Parallelism and historical contingency during rapid ecotypedivergence in an isopod

F. EROUKHMANOFF,* A. HARGEBY,� N. N. ARNBERG,� O. HELLGREN,§ S. BENSCH*

& E. I. SVENSSON*

*Section for Animal Ecology, Lund University, Lund, Sweden

�Division of Biology, Linkoping University, Linkoping, Sweden

�Department of Ecology & Evolutionary Biology, University of California, Santa Cruz, CA, USA

§The EGI, Department of Zoology, Oxford, UK

Introduction

Evolution is often repeatable to some extent, as recent

studies of parallel evolution have shown (Losos et al.,

1998; Johannesson, 2001; Schluter et al., 2004). Yet

history does not always repeat itself in exactly the same

ways (Harvey & Partridge, 1998; Lee, 1999). Numerous

studies on parallel evolution have provided ample

opportunities to address this issue, as recently reviewed

by Arendt & Reznick (2008). Historical contingencies

mean that history and ancestral conditions unique to

evolving populations might influence both the direction

and the magnitude of evolutionary trajectories (Bell,

1987; Huey et al., 2000; Langerhans & DeWitt, 2004;

Langerhans et al., 2006). Shared selection pressures in

similar ecological environments are likely to operate

in concert with history and ancestral conditions resulting

in parallel evolution but with a historical signature

(Langerhans & DeWitt, 2004; Langerhans et al., 2006).

A central question is how shared selection pressures in

novel environments interact with such contingencies

during parallel evolution and the relative importance of

determinism (or selection) and history (Gould, 1989;

Losos et al., 1998; Huey et al., 2000; Langerhans et al.,

2006).

Convergence to similar phenotypic states in similar

selective environments has long fascinated evolutionary

biologists, because it provides opportunities to quantify

the predictability of adaptive evolution and the role of

selective determinism (Langerhans et al., 2006). Under-

standing the relative importance of history and selection

is a major remaining challenge in evolutionary biology,

but this issue has so far mainly been explored in

laboratory selection experiments on microorganisms

(Travisano et al., 1995) and only rarely in natural settings

(Losos et al., 1998; Taylor & McPhail, 2000; Schluter

Correspondence: Fabrice Eroukhmanoff, Section for Animal Ecology,

Ecology Building, Lund University, SE-223 62 Lund, Sweden.

Tel.: +46 46 222 38 19; fax: +46 46 222 47 16;

e-mail: fabrice.eroukhmanoff@zooekol.lu.se

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Keywords:

adaptive radiation;

historical contingency;

mating propensity;

parallel evolution;

phenotype sorting;

pigmentation.

Abstract

Recent studies on parallel evolution have focused on the relative role of

selection and historical contingency during adaptive divergence. Here, we

study geographically separate and genetically independent lake populations of

a freshwater isopod (Asellus aquaticus) in southern Sweden. In two of these

lakes, a novel habitat was rapidly colonized by isopods from a source habitat.

Rapid phenotypic changes in pigmentation, size and sexual behaviour have

occurred, presumably in response to different predatory regimes. We

partitioned the phenotypic variation arising from habitat (‘selection’: 81–

94%), lake (‘history’: 0.1–6%) and lake · habitat interaction (‘unique diver-

sification’: 0.4–13%) for several traits. There was a limited role for historical

contingency but a strong signature of selection. We also found higher

phenotypic variation in the source populations. Phenotype sorting during

colonization and strong divergent selection might have contributed to these

rapid changes. Consequently, phenotypic divergence was only weakly

influenced by historical contingency.

doi:10.1111/j.1420-9101.2009.01723.x

et al., 2004; Boughman et al., 2005; Hoekstra et al.,

2006).

