Local selection modifies phenotypic divergence amongRana temporaria populations in the presence of geneflow

ALEX RICHTER-BOIX, CELINE TEPLITSKY* , BJORN ROGELL and ANSSI LAURILAPopulation Biology and Conservation Biology ⁄Department of Ecology and Evolution, Evolutionary Biology Centre, UppsalaUniversity, Norbyvagen 18D, 75236 Uppsala, Sweden

Abstract

In ectotherms, variation in life history traits among populations is common and suggestslocal adaptation. However, geographic variation itself is not a proof for local adaptation,as genetic drift and gene flow may also shape patterns of quantitative variation. Westudied local and regional variation in means and phenotypic plasticity of larval lifehistory traits in the common frog Rana temporaria using six populations from centralSweden, breeding in either open-canopy or partially closed-canopy ponds. To separatelocal adaptation from genetic drift, we compared differentiation in quantitative genetictraits (QST) obtained from a common garden experiment with differentiation inpresumably neutral microsatellite markers (FST). We found that R. temporaria popula-tions differ in means and plasticities of life history traits in different temperatures atlocal, and in FST at regional scale. Comparisons of differentiation in quantitative traitsand in molecular markers suggested that natural selection was responsible for thedivergence in growth and development rates as well as in temperature-induced plasticity,indicating local adaptation. However, at low temperature, the role of genetic drift couldnot be separated from selection. Phenotypes were correlated with forest canopy closure,but not with geographical or genetic distance. These results indicate that local adaptationcan evolve in the presence of ongoing gene flow among the populations, and that naturalselection is strong in this system.

Keywords: countergradient variation, gene flow, genetic drift, local adaptation, phenotypicplasticity

Received 7 April 2009; revision received 24 November 2009; accepted 26 November 2009

Introduction

Divergent selection among local environments cancause populations to evolve in response to the specificecological conditions, leading to local adaptation. Therelationship between selection, gene flow and geneticdrift in adaptive phenotypic divergence among popula-tions has been considered theoretically and empiricallyin several recent studies (e.g. Garcıa-Ramos & Kirkpa-

trick 1997; Kawecki & Ebert 2004; Nosil & Crespi 2004;Crispo 2008; Rasanen & Hendry 2008). Populationgenetic theory predicts that even small amounts of geneflow between populations can effectively homogenizegene pools in the absence of very strong selection(Crow & Kimura 1970). Gene flow has thus beenthought to inhibit local adaptation in response to heter-ogeneous environments (Lenormand 2002), however,local adaptation can occur in the face of gene flow (e.g.Antonovics 1968; Von Wettberg et al. 2008). To under-stand the interplay of local selection and gene flow, it isnecessary to examine adaptation at a spatial scale overwhich gene flow, natural selection and drift can pro-duce varying evolutionary outcomes (Coyne & Orr

Correspondence: Alex Richter-Boix, Fax: +46 18 471 6424;E-mail: alex.richter@ebc.uu.se*Present address: Unite Mixte de Recherche 7204, MuseumNational d’Histoire Naturelle, 55, Rue Buffon, CP51, 75005Paris, France.

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Molecular Ecology (2010) 19, 716–731 doi: 10.1111/j.1365-294X.2009.04502.x

2004). The relative roles of selection, genetic drift andgene flow can be analysed with comparative studies ofvariation in molecular neutral markers (FST) and quanti-tative genetic traits (QST; Spitze 1993; Merila & Crno-krak 2001; Leinonen et al. 2008).Phenotypic plasticity is an evolutionary factor that

is rarely considered in studies of local adaptation andphenotypic diversification (Ghalambor et al. 2007; Cri-spo 2008). Nevertheless, phenotypic plasticity maymodify the relationship among selection, drift andgene flow. Plasticity may allow increased gene flow,if the plastic individuals are more likely to surviveunder a wide range of selective pressures (Sultan &Spencer 2002). Consequently, the traditional view isthat plasticity reduces adaptive genetic divergence bybuffering individuals from the action of negativeselection (Schlichting & Pigliucci 1998). However,depending on the costs and limits associated with theplastic response, phenotypic plasticity can promote orretard genetic change (Price et al. 2003; Paenke et al.2007). According to the alternative view, adaptationoccurs faster because plasticity allows an entire popu-lation to adapt simultaneously, whereas natural selec-tion works upon standing genetic variation amongindividuals (Ghalambor et al. 2007). Some studies sug-gest that plasticity may drive the initial phenotypicdivergence and the genetic change follows in thedirection of the plastic response (Ghalambor et al.2007; Crispo 2008).However, these predictions are only valid for adap-

tive plasticity. In the case of maladaptive plasticity, i.e.when plasticity opposes the direction of adaptivegenetic change among populations (countergradientvariation; Conover & Schultz 1995), the predictions forthe relationship among plasticity, selection and geneflow change (Crispo 2008). Under a countergradientvariation scenario, theory suggests that gene flowshould be reduced between the different selective envi-ronments, favouring philopatry, because well-adaptedresidents would be favoured over dispersers exhibitingmaladaptive plasticity, and genetic adaptation shouldoccur to compensate for the maladaptive plasticresponses (Grether 2005; Crispo 2008).Thermal reaction norms across latitudinal and altitu-

dinal clines in ectothermic organisms are classicalexamples of countergradient variation (Berven et al.1979; Laugen et al. 2003). In ectotherms, ambient tem-perature affects body temperature and physiologicalrates, and thereby development and growth rate gener-ally declines with decreasing temperatures (Angillettaet al. 2002). As lower development and growth ratesand the resultant smaller body size are expected toreduce individual fitness, environmental temperature isconsidered as a strong selective force (Gilchrist & Huey

