ORIGINAL ARTICLE

doi:10.1111/j.1558-5646.2011.01472.x

ADAPTIVE DIVERGENCE IN MOOR FROG(RANA ARVALIS) POPULATIONS ALONG ANACIDIFICATION GRADIENT: INFERENCESFROM QST – FST CORRELATIONSSandra Hangartner,1,2 Anssi Laurila,3 and Katja Rasanen1

1EAWAG, Department of Aquatic Ecology and ETH-Zurich, Institute of Integrative Biology (IBZ), Ueberlandstrasse 133,

CH-8600 Duebendorf, Switzerland2E-mail: sangartner@hotmail.ch

3Population and Conservation Biology/Department of Ecology and Genetics, Evolutionary Biology Center,

Uppsala University, Norbyvagen 18D, SE-752 36 Uppsala, Sweden

Received June 14, 2011

Accepted September 13, 2011

Microevolutionary responses to spatial variation in the environment seem ubiquitous, but the relative role of selection and neutral

processes in driving phenotypic diversification remain often unknown. The moor frog (Rana arvalis) shows strong phenotypic

divergence along an acidification gradient in Sweden. We here used correlations among population pairwise estimates of quan-

titative trait (PST or QST from common garden estimates of embryonic acid tolerance and larval life-history traits) and neutral

genetic divergence (FST from neutral microsatellite markers), as well as environmental differences (pond pH, predator density, and

latitude), to test whether this phenotypic divergence is more likely due to divergent selection or neutral processes. We found that

trait divergence was more strongly correlated with environmental differences than the neutral marker divergence, suggesting

that divergent natural selection has driven phenotypic divergence along the acidification gradient. Moreover, pairwise PSTs of em-

bryonic acid tolerance and QSTs of metamorphic size were strongly correlated with breeding pond pH, whereas pairwise QSTs of

larval period and growth rate were more strongly correlated with geographic distance/latitude and predator density, respectively.

We suggest that incorporating measurements of environmental variation into QST – FST studies can improve our inferential power

about the agents of natural selection in natural populations.

KEY WORDS: Acid stress, adaptive divergence, amphibians, environmental gradient, environmental stress, QST – FST comparisons.

One of the central goals of evolutionary biology is to under-

stand which forces shape phenotypic diversification (Fisher 1930;

Wright 1931). Although microevolutionary responses to spa-

tial and temporal variation in natural selection seem ubiquitous

(Kawecki and Ebert 2004; Leinonen et al. 2008), neutral pro-

cesses (mutation, gene flow, and genetic drift) can also influence

diversification (e.g., Lande 1992). In particular, genetic drift can

give rise to random phenotypic variation, especially in small pop-

ulations (Jones et al. 1968; Spitze 1993). Gene flow can constrain

adaptive divergence (reviewed in Lenormand 2002; Rasanen and

Hendry 2008) and nonrandom dispersal/gene flow can facilitate

divergence (e.g., Sacks et al. 2008; Mendez et al. 2011). Although

the potential contribution of selection and neutral processes has

been long acknowledged, their relative importance is still much

debated (O’Hara 2005).

Comparisons of divergence in quantitative traits (as estimated

by QST) and divergence in neutral marker genes (as estimated by

FST) are increasingly used to disentangle adaptive from neutral

phenotypic differentiation (Leinonen et al. 2008; Whitlock 2008).

Stronger divergence in quantitative traits than in neutral markers

8 6 7C© 2011 The Author(s). Evolution C© 2011 The Society for the Study of Evolution.Evolution 66-3: 867–881

SANDRA HANGARTNER ET AL.

(QST > FST) is taken as evidence for divergent selection, and lower

divergence (QST < FST) as evidence for stabilizing selection. A

lack of significant differences (QST = FST) implies that the relative

effects of neutral processes and selection on population differen-

tiation cannot be separated (e.g., Raymond and Rousset 1995;

Merila and Crnokrak 2001). However, although QST – FST com-

parisons are a valuable technique to eliminate neutral processes

as an explanation for divergence, additional approaches—such as

correlations of divergence with environmental differences and/or

direct measurements of selection—are required to understand the

nature of selection (Whitlock 2008).

In this context, comparisons of populations along environ-

mental gradients can be particularly useful. Intraspecific cline

studies have provided strong evidence for adaptation to spatially

varying selection (e.g., Antonovics and Bradshaw 1970; Conover

and Schultz 1995; Gilchrist and Partridge 1999). However, the rel-

ative contributions of different processes that contribute to adap-

tive divergence along geographical clines often remain unclear

(e.g., Huey et al. 2000). In particular, the homogenizing effects

of gene flow should be strong at shorter geographic distances,

and its relative importance become weaker at larger geographic

distances. Phenotypic divergence among geographically distant

populations could therefore arise from nonadaptive isolation-by-

distance (IBD) processes rather than divergent selection (Gould

and Johnston 1972; Hutchison and Templeton 1999). The relative

importance of selection versus neutral processes in phenotypic

divergence along environmental gradients should then depend on

the spatial correlation between the selective environment and ge-

ographic distance.

QST – FST comparisons along environmental gradients, com-

bined with environmental measurements, should be a particularly

powerful method to disentangle evolutionary forces: not only do

they allow one to test if phenotypic divergence is likely caused by

selection, but they also allow inferences about what the selective

agent might be (Whitlock 2008). Although geographic distance

is often used as a proxy for spatially correlated selection—in par-

ticular in studies on large-scale clinal variation (Storz 2002; Palo

et al. 2003; Antoniazza et al. 2010)—measurements of local vari-

ation in selective agents may allow stronger arguments about the

putative agents of natural selection (Richter-Boix et al. 2010).

Environmental stress, defined as an environment that lies

outside the range of preferred conditions and challenges an or-

ganism’s ability to maintain function (Ghalambor et al. 2007),

can be a powerful evolutionary force (Hoffmann and Parsons

1991). It can alter the expression of phenotypic and genetic vari-

ation (Hoffmann and Merila 1999) and drive local adaptation

(e.g., Antonovics and Bradshaw 1970; Rasanen et al. 2003b;

Gomez-Mestre and Tejedo 2004; Jimenez-Ambriz et al. 2007).

However, to what extent phenotypic divergence along environ-

mental stress gradients reflects selection versus neutral processes

has been rarely studied (but see Gomez-Mestre and Tejedo 2004;

Jimenez-Ambriz et al. 2007; Lee and Boulding 2010; Miner and

Kerr 2011). Moreover, the inferences on the relative role of se-

lection versus neutral processes in QST – FST comparisons may

be influenced by the environmental conditions under which the

phenotype is expressed: populations may differ in their responses

to the environment (Palo et al. 2003; Gomez-Mestre and Tejedo

2004; Alho et al. 2011), as reflected in genotype-by-environment

interactions (G × E; Via and Lande 1985). To make accurate infer-

ences about how natural populations respond to natural selection,

it is necessary to study quantitative trait divergence under relevant

environmental conditions (e.g., Palo et al. 2003; Leinonen et al.

