649 10 14 H1 (14) 3 H1 (6) DA_SIL Sillersdorf Germany 47.852189 12.922139 5 5 H5 (3), H6

(2) 4 H1 (8)

DA_SNE La Sneira, Palude della Gaia

Italy 45.4969 7.8978 7 8 H1 (6), H3 (1), H25 (1)

10 H1 (20)

DA_SOU Soudan at Chausseroy

France 46.425349 -0.110661 22 H1 (22)

DA_STA Starnberg Germany 48.006922 11.34407 1 H1 (1) 1 H1 (2) DA_SWB Blekinge Sweden 56.190036 15.662184 24 2 H1 (2) DA_THI Ehrhorn Germany 53.194667 9.886122 9 9 H1 (9) 4 H1 (8)

10

DA_UND Undeloh Germany 53.201193 9.973469 5 8 H1 (8) 5 H1 (10) DA_VER Križovec Croatia 46.4976 16.4892 5 H1 (5) DA_VIL Heider Bergsee Germany 50.833186 6.824426 9 13 H1 (13) 5 H1 (10) DA_WIL Wilsede Germany 53.164571 9.960423 4 6 H1 (6) 3 H1 (6) DA_ZAB Žabnik Croatia 46.528485 16.372962 1 H1 (1) R. iberica

IB_ARF La Alberca Spain 40.51455 -6.080913 11 IB_BAU Monasterio de

Batuecas Spain 40.462139 -6.148948 1

IB_CAV Calvelo Spain 42.208651 -7.62712 18 10 IB_MAN Manzaneda Spain 42.253889 -7.298689 8 IB_PTR Pena Trevinca Spain 42.218601 -6.867247 1 IB_SEO Seoane Spain 42.641214 -7.150855 6 R. italica

IT_LAS Palombara

Sabina Italy 42.094032 12.74149 5 10

R. latastei

LA_CAS Bosco del Castagnolo

Italy 45.269 8.971 6 8

LA_GIA Bosco Bel Giardino

Italy 45.334 9.487 11 7

LA_MAC Chiaverano Italy 45.4911 7.9122 4 R. pyrenaica

PY_BUJ Bujaruelo Spain 42.678697 -0.123316 4 3 PY_ZUR Zuriza Spain 42.899454 -0.795938 3 R. temporaria

TE_ADU Aduy River Russia 57.3167 61 2 H26 (2) 2 H8 (1), H9 (3) TE_AHR Ahrensburg Germany 53.7328 10.4511 9 H26 (7),

H30 (2)

TE_AIG Aigualluts Spain 42.663097 0.668886 1 H3 (1) TE_ALB Albertshofen Germany 49.7556 10.1840 6 H19 (6) TE_ALL Allgäu Germany 1 H19 (1) 3 H4 (1), H11 (1),

H18 (4) TE_ALT Puerto de

Altube Spain 42.972349 -2.861677 1 H5 (1), H14 (1)

TE_ANA Anayet Spain 42.778214 -0.413632 6 H2 (6) 7 H1 (10), H3 (1), H7 (1), H35 (2)

TE_ANC Ancares Spain 42.848219 -6.877509 4 H35 (2), H46 (2)

TE_AQU Kauleteich Germany 52.255451 10.595859 2 H19 (2) 5 H4 (3), H5 (6), H16 (1)

TE_ARA Aranga (Montesalgueir

Spain 43.20364 -8.050282 2 H41 (2)

TE_ART Artouste-Arremoulit

France 42.849964 -0.333799 4 H1 (1), H2 (3)

4 H1 (2), H19 (1), H24 (1), H35 (2), H41 (2)

TE_BAG Bagrationovsk Russia 54.3667 20.6167 1 H26 (1) 2 H7 (2), H8 (1), H9 (1)

TE_BAL Ballynahinch Ireland 53.4884 -9.88354 3 H22 (3) 3 H6 (6) TE_BAR Ibon Chico de

Barbarisa Spain 42.608892 0.406751 1 H34 (1)

TE_BAT Ibonet de Batisielles

Spain 42.650115 0.515889 11 H3 (6), H7 (3), H34 (2)

10 H1 (14), H21 (1), H35 (5)

TE_BAY Bayreuth Germany 49.921771 11.536989 5 H26 (5)

11

TE_BEL Bel'sky Russia 57 63.8333 3 H26 (3) 2 H8 (3), H9 (1) TE_BIE Biedacz Poland 50.02362 20.76024 1 H26 (1) TE_BIL Ibon Baixo de

Billabierma Spain 42.623962 0.617879 4 H3 (4) 4 H15 (2), H26

(2), H31 (4) TE_BIY Lac Antigus-

Biyous France 42.865453 -0.455481 1 H2 (1) 1 H1 (1), H35 (1)

TE_BOB Bobbio Pellice Italy 44.81049 7.117581 12 11 H1 (8), H15 (2), H17 (1)

TE_BOU Lac de Bouillouses

France 42.561098 1.993363 1 H5 (1), H31 (1)

TE_BRE Brennhof Germany 50.1205 10.2317 20 20 H19 (8), H26 (12)

10 H4 (6), H9 (3), H11 (2), H12 (4), H14 (2), H16 (1), H34 (2)

TE_BRZ Ibons del Brazato Alto Panticosa

Spain 42.755194 -0.211196 10 H1 (9), H5 (1)

10 H1 (19), H44 (1)

TE_BUR Lagos de Burbia Spain 42.813466 -6.846235 2 H46 (2) TE_CAF Canfranc Spain 42.715573 -0.525099 1 H1 (1), H3 (1) TE_CAL Ibon de

l'Escaleta Spain 42.606503 0.647492 17 18 H1 (12),

H3 (5), H34 (1)

4 H1 (2), H2 (1), H5 (4), H8 (1)

TE_CAN Mont Canigou France 42.505578 2.455227 6 H19 (6) TE_CAR Carregosa Spain 43.113692 -8.056844 9 8 H41 (6),

H40 (1), H45 (1)

6 H5 (3), H28 (1), H29 (5), H30 (3)

TE_CBA Carballo Spain 43.243082 -8.728821 1 H37 (1) TE_CED Cedeira Spain 43.699403 -8.004144 3 H46 (2),

H40 (1)

TE_CER Cerca de Cerren Ireland 53.03082 -9.05992 4 H19 (1), H22 (3)

4 H4 (1), H6 (5), H33 (1), H36 (1)

TE_COI Coirós Spain 43.238729 -8.150793 2 H41 (1), H44 (1)

TE_COL Colomers Spain 42.621234 0.923091 5 H1 (1), H3 (4)

4 H1 (3), H5 (4), H41 (1)

TE_CON Connemara Ireland 53.42967 -10.04284 4 H22 (4) 4 H6 (5), H12 (3) TE_COR Coristanco Spain 43.112283 -8.723168 4 H42 (3),

H38 (1)

TE_CUR Curtis Spain 43.134639 -8.135697 4 H41 (4) TE_DIT Dittelbrunn Germany 50.0822 10.2260 10 9 H19 (7),

H24 (1), H26 (1)