The freshwater isopod (Asellus aquaticus) is in the

process of parallel phenotypic divergence, as similar

ecotypes (in terms of pigmentation and size) have

emerged in similar habitats in several geographically

separate lakes in southern Sweden (Hargeby et al., 2004,

2005). In a previous study, we have shown that although

isopods belonging to the reed ecotype have remained

phenotypically similar during the last 20 years, in the

newly emerged stonewort ecotype, the isopods have

become locally adapted to their new habitat (Hargeby

et al., 2004). However, it is not yet known to what extent

historical factors have influenced these divergence pro-

cesses and how genetically independent these different

ecotypes are. In this study, we address these issues by

presenting new molecular genetic data which suggests

that the phenotypically similar novel stonewort isopods

in two of these south Swedish lakes are likely to have

separate origins. Combined with our previous work on

the temporal changes in phenotypic differentiation, these

data strongly suggest that the stonewort isopods became

locally adapted after colonization of the new habitat

(Hargeby et al., 2004). Thus, the data that we present

here suggest that these ecotypes are unlikely to have

emerged and evolved in only one lake, thereafter they

spread to the other. We also present novel results on

morphological and behavioural differentiation between

and within lakes with the specific goal of quantifying

the relative importance of habitat (‘selection’), lake

(‘history’) and their interaction (‘selection · history’ or

unique diversification) in phenotypic divergence of these

isopod populations (Langerhans et al., 2006). We discuss

the role of both historical contingency (the effect of lake

origin because of different phylogenetic or ecological

background) and selection during adaptation to a novel

habitat (stonewort) in these isopod ecotypes. We also

present new experimental results, documenting a strong

parallelism in mating behaviours between habitats, with

only a limited role for historical contingency. This study

therefore adds to the increasing evidence that behavio-

ural traits might respond more rapidly than morpholog-

ical traits to divergent predation pressures (Dill et al.,

1999; Bernal et al., 2007) and behaviours have also been

suggested to evolve faster than other types of traits (Rice

& Holland, 1997).

The two lakes in this study are not the only lakes

where this apparently adaptive phenotypic variation has

been documented for this species. In a recent study

investigating 29 Swedish lakes and ponds (Hargeby et al.,

2005), we have shown that at least seven lakes harbour

two phenotypically divergent and variable populations of

A. aquaticus. Moreover, there is a strong correlation

between the background colour of the local habitats

and the average pigmentation of the isopods, indicating

locally cryptic and adaptive pigmentation (Hargeby et al.,

2005). A total of seven lakes in southern Sweden host

two phenotypically divergent populations (Hargeby et al.,

2005), including the two lakes in this study. Here, we

focus more into details on two lakes in which the

diversification events have been monitored from the start

(Hargeby et al., 2007), with the aim to quantify the

influence of history and selection during phenotypic

diversification. Although the results we present are likely

to be applicable to other lakes where this phenotypic

diversification has taken place the role of history and

selection during parallel evolution in other lakes remains

to be examined to draw any general conclusions on this

system. In addition, we also show that these isopods are

phenotypically and genetically highly variable in their

source habitat (reed). These results suggest that the rapid

(less than 40 generations) parallel divergence in these

different lakes can partly be a result of sorting of already

existing phenotypic and genetic variation (Rice & Pfen-

nig, 2007). Phenotype sorting during colonization, in

addition to the already documented phenotypic changes

in the novel stonewort habitat, stands of submerged

vegetation in the centre of the lakes (Hargeby et al.,

2004) could thus partly explain the rapid phenotypic

divergence over a time-scale of only a few years in these

lakes. Phenotype sorting is a recently proposed mecha-

nism through which pre-existing divergent phenotypes

increase in frequency (Rice & Pfennig, 2007) and can be

viewed as form of selection on standing genetic variation

(Barrett & Schluter, 2008).

We base our study and general approach on a recently

developed conceptual framework outlined by Langer-

hans & DeWitt (2004) and Langerhans et al. (2006).

When a parallel phenotypic change to a novel habitat

takes place in two different regions (here labelled ‘A’ and

‘B’), there are several different outcomes, depending on

the relative importance of history and selection to the

novel habitat (Fig. 1). First, phenotypic changes to the

novel habitat can show evidence of perfect parallelism, as

animals from an ancestral habitat invade the novel

habitat (Fig. 1a,b). This parallelism could either occur

without any historical signature (Fig. 1a) or show a

historical signature in the form of different starting

phenotypes in the source habitat of the two regions that

affects the final outcome (Fig. 1b). Alternatively, there

could be an interaction between history (region) and

selection (habitat), so that the magnitude of the pheno-

typic change differs between regions (Fig. 1c,d). In

Fig. 1c, there is convergence towards a common pheno-

typic state across both regions and a moderate interaction

effect between history and selection, which is here

termed as ‘unique diversification’. In Fig. 1d, there is a

stronger interaction effect and more pronounced unique

diversification as the rank ordering of the phenotypes

change. The relative effects of habitat, history and unique

diversifications can thus be estimated using variance

component analysis, an approach that was pioneered by

Langerhans & DeWitt (2004) and Langerhans et al.