1999). Several studies have shown that ectotherms dis-play thermal or latitudinal compensation, by whichindividuals from the colder environment are able tomaintain physiological rates as high as those from thewarmer environment. Common garden experimentsdemonstrate that individuals from cool-climate popula-tions grow faster than individuals from warm-climatepopulation across a broad range of temperatures (Co-nover & Present 1990; Yamahira et al. 2007). Despite thelarge interest on the topic, very little if no studies haveinvestigated the interplay between gene flow and plas-ticity in a countergradient variation context (Crispo2008).In this study, we investigated local and regional vari-

ation in the means and plasticity of larval life historytraits (larval period, growth rate and mass at metamor-phosis) in the common frog Rana temporaria by studyingsix populations along a 230-km latitudinal gradient. Thebreeding ponds used by these populations differ inthermal environment, and exchange of individualsamong the most closely located populations can beexpected. Previous studies on this species have demon-strated a countergradient in larval life history traits atmacrogeographic scale (1600 km latitudinal gradientacross Sweden: Laugen et al. 2003; Lindgren & Laurila2005). However, at the local scale, the temperature ofthe aquatic environment in which the amphibian larvaedevelop is strongly related to forest canopy generatinga mosaic of water temperatures across the landscape(Halverson et al. 2003; Skelly 2004). Ponds with morecanopy cover have lower temperature than open can-opy ponds, which has been found to select for fasterembryonic development rate in wood frogs Rana sylvati-ca breeding in closed-canopy ponds (Skelly 2004). Varia-tion in canopy cover may also select for differences inthe thermal preferences of R. sylvatica tadpoles (Freiden-burg & Skelly 2004).Here, we examine the mechanisms of population

divergence in the R. temporaria using comparisonsbetween neutral genetic population divergence (FST)and population divergence in quantitative life historytraits (QST). We address the following questions: (i) Arepopulations adapted to their local environments? Localdifferentiation owing to a selective response is inferredif the population differentiation in quantitative traitsexceeds the neutral genetic population divergence (Spit-ze 1993; Merila & Crnokrak 2001). In addition to differ-entiation in trait means, we also examined populationdivergence in plastic responses. (ii) Are populationsadapted according to the latitudinal gradient or to thelocal pond conditions? If populations are adapted to thelatitudinal gradient as in previous studies, we expectthat geographical distance is a good predictor of pheno-typic differentiation, and gene flow is restricted among

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the populations (Crispo 2008). However, if phenotypesare adapted to local pond-specific conditions, we expectto find a correlation between phenotypic differentiationand local conditions.

Material and methods

The common frog, Rana temporaria, is the most wide-spread frog species in Europe, inhabiting a wide rangeof habitats from northern Spain to northernmost Nor-way and Russia. In northern Europe, spawning beginsas early in the spring as the temperature permits(minimum temperature for spawning migration is5 "C; Haapanen 1982). Breeding sites are very variable,and reproduction occurs in shallow areas in a widerange of natural and artificial water bodies (Haapanen1982).

Study populations

In April 2004, we sampled freshly laid eggs of R. tempo-raria in six medium-sized, permanent breeding pondsin central Sweden (Fig. 1, Table 1). The minimum dis-tance between the ponds was 4.5 km and the maximumdistance 227 km, with a mean distance of 88 kmbetween localities. The average growth season length inthis area is c. 180 days (Laugen et al. 2003). To estimatethe local water temperature conditions in the ponds, thepercentage of forest canopy closure defined as the pro-portion of nonvisible sky within an angle of 40"–45"was estimated visually by the same person into 10%categories with a standard scale (Jennings et al. 1999).Simple visual assessment of canopy normally providesunderestimated values as compared with more accuratetechniques, but allows comparisons among sites when

(b) (a)Fig. 1 Map showing the location thestudy region (a) and the locations of thesix study populations (b).

Table 1 Population-specific information on the latitude of location, percentage of forest canopy closure, mean and variance of watertemperature during 2009 spring (April–June), invertebrate predator abundance on the basis of catch per unit effort, number of indi-viduals sampled for the molecular analyses per population (n), average observed heterozygosity (HO), average expected heterozygos-ity (HE), average allelic richness, loci showing signs of a null allele and significant departures from Hardy–Weinberg equilibrium

Population Latitude

Forestcanopy(%)

Temperaturemean ±variance

Predatorabundances n HO HE ± SE

Allelicrichness ±SD

Nullallele

HWdisequilibrium

Gullsmyra 60" 7¢12.28¢¢N 60 13.09 ± 10.34 0.82 30 0.55 0.57 ± 0.045 5.08 ± 3.07 — —Haggedal 59"51¢40.27¢¢N 20 13.52 ± 15.98 1.06 30 0.58 0.59 ± 0.045 5.17 ± 3.71 Rtempl7 —Katrineholm 58"59¢0.00¢¢N 50 13.38 ± 11.48 1.53 25 0.52 0.53 ± 0.061 5.00 ± 3.72 Rtempl7 —Mortebo 60"59¢08.14¢¢N 0 14.52 ± 30.96 2.13 31 0.55 0.56 ± 0.044 6.22 ± 4.16 Rtempl4 —StoraAlmby 59"51¢10.87¢¢N 10 15.25 ± 8.39 2.26 29 0.57 0.58 ± 0.044 5.00 ± 3.70 — —Kolva 59"53¢29.78¢¢N 80 11.70 ± 7.93 0.73 21 0.63 0.65 ± 0.051 6.03 ± 4.62 RRD590,