9 H4 (8), H8 (1), H9 (2), H11 (2), H12 (4), H45 (1)

TE_DUN Gap of Dundee Ireland 52.02828 -9.63557 3 H19 (1), H22 (2)

3 H6 (4), H12 (2)

TE_ESC Ibones de Escarpinosa

Spain 42.645718 0.504381 6 H3 (4), H7 (2)

TE_ESP Espelette France 43.35133 -1.441791 7 H1 (1), H2 (6)

5 H1 (4), H5 (2), H7 (1), H8 (1), H10 (1), H19 (1)

TE_ETO Refugio de Estos

Spain 42.664865 0.511633 5 4 H3 (1), H34 (3)

TE_FDE Fuente De Spain 43.166544 -4.772499 2 H32 (1), H33 (1)

2 H5 (1), H7 (1), H27 (1), H30 (1)

TE_FLE Flensburg Germany 54.744885 9.35586 17 H26 (15), H27 (1), H28 (1)

TE_FOQ Prats-de-Mollo-la-Preste

France 42.437694 2.392911 1 H26 (1), H31 (1)

TE_FOR Formigal Spain 42.782058 -0.385028 2 H2 (2) 2 H1 (3), H41 (1)

12

TE_GAB Gabas France 42.889297 -0.426832 7 H2 (7) 7 H1 (7), H19 (1), H35 (3), H41 (3)

TE_GAV Forêt de Gâvre France 47.520879 -1.801695 19 20 H21 (1), H19 (1), H22 (16), H24 (2)

TE_GLA Glanteenassig Ireland 52.22418 -10.02245 20 10 H19 (8), H22 (2)

8 H6 (13), H12 (3)

TE_GOR Ibon Ladera Gorgutes

Spain 42.697133 0.60218 2 H1 (1), H3 (1)

2 H41 (4)

TE_GRM Grmošćica Croatia 45.8156 15.9192 1 H4 (1) 1 H5 (2) TE_HAR Hartmannshofer

Bach Germany 48.163094 11.459577 4 H19 (4)

TE_HEL Helgoland Germany 54.183357 7.887139 5 H26 (3), H30 (2)

TE_INO Cariño Spain 43.697271 -7.980899 4 H41 (1), H46 (2), H39 (1)

TE_IRA Iraty France 43.03902 -1.071867 7 H1 (4), H2 (3)

7 H1 (1), H3 (1), H5 (2), H19 (1), H20 (1), H31 (2), H35 (4), H41 (2)

TE_JYK Jyväskylä Finland 62.209709 25.742816 3 2 H26 (2) TE_KAR Kartezh Station Russia 66.3333 33.6667 1 H26 (1) TE_KOP Kopanina Poland 52.5567 16.5375 2 H26 (2) TE_KRA Krasnyi kamen' Russia 66.9333 65.6667 3 H26 (3) 3 H8 (1), H9 (5) TE_KUC Malyi Kuchuriv Ukraine 48.459 25.923 3 H26 (3) TE_LAG Monti della

Laga Italy 42.70345 13.32125 14 10 H16 (10) 5 H6 (1), H13 (3),

H37 (5), H38 (1)

TE_LAP Lapitzurri France 43.30758 -1.491366 6 H1 (3), H2 (3)

6 H1 (2), H7 (2), H32 (6), H35 (1), H46 (1)

TE_LIA Estany de Liat Spain 42.803659 0.876077 5 H3 (5) 5 H1 (4), H2 (4), H5 (2)

TE_LOK Lokve- Mrzla Vodica

Croatia 45.3700 14.6592 9 10 H11 (9), H12 (1)

3 H8 (4), H19 (2)

TE_LOP Lopushna Ukraine 48.0667 25.2833 1 H26 (1) TE_MAI Maibach Germany 50.1756 10.3089 11 9 H19 (2),

H26 (7)

TE_MAK Dramas, Stravorema

Greece 41.4977 24.3441 2 H13 (1), H14 (1)

TE_MAN Manzaneda-Queixa

Spain 42.253889 -7.298689 9 H40 (8), H47 (1)

TE_MAS Mascherode Germany 52.2313 10.5713 13 13 H19 (13) 12 H4 (13), H5 (2), H8 (1), H9 (4), H11 (3), H39 (1)

TE_MAZ Mazaricos Spain 42.921729 -9.007119 4 H42 (2), H41 (1), H43 (1)

TE_MIS Miskoltc Hungary 48.1 20.6167 1 H26 (1) 1 H14 (1), H25 (1)

TE_MOK Mokrsko nad Nidą

Poland 50.683122 20.4443119 1 H31 (1)

TE_NIU Estany Malniu Spain 42.472507 1.791155 4 H1 (3), H8 (1)

5 H1 (1), H5 (4), H10 (2), H31 (3)

TE_ODR Oza dos Ríos Spain 43.157223 -8.12394 1 H40 (1) TE_ORD Barranco

Ordiso Spain 42.678697 -0.123316 2 H1 (2) 4 H1 (4), H7 (1),

H19 (2), H31 (1)

TE_ORO Oropa Italy 45.633838 7.97945 2 H1 (2)

13

TE_OZA Oza Spain 42.834932 -0.636509 11 H1 (2), H2 (9)

11 H1 (7), H7 (5), H19 (3), H20 (1), H26 (3), H35 (1), H41 (1), H43 (1)

TE_PAD Paderne Spain 43.264389 -8.162512 2 H44 (2) TE_PAM Col del Pam France 42.532512 2.043004 3 H1 (3) 3 H1 (3), H26 (2),

H31 (1) TE_PAN Panticosa Spain 42.758471 -0.231569 19 H1 (18),

H5 (1)

TE_PFA Pfändhausen Germany 50.2611 10.3669 15 10 H19 (4), H26 (6)

TE_PIF Ibon de Piedrafita

Spain 42.694419 -0.356611 4 H2 (4) 9 H1 (12), H7 (3), H23 (2), H41 (1)

TE_PII Kesamonsaari Finland 61.793973 28.77099 7 7 H26 (7) 5 H8 (2), H16 (7), H17 (1)

TE_PLL Ibones Pllan d´Estan

Spain 42.682882 0.638112 4 H3 (4) 4 H1 (6), H41 (2)

TE_POR Portalet Spain 42.806786 -0.418205 7 H2 (7) 4 H1 (5), H7 (3) TE_PRA Etang Pradeilles France 42.544044 2.012439 3 H1 (1), H3

(1), H8 (1) 6 H1 (1), H5 (3),

H10 (6), H26 (1), H31 (1)

TE_PRI Pribytkovo Russia 59.45 30.0833 2 H26 (2) 2 H8 (2), H16 (2) TE_RAT Port de Rat France 42.628777 1.46549 2 H1 (2) 2 H1 (2), H5 (2) TE_REM Valle de

Remuñé Spain 42.686277 0.578234 13 H3 (13)

TE_RES Respomuso Spain 42.814706 -0.281872 19 46 H1 (3), H2 (42), H18 (1)