(2006).

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Methods

Study organism and ecology

Asellus aquaticus is a freshwater isopod that is widespread

in lakes, ponds and slow-flowing rivers in Eurasia

(Whitehurst, 1991; Verovnik et al., 2005). Populations

of A. aquaticus occupy various littoral habitats in lakes,

including reed belts (Phragmites australis) where they

mainly feed on decaying leaves (Adcock, 1982; Zimmer &

Bartholme, 2003). It is a nonswimming isopod and is

therefore a slow colonizer of new habitats. Two shallow

Swedish lakes, Lake Krankesjon (55�42¢N, 13�28¢E) and

Lake Takern (58�21¢N, 14�50¢E), have in the past

20 years (starting in 1987 in Lake Krankesjon and in

2000 in Lake Takern) experienced dramatic shifts from a

phytoplankton dominated state towards a macrophyte

dominated state (Hargeby et al., 1994, 2007). These lake

shifts resulted in the colonization of old sediment areas

by submerged vegetation such as stonewort (Chara

tomentosa). In both lakes, data suggest that the isopods

colonized the novel stonewort habitat from the already

existing reed habitat. Rapid habitat-specific changes in

the pigmentation of the isopods were observed after

colonization of the stonewort habitat: A. aquaticus pop-

ulations became brighter and smaller in these newly

emerged stonewort stands compared to the darker and

larger source populations in the reed (Hargeby et al.,

2004, 2005). The substrate in the reed consists of organic

detritus that forms a black background, whereas in the

stonewort the substrate consists of light green vegetation

growing above a light grey mineral substrate. Local

adaptation in isopod pigmentation is therefore likely to

be a consequence of divergent selection pressures caused

by different background colours and different predator

faunas in the two different habitats (Hargeby et al., 2004,

2005). Predation from visually hunting fish is likely to be

more intense in the novel stonewort habitat than in the

reed (Wagner & Hansson, 1998). This ecological differ-

ence between the two habitats is thought to select for

crypsis which presumably favours smaller and brighter

isopods in the stonewort where visually hunting fish

predators is the major potential threat (Wagner &

Hansson, 1998; Hargeby et al., 2005). In contrast, in the

reed habitat, invertebrate predators relying on tactile

cues (i.e. dragonfly larvae) predominate (Hargeby et al.,

2004), which presumably favours larger body size. We

incorporated molecular data from two additional lakes

Fig. 1 Graphical illustrative model of the interplay between historical contingency and selection during parallel phenotypic adaptation to a

novel habitat. Average phenotypes in source and novel habitat in two geographically separate regions (a and b, e.g. different lakes) are

illustrated. In each region, a novel habitat emerges which is invaded from a source habitat. Parallel changes in phenotypic trait may occur

without or with a very weak historical component (a), i.e. if animals in the source habitat do not differ markedly between regions.

Alternatively, there could a historical signature but otherwise perfectly parallel changes if the source habitat phenotypes differ between

regions (b). Phenotypic change to the new habitat may also show evidence of ‘‘unique diversification’’, i.e. a significant interaction between

history (region) and selection (adaptation to novel habitat) (c, d). In (c), the interaction term and the extent of unique diversification of

phenotypes is moderate and the rank ordering of the average phenotypes in the different regions do not change between habitats. In (d), there

is a strong interaction term and pronounced unique diversification, resulting in a reversal of the rank ordering of phenotypes. Modified and

interpreted based on the conceptual framework by Langerhans & DeWitt (2004).

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located in southern Sweden for the molecular analyses,

Lake Rabelovsjon (RAB) and Lake Fardume (Fig. 2), both

studied in a previous paper (Hargeby et al., 2005) and

shown to exhibit similar patterns of divergence between

populations of A. aquaticus.