2008; Alho et al. 2011).

In a recent common garden study on Rana arvalis along a

160 km acidification gradient in Sweden, we found strong phe-

notypic divergence among eight populations. Embryonic acid tol-

erance, larval growth and metamorphic size increased, and larval

development rate decreased with decreasing breeding pond pH

(Hangartner 2010). However, the magnitude of phenotypic di-

vergence among populations was correlated with environmental

differences (in particular, breeding pond acidity, predator density,

and latitude). Because phenotype-environment correlations may

be confounded by neutral IBD processes (Gould and Johnston

1972; Hutchison and Templeton 1999), we here use correlations

between population pairwise QSTs (embryonic acid tolerance and

larval life-history traits), FSTs (microsatellite markers) and envi-

ronmental differences (pond pH, latitude, and predator density)

to estimate the relative importance of divergent natural selection

and neutral genetic processes in trait divergence. We made the

following main predictions. First, if phenotypic divergence along

the acidification gradient is solely due to neutral IBD, QSTs and

FSTs should show similar patterns of increase with increasing

geographic distance. Second, if phenotypic divergence reflects

response to acidity (or predation/latitude) mediated selection,

QSTs should increase more strongly with increasing differences

in breeding pond pH (or predation/latitude) than FSTs. To account

for potential G × E interactions, we further investigated to what

extent inferences based on QST – FST correlations depend on the

environment under which the phenotype is expressed. Finally,

to gain insight into the potential use of neutral marker varia-

tion as a proxy for fitness trait variation (e.g., Leinonen et al.

2008), we investigated to what extent divergence in quantita-

tive traits and neutral genetic markers are correlated in our study

system.

Material and MethodsSTUDY SYSTEM

R. arvalis is a brown frog that occurs through the western Palearc-

tic and inhabits a range of pHs (Glandt 2006). Our previous

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Q ST AND F ST COMPARISONS IN RANA ARVALIS

Table 1. Descriptive information on Rana arvalis study populations. The columns show geographic coordinates (N/E), pond pH

(mean ± SD), approximate number of breeding females (clutch counts in 2007–2009), predator density (individuals/five sweeps), al-

lelic richness (mean ± SD), average observed heterozygosity (H0), expected heterozygosity (HE, mean ± SD), and loci showing signs of

null alleles.

Breeding Predator Allelic NullPopulation Latitude N Longitude E Pond pH females density richness H0 HE alleles

Kungsbacka (Ku) 57◦49′69.90” 12◦06′02.23” 4.9±0.2 80 10.2 4.68±3.02 0.60 0.64±0.18Tottajarn (To) 57◦60′33.51” 12◦57′94.96” 4.0±0.2 100 6.0 4.73±2.95 0.58 0.60±0.25Viskafors (Vi) 57◦65′39.55” 12◦87′23.07” 5.6±0.3 270 6.2 4.70±3.16 0.60 0.61±0.24Lomsjo (Lo) 57◦76′32.37” 12◦88′42.22” 4.0±0.2 50 14.2 5.01±3.14 0.56 0.63±0.22 WRA160, RC1Bergsjo (Be) 58◦20′10.07” 13◦48′42.59” 6.1±0.3 >500 4.7 5.13±3.79 0.59 0.59±0.24Stubberud (St) 58◦45′72.25” 13◦76′48.39” 7.3±0.2 250 7.0 4.82±3.49 0.61 0.63±0.19 RECRud (Ru) 58◦59′07.65” 13◦79′05.51” 7.0±0.2 300 6.5 4.77±2.93 0.50 0.58±0.25 RtsB14

comparisons between populations originating from acid and

neutral environments suggest that environmental acidity drives

divergence in maternal investment (larger but fewer eggs in

acid origin populations), in embryonic acid tolerance (higher

survival of acid origin embryos at low pH), and in lar-

val life-history traits (faster growth and slower development,

and larger metamorphic size of acid origin larvae; Rasanen

et al. 2003a, 2005; Rasanen et al. 2008; Hangartner 2010). Quan-

titative genetic crosses further indicate that variation in embryonic

acid tolerance is driven by maternal effects (Rasanen et al. 2003b;

Merila et al. 2004; Persson et al. 2007; Hangartner 2010), whereas

divergence in larval traits is primarily due to direct genetic effects

(Hangartner 2010).

STUDY POPULATIONS AND ENVIRONMENTAL

VARIATION

Seven populations, for which both phenotypic and genetic data

were available, from about 160 km transect in southwestern Swe-

den were used in this study (Table 1, Fig. 1A and B; for further

details see Hangartner 2010). As stated above (see Introduction),

these populations show strong phenotypic divergence, which cor-

relates with environmental differences along the gradient in a trait

specific manner (Hangartner 2010). All study populations inhabit

permanent ponds or small lakes in forested areas, with average

breeding pond pH ranging from 4.0 to 7.3 (Table 1). The exact

acidification history of our study ponds is not known, but studies

on nearby lakes show a mix of anthropogenic and natural acidifi-

cation (Renberg et al. 1993). The two most neutral sites (Rud and

Stubberud) are naturally buffered due to limestone bedrock and

presumably have no history of acidification (Table 1, Fig. 1A).

Three environmental measures were selected as indicators of

spatially varying selection, based on our previous study (Hangart-

ner 2010): pond pH (strongly correlated with divergence in em-

bryonic acid tolerance and metamorphic mass), latitude (strongly

correlated with divergence in larval period), and predator density

(correlated with divergence in larval growth). The details of the

environmental measurements are given in Hangartner (2010).

COMMON GARDEN EXPERIMENTS

QST estimates were calculated from two common garden experi-

ments that were performed to estimate the extent (2008) and the

quantitative genetic basis (2009) of among population divergence

along the acidification gradient (Hangartner 2010). All seven pop-

ulations were used in 2008, and five of them also in 2009 (Table 2).

For populations where data were available for both years, data

were pooled to increase the number of families per population,

and hence accuracy of the QST estimates (Whitlock and Guillaume

2009).

Similar experimental conditions were used in both years. The

details for these experiments are described elsewhere (Hangartner

2010), and we here only provide a short summary. Adult frogs

were collected from the wild, brought to Uppsala University, Swe-

den (59◦50′N, 17◦50′E), and maintained in climate chambers at

+2◦C to 4◦C until crossed artificially a few days later. Artificial

mating prevented any bias due to differences in exposure in the

early environment and assured that the offspring in each family

were full siblings. In males, sperm production was stimulated with

fish hormone LHRH (H-7525, Bachem Bioscience Inc. Buben-

dorf, Switzerland, Browne et al. 2006), whereas females were

not treated with hormones as they had already ovulated. Sperm

from males was collected into 0.9-l plastic vials containing 10%

Amphibian Ringer solution and eggs from the females were subse-

quently stripped into the sperm solution and treated using standard

procedures (Rasanen et al. 2003a).