6 H1 (7), H7 (4), H19 (1)

TE_RIG Riga Latvia 57.0167 24.15 1 H26 (1) 1 H9 (1), H16 (1) TE_ROM Font Romeu France 42.509838 2.03647 1 H1 (1) 1 H10 (1), H31

(1) TE_ROY Canal Roya-

Fondo Spain 42.787 -0.447953 7 H1 (1), H2

(5), H18 (1) 8 H1 (10), H3 (1),

H7 (1), H19 (1), H35 (2), H44 (1)

TE_SAL Espina near Salas

Spain 43.39612 -6.339604 3 H35 (1), H36 (2)

3 H5 (2), H28 (1), H29 (1), H30 (2)

TE_SCH Schwebheim Germany 50.0158 10.2594 4 H26 (4) TE_SCO Santa Comba Spain 43.015821 -8.949452 2 H42 (2) TE_SEE Seealpsee Switzerland 47.268323 9.400384 4 H19 (4) 3 H18 (3), H11

(3) TE_SHY Shypyntsi Ukraine 48.373 25.730 9 8 H26 (7),

H29 (1) 7 H8 (1), H14

(10), H16 (2), H40 (1)

TE_SLA Lough Slat Ireland 52.21308 -10.02804 7 H19 (4), H22 (3), H23 (1)

5 H6 (5), H12 (4), H19 (1)

TE_SOE Schöningen Germany 52.127968 10.966966 5 H19 (3), H25 (2)

5 H4 (8), H9 (1), H45 (1)

TE_SOK Sokolniki Poland 52.52398 16.50156 22 16 H26 (14), H31 (2)

TE_SOM Puerto de Somiedo

Spain 43.014158 -6.228834 2 H35 (2) 2 H28 (1), H29 (1), H30 (2)

TE_SOU Commune of Soudan at Chausseroy

France 46.425349 -0.110661 5 H1 (3), H9 (1), H10 (1)

TE_SVI Svityaz'ke Lake Ukraine 51.4833 23.8333 1 H26 (1) 2 H8 (1), H16 (2), H45 (1)

TE_TAL La Preste/ Val du Tal

France 42.407902 2.409152 1 H6 (1) 1 H10 (1), H31 (1)

TE_TER Terzenbrunn Germany 50.151496 10.089054 1 H19 (1) TE_THE Etang Thévenon France 46.897196 5.504959 6 H20 (6) 5 H4 (4), H8 (2),

14

H11 (2), H19 (1), H42 (1)

TE_TRE Banhs de Tredos

Spain 42.656212 0.928904 5 H1 (1), H3 (4)

5 H1 (4), H5 (4), H35 (1), H47 (1)

TE_TRV Trevinca-Segundeira

Spain 42.173712 -6.79283 5 H40 (5)

TE_TUE Aguas Tuertas Spain 42.826415 -0.624801 7 H2 (7) 7 H1 (8), H5 (1), H7 (1), H26 (1), H31 (1), H35 (2)

TE_TUM Tumannyi Russia 68.8667 35.9833 3 H26 (3) 2 H16 (4) TE_TUR Cabaña de

Turmo Spain 42.665466 0.511583 5 2 H3 (2)

TE_VAL Valaam Island Russia 61.35 30.9333 3 H26 (3) 1 H8 (2) TE_VIG Tercis-les-Bains France 43.6792 -1.115703 16 15 H1 (7), H3

(8)

TE_VOG Markneukirchen Germany 50.311788 12.327676 2 H26 (2) TE_WAS Wasserauen Switzerland 47.284658 9.427249 2 H19 (2) 1 H18 (2) TE_XX1 Elm Germany 52.1525 10.9098 1 H22 (1) TE_ZEL Zell Germany 50.0974038 10.2327347 4 H19 (3),

H26 (1)

TE_ZUG Zhudryo Spain 43.270784 -1.541351 14 H1 (6), H2 (8)

15 H1 (9), H7 (16), H19 (1), H32 (2), H35 (1), H41 (1)

229 230 231 232 233 234

15

235 Table S4. Genetic diversity in a fragment of the mitochondrial COB gene in populations of 236 Rana dalmatina, R. temporaria, and other European brown frogs. The column "Refugial area" 237 denotes whether a population occurs in a region flagged as containing possible glacial refugia 238 or sanctuaries by the paleomodelling analysis for the respective species (M denotes montane 239 regions within refugial areas). 240 241 Population Country Refugial

area N N

HaplotypesVariable

sites Haplotype diversity

Nucleotide diversity

Rana dalmatina

DA_DEL Croatia Yes 11 5 4 0.618 0.00184 DA_GRM Croatia Yes 16 1 0 0 0 DA_KOR Croatia Yes 9 2 1 0.222 0.00056 DA_MAL Croatia Yes 19 2 1 0.456 0.00115 DA_MOT Croatia Yes 12 2 1 0.167 0.00042 DA_DKF Denmark No 10 1 0 0 0 DA_SOU France No 22 1 0 0 0 DA_KOT Germany No 11 1 0 0 0 DA_LEL Germany No 17 1 0 0 0 DA_MAS Germany No 7 1 0 0 0 DA_RAE Germany No 14 1 0 0 0 DA_VIL Germany No 13 1 0 0 0 DA_COS Italy Yes 9 2 5 0.556 0.00701 DA_END Italy Yes 15 1 0 0 0 DA_FOG Italy Yes 9 5 8 0.806 0.00898 DA_IVR Italy Yes 11 2 1 0.327 0.00083 DA_LAT Italy Yes 15 4 9 0.667 0.00548 DA_MAC Italy Yes 21 4 4 0.348 0.00118 DA_MIC Italy Yes 13 1 0 0 0 DA_QUA Italy Yes 13 2 1 0.154 0.00039 DA_SNE Italy Yes 8 3 3 0.464 0.00189 mean 13.10 2.05 1.81 0.22786 0.00142 SD 4.16 1.36 2.71 0.2690 0.0025 Rana temporaria

TE_LOK Croatia Yes 10 2 1 0.2 0.0006 TE_GAB France M 7 1 0 0 0 TE_GAV France No 20 4 4 0.363 0.00219 TE_IRA France Yes 7 2 1 0.571 0.00173 TE_VIG France No 15 2 1 0.533 0.00161 TE_AHR Germany No 9 2 1 0.389 0.00117 TE_BRE Germany No 20 2 8 0.505 0.01221 TE_DIT Germany No 9 3 8 0.417 0.00587 TE_FLE Germany No 17 3 2 0.228 0.00071 TE_MAS Germany No 13 1 0 0 0 TE_PFA Germany No 10 2 8 0.533 0.01289 TE_GLA Ireland Yes 10 2 1 0.356 0.00107 TE_BOB Italy Yes 11 3 5 0.473 0.00593 TE_LAG Italy Yes 10 1 0 0 0 TE_SOK Poland No 16 2 2 0.233 0.00141 TE_ANA Spain M 6 1 0 0 0 TE_BAT Spain M 11 3 16 0.655 0.01681 TE_BRZ Spain M 10 2 1 0.2 0.0006 TE_CAL Spain M 18 3 15 0.503 0.00598 TE_CAR Spain Yes 8 3 2 0.464 0.00151 TE_OZA Spain M 11 2 1 0.327 0.00099 TE_PAN Spain M 19 2 1 0.105 0.00032 TE_REM Spain M 13 1 0 0 0 TE_RES Spain M 46 3 2 0.165 0.00051