Molecular analyses

The following four isopod populations were used for

amplified fragment length polymorphism (AFLP) analy-

sis: Takern Reed (TR), Takern Stonewort (TS), Kra-

nkesjon Reed (KR) and Krankesjon Stonewort (KS). An

additional population from the reed ecotype of Lake RAB

was analyzed and compared with the other populations

and used as an ‘outgroup’ population. Eighteen individ-

uals (nine females and nine males) were chosen at

random from each population (n = 90). For the mito-

chondrial DNA (mtDNA) analyses, we used an additional

lake where we had access to both ecotypes (Lake

Fardume, n = 18). This enabled us to build a more

general haplotype network composed of seven different

populations and to test more accurately the hypothesis of

independent evolution of the stonewort ecotypes. Sam-

ples were preserved in 95% ethanol until use. Individual

isopods were then cut longitudinally to avoid possible gut

parasites. Body fragments were placed in 500 lL SET

buffer (0.15 MM NaCl, 0.05 MM Tris, 0.001 MM EDTA (ethy-

lenediaminetetraacetic acid), pH 8.0). Then 13 lL of SDS

(sodium dodecyl sulfate) was added with 15 lL protein-

ase K (10 mg mL)1) for overnight digestion at 56 �C.

DNA was isolated following a standard phenol ⁄ chloro-

form extraction protocol (Laird et al., 1991). The DNA

concentration was checked on a spectrophotometer and

samples were diluted to approximately 5 ng lL)1.

We used the AFLP protocol of Vos et al. (1995) with

slight modifications as described in Bensch et al. (2002).

The fragments were separated on 6% polyacrylamide gels

and visualized using fluorescein-labelled E-primers in a

Typhoon FluorImager. We used two selective primer

combinations: A (ETGA · MCGC) and B (ETCG · MCAA).

These primer combinations amplified relatively few, but

easy to score bands. We used 54 bands for combination A

and 18 bands for combination B. Polymorphic AFLP

bands were transformed into a binary rectangular matrix.

AFLP bands on the gel were scored as 1 (present) or 0

(absent) and missing data points were assigned a 9. The

analysis of molecular variance (AMOVAAMOVA) component in

the ARLEQUINARLEQUIN 2.0 software (Excoffier et al., 2005) was

used on both the AFLP data and mtDNA data (see

below). Fst values for the AFLP data were calculated from

the binary rectangular matrices and Fst values for the

mtDNA were calculated using a Jukes-Cantor distance

matrix and between populations P-values were

calculated using a resampling procedure with 10 000

permutations. We also used an AMOVAAMOVA to partition the

total genetic variance into within-lake and among-lake

components, with the expectation that a parallel evolu-

tion of the stonewort ecotype scenario would result in

higher among-lake variation and whether a scenario of

unique emergence of the stonewort ecotype followed

by colonization would result in higher within-lake

variation.

A total of 92 individuals were used for mtDNA

sequencing: 15 TR, 12 TS, 12 KR, 12 KS as well as

respectively 11 and 14 individuals from respectively the

reed and the stonewort ecotype from Lake Fardume and

finally 16 individuals from the reed ecotype of RAB. Lake

Rabellovsjon and Lake Fardume were included in this

study to obtain a broader phylogeographic perspective on

mtDNA variation. The geographic locations of each lake

are shown in Fig. 2. For each of the specimens, a 641 bp

fragment of the first subunit of cytochrome oxidase

mitochondrial gene (COI) was amplified using primers

LCO1490 and HCO2198 (Folmer et al., 1994). The

100 Km12

3

4

Reed Stonewort

(1) Lake Krankesjön(2) Lake Tåkern(3) Lake Fardume(4) Lake Råbelövsjön

Fig. 2 Median-joining network of mtDNA

haplotypes obtained from 97 individual

freshwaters isopods from four southern

Swedish lakes, including the two main study

lakes Krankesjon (blue) and Takern (green).

Light colours represent individuals caught in

stonewort habitat and dark colour individu-

als caught in reed habitat. The size of the

circles represents the frequencies of the

different haplotypes and each step on the

branches represents a single mutation.

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polymerase chain reaction cycles were 1· (2 min at

94 �C), 35· (30 s at 94 �C, 30 s at 52 �C, 1 min at 72 �C),

1· (10 min at 72 �C) in volumes of 25 lL that included

20 ng of template DNA. We used the BigDye Sequencing

Kit loaded on ABI 310 or ABI 377 (PerkinElmer, Vasby,

Sweden) automated sequencers. All COI sequences were

aligned by hand using BioEdit (Hall, 1999) and all

chromatograms were checked by eye to ensure that no

bases had been misread. The mtDNA haplotypes were

used to construct a median-joining haplotype network

(Bandelt et al., 1999) using the software NETWORKNETWORK 4.5.