Fertilized eggs were divided in the treatments two hours after

fertilization. Embryos and tadpoles were reared in reconstituted

soft water (RSW) at two pHs (acid: embryos pH 4.0; larvae pH

4.3; neutral: pH 7.5). RSW was used untreated in the neutral

treatment and adjusted with 1 M H2SO4 in the acid treatments, and

changed every three (embryos) and two to three days (larvae) to

EVOLUTION MARCH 2012 8 6 9

SANDRA HANGARTNER ET AL.

Figure 1. (A) Maps showing the location of the study region within Sweden (left-hand side) and the location of the seven Rana arvalis

populations (right-hand side, solid dots) in relation to large-scale anthropogenic acidification history. (Source: Swedish Environmen-

tal Protection Agency: http://www.internat.environ.se/index.php3). (B) Pond pH of the study populations plotted against geographic

distances from the Ku population ( = 0 km). The pairwise differences between each study population are given in Table S1.

maintain appropriate pH and water quality (Hangartner 2010). The

experiments were conducted in a walk in climate room (+16◦C)

with a 17L:7D photoperiod.

For the embryonic experiment, 30–50 embryos per replicate

were reared in 0.9-l plastic vials containing 0.5-l of treatment

water. Embryos were reared from fertilization (day 0) to day 12,

that is, when all possible larvae had hatched and most had reached

the free-swimming stage (about Gosner stage 20, Gosner 1960).

Survival of embryos was recorded during each water change. For

the larval experiment, a randomly selected subset of tadpoles from

each family was selected from the neutral embryonic treatment as

soon as they had reached Gosner stage 25 (complete gill absorp-

tion and initiation of independent feeding, Gosner 1960, about

12–15 days after fertilization). We used tadpoles from the neutral

treatment to avoid any potential bias due to selective mortality in

the acid treatment (survival is strongly reduced in acid, but high

in the neutral treatment). Tadpoles were randomly assigned to one

of the two pH treatments and reared singly in 0.9-l opaque plastic

vials containing 0.7-l of treatment water. They were fed ad libitum

with finely chopped and parboiled spinach every second to third

day in conjunction with water change and the amount of food was

increased with increasing age of the tadpoles. When the tadpoles

approached metamorphosis (emergence of at least one front leg;

Gosner stage 42) the vials were checked daily.

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Q ST AND F ST COMPARISONS IN RANA ARVALIS

Table 2. Number of families used and phenotypic variation (mean ± SD embryonic survival, metamorphic mass, larval period, and

growth rate) in different pH treatments (pH 4.0 = embryonic, pH 4.3 = larval) for seven Rana arvalis populations (pop). Data from

common garden experiments in 2008 (08) and 2009 (09). Details for environmental measurements are given in text. na = data not

available.

Embryonic experiment Larval experiment

Families Survival Families Mass Larval period Growth rate(N) (%) (N) (mg) (days) (mg/day)

Pop pH treatment 08 09 08 09 08 09 08 09 08 09 08 09

Ku pH 7.5 8 9 96.9 ± 4.2 92.7 ± 11.6 8 9 698 ± 77 424 ± 40 61 ± 4 66 ± 5 11.5 ± 1.6 6.4 ± 0.8pH 4.0/4.3 20.7 ± 15.6 11.0 ± 9.5 621 ± 55 383 ± 54 62 ± 5 71 ± 6 10.0 ± 1.1 5.4 ± 0.6

To pH 7.5 5 8 97.9 ± 4.6 98.8 ± 2.8 5 9 775 ± 67 453 ± 55 62 ± 3 65 ± 3 12.5 ± 1.1 7.0 ± 0.8pH 4.0/4.3 38.1± 22.0 34.4 ± 21.4 649 ± 82 404 ± 68 62 ± 4 68 ± 5 10.5 ± 1.5 5.9 ± 0.7

Vi pH 7.5 6 0 99.6 ± 1.2 na 6 0 719 ± 91 na 60 ± 4 na 12.1 ± 1.7 napH 4.0/4.3 23.9 ± 20.3 na 650 ± 92 na 63 ± 5 na 10.3 ± 1.5 na

Lo pH 7.5 11 12 97.6 ± 5.4 98.6 ± 2.6 11 11 724 ± 62 451 ± 57 57 ± 4 67 ± 5 12.7 ± 1.4 6.8 ± 0.7pH 4.0/4.3 24.6 ± 19.0 12.6 ± 12.7 654 ± 53 395 ± 47 60 ± 4 69 ± 5 11.0 ± 1.1 5.7 ± 0.6

Be pH 7.5 9 0 98.1 ± 3.3 na 10 0 659 ± 52 na 56 ± 3 na 11.7 ± 0.9 napH 4.0/4.3 16.9 ± 20.0 na 592 ± 66 na 59 ± 4 na 10.1 ± 1.1 na

St pH 7.5 9 8 99.4 ± 1.4 96.3 ± 7.1 9 9 638 ± 67 394 ± 37 54 ± 4 61 ± 4 11.8 ± 1.2 6.5 ± 0.6pH 4.0/4.3 7.6 ± 8.9 3.4 ± 5.4 575 ± 68 340 ± 40 57 ± 3 68 ± 5 10.2 ± 1.3 5.0 ± 0.6

Ru pH 7.5 10 12 98.9 ± 3.8 97.4 ± 4.5 10 12 669 ± 76 398 ± 41 57 ± 3. 61 ± 5 11.8 ± 1.3 6.5 ± 0.7pH 4.0/4.3 7.1 ± 6.5 7.1 ± 6.0 598± 65 343 ± 59 60 ± 4 66 ± 5 10.1 ± 1.3 5.3 ± 0.9

The number of families for the embryonic and larval exper-

iments over the two study years ranged from six to 23 per pop-

ulation (Table 2). Within each year, the embryonic experiment

consisted of three replicates/family/treatment and the larval ex-

periment of nine replicates/family/treatment. Within both exper-

iments and years, the experimental vials were fully randomly

distributed over three blocks (embryos: one replicate per family

and treatment per block, larvae: three replicates per family and

treatment per block) according to a known temperature gradient

within the room. This design resulted in a total of 627 experimen-

tal units for the embryonic (2008: 348, 2009: 279) and a total of

1559 experimental units for the larval experiment (2008: 1011,

2009: 548).

Final survival (day 12) was used in the statistical analyses

of embryonic survival. Any unfertilized eggs were determined in

conjunction to the first water change (day 3) and were excluded

from the survival analyses, as were any abnormal hatchlings.

Embryonic survival was estimated as healthy hatched larvae at

day 12/total number of fertilized eggs in each experimental unit.