16

TE_ZUG Spain Yes 14 2 1 0.527 0.00159 TE_SHY Ukraine No 8 2 1 0.25 0.00076 mean 13.38 2.15 3.15 0.3076 0.0029 SD 7.83 0.78 4.39 0.2065 0.0045 Rana arvalis

AR_DRO Germany 18 5 5 0.484 0.00124 Rana iberica

IB_CAV Spain 17 6 9 0.515 0.00296 IB_ARF Spain 11 2 1 0.182 0.00046 IB_MAN Spain 8 2 5 0.429 0.00542 mean 12 3.33 5 0.3753 0.00295 Rana italica

IT_LAS Italy 4 1 0 0 0 Rana latastei

LA_GIA Italy 11 2 2 0.182 0.00092 LA_CAS Italy 6 1 0 0 0 mean 8.5 1.5 1 0.091 0.0005 Rana pyrenaica

PY_BUJ Spain 4 1 0 0 0 242 243 244

17

Table S5. Genetic diversity in a fragment of the nuclear RAG1 gene in populations of Rana 245 dalmatina, R. temporaria, and other European brown frogs. The column "Refugial area" 246 denotes whether a population occurs in a region flagged as containing possible glacial refugia 247 or sanctuaries by the paleomodelling analysis for the respective species (M denotes montane 248 regions within refugial areas). 249 250 Population Country Refugial

area N N

HaplotypesN

variable sites

Heterozygosity N heterozygous

sites

Haplotypediversity


Rana dalmatina

DA_MAL Croatia Yes 40 2 1 0.05 1 0.05 0.0001 DA_MOT Croatia Yes 18 1 0 0 0 0 0 DA_LEL Germany No 12 1 0 0 0 0 0 DA_MAS Germany No 40 1 0 0 0 0 0 DA_COS Italy Yes 16 1 0 0 0 0 DA_IVR Italy Yes 14 2 2 0.14 2 0.143 0.00059 DA_MAC Italy Yes 24 1 0 0 0 0 0 DA_FOG Italy Yes 16 1 0 0 0 0 0 DA_SNE Italy Yes 20 1 0 0 0 0 0 mean 22.22 1.22 0.333 0.021 0.375 0.0214 0.0001 SD 10.65 0.44 0.71 0.05 0.74 0.05 0.0002 Rana temporaria

TE_GAB France Yes 14 4 8 0.86 8 0.679 0.00589 TE_IRA France Yes 14 8 9 0.86 9 0.901 0.00681 TE_MAS Germany No 24 6 6 0.5 5 0.681 0.00517 TE_DIT Germany No 18 6 8 0.55 7 0.765 0.00643 TE_BRE Germany No 20 7 9 0.7 8 0.858 0.00668 TE_GLA Irland No 16 2 5 0.12 5 0.325 0.00322 TE_LAG Italy M 10 4 5 0.8 4 0.711 0.00295 TE_BRZ Spain M 20 2 5 0.1 5 0.1 0.00099 TE_BAT Spain M 20 3 6 0.6 6 0.468 0.00478 TE_ANA Spain M 14 4 8 0.57 8 0.495 0.00409 TE_PIF Spain M 18 4 4 0.66 4 0.542 0.00312 TE_RES Spain M 12 3 4 0.66 4 0.591 0.00339 TE_ZUG Spain Yes 30 6 10 0.66 10 0.639 0.00418 TE_CAR Spain Yes 12 4 5 1 5 0.758 0.00435 TE_OZA Spain M 22 8 10 0.91 9 0.84 0.0055 TE_SHY Ukraine No 14 4 3 0.28 2 0.495 0.00133 mean 17.38 4.69 6.56 0.61 6.19 0.62 0.0043 SD 5.20 1.92 2.28 0.26 2.29 0.21 0.0018 Rana arvalis

AR_DRO Germany 36 1 0 0 0 0 0 Rana latastei

LA_GIA Italy 14 2 1 0.142 1 0.143 0.00026 LA_CAS Italy 14 1 0 0 0 0 0 mean 14 1.5 0.5 0.07 0.5 0.07 0.0001 Rana iberica

IB_CAV Spain 20 1 0 0 0 0 0 IB_ARF Spain 14 2 2 0.571 2 0.44 0.00156 IB_MAN Spain

18

mean 17 1.5 1 0.286 1 0.22 0.0008 Rana italica

IT_LAS Italy 20 1 0 0 0 0 0 Rana pyrenaica

PY_BUJ Spain 8 2 1 0.5 1 0.571 0.00103 251 252

19

253 Table S6. Genetic diversity in fragments of three nuclear genes (TYR, POMC, BDNF) in 254 populations of Rana dalmatina and R. temporaria. 255 256 257 Population N N

Haplotypes N variable

sites Heterozygosity N heterozygous

sites Haplotype diversity


Tyrosinase

Rana dalmatina

DA_RAE 16 1 0 0 0 0 0 DA_LEL 18 3 2 0.22 2 0.216 0.00046 DA_ALT 8 1 0 0 0 0 0 DA_KOR 10 2 2 0.1 2 0.2 0.00083 DA_LAT 14 2 2 0.071 2 0.143 0.00059 DA_MAS 4 1 0 0 0 0 0 Rana temporaria

TE_CAR 8 3 2 0.5 2 0.75 0.00222 TE_SOK 18 5 5 0.667 5 0.824 0.00449 TE_MAS 24 8 16 0.417 11 0.562 0.00608 POMC Rana dalmatina

DA_RAE 26 2 1 0.077 1 0.077 0.00021 DA_LEL 24 1 0 0 0 0 0 DA_ALT 8 1 0 0 0 0 0 DA_KOR 10 2 1 0.25 1 0.556 0.00149 DA_LAT 18 2 1 0.111 1 0.111 0.0003 DA_MAS 10 1 0 0 0 0 0 Rana temporaria

TE_CAR 10 3 7 0.4 6 0.6 0.00914 TE_SOK 20 4 4 0.3 4 0.616 0.00216 TE_MAS 24 4 8 0.5 7 0.525 0.00372 BDNF Rana dalmatina

DA_RAE 14 1 0 0 0 0 0 DA_LEL 12 1 0 0 0 0 0 DA_KOR 12 1 0 0 0 0 0 DA_LAT 18 1 0 0 0 0 0 DA_MAS 8 1 0 0 0 0 0 Rana temporaria

TE_CAR 8 3 2 0.25 1 0.679 0.00158 TE_SOK 18 3 2 0.333 2 0.307 0.00097 TE_MAS 20 4 3 0.7 3 0.595 0.00131

258

20

259 Table S7. Overall number of alleles observed in each of the 8 microsatellite loci studied for 260 Rana temporaria and R. dalmatina. 261 262 263