(available at http://www.fluxus-engineering.com). The

AMOVAAMOVA was constructed using haplotype frequencies and

genetic distances.

Field work and morphological data collection

Isopods were captured at sexual maturity from Lake

Takern and Lake Krankesjon during the reproductive

season (February–June) in 2005 and 2006. We only

used individuals captured as pairs in pre-copula, a state

where the male is holding the female until she moults

and is ready to be fertilized, which can last up to a

couple of weeks. The pairs were subsequently separated

after capture and kept apart in single containers. All

individuals were photographed live in a Petri dish with

water under natural light conditions. Each individual

was photographed with a metric reference and six

colour plates (black, grey, white, red, yellow and blue).

Pictures were then calibrated according to these refer-

ences and analyzed using our own software ‘Picstats’

(code of the program available from the authors upon

request). We measured seven morphological traits: total

length, width of the first, fourth and seventh seg-

ments (W1, W4, W7) and the colour (H), saturation

(S) and brightness (V) of the total shell of the individual

(H, S and V being colouration components ranging

from 0 to 1). For all the morphological analyses, a total

of 805 individuals were measured, ranging from 99

to102 individuals for each category of sex, habitat and

lake.

Mating behaviour experiments

To estimate habitat and population differences in

mating propensity, we performed no-choice experi-

ments (Jennions & Petrie, 1997). We randomly selected

one sexually mature male and one sexually mature

female from the same population (caught in the wild

in amplexus) and placed them together in a Petri dish

filled with water. In many isopod species such as

A. aquaticus, the male forms a couple and carries a

female in a pre-copula (amplexus) until she moults

into mating state, which can take place several days

after mate guarding has been initiated (Hargeby et al.,

2005). In this species, pre-copula formation almost

always results in female fertilization (on average in

84.1% of the cases (n = 283; SD = 3.46). We therefore

measured the time until the male and female formed a

pre-copula. In cases where no pre-copula formation

had occurred within the first 10 min, individuals were

checked every 5 min during the next 2 h. We could

then estimate time to pre-copula formation (which we

call ‘time to copulation’ for simplicity) for all males and

females and the average propensity to mate. From the

data based on all the measured individuals, the median

time to copulation was found to be 512 s (n = 283).

We used this time as a threshold to determine if

individuals would have mated or not under natural

conditions and then attributed all couples to value of

either 0 (did not mate) or 1 (mated). In total, we

obtained data from 60 pairs from Lake KS population,

63 from Lake KR population, 89 from Lake Takern

stonewort population and 71 pairs from Lake Takern

reed population.

General linear models and generalized linear models

All the statistical analyses in this study, except the

population genetic analyses (see above), were per-

formed with STATISTICASTATISTICA (Statsoft, Inc., 2004). To

quantify the relative effects of habitat and lake on

parallelism and historical contingency in morphology

and sexual behaviour, we constructed sigma-restricted

general linear models (GLM) with length, brightness

and time to copulation as dependent variables, and lake,

habitat and their interaction (lake · habitat) as indepen-

dent factors. We also included sex as an independent

factor in all analyses to control for inherent differences

in sexual dimorphism (males are bigger than females in

all four ecotypes) between lakes or habitats and the

levels of significance for all tests remained unchanged.

We also estimated for each of the three factors, using

their respective sums of squares (SS), the percentage of

non-error variance explained by the model. For the

data on mating probabilities, we used a generalized

linear model (GLZ) with probability of copulation

(1 = copula; 0 = no copula) of pairs as the dependent

variable, and lake, habitat and their interaction

(lake · habitat) as independent factors. In this GLZ-

model, we used a binomial error structure with logit

link function (Type 3).

We interpret the biological significance of the three

terms in these models as explained briefly below (see also

Fig. 1). The lake term should reflect any historical

signature of isopod divergence, and would arise if the

different historical environments (the reed habitat in

both lakes) had caused an imprint on the parallel

divergence in the new habitat. The habitat term should

reflect parallel and similar changes in phenotypes that

are caused entirely by the invasion of the new stonewort

habitat and would reflect identical or similar divergent

selection in the same direction in both lakes (‘shared

diversification’). Finally, the lake · habitat interaction

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should reflect the combined effect and interaction of

historical contingency and determinism, or ‘unique

diversification’ (Langerhans et al., 2006). We stress that

all these three terms could be important and are by no

means mutually exclusive. The empirical question in any

particular study system is rather to what extent each of

these factors is important and what their relative mag-

nitudes are. Recent studies on ecotype divergence on the

classical adaptive radiation of Anolis lizards have, for

instance, revealed evidence for strong parallel and

deterministic evolution (Losos et al., 1998), but this

parallel evolution is nevertheless still accompanied by a

significant signature of historical contingency (Langer-

hans et al., 2006).