In the larval experiment, mass at metamorphosis, larval period

and growth rate was measured for each individual as described

in Hangartner (2010). Wet mass was measured with an elec-

tronic balance (to the nearest 0.1 mg). Larval period was mea-

sured as the number of days from Gosner stage 25 (day 0 of

larval experiment) to metamorphosis. Average daily growth rate

(mg/day) was defined as the ratio of mass at metamorphosis/larval

period.

MOLECULAR GENETIC DATA

Neutral genetic differentiation was assessed for all seven pop-

ulations. Embryos from 25 to 36 clutches at Gosner stage

10–19 were randomly collected in each population in 2007, ex-

cept in Stubberud, which was sampled in 2009. One embryo per

clutch was used for the genetic analyses to assure nonindepen-

dence of the genetic material. The 12 microsatellite loci used

were: RtCa2–22 (T. Garner, unpubl. data), WRA1–22, WRA1–

28, WRA1–160, WRA6–8 (P. Arens, unpublished, Genbank ac-

cession AJ1419881–84), RECALQ, RCO8604, and RCIDII (Vos

et al. 2001), RLaCa41 (Garner and Tomio 2001), RtsB14 (Berlin

et al. 2000), RtμP (Pidancier et al. 2002), and Ga1a19 (Zeisset

et al. 2000). Egg capsules were first removed from the eggs and

DNA extracted from embryos using a high-salt extraction pro-

cedure (Aljanabi and Martinez 1997). Multiplexed polymerase

chain reaction (PCR) amplifications were performed in a total

volume of 5 μl containing some 50 ng DNA template, 2.5 μg

PCR mix (QIAGEN Multiplex PCR Kit), 1 μl 10xprimer mix,

1.5 μl RNAse-free water in a TProfessional Basic (Biometra)

thermal cycler. PCR profiles consisted of 15 min denaturation at

95◦C followed by 30–38 cycles of 30 sec denaturation at 94◦C,

90 sec annealing at 50–60◦C, and 60 sec extension at 72◦C with a

final step of 30 min at 72◦C. PCR products were visualized using

an ABI 3739xI DNA analyzer (Applied Biosystems) and alleles

were scored using GeneMapper software v3.7 (Applied Biosys-

tems). A total of 173 R. arvalis embryos (22–34 individuals per

population) were genotyped.

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SANDRA HANGARTNER ET AL.

To estimate neutral genetic differentiation, global and pair-

wise FSTs were estimated according to Weir and Cockerham

(1984). This method is appropriate for pairwise FST – QST

comparisons, because variance among groups is calculated in the

same way as for QST (Whitlock 2008). In addition, genetic diver-

gence was also assessed using Slatkin’s RST (Ohta and Kimura

1973; Valdes et al. 1993; Slatkin 1995). RST (based on the stepwise

mutation model) accounts for microsatellite mutation pattern and

is better suited than FST when mutation is important relative to

migration (Slatkin 1995; Whitlock 2011). Eight markers (RtμP,

Ga1a 19, RtCa2–22, RLaCa41, WRA1–22, WRA1–160, WRA6–

8, and RCO8604) were used for estimating RSTs, as four (RtCa2–

22, WRA1–28, RECALQ, and RCIDII) of the 12 markers violated

the assumptions of the stepwise mutation model. After standard-

izing allele lengths (zero mean, unit variance), Goodman’s (1997)

ρ was used as an estimator of RST. The program FSTAT version

2.9.3.2 (Goudet 1995) was used to estimate pairwise FSTs and al-

lelic richness, and RSTCalc 2.2 (Goodman 1997) to estimate RSTs.

Measures of genetic differentiation (hereafter, “FST”) were calcu-

lated for all pairwise combinations of populations. When multiple

comparisons were made, a sequential Bonferroni correction of the

significance level was applied using a global significance level of

0.05 (Rice 1989).

Arlequin (Excoffier et al. 2005) was used to test for link-

age disequilibrium between pairs of loci, to estimate global FST

and to estimate expected and observed heterozygosities. Tests of

departures from Hardy–Weinberg expectations were performed

using the Markov chain method (Raymond and Rousset 1995)

with 10,000 permutations. Null alleles (Pemberton et al. 1995)

can lead to unknown biases in estimates of allele frequencies and

population differences. Microchecker 2.2.3 (Van Oosterhout et al.

2004) was used to test for the presence of null alleles, and potential

genotyping errors due to stuttering and allelic dropouts. Evidence

for null alleles was detected in three populations at four differ-

ent markers (Table 1). Corrected allele frequencies for each locus

and population containing null alleles were estimated using the

Brookfield Estimator 2 (Brookfield 1996), under the assumption

that the nonamplified samples represent null allele homozygotes.

All estimates of population differentiation were carried out using

both the original (nonadjusted for null alleles) data and the ad-

justed data. As the results were qualitatively similar, we present

the results for the nonadjusted genotypes only.

STATISTICAL ANALYSES

All statistical analyses were performed using SAS 9.2 (SAS Insti-

tute, Inc.). To test for pH treatment effects and phenotypic differ-

ences among populations, mixed model analyses were conducted

with pH treatment, year, block (nested under year), and popula-

tion as fixed factors and family (nested under population) as a

random factor. Embryonic survival was analyzed using general-

ized linear mixed models with binomial errors, logit link function,

and Kenward–Roger degrees of freedom method in the GLIM-

MIX procedure (Littell et al. 2006). Metamorphic mass, larval

period, and growth rate were log transformed to homogenize vari-

ances and analyzed using mixed model analyses of variance using

the MIXED procedure and Kenward–Roger degrees of freedom

method (Littell et al. 2006).

Quantitative trait divergence indices (PST or QST, see below)

were generated for each trait based on mixed model analyses for

each population pair and within each pH treatment. In these mod-

els, year and block were included as fixed factors and population

and family (nested within population) as random factors. As di-

vergence in embryonic acid tolerance in R. arvalis is driven by

maternal effects (e.g., Rasanen et al. 2003b; Persson et al. 2007)

and it is not known to what extent these reflect environmental

versus genetic maternal effects, we here call population differ-

entiation in embryonic survival PST (for phenotypic or pseudo –

QST)—as proposed by Saether et al. (2007). PSTs for embryonic

survival were extracted from the generalized linear mixed mod-

els, and QSTs for metamorphic mass, larval period, and growth

rate from the mixed model analyses of variance. Variance compo-

nents were extracted from these analyses for each trait and each

pH treatment separately and used for estimating QST using the

equation (Spitze 1993; O’Hara and Merila 2005):

QST trait =Vb/(2VW + Vb),

where Vb is the between population genetic variance and Vw the

within population genetic variance. Vb was obtained directly from

the estimated variance component for the population effect (be-

tween populations) and the within population variance (Vw) was

estimated using the among family variance component (Vf ) and

multiplied by 2 (Vw = 2Vf ) as we used a full-sibling design (Roff

1997).