Rtempl4Rtempl4,RtCa2-22

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the measurements are done by the same person (Korho-nen et al. 2006). In addition to estimating forest canopyclosure in the ponds, we measured water temperatureand the abundance of invertebrate predators and adultsnewts during the spring (from April to June 2009). Sur-face water temperature data were collected at 15 minintervals with underwater temperature data loggers(HOBO Water Temp Pro v2 Data Logger) from thebreeding season in April until early July. Predatoryinvertebrates were sampled with dipnet sweeps (32 cmdiameter, 0.08 cm mesh size) during the visits in earlyMay and early July. A minimum of 10 dipnet sweepswere taken at depth of 0.5–1.5 m over a distance of 2–3 m. Invertebrates considered as potential predators(aeshnid and libellulid dragonfly larvae, notonectidbugs and diving beetles) were identified in the field,counted and returned to the pond. Total counts forinvertebrate predators and adult newts captured in eachlocality were divided by the number of dipnet sweepstaken in each pond. This procedure yielded abundanceon the basis of catch per unit effort, comparable acrossthe localities (Babbitt et al. 2003). The relationshipsbetween canopy cover, temperature and predator abun-dance were analysed by Pearson correlations.Freshly laid eggs from 21 to 31 clutches were collected

in each of the ponds. The eggs were transported to thelaboratory in Uppsala and reared in 0.9 L plastic vials at15 "C until gill absorption was complete (developmentalGosner stage 25). For molecular analyses a sample of 3–5 eggs per clutch were collected, for the quantitativetraits study the sample size per clutch was 20–25 eggs.

Experimental design

As a results of logistic limitations in the low-tempera-ture treatment, we were able to obtain data on quantita-tive traits for Haggedal population only at 19 "C. Theanalyses at 19 "C were done with the six populations,but the analyses at 15 "C, as well as plasticity analyses,are based on the data from five populations, excludingHaggedal population.We used 12 families (or clutches) from each of the six

populations in the quantitative trait study. Twelve tad-poles (six in Haggedal) were taken at random fromeach family and six tadpoles were placed in each of thetwo temperature treatments (15 and 19 "C water tem-perature) arranged in two temperature-regulated rooms(in total 864 tadpoles). The temperature treatments rep-resent normal temperatures tadpoles encounter in natu-ral ponds. The high-temperature treatment correspondsto the mean temperature in open forest canopy pondsduring the warmest hours of the day (18.32 ± 1.58 SD;Fig. A1 in Appendix S1, Supporting information),whereas the low-temperature treatment corresponds to

the similar temperature in closed forest canopy ponds(14.78 ± 1.05 SD). These high temperatures can contrib-ute substantially to gain in growth and development(Hertz et al. 1988), and during these hours, the temper-ature differences between the closed- and open-canopyponds are the largest (Fig. A1, Supporting information).The temperature treatments were called 19 and 15 "Caccording to the air temperature of experimental cham-bers, and resulted in mean water temperatures of 18.26and 15.23 "C, respectively. Tadpoles were raised indi-vidually in 0.9 L opaque plastic containers and fedchopped spinach ad libitum. The photoperiod was keptconstant at 16L:8D in both rooms. The water in eachcontainer was changed every 3–4 days. Throughout thestudy, we used reconstituted soft water, prepared fromdeionised water and NaHCO3 (48 mg ⁄L), CaSO4Æ2 H2O(30 mg ⁄L), MgSO4Æ7 H2O (61 mg ⁄L) and KCl (2 mg ⁄L)(APHA 1985), to ensure homogeneous water quality.The timing of metamorphosis, defined as the emergenceof the first forelimb (Gosner stage 42), was determinedby daily checks. For each individual we measured: (i)larval period, defined as the number of days elapsedfrom the beginning of experiment to metamorphosis,(ii) wet mass at metamorphosis measured to the nearest0.1 mg, and (iii) growth rate (g ⁄d), defined as the massat metamorphosis divided by the larval period.

Genetic analyses

DNA was extracted using the tail tip of one randomhatchling from each spawn clump (mean of 27.6 indi-viduals per population) using a standard salt extractionprocedure (Paxton et al. 1996). Six microsatellite loci,RRD590 (Vos et al. 2001), RtCa2-22, RtCa25 (T. Garner,unpublished), Rtempl4, Rtempl7 (Rowe & Beebee2001) and RtlH (Pidancier et al. 2001), were amplifiedusing the extracted DNA and polymerase chain reaction(PCR) procedures described in Palo et al. (2003). PCRproducts were separated using a denaturating 5%LongRanger gel (BMA Rockland) and visualized usingan ABI PRISM 377 (Applied Biosystems). The scoring ofalleles was done using GENESCAN 3.1 and GENOTYP-ER 2.5 (Applied Biosystems). A total 166 individualswere scored for the six polymorphic microsatellite loci.Potential genotyping errors such as presence of null

alleles, stuttering and allelic dropouts were investigatedusing MICROCHECKER 2.2.3 (Van Oosterhout et al.2004). We detected null alleles in three loci in four ofthe populations (Table 2). Corrected allele frequenciesfor each locus and population containing null alleleswere estimated using the Brookfield Estimator 2 (Brook-field 1996), under the assumption that the nonamplifiedsamples represent null allele homozygotes. All esti-mates of population differentiation were carried out

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using both the original (non-adjusted) data and theadjusted data. As the results were generally similar, wepresent the results on the adjusted genotypes, unlessotherwise specified. Expected, observed heterozygosityand allelic richness for each population were calculatedusing PEDANT version 1.0 (Johnson & Haydon 2007)and GENETIX version 4.05.2 (Belkhir et al. 2004).GENEPOP version 4.0.6 (Rousset 2008) was used to testfor deviations from Hardy–Weinberg equilibrium(HWE) at all loci and for linkage disequilibriumbetween pairs of loci. Linkage analyses between allelesof different loci were conducted with the original unad-justed data. We estimated one-tailed probabilities ofdeparture from HWE within each population for eachlocus to test for heterozygote deficiency. Deviation fromHWE was tested with a Markov Chain approximation(Raymond & Rousset 1995). The critical probability foreach HWE test was adjusted using the sequential Bon-ferroni procedure to maintain an overall significancelevel of 0.05. We also conducted linkage disequilibriumtests for each pair of loci within each population usingMarkov Chain approximation followed by sequentialBonferroni correction.To quantify the magnitude of differentiation among

populations in allele frequencies, we calculated overalland pairwise FST values (Weir & Cockerham 1984) overmultiple loci in FSTAT version 2.9.3.2 (Goudet 1995). Wetested if the FST values deviated significantly from zeroby bootstrapping with 1500 permutations and with Bon-ferroni corrections to give table-wide significance levelsof 0.05 in FSTAT.