In this paper, we tentatively assume that the morpho-

logical and behavioural traits we have measured in field-

caught isopods are heritable, at least to some degree, and

are not entirely caused by phenotypic plasticity in the

different ecotypes. At present, we do only have strong

quantitative evidence for a genetic basis for pigmentation

(Hargeby et al., 2004). However, ongoing, but not yet

published, laboratory experiments in which we have

raised isopod families from different lakes and habitats in

a common garden environment have revealed significant

family, habitat and lake differences in both size and

pigmentation in Lake Krankesjon and Lake Takern

(F. Eroukhmanoff, E.I. Svensson & A. Hargeby, unpub-

lished data). Heritability estimates ranged from 0.14 and

0.89 for all traits measured in the different populations

and were all significant. These results will be presented in

depth in a future paper. Preliminary results from

behavioural experiments also indicate that mating

propensity is not phenotypically plastic and is not

affected by food conditions or density (F. Eroukhmanoff

& E.I. Svensson, unpublished data).

Multivariate analyses

We performed a discriminant function analysis (DFA) to

investigate if the isopods from the different populations

had diverged in overall morphology (including both

the metric traits and pigmentation differences). Wilks’

lambda was used as test-statistic to evaluate the signif-

icance and the discriminatory power of this DFA. Its

value ranges from 1 (no discriminatory power) to 0

(perfect discriminatory power). The DFA was followed up

by a canonical variate analysis to compute the discrimi-

nant functions and to investigate if we could discriminate

between the different populations on the basis of the

seven variables earlier described. We also estimated

the percentage of correctly classified individuals, the

multivariate phenotypic distances between population

centroids (squared Mahanalobis distances) and the

significance of these multivariate distances between

populations phenotypic centroids using standard proce-

dures in STATISTICASTATISTICA (Statsoft, Inc., 2004). We also used a

principal component analysis (PCA) based on covari-

ances of four morphological traits (length and the three

width traits W1, W4 and W7) and three pigmentation

traits (H, S and V), to visualize these phenotypic

population differences in multivariate space.

Cluster analyses

To further visualize the phenotypic and genetic rela-

tionships between the different populations, we per-

formed a clustering analysis using the joining method

(also called tree clustering) (Statsoft, Inc., 2004). The

aim with these cluster analyses was neither primarily to

test for significant differences between populations,

which was already addressed by the DFA and the

PCA-approaches (see above), nor to make any kind of

evolutionary inference. Instead, our primary aim was to

use the phenotypic and genetic dissimilarities and

distances between populations to investigate and visu-

alize if there was any tendency for clustering patterns of

the different populations. To compute the distances

between populations in a multi-dimensional space, we

calculated the Euclidean distances using the following

formula:

distance ðp1; p2Þ ¼X

iðp1i � p2iÞ2

n o1=2

where p1 and p2 are two populations compared, and pi

the mean of measure i for a given population. We

computed three different distances (one phenotypic, one

behavioural and one genetic) and visualized these

differences in three separate cluster diagrams: the mor-

phological raw data, the data on mating behaviours and

the AFLP-data.

Results

AFLP and mtDNA analyses

Pairwise Fst values between populations, ranged from 0

to 0.18, for AFLP data and between 0 and 0.84 for

mtDNA data. Four of six population differences were

significant (Table 1). For both the AFLP and mtDNA

data, the significant population differences all involved

comparisons between populations from different lakes

(Table 1). In contrast, the genetic distances between

populations within lakes were close to 0 and lower

than the reed population from RAB (Table 1). The

population differentiation estimates between lakes from

the different populations were in all cases considerably

higher than those within lakes (> 0.088) (Table 1). In

addition, the Fst values between lakes and including all

individuals from a given lake irrespective of ecotypes

were calculated using a similar procedure as above.