Because our data are based on common garden rearing, the

environmental source of phenotypic variance that can bias QST

estimates (Pujol et al. 2008) can be excluded. However, because

the data is based on full-sibling analyses on F1 offspring from

adults collected from the wild, the genetic estimates are broad

sense estimates of genetic variance and can be influenced by

nonadditive genetic and maternal sources of variance (Lynch and

Walsh 1998).

MANTEL TESTS

Mantel tests and partial Mantel tests (Mantel 1967; Manly 1997)

were used to analyze correlations between geographic (distance)

and environmental (pond pH and predator density) variables and

genetic (FST) and phenotypic (QST) components (Antoniazza

et al. 2010). The basic Mantel test examines the relationship

between one dependent (e.g., FST) and one independent (e.g.,

8 7 2 EVOLUTION MARCH 2012

Q ST AND F ST COMPARISONS IN RANA ARVALIS

geographic distance) variable matrix, whereas a partial Mantel

test allows comparisons among one dependent variable matrix and

two (or more) independent variable matrices (Mantel 1967; Manly

1997). Castellano and Balletto (2002) used repeated simulations

to show that partial Mantel test is a valid method for analyzing

association patterns between distance matrices. All Mantel tests

were performed in FSTAT , separately for the two pH treatments

and the four phenotypic traits, using 10,000 permutations. First,

a correlation between pairwise FSTs and the geographic distance

matrices was performed to test for an IBD relationship. Second,

correlations between population pairwise FSTs and QSTs were

performed to test if neutral genetic differentiation explains diver-

gence in quantitative traits. Third, correlations between popula-

tion pairwise QSTs with the matrix of environmental differences

were performed to test if phenotypic differentiation is related to

environmental differences (results shown in Table S2). A simple

way to then test if phenotypic divergence is more likely driven by

divergent selection or neutral processes is to correlate the matrix

of pairwise differences between QST and FST (QST – FST) with

the matrix of environmental differences (Antoniazza et al. 2010).

This approach enables one to test if phenotypic differentiation

along the pH gradient remains even after accounting for neutral

genetic divergence. In particular, if phenotypic divergence is only

due to neutral processes, the two matrices (the QST – FST matrix

and the environmental difference matrix) should be uncorrelated.

In contrast, a positive correlation between them would strongly

indicate that selection by the environmental factor is driving phe-

notypic divergence (Antoniazza et al. 2010). Moreover, because

comparing pairwise FST and QST with environmental differences

does not depend on the absolute values of phenotypic and genetic

divergence but on the slopes of the respective regressions, this

approach also provides a more robust test of gradually varying

selection (Antoniazza et al. 2010).

In addition to pH, several other environmental factors vary

along the acidity gradient and may cause divergent selection

(Hangartner 2010). First, we asked if acidity (pH or something

directly correlated with it) drives phenotypic divergence along the

acidity gradient. As pond acidity and geographic distance are cor-

related along this gradient (Hangartner 2010), we tested if pheno-

typic divergence is more strongly related to pond pH than to geo-

graphic distance. Second, as divergence in larval period correlates

with latitude and divergence in growth rate with predator density

(Hangartner 2010), we tested if these correlations between phe-

notype and environment remain after neutral genetic divergence

is accounted for (by performing Mantel tests comparing the pair-

wise QST – FST matrix to the matrix of pairwise differences in

latitude or predator density). However, as the differences in lati-

tude and geographic distance are very highly correlated along this

acidification gradient (Mantel’s r = 0.97, P < 0.001), we used ge-

ographic distance as an indicator of geographically/latitudinally

ordered selection. If more than one habitat variable was corre-

lated with a given quantitative trait, partial Mantel tests were

subsequently performed to estimate which habitat variable best

explains phenotypic divergence in the quantitative trait when ac-

counting for neutral genetic processes. In these tests, pairwise

QST - FST matrix was used as the dependent variable matrix and

the two habitat matrices as two independent matrices.

ResultsPHENOTYPIC DIVERGENCE

As the detailed tests of among population divergence in pheno-

typic traits are presented elsewhere (Hangartner 2010), we here

only provide a short summary of trait means and statistical re-

sults to confirm that these populations differ significantly even

after accounting for annual variation. For all populations and

across both years, low pH treatment reduced embryonic survival

(Table 2, pH treatment: P < 0.001), metamorphic size and growth

rate, and increased larval period (Table 2; pH treatment: all P <

0.001). The seven populations showed clear divergence in all four

traits at both acid and neutral pHs (Table 2; population main effect:

all P < 0.001), with the exception of embryonic survival at pH

7.5, which was very high and similar among populations (range

95–99%, P = 0.290). In short, acid origin populations had higher

embryonic acid tolerance (survival at pH 4.0), larger metamorphic

mass, faster growth, and slower development rates. Among popu-

lations, differences in larval traits were roughly similar in both pH

treatments (Table 2). However, there were substantial differences

between the two years in all traits (Table 2, all P < 0.004). As the

conditions in the laboratory were very similar between the two

years, the differences found here may be due to annual variation

in the environmental conditions in nature, as has been suggested

in Dahl (2011).

NEUTRAL GENETIC DIFFERENTIATION

Variability in the 12 microsatellite markers ranged from one to

24 alleles per locus (with an average of 6.2) and population.

Population average allelic richness ranged from 4.45 to 5.40, and

expected heterozygosity from 0.581 to 0.637 (Table 1). There

was evidence for the presence of null alleles in three populations

at four different loci, with no loci showing systematic presence

of null alleles across populations (Table 1). Across loci, Lomsjo

(Lo) and Rud (Ru) showed significant FIS values (0.11 and 0.13,

respectively). When all populations were pooled, none of the loci

showed significant linkage disequilibrium.

The average FST (0.017; P < 0.001) revealed low but sig-

nificant genetic differentiation among the seven populations.

Pairwise FSTs between populations ranged from 0.003 to 0.044

(Table S1), and were significantly different from zero except for

three population pairs (Table S1). The average RST (0.037) was

EVOLUTION MARCH 2012 8 7 3

SANDRA HANGARTNER ET AL.

A

B

Figure 2. Pairwise neutral genetic differentiation using (A) FST

and (B) RST plotted against pairwise geographic distances among

seven Rana arvalis population pairs.

somewhat higher than the average FST (0.017). Pairwise RSTs

between populations ranged from 0.002 to 0.070 and were signif-

icantly correlated with FSTs (Mantel’s r = 0.51, P = 0.016, N =7). A significant positive correlation between pairwise FSTs and

geographical distance (Mantel’s r = 0.56, P = 0.013, Fig. 2A)

suggested IBD in neutral divergence among these populations.