ANOVA

The effects of temperature, population, family and theirinteractions on larval life history traits were analysed

with mixed model ANOVAs with restricted maximumlikelihood estimation using Proc Mixed in SAS. Family(nested under population) was considered a randomeffect. Larval period, mass at metamorphosis andgrowth rate were log-transformed before the statisticalanalyses.

Test of adaptive differentiation

To test if differences between populations were a conse-quence of neutral or adaptive differentiation, we com-pared quantitative genetic divergence (QST) todivergence in putatively neutral polymorphic microsat-ellites (FST). Using the variance components extractedfrom ANOVA as described above, we estimated QST val-ues for quantitative traits using the equation (Spitze1993; O’Hara & Merila 2005):

QST trait ! VB"2VW # VB$;

where VB was obtained directly from the estimated vari-ance component for the population effect (between popu-lations). As we had full-sib design, the among familyvariance Vf was converted into within-population(genetic) variance (VW) by multiplying it by two (VW = 2Vf; Roff 1997). We calculated QST values for each trait atthe two temperature treatments, and on the traits pooledacross the two treatments. In addition, we estimated QST

values for plasticity of each trait using the equation:

QST trait plasticity ! VB%E

"2VW%E # VB%B$;

where VB·E is the estimated variance component for thepopulation · temperature interaction effects and VW·E

is the estimated variance component for the fam-ily · temperature interaction effect (Banta et al. 2007).

Table 2 Results of mixed models on the effects of experimental temperature, population and family (nested within population) onlarval period, mass at metamorphosis and growth rate in the laboratory experiment. Bold values indicate P < 0.005

Fixed effects

Source of variation

Larval period Body mass Growth rate

d.f. DenDF F P d.f. DenDF F P d.f. DenDF F P

Temperature 1 582 1760.65 <0.0001 1 583 4.96 0.0263 1 582 346.9 <0.0001Population 4 50.3 13.11 <0.0001 4 52.4 15.12 <0.0001 4 53 14.70 <0.0001Temperature ·Population

4 579 1.21 0.3064 4 583 4.57 0.0012 4 581 8.24 <0.0001

Random effects Var Var SE Z P Var Var SE Z P Var Var SE Z P

Family(population) 0.00055 0.00013 4.26 <0.0001 0.00054 0.00012 4.34 <0.0001 9.1E)6 2.01E)6 4.59 <0.0001Family(population) ·Temperature

)1.1E)7 )5.4E)7 — — )8.7E)7 )5.4E)7 — — )4.0E)9 )1.1E)8 — —

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To evaluate whether QST was significantly differentfrom FST, we jackknifed the difference between QST andFST at the population level and calculated the probabil-ity that the difference was more than zero according tothe Student’s t-distribution using a one-tailed test(Banta et al. 2007). The sign of the statistics then allowsus to interpret the results. If QST is larger than FST, thisis interpreted as evidence of divergent selection andadaptation to local environments. If QST and FST do notdiffer, genetic drift alone is sufficient to explain the pat-tern of differentiation, if QST is smaller than FST uni-form selection favours the same phenotype in differentenvironments (Merila & Crnokrak 2001). Although jack-knife over populations has some problems associatedespecially with large QST values (around 0.8), it worksbetter than any other standard resampling method forlow QST (O’Hara & Merila 2005).We also evaluated if the QST estimates for all the

studied traits differed from each other at different con-ditions (low temperature, high temperature, across tem-perature treatments or trait plasticities) using one-tailedt-tests (Banta et al. 2007).

Correlations between genotype, geography, forestcanopy closure and phenotype

We analysed the relationship among genetic, geo-graphic, forest canopy closure and phenotypic compo-nents using Mantel and Partial Mantel tests (Ver Hoef& Cressie 1993). We tested for isolation by distancebetween populations by analysing the relationshipbetween the natural logarithm of geographical distance(using the pairwise Euclidean linear distance betweenponds) and the genetic distance FST ⁄ 1 ) FST (Rousset1997). Similarity of forest canopy closure betweenponds was estimated by Euclidean distances betweenpairwise locations. For the phenotypic distance matrixbetween populations, we calculated a total of fourmatrices using Euclidean distances based on the familymeans across temperature treatments for the three lifehistory traits measured, the within-temperature familymeans of the traits (one matrix for the 15 "C and onefor the 19 "C temperature treatment) and the familymeans for the plasticities of the traits (the differencebetween the treatments). As the traits have all differentsizes, the traits were scaled by subtracting the meanand dividing by the standard deviation to obtain stan-dardized scores before calculation of Euclidean dis-tances between populations. We obtained four matricesof dimension five (excluding Haggedal population) withthe similarities among the populations for all pheno-typic traits. Statistical significance for all Mantel testswas assessed with 10 000 permutations. We also usedpartial Mantel tests, holding one matrix constant and

testing for an association between the remaining matri-ces (see Table 6 for all combinations tested). Finally, wetested for correlations between log-transformed meangrowth rate at 19 "C treatment and angularly trans-formed forest canopy closure, as well as betweengrowth rate and latitudinal position of the populations,to test if the study populations are adapted accordingto the latitudinal gradient or local pond properties.

Results

Habitat characterization

There was a negative correlation between estimated for-est canopy cover and water temperature measured dur-ing the larval period (r = )0.958; P = 0.0025; n = 6), andbetween canopy cover and abundance of potential pre-dators (r = )0.817; P = 0.046; n = 6), indicating that tad-poles from open ponds encountered higher watertemperatures and greater predation risk than individu-als from forested ponds (Fig. A3 in Appendix S1, Sup-porting information). As a result of the high correlationbetween traits, we used forest canopy closure, the prox-imal cause for variation in temperature and probablyalso for predation risk, to define the environments inthe following analyses.