Rana temporaria (155 Individuals) Rana dalmatina (239 Individuals) Locus Number of alleles Locus Number of alleles BFG-237 20 F5 16 BFG-161 16 F3 30 BFG-250 22 H1 16 BFG-151 43 F11 15 BFG-090 30 B5X 27 BFG-242 32 B10* 34 BFG-129 25 G12 21 BFG-134* 30 E8 26 264 *= Dinucleotide locus, all others are tetranucleotide loci. 265 266

21

Table S8. Results of mismatch distribution analysis and Tajima's D and Fu's Fs for the complete species, cluster, and population datasets of COB sequences in Rana dalmatina and R. temporaria. Populations presenting tests indicative of population expansions are marked in red and the corresponding significant results are also highlighted in red. Populations and clusters with only a single haplotype are not shown. See captions to Tables A.11 and A.14 for definitions of clusters. tau theta0 theta1 sample size haplotypes TajD pvalue Fs pvalue SSD pvalue Raggdness pvalue Estimated LB UP Estimated LB UP Estimated LB UP R. dalmatina 193 17 -1.379 0.004 -0.93881 0.14 0.23335098 0 0.22024309 0.985 0 0 0.449 0 0 0.021 99999 99999 99999 (COS excluded) 184 15 -1.87004 0 -8.87444 0 0.01934 0.33 0.2626 0.47 2.75 0 4.475 0.33 0 0.021 0.606 0 99999 cluster 1 47 7 -1.48709 0.008 -3.35744 0.004 0.2409 0 0.212 0.986 0 0 0.502 0 0 0.005 99999 99999 99999 cluster 2 24 7 0.77783 0.225 1.31828 0.443 0.04397905 0.231 0.07346146 0.364 8.43 0 97.43 0.002 0 5.089 6.173 3.25 99999 cluster 6 42 2 2.04024 0.824 2.12776 0.64 0.29223179 0.104 0.67380527 0.002 3.07 0 88.07 0.9 0 6.126 3.6 1.598 99999 DA_COS 9 2 2.15996 0.431 4.82931 0.925 0.34849346 0.072 0.81481481 0.011 6.186 0 95.186 0.002 0 7.566 4.658 0.814 99999 DA_FOG 9 5 1.71478 0.295 0.71956 0.372 0.07796608 0.19 0.1751543 0.32 8.375 0 97.375 0 0 8.387 7.771 2.451 99999 DA_LAT 15 4 -0.62742 0.031 1.70025 0.62 0.57396793 0 0.19020408 1 0 0 0.555 0 0 0.002 99999 99999 99999 DA_MAC 21 4 -1.40948 0.007 -1.25725 0.011 0.01116773 0.4992 0.2612 0.508 3 0.41 4.312 0 0 0.005 0.545 0 99999 DA_MAL 19 2 4.03292 0.921 2.65038 0.762 0.17063 0.152 0.7119 0.079 2.844 0 24.844 0 0 1.464 1.262 0.061 99999 DA_MOT 12 2 0.01521 0.576 0.43183 0.212 0.037 0.074 0.75 0.674 3 0.541 3 0 0 0 0.133 0 99999 DA_SIL 5 2 5.10593 0.77 2.42855 0.773 0.3025 0.071 0.88 0.093 3.811 0 92.811 0.004 0 8.814 4.573 0.411 99999 DA_SNE 8 3 -1.44751 0.002 -0.30459 0.06 0.01038545 0.676 0.13647 0.864 2.213 0 4.107 0 0 0.032 0.954 0 99999 R. temporaria 235 20 -0.43763 0.057 0.83332 0.289 0.0207788 0.381 0.037 0.318 8.248 1.326 14.535 0 0 2.456 7.363 3.309 99999 cluster 1 18 2 -0.68482 0.085 1.01308 0.364 0.0557 0.034 0.7129 0.551 3 0.365 3.5 0 0 0 0.195 0 99999 cluster 2 24 3 0.44417 0.166 7.80767 0.964 0.07357 0.027 0.1682 0.129 0.809 0.25 1.635 0 0 0.128 99999 0.1786 99999 cluster 3 38 8 1.55253 0.421 2.65055 0.569 0.0727 0.024 0.155 0.015 8.391 0.621 13.582 0.002 0 3.749 9.622 6.086 99999 cluster 4 48 3 3.43221 0.835 9.61227 0.941 0.5115 0 0.655 0.91 0 0 0.531 0 0 0.007 99999 99999 99999 cluster 5 10 2 2.59036 0.792 1.52347 0.559 0.1267 0.081 0.6681 0.306 3.156 0 45.781 0 0 0.334 0.635 0 99999 cluster 6 46 3 -0.9744 0.076 -1.3073 0.041 0.00055083 0.341 0.5164 0.687 3 0.449 3 0 0 0.007 0.191 0 99999 cluster 7 15 2 1.50272 0.734 1.31784 0.47 0.03031818 0.118 0.28888 0.1 0.828 0 43.451 0 0 0.014 99999 0.902 99999 cluster 10 11 3 0.57582 0.246 2.10659 0.674 0.3305783 0 0.4816 0.92 0 0 0.582 0 0 0.002 99999 99999 99999 cluster 11 8 3 -1.31009 0.004 -0.99899 0.004 0.01357 0.593 0.167 0.776 0.627 0 1.717 0 0 0.018 99999 4.342 99999 SOK+PII+SHY +JYK

112 11 -1.37585 0.009 -0.87229 0.068 0.0145 0.176 0.6065 0.65 3 0.438 3.5 0 0 0.217 0.173 0 99999

RES+GAV+VIG +GLA+LAG+BOB

33 3 -0.13409 0.14 -0.29289 0.201 0.01055 0.634 0.0349 0.706 4.268 0.6 9.1 0 0 1.037 4.187 1.701 99999

TE_BOB 11 3 0.57582 0.228 2.10659 0.692 0.33 0 0.4816 0.94 0 0 0.41 0 0 0.002 99999 99999 99999 TE_BRE 20 2 2.67114 0.635 9.1579 0.983 0.51 0 0.755 0.922 0 0 0.512 0 0 0 99999 99999 99999 TE_CAL 18 3 -2.06997 0 2.9971 0.778 0.02335 0.162 0.174 0.293 0.637 0 1.398 0 0 0.035 99999 4.518 99999 TE_CAR 8 3 -1.31009 0.008 -0.99899 0.01 0.0135706 0.613 0.167 0.796 0.627 0 1.717 0 0 0.065 99999 4.342 99999 TE_DIT 9 3 -1.54052 0 1.72467 0.628 0.05076957 0.191 0.2554 0.608 3.25 0.508 4.109 0.447 0 0.217 0.588 0 99999 TE_GAV 20 4 -0.16515 0.15 0.13355 0.218 0.07554429 0.096 0.4225 0.435 3 0.316 4.541 0 0 0.011 0.496 0 99999 TE_GLA 10 2 2.59036 0.791 1.52347 0.512 0.12679113 0.072 0.6681 0.319 3.156 0.221 45.781 0 0 0.498 0.635 0 99999 TE_LAG 10 1 TE_LOK 10 2 -1.11173 0.05 -0.33931 0.043 0.33100869 0.1 0.4 0.209 2.93 0 87.93 0.9 0 3.6 3.6 1.093 99999 TE_MAI 9 2 0.25846 0.082 5.23125 0.946 0.20718107 0.023 0.6759 0.466 3 0.508 3.734 0 0 0.217 0.388 0 99999 TE_PFA 10 2 2.19508 0.43 6.85861 0.982 0.56888868 0 0.78667 0.93 0 0 0.629 0 0 0.004 99999 99999 99999 TE_PII 7 1 TE_RES 46 3 -0.9744 0.074 -1.3073 0.049 0.00055083 0.366 0.5164 0.668 3 0.418 3 0 0 0.011 0.191 0 99999 TE_SHY 8 2 -1.05482 0.061 -0.18197 0.06 0.27913606 0.075 0.3125 0.406 2.93 0 87.93 0.9 0 9.148 3.6 1.636 99999 TE_SOK 16 2 -0.57783 0.085 1.12688 0.439 0.06720502 0.031 0.69667 0.503 3.176 0.412 3.676 0 0 0.005 0.24 0 99999 TE_VIG 15 2 1.50272 0.746 1.31784 0.468 0.03031818 0.124 0.288 0.114 0.828 0 43.451 0 0 0.014 99999 0.902 99999