A significant positive correlation between pairwise FSTs and dif-

ference in pond pH (Mantel’s r = 0.55, P = 0.015) most likely

arose due to the strong correlation between differences in pond

pH and geographic distance (Mantel’s r = 0.62, P = 0.003). Ad-

ditionally, pond pH was highly positively correlated with latitude

(Pearson’s r = 0.86, P = 0.012), but not significantly correlated

with predator density (Mantel’s r = −0.50, P = 0.254). There was

no significant correlation between pairwise RSTs and geographi-

cal distance (Mantel’s r = -0.09, P = 0.708; Fig. 2B) or between

RSTs and differences in pond pH (Mantel’s r = 0.186, P = 0.426).

For reasons explained in the Discussion, only estimates of FSTs

were considered in further analyses.

COMPARISONS OF NEUTRAL GENETIC AND

QUANTITATIVE TRAIT DIFFERENTIATION

For all traits, average divergence in quantitative traits was higher

than average divergence in neutral markers (FST = 0.017).

Divergence in embryonic survival was about eight times higher

at pH 4.0 (PST = 0.139), but about four times smaller at pH 7.5

(PST = 0.004). Divergence in larval traits was about 14 times

higher for metamorphic mass (QST at pH 4.3 = 0.218, at pH

7.5 = 0.277) and about six times higher for growth rate (QST at

pH 4.3 = 0.123, at pH 7.5 = 0.105) at both pHs, but for larval

period about six times higher at pH 4.3 (QST = 0.113) and about

13 times higher at pH 7.5 (QST = 0.221).

Pairwise FSTs were significantly correlated with pairwise

PSTs for embryonic survival at pH 4.0 (Table 3A) and with QST for

mass at metamorphosis at pH 4.3 (Table 3A), but only marginally

so for mass at metamorphosis at pH 7.5 (Table 3A). Pairwise FSTs

were not significantly correlated with pairwise PSTs for embryonic

survival at pH 7.5, or QSTs for growth rate and larval period at

either pH 4.3 or pH 7.5 (Table 3A).

QST DIVERGENCE AFTER CONTROLLING FOR

NEUTRAL DIVERGENCE

For embryonic survival, Mantel test was only produced at pH 4.0,

as there was no significant divergence in embryonic survival at

pH 7.5. At pH 4.0, the PST – FST matrix of embryonic survival was

strongly correlated with differences in pond pH and geographic

distance (Fig. 3A and B, Table 3A). Partial Mantel test including

both geographic distance and pond pH matrices revealed that

only pond pH had a significant effect on the PST – FST matrix

(Table 3B), indicating that embryonic acid tolerance has diverged

as a result of pond acidity related divergent selection.

For larval traits, the QST – FST matrix correlations differed

among traits and between treatments. For metamorphic mass, the

QST – FST matrix was strongly correlated both with difference

in pond pH and with geographic distance at both pHs (Fig. 4A,

Table 3A). Partial Mantel tests further revealed that the QST –

FST matrix was more strongly correlated with differences in pond

pH than with geographic distance, suggesting that divergence in

metamorphic size is a result of pond pH-mediated divergent selec-

tion and that this effect is independent of the rearing environment

(Table 3B).

For larval period, the QST – FST matrix correlated with differ-

ences in pond pH at pH 7.5, but not at pH 4.3 (Fig. 4B, Table 3A).

However, it was strongly correlated with the geographic distance

matrix at both pH treatments (Fig. 4B, Table 3A). Partial Mantel

test including both pond pH and geographic distance matrices re-

vealed that only the geographic distance matrix was significantly

correlated with the QST – FST matrix at both pHs (Table 3B),

suggesting that among population divergence in larval period is

8 7 4 EVOLUTION MARCH 2012

Q ST AND F ST COMPARISONS IN RANA ARVALIS

Table 3. (A) Mantel and (B) partial Mantel tests for correlations between phenotypic trait (QST; embryonic survival, metamorphic

mass, larval period, and growth), genetic (FST), environmental (pond pH or predation), and geographic (geo. distance) distance matrices

among seven Rana arvalis populations. Matrix 1 is the dependent variable matrices (QST or QST – FST), whereas matrices 2 and 3 are the

independent variable matrices (genetic, geographic, or environmental). Partial Mantel tests were only performed when matrix 1 was

correlated with more than one other of the independent matrices.

(A) Mantel testsMatrix 1 Matrix 2Dependent Independentmatrix Trait Treatment matrix r p

QST Survival pH 7.5 FST −0.15 0.523pH 4.0 FST 0.58 0.008

Mass pH 7.5 FST 0.39 0.077pH 4.3 FST 0.53 0.015

Larval period pH 7.5 FST 0.34 0.138pH 4.3 FST 0.22 0.330

Growth pH 7.5 FST 0.14 0.570pH 4.3 FST 0.23 0.326

QST – FST Survival pH 7.5 Pond pH −0.32 0.157pH 4.0 Pond pH 0.72 <0.001pH 7.5 Geo. distance −0.19 0.446pH 4.0 Geo. distance 0.61 0.004

Mass pH 7.5 Pond pH 0.66 0.002pH 4.3 Pond pH 0.77 <0.001pH 7.5 Geo. distance 0.50 0.021pH 4.3 Geo. distance 0.57 0.006

Larval period pH 7.5 Pond pH 0.51 0.018pH 4.3 Pond pH 0.06 0.795pH 7.5 Geo. distance 0.66 0.002pH 4.3 Geo. distance 0.47 0.029

Growth pH 7.5 Pond pH 0.01 0.963pH 4.3 Pond pH 0.29 0.202pH 7.5 Predation 0.43 0.047pH 4.3 Predation 0.60 0.003pH 7.5 Geo. distance −0.12 0.613pH 4.3 Geo. distance 0.01 0.972

(B) Partial Mantel testsMatrix 1 Matrix 2 Matrix 3Dependent matrix Trait Treatment Independent matrix r p Independent matrix r p

QST – FST Survival pH 4.0 pond pH 0.44 0.046 Geo. distance 0.22 0.336Mass pH 7.5 pond pH 0.45 0.043 Geo. distance 0.12 0.605

ph 4.3 pond pH 0.53 0.001 Geo. distance 0.12 0.607Larval period pH 7.5 Geo. distance 0.43 0.048 pond pH 0.13 0.588

ph 4.3 Geo. distance 0.55 0.015 pond pH −0.30 0.183

likely driven by selection that is more strongly correlated with

geographic distance than with pond pH. Because geographic

distance and latitude are very highly correlated (see Methods),

the pattern for larval period indicates climate-mediated selection

or that the putative selective agent strongly correlates with geo-

graphic distance/latitude.

For growth rate, the QST – FST matrix was not correlated

with differences in pond pH or geographic distance (Fig. 4C,

Table 3A). However, it was correlated with differences in predator

density at both pH treatments (Fig. 4C, Table 3A), suggesting

predator density-mediated divergent selection on growth rate. As

growth rate was not related to geographic distance or difference

EVOLUTION MARCH 2012 8 7 5

SANDRA HANGARTNER ET AL.