Phenotypic variation

Larval period was longer in the low-temperature treat-ment, with strong differences among populations andfamilies within populations (Table 2). Populations fromponds with more forest canopy closure (Kolva, Gulls-myra, Katrineholm) had faster developmental rates thanpopulations from ponds with little canopy closure(Mortebo, Stora Almby, Haggedal). All populationsshowed the same reaction norms with developmentaltime decreasing with temperature (Fig. 2a). Popula-tion · temperature and family · temperature interac-tions were not significant (Fig. 3a).The effects on mass at metamorphosis were statisti-

cally significant for all traits except for tempera-ture · family interaction (Table 2). Gullsmyrapopulation had the largest individuals and Mortebo thesmallest. Temperature · population interaction was sig-nificant indicating population differences in the plastic-ity of this trait: mass at metamorphosis was higher athigh temperature in Kolva and Gullsmyra but remainedsimilar in the other populations (Table 2, Fig. 2b). Fam-ilies within populations differed in size (Table 2,Fig. 3b).Growth rate was significantly affected by all the fac-

tors except temperature · family interaction (Table 2).Growth rate increased with temperature (Fig. 2c).

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Again, Gullsmyra population had the largest traitvalues and Mortebo the smallest. A significant tempera-ture · population interaction indicated that the reactionnorms differed among populations with higher differ-ences between the treatments in populations with morecanopy closure (Fig. 3).

Genetic variation

The number of alleles in the microsatellite loci rangedfrom two to 23 per population, with an average of 6.1alleles per population and locus (Table A1 in Appen-dix S1, Supporting information). Expected heterozygos-ity ranged between 0.501 and 0.609, and observedheterozygosity from 0.523 to 0.639 (Table 1). Afterpopulation-by-locus tests for HWE and Bonferroni

corrections, one population (Kolva) exhibited significantdeviations towards excess homozygosity in two loci(Table 1). No comparisons between loci were significantfor linkage disequilibrium when all the populationswere pooled (Table A2 in Appendix S1, Supportinginformation).The overall FST value revealed genetic differentiation

among populations (FST = 0.058 for the six populations,FST = 0.066 for the five populations after exclusion ofHaggedal). Pairwise FST values between populationsranged from 0.018 to 0.155 (Table 3). Most FST valuesdiffered significantly from zero in all pairwise popula-tion combinations with the exception of the populationslocated at 4 and 14 km from another (Table 3). Correc-tion for null alleles by MICROCHECKER produced lit-tle change in the values for FST and pairwise statistical

(a)

(c)

(b)

Fig. 2 Mean and ±1 SE of (a) larval period, (b) mass at metamorphosis and (c) growth rate from each temperature treatments (fullcircles = 19 "C; open circles = 15 "C). Populations are ranked according to canopy cover, from open-canopy ponds to closed-canopyponds: Mo = Mortebo, StA = Stora Almby, Ha = Haggedal, Ka = Katrineholm, Gu = Gullsmyra and Kol = Kolva. For Haggedal pop-ulation, we only have data at 19 "C (see text).

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differences (Table A3 in Appendix S1, Supportinginformation).

Test of adaptive differentiation

QST was significantly higher than FST in all traits at19 "C (Table 4; Fig. 4). However, the results weresomewhat different if we exclude Haggedal populationat 19 "C: QST was significantly higher than FST only forthe larval period (QST for mass = 0.266 ± 0.26 SD; larvalperiod = 0.264 ± 0.23 SD, growth rate = 0.285 ± 0.26SD), suggesting an increased power of the analyses asthe number of populations increases (see O’Hara &Merila 2005).

Other comparisons involved only five populations.QST were significantly higher than FST for growth ratesmeasured across the two temperature treatments aswell as for growth rate plasticity (Table 4). None of theQST values was statistically different from FST values at15 "C.The higher differentiation among populations at high

than at low temperature was confirmed in a series ofone-tailed t-tests: QST point estimates for high-tempera-ture treatment were, on average, significantly higherthan values at low temperature (Table 5). Traits pooledacross both temperatures showed QST values similar tovalues observed at high temperature. Trait plasticitiesshowed QST values intermediate to those found for tem-

Fig. 3 Thermal reaction norms based on family and population means for larval period, weight at metamorphosis and growth rate.Mo = Mortebo, StA = Stora Almby, Ha = Haggedal, Ka = Katrineholm, Gu = Gullsmyra and Kol = Kolva.

Table 3 Genetic differentiation (FST) between populations estimates for all loci with FSTAT (above the diagonal) after adjusting fornull alleles

Population Gullsmyra Haggedal Katrineholm Mortebo StoraAlmby Kolva

Gullsmyra — 0.0180*** 0.0738*** 0.0252*** 0.0274*** 0.0264*Haggedal 35.44 — 0.0863* 0.0387*** 0.0303ns 0.0302nsKatrineholm 131.49 108.97 — 0.1551*** 0.1295** 0.1051*Mortebo 99.48 131.47 227.77 — 0.0478*** 0.0377***StoraAlmby 42.75 14 117.64 133.38 — 0.0339nsKolva 31.32 4.39 113.59 126.84 13.88 —

The Euclidean distance in km between populations is shown below the diagonal.*P < 0.05; **P < 0.01; ***P < 0.001, in bold ns denotes FST values which were nonsignificantly different from zero.

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perature means, but the differences were not signifi-cant.