22

Table S9. Results of the applied tests used to detect signatures of bottlenecks in the microsatellite data for populations and clusters of populations in Rana dalmatina and R. temporaria. Results in red font mark positive indications for a bottleneck.Abbreviations: SMM=Stepwise Mulation Model, TPM= Two Phased Mutation molde.

Sign Test

Standardized Difference Test Wilcoxon Test Mode Shift

sample size

Loci Excess

SMM Loci Excess

TPM T2 SMM T2 TPM SMM TPM

DA_SWB 36 3 0.543 2 0.24229 1.149 0.12527 1.796 0.03623 0.03711 0.01367 shifted mode DA_ESR 14 5 0.10453 4 0.29857 -1.519 0.06433 -0.786 0.21606 0.98047 0.76563 Normal L shape DA_MAS 12 1 0.11003 1 0.05071 0.686 0.24647 1.125 0.13031 0.09766 0.09766 Normal L shape DA_RAE 16 3 0.58492 2 0.28244 -0.601 0.27377 0.25 0.4014 0.42188 0.23047 shifted mode DA_LEL 34 4 0.43296 4 0.46129 -1.391 0.08207 0.256 0.39886 0.84375 0.42188 Normal L shape DA_VIL 16 3 0.54997 2 0.28962 0.416 0.33857 1.237 0.10805 0.19141 0.15625 shifted mode DA_KOT 10 4 0.48889 2 0.26668 -0.112 0.45555 0.532 0.29748 0.42188 0.125 shifted mode DA_ALB 22 4 0.3564 3 0.61168 0.286 0.38741 0.817 0.20699 0.40625 0.23438 shifted mode DA_FON 12 2 0.50164 1 0.12523 1.09 0.13793 1.527 0.06343 0.125 0.02734 shifted mode DA_OVA 12 2 0.28973 1 0.06426 0.377 0.35326 0.777 0.21857 0.15625 0.09766 Normal L shape DA_LUM 10 4 0.34053 3 0.62528 -0.05 0.48017 0.502 0.3077 0.53125 0.34375 shifted mode DA_MOT 20 2 0.29617 1 0.0928 -0.596 0.27569 0.555 0.28929 0.125 0.09766 Normal L shape DA_MAL 10 4 0.405 2 0.31763 0.102 0.45926 0.98 0.16348 0.47266 0.09766 Normal L shape DA_FOG 18 1 0.11062 0 0.01561 1.246 0.10645 1.855 0.03176 0.00586 0.00195 Normal L shape DA_SNE 14 5 0.16433 4 0.41058 -1.352 0.08812 -0.177 0.42958 0.80859 0.67969 Normal L shape DA_MAC 34 7 0.00981 4 0.42571 -3.832 0.00006 -1.138 0.12746 0.67969 0.99805 Normal L shape DA_LAT 30 5 0.17647 3 0.59787 -1.423 0.07732 0.516 0.3028 0.84375 0.27344 Normal L shape

TE_CAR 14 2 0.39021 2 0.4607 0.475 0.31735 0.956 0.16957 0.15625 0.02734 Normal L shape TE_RES 28 5 0.19394 2 0.31292 -1.152 0.12472 0.512 0.30425 0.875 0.23047 Normal L shape TE_ETO 10 3 0.5718 3 0.5 -0.716 0.23698 -0.142 0.44352 0.42188 0.37109 shifted mode TE_TUR 10 5 0.20029 5 0.23132 -1.64 0.0505 -0.868 0.19283 0.80859 0.72656 shifted mode TE_CAL 24 3 0.55821 2 0.3319 0.789 0.21494 1.728 0.04199 0.19141 0.01367 Normal L shape TE_GAV 26 4 0.42065 3 0.5991 -1.12 0.13142 0.351 0.36277 0.67969 0.19141 Normal L shape TE_VIG 28 5 0.17401 3 0.59351 -1.057 0.14519 0.577 0.28207 0.76953 0.23047 Normal L shape TE_GLA 32 3 0.5664 3 0.58771 -2.864 0.00209 -0.485 0.31394 0.67969 0.62891 Normal L shape TE_LAG 22 4 0.27891 4 0.31418 -1.931 0.02671 -0.573 0.28329 0.94531 0.65625 Normal L shape TE_BOB 24 5 0.21472 5 0.1886 -1.705 0.04412 -0.217 0.41425 0.90234 0.72656 Normal L shape TE_DIT 20 4 0.43086 4 0.47813 -1.303 0.09627 0.088 0.46509 0.875 0.47266 Normal L shape TE_BRE 40 5 0.19062 1 0.10889 -0.939 0.17397 0.84 0.2004 0.96289 0.01953 Normal L shape TE_MAI 12 2 0.36281 2 0.33524 0.729 0.23285 1.181 0.11888 0.15625 0.02734 Normal L shape TE_PFA 20 3 0.57958 2 0.30462 0.439 0.33046 1.295 0.09765 0.27344 0.03711 shifted mode TE_MAS 24 4 0.30055 2 0.42486 -0.958 0.16895 0.377 0.353 0.71094 0.28906 Normal L shape TE_LOK 14 4 0.35145 4 0.40238 -1.141 0.12702 -0.356 0.36105 0.72656 0.57813 Normal L shape TE_SOK 36 5 0.18223 3 0.58683 -1.1 0.13559 0.763 0.22277 0.875 0.15625 Normal L shape TE_SHY 14 6 0.01705 6 0.02929 -1.87 0.03075 -0.959 0.16875 0.97266 0.80859 Normal L shape TE_PII 14 2 0.3057 1 0.09254 0.637 0.26201 1.294 0.09781 0.125 0.09766 Normal L shape