Figure 3. Pairwise quantitative trait (PST) divergence of embryonic survival (%) and neutral genetic (FST) divergence between population

pairs plotted against pairwise (A) differences in pond pH and (B) geographic distance (km). FST are shown as gray triangles, PST at pH 7.5

as black circles, and PST at pH 4.0 as gray circles.

in pond pH, no partial Mantel tests were performed for growth

rate.

DiscussionStudies of environmental gradients, in conjunction with QST –

FST comparisons, can provide a powerful means for inferring the

relative role of divergent natural selection and neutral process in

phenotypic diversification (e.g., Storz 2002; Palo et al. 2003; An-

toniazza et al. 2010). We found that quantitative trait divergence

(PSTs/QSTs of embryonic survival and larval life-history traits)

of R. arvalis along an acidification gradient is more strongly

correlated with environmental differences (acidity, latitude, and

predation) than with neutral genetic divergence (as measured by

FST)—indicating that divergence is more likely driven by natural

selection than by neutral genetic processes. However, the rela-

tionship between trait divergence and particular environmental

factors depended on the particular trait and was influenced by

the rearing environment (i.e., G × E interactions). We next dis-

cuss the strength of evidence for adaptive divergence along this

environmental gradient, as well the general implications of our

study.

EVIDENCE FOR ADAPTIVE DIVERGENCE

Higher QSTs than FSTs seem to be rather the norm than the excep-

tion in natural populations (Merila and Crnokrak 2001; Leinonen

et al. 2008), providing general evidence for selection as a driver

of phenotypic diversification in nature (Leinonen et al. 2008).

In our study, divergence in FSTs showed a weak but signifi-

cant IBD pattern, suggesting that—at the geographic distances

studied here (population pairwise geographic distances about 10–

160 km)—ongoing or historical gene flow has constrained neutral

genetic differentiation between more closely situated populations

(Hutchison and Templeton 1999). However, QST divergence was

stronger than the neutral baseline divergence (FSTs) for all traits,

and comparisons of QST and FST differentiation together with en-

vironmental measurements (i.e., putative selective agents) showed

that the extent of phenotypic divergence of R. arvalis along the

acidity gradient is strongly correlated with the magnitude of

environmental differences (i.e., divergence was largest among

populations with largest environmental differences and weaker

for intermediate populations). These observations indicate that

(1) divergence along the gradient is most likely due to divergent

selection and that (2) the magnitude of divergence presumably

reflects variation in the strength of divergent selection (Porcher

et al. 2004). The specific selective agents contributing to this di-

vergence require formal tests, but current evidence indicates a role

for pH, climate, and predators.

In particular, divergence in embryonic acid tolerance (sur-

vival at pH 4.0) and metamorphic mass correlated most strongly

with differences in breeding pond pH, strengthening the argument

that environmental acidity (low pH or some correlated factor) se-

lects for higher embryonic acid tolerance (e.g., Rasanen et al.

2003b; Persson et al. 2007; Hangartner 2010) and larger meta-

morphic size (e.g., Rasanen et al. 2005; Hangartner 2010). In

contrast, divergence in larval period was most strongly related

to geographic distance/latitude and divergence in growth rates

with predator abundance (higher growth rates in populations with

more predators, Hangartner 2010). We suggest that these patterns

may have arisen due to latitudinally mediated countergradient

selection (Conover and Schultz 1995) on developmental rates

(Hangartner 2010) and by predator-mediated selection on tadpole

size, as large tadpole size increases survival under gape-limited

predation (Urban 2007). Future work should aim to explicitly test

for divergent selection and the putative selective agents acting on

R. arvalis along this gradient.

8 7 6 EVOLUTION MARCH 2012

Q ST AND F ST COMPARISONS IN RANA ARVALIS

A

B

C

Figure. 4. Pairwise quantitative trait (QST) divergence of (A) metamorphic mass (mg), (B) larval period (days), and (C) growth rate

(mg/days) and neutral genetic (FST) divergence between population pairs plotted against pairwise differences in pond pH, geographic

distance (km), latitude (N), and/or predator density (individuals/five sweeps). FST are shown as gray triangles, QST at pH 7.5 as black

circles, and QST at pH 4.3 as gray circles.

ACCURACY OF FST AND QST ESTIMATES

We found weak but significant divergence in pairwise FSTs among

most of the seven populations studied here. Differentiation in neu-

tral genetic markers can be affected by drift, migration, and mu-

tation (e.g., Slatkin 1985). For loci with high mutation rates, such

as microsatellites, FSTs can be biased by the amount of mutation,

which could potentially mask a pattern of IBD (Slatkin 1993).

Estimates of RSTs (Slatkin 1995) have been suggested as an al-

ternative that accounts for mutation processes, and we therefore

analyzed divergence in both RSTs and FSTs. However, we used

FSTs in the final analyses for three main reasons. First FSTs are

suggested to be the best estimates of neutral genetic divergence if

EVOLUTION MARCH 2012 8 7 7

SANDRA HANGARTNER ET AL.

mutation rate is much smaller than migration rate (Slatkin 1993).

We expect this to be the case, as our study populations occur at a

relatively small spatial scale and show weak neutral genetic diver-

gence (Whitlock 2011). Second, FSTs were significantly related to

geographic distance, whereas RSTs were not (Fig. 2), suggesting

that potentially high mutation rates did not mask an IBD relation-

ship in FSTs. Moreover, RST tends to have larger variance and has

been shown to be less suited for detecting IBD than FST when

population structure is low (Balloux et al. 2000)—likely also the

reason why we found no IBD for RST. Third, FSTs provided a more

conservative neutral baseline for the QST – FST comparisons, as

FSTs (but not RSTs) were significantly correlated with geographic

distance, latitude, and pond pH.

With regard to QST estimates, several causes of bias exist for

inferences of adaptive divergence. These include bias in the QST

estimates per se and environmental dependency of the expression

of phenotypes. First, QST estimates, when used in their most strict

sense, should be based on estimates of additive genetic variance

to avoid potential bias due to environmental variation, maternal

effects, or nonadditive genetic effects (O’Hara 2005). As we here

used common garden-reared individuals, we can discard biases

arising from direct environmental effects typical in QST (PST)

studies on individuals collected from the wild (Pujol et al. 2008).

A potential limitation in our study, however, is that it uses full-

sibling families and the QST estimates may therefore be biased by

maternal or nonadditive genetic effects (e.g., Lopez-Fanjul et al.

2007).

Quantitative genetic studies on R. arvalis have repeat-

edly shown that both within and among population variation

in embryonic acid tolerance is maternally determined (e.g.,

Rasanen et al. 2003b; Persson et al. 2007; Hangartner 2010),

and we therefore considered embryonic survival to be a phe-

notypic estimate (PST, Saether et al. 2007). However, ma-

ternal effects can also be genetically determined (reviewed

in Rasanen and Kruuk 2007), and it is then possible (using

appropriate study designs) to estimate maternal QSTs (Perry

et al. 2005). Using this approach, Perry et al. (2005) suggested

that QSTs for traits determined by genetic maternal effects should

be higher than QSTs for traits determined by direct genetic effects.