Phenotypic differentiation

The relationship between pairwise genetic distances andgeographic distances was close to statistical significance,suggesting isolation-by-distance in microsatellite mark-ers. Moreover, we found no relationship betweengenetic distances and phenotypic differences betweenpopulations, and the correlations were further weakenedwhen we controlled for geographic distance (Table 6).Phenotypic differences between populations were posi-tively correlated with forest canopy differences betweenbreeding ponds, and this was also the case when wecontrolled for the effects of geographic distance andgenetic distance (Table 6). These results were in accor-dance with the positive correlation at population levelbetween growth rate and forest canopy at high-tempera-ture treatment (n = 6; r = 0.83; P = 0.041). These resultstogether with the lack of correlation between phenotypicdifferences and geographic distances, or growth rateacross latitudinal range (n = 6; r = )0.29; P = 0.571),demonstrated that faster phenotypes are found inclosed-canopy ponds and not following the latitudinalgradient at the present geographical scale.

Discussion

Population differentiation

We found that local populations of a common amphib-ian species differed in life history traits and in plasticresponses of these traits to different temperatures. Thehigher values of QST than FST found for all traits at high

temperature, and both in the mean and the plasticity ofgrowth rate, indicate stronger among-population differ-entiation than can be accounted for by neutral diver-gence, supporting divergent natural selection and,consequently, adaptive population differentiation. Theseresults corroborate the study of Johansson et al. (2007)on the importance of local selection for Rana temporarialarval life history traits and support the notion thatpopulation structure in pond-breeding amphibians islikely to favour local adaptation in both coarse andfine-grained environmental variation (see Beebee 2005for review). Local populations can be described as dis-crete units which occupy well-delimited habitat patchesconnected with a relatively restricted gene flow andexperience divergent natural selection in different habi-tats, which favour local adaptation (Kawecki & Ebert2004).The results on the FST–QST comparison at high-tem-

perature treatment changed depending on whether weincluded Haggedal population in the analyses, suggest-ing that our ability to detect adaptive divergencedepended on the power of the analyses. The increase inthe power of FST–QST comparison is expected, as QST

estimates derived from few populations (<20) tend tobe biased downwards and precise estimation is difficultbecause of the large variance in QST (O’Hara & Merila2005; Goudet & Buchi 2006). Another point to keep inmind about our estimation of QST is the potential biasbecause of maternal effects: as we used offspring col-lected from nature (full-sibs), we could not control formaternal effects, and both among- and within-popula-tion variance components may include maternal envi-ronmental effects. In the case of Swedish R. temporariapopulations, quantitative genetic studies have foundsignificant maternal effects, but they are usually consid-erably weaker than additive genetic effects (Laugenet al. 2002, 2005; Laurila et al. 2002).The role of local selection was further ascertained by

Mantel tests showing that local habitat variation interms of canopy closure explained a large proportion ofthe variation in quantitative traits. Furthermore, the lackof correlation between phenotypic traits and geographi-cal distances or genetic similarity suggest that pheno-typic differentiation is most probably because ofadaptation to local conditions in the ponds rather thangenetic similarity between closely located populationsor selection along a latitudinal gradient. Our results onincreased development and growth rates in ponds withmore canopy closure are in accordance with previousstudies on R. temporaria showing that individuals fromhigh-latitude areas with cooler temperatures andshorter season length tend to have higher rates across arange of temperatures (i.e. countergradient variation;Laugen et al. 2003; Palo et al. 2003; Lindgren & Laurila

Table 4 Mean and SD of QST estimates for traits within thetemperature treatments, for pooled traits across r temperaturesand for trait plasticities

Trait

Water temperature

Across Plasticity19 "C 15 "C

Weight 0.243 ± 0.24* 0.145 ± 0.13 0.248 ± 0.22 0.163 ± 0.15Larvalperiod

0.214 ± 0.20* 0.150 ± 0.15 0.325 ± 0.34 0.277 ± 0.45

Growthrate

0.245 ± 0.23* 0.148 ± 0.13 0.262 ± 0.23* 0.312 ± 0.15***

Significant P-values indicate that QST was significantly higherthan FST (19 "C: FST = 0.058, n = 6 populations; 15 "C, acrossand plasticity: FST = 0.066, n = 5 populations) according to aone-tailed t-test.*P < 0.05; ***P < 0.001.Bold values indicate P < 0.05.

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2005). The positive correlation between growth rateand forest canopy suggests that differences in watertemperature among the ponds contribute to phenotypicdifferentiation even at smaller geographical scale.

Local adaptation and gene flow

Previous studies have demonstrated the adaptivevalue of genetic differentiation in life history traits inamphibians over wider climatic gradients and largergeographic distances than investigated in this study(e.g. Berven 1982; Palo et al. 2003). Few studies have

been performed at spatial scales over which dispersal isfeasible allowing investigations of migration-selectionbalance and local evolution of the phenotypes (Relyea2002; Skelly 2004; Ficetola & De Bernardi 2005; VanBuskirk & Arioli 2005; Johansson et al. 2007; Meier2007). Local adaptation was found in some studieswhere phenotypes could be coupled with environmentconditions (forest canopy: Skelly 2004; Ficetola & DeBernardi 2005; predation risk: Van Buskirk & Arioli2005), but the adaptive explanation remained unclear inothers (Relyea 2002; Johansson et al. 2007; Meier 2007).However, while these studies reported differentiation in

(a)

(c)

(b)

Fig. 4 Comparison of overall FST and QST estimates for (a) larval period, (b) weight at metamorphosis, and (c) growth rate in thetwo temperature treatments, when pooled across both temperatures, and for trait plasticity. Grey boxes depict FST ± 95% CI (at19 "C six populations; at 15 "C five populations) and horizontal bars QST ± 95% CI.