Sign

Test Standardized Difference Test Wilcoxon Test Mode Shift

sample size

Loci Excess

SMM Loci Excess

TPM T2 SMM T2 TPM SMM TPM

DAcluster 1 90 7 0.01105 4 0.40939 -7.275 0 -2.006 0.02243 0.99609 0.67969 Normal L shape DAcluster 2 48 5 0.17924 2 0.30834 -0.612 0.27034 1.236 0.10816 0.72656 0.01953 Normal L shape DAcluster 6 46 3 0.58567 2 0.31115 -0.581 0.28067 1.251 0.10554 0.57813 0.15625 Normal L shape DAcluster 7 76 5 0.20005 4 0.46687 -3.66 0.00013 -0.559 0.28805 0.875 0.57813 Normal L shape DAcluster 8 36 3 0.51946 2 0.23404 1.234 0.10854 1.831 0.03358 0.03711 0.01367 shifted mode DAcluster 9 22 4 0.35335 3 0.56908 0.351 0.36292 0.851 0.19741 0.40625 0.23438 shifted mode

TEcluster 1 42 5 0.20269 4 0.40909 -0.935 0.17497 0.17613 0.93 0.84375 0.15625 Normal L shape TEcluster 2 44 5 0.19133 2 0.31485 -0.902 0.18352 1.114 0.13266 0.67969 0.03711 Normal L shape TEcluster 3 54 5 0.18137 2 0.32666 -1.539 0.06189 0.635 0.26273 0.98047 0.125 Normal L shape TEcluster 4 110 4 0.43535 1 0.0966 0.04 0.48388 2.28 0.01131 0.47266 0.00586 Normal L shape TEcluster 5 32 3 0.59208 3 0.58365 -2.749 0.00299 -0.365 0.35769 0.67969 0.62891 Normal L shape TEcluster 6 28 5 0.18525 2 0.31845 -1.079 0.14022 0.456 0.32422 0.90234 0.23047 Normal L shape TEcluster 7 28 5 0.18481 3 0.60743 -1.206 0.11383 0.574 0.28286 0.84375 0.32031 Normal L shape TEcluster 9 22 4 0.28338 4 0.30249 -1.979 0.02391 -0.644 0.25978 0.94531 0.71094 Normal L shape TEcluster 10 24 5 0.22468 5 0.18778 -1.714 0.04323 -0.219 0.41319 0.90234 0.67969 Normal L shape

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Table S10. Results of the k and g tests applied to detect the signature of population expansion in the microsatellite data for populations and clusters of populations in Rana dalmatina and R. temporaria. Results in red indicate possible population expansions (none observed in the more powerful g test). Results in orange are two examples of non-significant g tests that are close to the threshold value indicative of an expansion. Dataset k test (#

loci negative k)

pvalue g test Expected g at 5%

sample size (haploid)

ration obs(g)/exp(g)

ABS (exp-obs)

DA_cluster1 7 0.02906845 0.75877124 0.15 90 5.05847497 0 0.60877124 DA_cluster2 6 0.12572313 0.41153314 0.15 48 2.74355426 1 0.26153314 DA_cluster6 8 0.0030615 0.504967 0.15 46 3.36644669 0 0.354967 DA_cluster7 6 0.12572313 4.18029832 0.15 56 27.8686555 0 4.03029832 DA_cluster8 4 0.60344424 0.43307516 0.16 36 2.70671978 1 0.27307516 DA_cluster9 4 0.60344424 0.77624802 0.16 22 4.85155012 0 0.61624802 DA_cluster10 5 0.33098979 1.25912501 0.19 12 6.62697375 0 1.06912501

28.5714286 0.60877124

DA_ALB 4 0.60344424 0.77624802 0.16 22 4.85155012 0 0.61624802 DA_ESR 7 0.02906845 4.83016348 0.19 14 25.4219131 0 4.64016348 DA_FOG 6 0.12572313 0.52743526 0.19 18 2.77597504 1 0.33743526 DA_FON 4 0.60344424 1.81543127 0.19 12 9.55490145 0 1.62543127 DA_LAT 6 0.12572313 0.44495046 0.16 30 2.78094036 1 0.28495046 DA_LUM 3 0.83489009 0.53018944 0.19 10 2.79047075 1 0.34018944 DA_MOT 5 0.33098979 0.82121968 0.16 20 5.13262301 0 0.66121968 DA_OVA 5 0.33098979 1.25912501 0.19 12 6.62697375 0 1.06912501 DA_RAE 5 0.33098979 3.72015687 0.19 16 19.579773 0 3.53015687 DA_MAS 3 0.83489009 4.99757371 0.19 12 26.3030195 0 4.80757371 DA_MAL 4 0.60344424 0.63250516 0.16 20 3.95315722 0 0.47250516 DA_LEL 5 0.33098979 4.17846999 0.16 34 26.1154374 0 4.01846999 DA_KOT 5 0.33098979 2.34149515 0.19 10 12.3236587 0 2.15149515 DA_SINE 4 0.60344424 0.68059214 0.19 14 3.58206391 0 0.49059214 SA_SWB 4 0.60344424 0.43307516 0.16 36 2.70671978 1 0.27307516

26.6666667 0.66121968

TE_cluster1 5 0.33098979 0.82418895 0.15 42 5.49459303 0 0.67418895 TE_cluster2 7 0.02906845 0.76923277 0.15 44 5.12821846 0 0.61923277 TE_cluster3 7 0.02906845 0.37657725 0.15 54 2.510515 1 0.22657725 TE_cluster4 5 0.33098979 0.35362476 0.15 110 2.35749838 1 0.20362476 TE_cluster5 3 0.83489009 0.33628152 0.16 32 2.10175949 1 0.17628152 TE_cluster6 5 0.33098979 1.13956326 0.16 28 7.12227036 0 0.97956326 TE_cluster7 4 0.60344424 1.8416237 0.16 28 11.5101481 0 1.6816237 TE_cluster9 5 0.33098979 0.48541691 0.16 22 3.03385569 1 0.32541691 TE_cluster10 4 0.60344424 0.97427963 0.16 24 6.08924768 0 0.81427963

44.4444444 0.61923277

TE_BOB 4 0.60344424 0.97427963 0.16 24 6.08924768 0 0.81427963 TE_CAL 6 0.12572313 0.74470189 0.16 24 4.65438681 0 0.58470189 TE_CAR 6 0.12572313 1.31341291 0.16 14 8.20883069 0 1.15341291 TE_DIT 4 0.60344424 0.53948035 0.16 20 3.37175219 0 0.37948035 TE_BRE 4 0.60344424 0.37830901 0.15 40 2.52206008 1 0.22830901 TE_VIG 4 0.60344424 1.8416237 0.16 28 11.5101481 0 1.6816237 TE_TUR 5 0.33098979 1.10239162 0.19 10 5.80206116 0 0.91239162 TE_SOK 5 0.33098979 0.99060269 0.16 36 6.19126682 0 0.83060269 TE_SHY 3 0.83489009 2.15028976 0.19 14 11.3173145 0 1.96028976 TE_PII 2 0.95777113 1.9566811 0.19 14 10.2983216 0 1.7666811 TE_PFA 4 0.60344424 0.22861895 0.16 20 1.42886842 1 0.06861895 TE_MAS 5 0.33098979 0.49233115 0.16 24 3.07706971 1 0.33233115 TE_MAI 2 0.95777113 0.57463216 0.19 12 3.02437978 1 0.38463216 TE_LOK 7 0.02906845 1.26756282 0.19 14 6.67138327 0 1.07756282 TE_LAG 5 0.33098979 0.48541691 0.16 22 3.03385569 1 0.32541691 TE_GAV 6 0.12572313 0.45082116 0.16 26 2.81763223 1 0.29082116 TE_ETO 4 0.60344424 1.93176416 0.19 10 10.1671798 0 1.74176416 TE_RES 5 0.33098979 1.13956326 0.16 28 7.12227036 0 0.97956326 TE_GLA 3 0.83489009 0.33628152 0.16 32 2.10175949 1 0.17628152