They were all significant (P < 0.01) and ranged from

0.32 (between RAB and Lake Fardume) to 0.74

(between Lake Takern and Lake Krankesjon). The

constructed haplotype network contained 35 different

6 F. EROUKHMANOFF ET AL.

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mtDNA haplotypes distributed over the four lakes

(Fig. 2). All lakes were dominated by one common

haplotype that was shared across habitats. The four

lakes were genetically differentiated from one another,

although they exhibited high levels of diversity

(Fig. 2). For distribution of individual haplotypes and

genebank numbers, see Appendix 1. The AMOVAAMOVA

revealed that there was higher molecular variation

among lakes (60.51%) compared to within lakes

(39.49%).

Parallelism in morphology and pigmentation

Morphological analyses showed that, in both lakes,

isopods from the stonewort habitat were smaller and

brighter in their pigmentation than the individuals

from the source reed habitat (Fig. 3, Table 2). How-

ever, the magnitude of the habitat differences differed

between lakes. The interaction term between habi-

tat · lake (‘unique diversification’) was significant

both for length and pigmentation brightness (Fig. 3,

Table 2). This revealed some unique lake-specific

effects of habitat divergence in size and pigmentation

brightness. The lake effect (‘history’) was significant in

the case of length but not for pigmentation brightness

(Fig. 3, Table 2). The difference in length between the

two lakes for the source habitat (reed) was not seen

when comparing the different stonewort populations,

as the stonewort isopods have converged to the same

average length in both lakes (Fig. 3, Table 2). The

percentage of variation explained by the three terms

showed a strong effect of selection (habitat) for both

length and pigmentation brightness (> 80%, Table 2), a

relatively weak effect of history (< 6%, Table 2) and

moderate effects of unique diversification (lake · habi-

tat: 19.6% for length and 13.2% for pigmentation

brightness, Table 2).

To investigate if this rapid parallel divergence has

potentially occurred through phenotype sorting, we

compared the amount of variation in male (because

males and females are sexually dimorphic, including

both sexes for this analyses would have increased the

total variance and biased the results) body length

and pigmentation between habitats in each lake,

using Levene’s tests. Consistent with a scenario of

phenotype sorting, the males from the reed popula-

tions were phenotypically more variable in pigmenta-

tion brightness than the males in the stonewort

populations in both lakes (Krankesjon: F1,198 = 14.30,

P < 0.001; Takern: F1,198 = 11.68, P < 0.001). More-

over, the average pigmentation brightness of the

stonewort ecotypes in both lakes (Krankesjon: 0.726;

Takern: 0.676) was within the range of the reed

ecotypes [Krankesjon: (0.240; 0.828), Takern (0.201;

0.809)]. For body length, the reed males also tended to

be more phenotypically variable than the males from

the stonewort habitat (Krankesjon: F1,198 = 3.49,

P = 0.06; Takern: F1,198 = 3.24, P = 0.07). Here as well,

the average length of the stonewort ecotypes for

both lakes (Krankesjon: 0.010281; Takern: 0.010338)

are well within the range of the reed ecotypes

[Krankesjon: (0.0095; 0.0140), Takern (0.0078;

0.0134)]. These results thus reveal the presence of

ample pre-existing phenotypic variation in the reed

habitat, also containing the average stonewort ecotype

within its range.

Discriminant analysis of morphological divergence

PC1 accounted for 69.3% and PC2 for 29.7% of the

variation, for a total of 99% of all phenotypic variation.

Ecotypes are primarily segregated along the first principal

axis, PC1, which is associated with positive factor

loadings for the traits (e.g. darker and larger individuals).

Fig. 3 Parallel changes in size and pigmentation brightness between

isopod populations in Lake Krankesjon and Lake Takern. Error bars

show 95% confidence limits. Filled circles (d) connected by solid

lines indicate population mean in Lake Krankesjon and empty

squares (h) connected by dashed lines population mean in Lake

Takern. General linear models (GLM) for length: Lake: F1,794 = 5.08,

P = 0.024; Habitat: F1,794 = 61.3, P < 0.001; Lake · Habitat:

F1,794 = 11.57, P < 0.001. GLM for brightness: Lake: F1,794 = 0.77,

P = 0.38; Habitat: F1,794 = 539.03, P < 0.001; Lake · Habitat:

F1,794 = 39.11, P < 0.001.

Table 1 Pairwise Fst values between populations form different

lakes and ecotypes.