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phenotypes and life history traits among populationsseparated by few kilometres, most of them could notdistinguish between three processes: high philopatry ofindividuals, selective migration between ponds withsimilar conditions, or strong local selection. For exam-ple, Meier (2007) reported high philopatry, but wasunable to identify if differences between populationsare a consequence of local selection or genetic driftbecause of the lack of information on gene flow amongthe populations. The novelty of this study lays in thecombination of neutral molecular markers and a quanti-

tative genetics approach with environmental informa-tion to differentiate local selection regimes and geneticdrift on phenotypic and plasticity differentiation amongpopulations. Unlike most of the earlier studies, wecould identify that although the differences in sometraits can be explained by genetic drift, in many casesthe differentiation is caused by local selection (see alsoJohansson et al. 2007).In our study, isolation-by-distance was very close to

significance and pairwise FST values ranged from 0.018to 0.155. These results indicate that some pairs of popu-lations were considerably differentiated, whereas othersformed population complexes of two or three pondsconnected with gene flow. These results are in accor-dance with previous studies on gene flow in anurans(Brede & Beebee 2004; Arens et al. 2007; Rowe & Beebee2007), but also other systems suggesting that ecologicaladaptation can be accomplished in the presence ofongoing gene flow (Via 1991; Jordan et al. 2005; Hem-mer-Hansen et al. 2007), sometimes within a few gener-ations (Antonovics et al. 1971; Freeman & Byers 2006;Franks et al. 2007). Furthermore, the strong phenotypicdifferentiation in the absence of neutral genetic differen-tiation among closely located populations with differentenvironmental conditions (Kolva, Haggedal and StoraAlmby) suggests that, despite high level of gene flow,selection may be sufficiently strong to cause life history

Table 5 P-values from a one-tailed t-test (99% confidenceinterval) comparing groups of QST values estimates amongenvironments or to QST values estimates for trait plasticities

QST comparison Mean QST Mean QST P-value

Traits15–traits19 0.234 0.378 <0.001Traits15–traits across 0.234 0.372 <0.001Traits15–traits plasticity 0.234 0.333 0.119Traits19–traits across 0.378 0.372 0.728Traits19–traits plasticity 0.378 0.333 0.535Traits across–traits plasticity 0.372 0.333 0.593

‘Traits across’ indicates that each QST is based on the traitpooled across both temperature treatments. 15 and 19 indicatethat QST was based on the trait values within a temperaturetreatment. Bold values indicate P < 0.001.

Table 6 Mantel and partial Mantel tests for correlations between genetic, geographic, forest canopy and phenotypic variation in fivepopulations

Mantel tests Partial Mantel tests

Matrixcomparison

ObservedZ Mantel’s r P-value

Matrixcomparison Observed Z Mantel’s r P-value

FST–Geo 3.0162 0.7362 0.052FC–Geo 1.6177 )0.1263 0.661FST–P15 2.3527 )0.2463 0.778 FST–P15_(Geo) )0.0569 )0.1906 0.601FST–P19 2.1276 )0.6062 0.956 FST–P19_(Geo) )0.2140 )0.6593 0.902FST–PAcross 3.1907 )0.4660 0.876 FST–PAcross_(Geo) )0.1902 )0.4740 0.825FST–Plasticity 1.5368 )0.0350 0.382 FST–Plasticity_(Geo) )0.1324 )0.4135 0.742P15–Geo 63.1663 )0.1595 0.634 P15–Geo_(FST) )0.0259 )0.0137 0.447P19–Geo 61.3823 )0.1429 0.656 P19–Geo_(FST) 0.5925 0.3527 0.265PAcross–Geo 88.5658 )0.1537 0.692 PAcross–Geo_(FST) 0.4200 0.1814 0.396Plasticity–Geo 41.5787 0.4332 0.282 Plasticity–Geo_(FST) 1.3057 0.5707 0.167P15–FC 1.5430 0.5722 0.050 P15–FC_(Geo) 0.1080 0.5637 0.062P19–FC 1.5084 0.5553 0.043 P19–FC_(Geo) 0.1139 0.5472 0.047PAcross–FC 2.1670 0.6147 0.016 Across–FC_(Geo) 0.1563 0.6073 0.028Plasticity–FC 1.0196 0.4849 0.014 Plasticity–FC_(Geo) 0.1240 0.6036 0.014

P15–FC_(FST) 0.1077 0.5704 0.063P19–FC_(FST) 0.1065 0.6343 0.045PAcross–FC_(FST) 0.1508 0.6514 0.029Plasticity–FC _(FST) 0.1107 0.4839 0.012

Geo, Euclidean distance; P15, phenotype at 15 "C; P19, phenotype at 19 "C; PAcross, phenotype across both temperature treatments;Plasticity, phenotype difference between temperature treatments; FC, forest canopy. Bold values indicate P < 0.05.

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divergence among the populations. Thus, at this spatialscale life history divergence appears to be driven moreby habitat differences than geographical isolation or alatitudinal climatic gradient, suggesting that selection inparapatry is more important than isolation in causingadaptive divergence in life history traits. Note thatwhile frogs disperse at juveniles or adult stage, we mea-sured gene flow using DNA from individuals collectedas embryos. In nature, selection against maladaptedalleles would occur later during larvae stage and thiscould explain the low FST values between some popula-tions. In future, it would be interesting to estimategenetic differences among populations using both eggsand metamorphosed juveniles the latter representingthe genotypes after selection has acted in the ponds.