36.8421053 0.81427963

24

Table S11. Results of an AMOVA analysis based on COB sequences of Rana temporaria, subdivided into populations and main mtDNA clades. Groups included the following populations: (1) TE_CAR; (2) TE_SOK, TE_JYK, TE_PII; (3) TE_BOB, TE_BRE, TE_CAL, TE_DIT, TE_GAV, TE_LAG, TE_LOK, TE_MAI, TE_PFA, TE_RES, TE_SHY, TE_VIG. The three variance components all were highly significant (P < 0.0001).

Source of Variation degrees of freedom SS Var comp % variationAmong Groups 2 213.377 3.13 (Va) 60.61 Among Pops Within Groups 13 254.767 1.40 (Vb) 27 Within Pops 203 130.093 0.64 (Vc) 12.39 Total 218 598.237 5.17 Table S12. Results of an AMOVA analysis based on COB sequences of Rana temporaria, subdivided into populations and clusters from the BAPS analysis. Groups included the following populations: (1) TE_SOK, (2) TE_CAL, (3) TE_GAV, TE_LOK, TE_SHY; (4) TE_DIT, TE_BRE, TE_MAI, TE_PFA; (5) TE_GLA; (6) TE_RES; (7) TE_VIG; (8) TE_PII; (9) TE_LAG; (10) TE_BOB; (11) TE_CAR. The variance component among groups was not significant (P = 0.22) while the other two components were highly significant (P < 0.0001).

Source of Variation degrees of freedom SS Var comp % variationAmong Groups 10 377.817 0.646 (Va) 22.15 Among Pops Within Groups 5 97.216 1.639 (Vb) 56.2 Within Pops 211 133.293 0.6317 (Vc) 21.65 Total 226 608.326 2.9175 Table S13. Results of an AMOVA analysis based on COB sequences of Rana dalmatina, subdivided only into populations. The two variance components were highly significant (P < 0.0001).

Source of Variation degrees of freedom SS Var comp % variationAmong Populations 14 35.159 0.189 (Va) 35.68 Within Populations 159 54.243 0.342 (Vb) 64.32 Total 173 89.402 0.53 Table S14. Results of an AMOVA analysis based on COB sequences of Rana dalmatina, subdivided into poulations and into clusters from the BAPS analysis. Groups included the following populations: (1) DA_VIL, DA_SIL, DA_SNE, DA_MAC; (2) DA_FOG, DA_LAT; (3) DA_THI, DA_UND, DA_WIL; (4) DA_KOT, DA_MOT, DA_MAL; (5) DA_MAS, DA_RAE, DA_LEL. The three variance components all were significant: among populations within groups and within populations, P < 0.0001; among groups, P < 0.05.

Source of Variation degrees of freedom SS Var comp % variationAmong Groups 4 16.19 0.056 (Va) 10.32 Among Pops Within Groups 10 18.96 0.142 (Vb) 26.36 Within Pops 159 54.24 0.341 (Vc) 63.32 Total 173 89.4 0.5388

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Figure S1. Haplotype network and geographic distribution of Rana dalmatina COB haplotypes (in populations with >10 sequenced individuals, and a few additional populations of 5-10 sequenced individuals to ensure geographic coverage.

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Figure S2. Haplotype network and geographic distribution of Rana temporaria COB haplotypes (in populations with >10 sequenced individuals, and a few additional populations of 5-10 sequenced individuals to ensure geographic coverage.

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Figure S3. Haplotype network and geographic distribution of Rana dalmatina RAG1 haplotypes (in populations with >10 sequenced individuals, and a few additional populations of 5–10 sequenced individuals to ensure geographic coverage. Numbers refer to haplotypes (2 per sequenced individual) after separation by phasing.

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Figure S4. Haplotype network and geographic distribution of Rana temporaria RAG1 haplotypes for populations with >10 sequenced individuals, and a few additional populations of 5–10 sequenced individuals to ensure geographic coverage. Numbers refer to haplotypes (2 per sequenced individual) after separation by phasing.

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Figure S5. Unrooted neighbor-joining tree based on the distance of proportion of shared alleles for the R. dalmatina microsatellite dataset. Only bootstrap values above 75% are shown.

Figure S6. Unrooted neighbor-joining trees based on the distance of proportion of shared alleles for the R. temporaria microsatellite dataset. Only bootstrap values above 75% are shown.

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Figure S7. Bayesian analysis of population structure based on microsatellite data. Clustering solution for Rana dalmatina. Each sampled population is defined in the figures as a box limited by two thin black lines. Populations belonging to the same cluster are labeled with the same color. Individuals are represented as colored vertical lines that indicate the proportion of the individual’s genotype belonging to a cluster. Admixed individuals are identified as bars of more than one color. Colors were defined in order to roughly match those used in the COB haplotype network and map.

Figure S8. Bayesian analysis of population structure based on microsatellite data. Clustering solution for Rana temporaria. Colors were defined in order to roughly match those used in the COB haplotype network and map. TE1–TE5 refers to major mtDNA clades.

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Figure S9. Results of mismatch distribution analyses for those populations of Rana dalmatina and R. temporaria that showed signs of population expansion. The observed curve of the dataset is plotted in blue. The simulated curves under the sudden expansion scenario are in purple (model), red (lower bound) and green (upper bound).

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Figure S10. Results of mismatch distribution analyses for selected main mtDNA clades of Rana dalmatina and R. temporaria. Clade T5 of R. temporaria, and R. dalmatina without DA_COS shows a clear signature of population expansion from the D and Fs statistics. The observed curve of the dataset is plotted in blue. The simulated curves under the sudden expansion scenario are in purple (model), red (lower bound) and green (upper bound).

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Figure S11. Last Glacial Maximum paleodistribution models for Rana temporaria (A-D) and R. dalmatina (E-F). Climate envelope models A,B,E and F were based on the global circulation climate model MIROC3, while models C,D,G and H were based on the CCSM3. Models on the left column were performed with the complete set of 19 climatic variables from worldclim, while models from the right column were performed with a subset of four variables. The potential distributions vary from yellow (zero probability of ocurrence) to dark brown (probability of 1). Blue represents the maximum extent of ice during the LGM.

Radically different phylogeographies and patterns of genetic variation in two European brown frogs,...

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