In our study system, maternal effects determine embryonic acid

tolerance and are clearly adaptive (e.g., Rasanen et al. 2003b;

Persson et al. 2007; Hangartner 2010). The extent to which they

are genetically determined or represent environmentally induced

transgenerational plasticity (Mousseau and Fox 1998) is currently

not known. Nevertheless, in our data PSTs of embryonic acid tol-

erance had similar levels of divergence (average PST fourfold to

10-fold higher than FST) than QSTs of larval traits, which are pri-

marily determined by direct genetic effects (see below). Although

maternal effects can strongly contribute to adaptive divergence,

their role is still little studied in this context (Mousseau and Fox

1998; Rasanen and Kruuk 2007). We want to further emphasize

that rather than treating maternal effects as nuisance parameters

in QST – FST comparisons, estimates of maternal QSTs would shed

further light on maternal effects as drivers of diversification (Perry

et al. 2005). Quantitative genetic studies of amphibians typically

find a genetic basis in larval life-history traits and that both ma-

ternal effects and nonadditive genetic effects often contribute to

trait divergence (e.g., Laugen et al. 2002; Sommer and Pearman

2003)—which is in line with our studies on R. arvalis (Hangartner

2010). However, as the relative contribution of maternal effects is

relatively weak (Hangartner 2010), and nonadditive effects do not

seem to introduce strong bias to QST – FST comparisons (Whitlock

2008)—our QST estimates for larval traits are likely conservative.

Second, the expression of genetic and phenotypic variation

and, subsequently, quantitative genetic parameters are depen-

dent on environmental conditions under which they are estimated

(e.g., Hoffmann and Merila 1999). We found that pairwise PSTs

of embryonic survival differed extremely between the two pH

treatments (divergence in survival was only apparent in the low pH

treatment), in accordance with environmentally dependent expres-

sion of maternal effects (Rasanen and Kruuk 2007). In contrast,

QSTs of metamorphic size were similar in both pH treatments,

whereas larval growth and larval period responded differently to

the pH treatments—possibly because of differences among popu-

lations in larval acid stress tolerance (Hangartner 2010). Neverthe-

less, these environment-dependent results further emphasize that

inferences based on QST – FST comparisons from wild-collected

specimens might be misleading (e.g., Pujol et al. 2008) and that

under—or overestimation of divergence is possible if the en-

vironment masks underlying genetic divergence (e.g., Conover

et al. 2009). It is therefore important that common garden stud-

ies of adaptive divergence do estimate quantitative trait variation

under a set of relevant environmental conditions.

DOES NEUTRAL GENETIC DIVERGENCE REFLECT

PHENOTYPIC DIVERGENCE?

Studies on the relationship between quantitative trait and neu-

tral marker divergence can be important also from a conserva-

tion biology perspective. Management decisions are often based

purely on population genetic analyses (e.g., Moritz 1995; Reed

and Frankham 2001), whereas variation in quantitative traits is

more likely to reflect variation important for fitness (e.g., Lynch

1996). As a positive correlation between FST and QST has been

found across different studies, it has been suggested that the de-

gree of genetic differentiation in neutral markers may at least

roughly predict the degree of differentiation in genes coding for

quantitative traits (Leinonen et al. 2008). We found low but sig-

nificant neutral genetic differentiation (FSTs) between most pop-

ulation pairs as well as an IBD pattern. However, although trait

divergence was clear in all larval traits, the extent of correlation

8 7 8 EVOLUTION MARCH 2012

Q ST AND F ST COMPARISONS IN RANA ARVALIS

between neutral marker divergence and quantitative trait diver-

gence was inconsistent. First, pairwise QSTs of larval period and

growth rate were not significantly correlated with neutral genetic

differences (i.e., pairwise FSTs). In contrast, pairwise PSTs of em-

bryonic survival at pH 4.0, and QSTs of metamorphic mass, were

positively correlated with neutral genetic differentiation. How-

ever, as differences in pond pH and latitude correlated positively

with geographic distance, strength of divergent selection and

neutral genetic processes are spatially autocorrelated (Storz 2002;

Antoniazza et al. 2010). Such spatial autocorrelations might

explain why FSTs often correlate with QSTs. A more powerful

approach to investigate correlations between neutral marker and

quantitative trait divergence might therefore be to study popula-

tions that are under divergent selection, but where the selective

agent is not correlated with geographic distance (Richter-Boix

et al. 2010). More studies are clearly needed to shed light on the

factors that might influence QST – FST correlations. However, our

results emphasize that both neutral markers and phenotypic traits

should be considered in conservation issues and management de-

cisions (e.g., Lynch 1996; Palo et al. 2003).

ConclusionsCorrelations between pairwise QSTs and FSTs, together with en-

vironmental differences, suggest that phenotypic divergence of R.

arvalis along an acidification gradient is most likely driven by

divergent natural selection—rather than by neutral genetic pro-

cesses. Our results further suggest that different selective forces

may be acting on different traits, and show that the expression

of phenotypic divergence depends on the environment. However,

explicit tests are needed to investigate how selection acts through

a multitude of selective forces and on a set of correlated traits

in this system. In general, we argue that inferences on adaptive

divergence from QST – FST comparisons can be strengthened by

including measurements of environmental variation and by esti-

mating the magnitude of phenotypic divergence under a range of

relevant environmental conditions. Our study also provides fur-

ther evidence that neutral marker divergence may not accurately

reflect divergence in traits relevant for fitness.

ACKNOWLEDGMENTSWe thank Beatrice Lindgren, Andres Egea-Serrano, Tove Nielson, NicoleHatt, and Claudio Brunold for invaluable help with field and labora-tory work, as well as all landowners for permission to use their sites.The molecular analyses were conducted at the Genetic Diversity Centre(GDC) of ETH Zurich. The experiments were conducted under permis-sions from the county boards in Halland and Vastra Gotaland coun-ties, and from the Ethical committee for animal experiments in UppsalaCounty. This study was supported by grants from Swiss National Sci-ence foundation (to KR) and Swedish Research Council Formas (to ALand KR).

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Associate Editor: R. Fuller

Supporting InformationThe following supporting information is available for this article:

Table S1. Population pairwise differences among seven Rana arvalis populations in neutral markers (FST: above the diagonal,

estimated with FSTAT) and geographic distance (km: below the diagonal).

Table S2. Mantel tests for correlations between phenotypic trait (QST; embryonic survival, metamorphic mass, larval period,

and growth), environmental (pond pH or predation), and geographic (geo. distance) distance matrices among seven Rana arvalis

populations.

Supporting Information may be found in the online version of this article.

Please note: Wiley-Blackwell is not responsible for the content or functionality of any supporting information supplied by the

authors. Any queries (other than missing material) should be directed to the corresponding author for the article.

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