Plasticity and differentiation

Although there is abundant evidence that amphibianpopulations may differ remarkably in the extent of theirthermal plastic responses when compared in commonenvironments (e.g. Berven et al. 1979; Loman 2002;Ficetola & De Bernardi 2005), studies comparing the dif-ferences over shorter geographic distances are few(Skelly 2004; Orizaola & Laurila 2009). We found signif-icant differentiation in plasticity of growth and bodymass at metamorphosis, and also evidence for naturalselection acting on growth plasticity.When the pattern and degree of environmental

change differ among habitats, different plastic responsesmay evolve among populations in different habitats.Interestingly, the reaction norms in growth were steeperin the forest ponds than in the open-canopy ponds indi-cating that forest pond tadpoles are better able toexploit high temperatures. A similar phenomenon wasfound in reaction norms of forest vs. field populationsin springtails (Liefting & Ellers 2008), suggesting thatsuch microgeographic variation in reaction norms mayoccur widely. However, while the differentiation inplasticity among the R. temporaria populations in thisstudy can reflect different selective pressures acting onplasticity, we cannot exclude the possibility that itreflects different limits to the maximization of plasticity,or a combination of both (Valladares et al. 2007).Countergradient variation implies the existence of a

covariance between genetic and environmental effects,with genotypes being nonrandomly distributed amongenvironments that influence the phenotypic trait (Fal-coner & Mackay 1996; coined ‘genetic compensation’ byGrether 2005). Crispo (2008) predicted that plasticitywould be maladaptive in this context, because migrantsadapted to different environments than residents wouldexpress a suboptimal phenotype when they develop in anew environment, and gene flow would be reduced

because well-adapted residents would be favoured overdispersers. In contrast with this prediction, the existenceof gene flow between closely located populations dis-playing strong plasticity suggests that temperature-induced countergradient variation does not provide astrong barrier for gene flow among populations differ-ing in thermal environment. This is in line with similarstudies in plant populations, which found no evidencefor decreased gene flow between agricultural or ruderalhabitats (Steinger et al. 2002) or between contaminatedand noncontaminated sites (Jimenez-Ambriz et al. 2007).Rather, temperature-induced plasticity may promote

gene flow because it allows fast initial modification of thephenotype in response to local conditions. However,gene flow is not sufficient to overcome local selectionwhich maintains the gradient, as evident from significantadaptive differentiation in populations’ means and plas-ticity of growth rate. Furthermore, these results suggestthat phenotypic divergence is governed by differentmechanisms than divergence in neutral markers andarises at spatial scales that are independent from varia-tion in neutral markers. The nonrandom distribution ofgenotypes in countergradient variation implies the actionof strong environment-specific selection over specific loci.More studies on the relationship between countergradi-ent variation and gene flow are needed to determine thegenerality of the present results, and their concordanceand contrast with predictions from the theory (Crispo2008) to understand the importance of phenotypic plas-ticity on local adaptation and population divergence.It should be noted that Crispo (2008) treated counter-

gradient variation as a form of maladaptive plasticitywhere the slopes of the reaction norms are in oppositedirections (Fig. 3c–d in Crispo 2008), and selection pro-motes a reduction in the gene flow between environ-ments, because plasticity moves phenotypes away fromcharacter optima. However, countergradient variationdoes not necessarily imply a pattern of crossing reactionnorms, and the observed plasticity is not necessarilymaladaptive but can be considered as adaptive (Conov-er et al. 2009). In this study, the reaction norms are non-parallel, in line with the predictions of seasonallatitudinal compensation model (Conover & Present1990; Yamahira et al. 2007).Non-crossing reaction norms in a countergradient

variation context imply that some genotypes displaytrait values with higher fitness across the range of envi-ronments, hence the same genotype would be favouredby selection in all thermal environments (Wolf et al.2004). The unresolved question is: why do the amphib-ian populations show noncrossing reaction norms andcountergradient variation? If the genetic variation forhigher growth capacity exists, why is the faster geno-type not present in all the environments? The most

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plausible explanation seems to be some trade-offs asso-ciated with high-genetic capacity for growth in someenvironments. To identify potential trade-offs, it is nec-essary to determine the mechanisms by which certaingenotypes achieve rapid growth (Storm & Angilletta2007). In the present case, it seems possible that temper-ature is not the most constraining factor in the pondenvironments. While several factors can select againstfast growth (reviewed by Arendt 1997, Metcalfe andMonaghan 2001), predation risk select against it. Studieson fish (Lankford et al. 2001) and anurans (Laurila et al.2006; for R. temporaria; Laurila et al. 2008) have demon-strated that fast-growing individuals from highlatitudes suffer higher predation mortality than slow-growing low-latitude individuals. The density of preda-tors is highly variable among the ponds, with higherdensities of predators in open canopy ponds (Freiden-burg & Skelly 2004). In environments with higher pre-dation risk, high mobility is dangerous and reducesfitness of the more active, fast-growing genotypes(Lankford et al. 2001; Laurila et al. 2006, 2008). Conse-quently, variation in growth rates and their plasticity maybe the result of an adaptive balancing of costs and ben-efits associated with growth, which may result in differ-ent optima in different environments (Gotthard 2001).

Conclusions

In summary, we have demonstrated that selection canlead to genetic differentiation in larval life history traitsand phenotypic plasticity at fine spatial scale in popula-tions with no physical barriers to gene flow. Much ofthis variation seems to be related to the quality of theindividual breeding ponds and in the conditions cre-ated by the surrounding terrestrial vegetation, espe-cially the presence of shading forest canopy.

Acknowledgements

We thank Mattias Sterner for expert help in the field andthree anonymous reviewers for valuable comments on an ear-lier draft of the manuscript. The work was supported by theSwedish Research Council (to AL), Formas (AL), grantMEC2007-0944 of the Spanish Ministry of Education and Cul-ture (ARB) and a travel grant from University of Barcelona(ARB).

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A.R.-B., currently a postdoctoral researcher in the EvolutionaryBiology Center (Uppsala University), is interested in popula-tion structure of natural populations and their effects on localadaptation, phenotypic plasticity and molecular genetic varia-tion. C.T. is a researcher at the Natural History Museum inParis and mainly interested in constraints on adaptation innatural populations. B.R. is a post doctoral researcher atthe Evolutionary Biology Center (Uppsala University) and ismainly interested in population structure, local adaptation and

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phenotypic plasticity of amphibian and fish populations. A.L.is interested in evolutionary processes in contemporary popu-lations.

Supporting information

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Appendix S1 Supplementary information about local habitatvariables and genetic variation.

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