KS KR TS TR RAB

KS 0 0.092 0.842** 0.825** –

KR 0.035 0 0.632** 0.629** –

TS 0.104* 0.166* 0 0 –

TR 0.105* 0.178* 0 0 –

RAB 0.113* 0.088* 0.156* 0.144* 0

Pairwise Fst values were calculated for mtDNA (above the diagonal)

and amplified fragment length polymorphism (below the diagonal)

for the four principal study populations [Krankesjon Reed (KR),

Stonewort (KS), Takern Reed (TR) and Stonewort (TS)] and one

additional reed population (Rabelovsjon, RAB), for AFLP only.

Significant (*P < 0.05 or **P < 0.01) pairwise Fst values were

obtained from permutation tests.

Parallelism and contingency during divergence 7

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Stonewort populations had low scores along the PC1

axis, whereas the source reed populations had high

scores along this axis (Fig. 4). The populations from the

same habitat in both lakes were phenotypically closer to

each other in multivariate phenotypic space than to the

populations from the other habitat in the same lake

(Fig. 4). The stonewort populations and the reed popu-

lations clustered together and tended to form two single

groups of similar trait combinations (Fig. 4). We only

included males in this comparison to avoid the con-

founding effects of sexual size dimorphism (females are

smaller than males in both habitats).

Our DFA analysis revealed significant and strong

discrimination between the four populations (Wilks’

Lambda = 0.31, F21,1126 = 27.10, P < 0.001). On average,

65% of all the isopods were classified to the correct

population in this DFA (Table 3). The second highest

classification probability for the stonewort populations in

both Lake Takern and Lake Krankesjon was to the other

stonewort habitat in the other lake (24.5% and 21.2%),

rather than to their closest genetic relatives in the reed

habitat within the same lake (Table 3). This was also the

case for the reed population in Krankesjon, in which

15.5% of the animals were incorrectly classified as

belonging to the reed population in Lake Takern.

Remarkably, only 1.9% of all reed individuals from Lake

Krankesjon were classified as belonging to the extremely

closely located stonewort population in the same lake

Fig. 4 Scatterplot of the first (PC1) and second (PC2) principal components analysis on morphological and pigmentation traits. Large grey

symbols represent the population averages for the two reed populations (circle = Krankesjon; square = Takern), large empty symbols the

population averages for the two stonewort populations (circle = Krankesjon; square = Takern). Note that populations from similar habitats are

morphologically closer to each other than to the populations from the other habitat but in the same lake (see also Table 4). PC1 accounted for

69.3% and PC2 for 29.7% of the total phenotypic variation. Ecotypes are primarily segregated along the first principal axis, PC1, which is

associated with positive factor loadings for the traits (e.g. darker and larger individuals).

Table 2 Parallelism and historical contingency in morphological and behavioural traits.

Length Brightness

Time to

copulation

Probability of

copulation

Shared diversification

(Habitat)

80.75%

F1,794 = 61.3

P = 0.0001

93.15%

F1,794 = 539.03

P = 0.0001

94.15%

F1,280 = 8.41

P = 0.0040

90.54%

v2 (1) = 19.8

P = 0.0001

Lake histories

(Lake)

5.99%

F1,794 = 5.08

P = 0.0244

0.14%

F1,794 = 0.77

P = 0.3805

5.41%

F1,280 = 0.48

P = 0.4875

0.42%

v2 (1) = 0.38

P = 0.7599

Unique diversification

(Habitat · Lake)

13.26%

F1,794 = 11.57

P = 0.0007

6.71%

F1,794 = 39.11

P = 0.0001

0.44%

F1,280 = 0.039

P = 0.8434

9.04%

v2 (1) = 2.32

P = 0.1578

F-values and v2 statistics (probability of copulation) shown below were obtained by general linear models and generalized linear model.

Percentages of non-error variance that were explained by each term of the model are shown and were calculated using the respective sums of

squares (SS) of each effect of the model.

8 F. EROUKHMANOFF ET AL.

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(Table 3). Thus, three of four populations had their

second highest classification probability to the same

habitat but in the other lake, rather than to their closer

genetic relatives in the same lake (Table 3). In total, 80%

of all individuals were classified in their respective

ecotype, regardless of their lakes origin, which indicates

that individuals from the same ecotypes are phenotyp-

ically very similar.

The multivariate phenotypic distances (squared Mah-

analobis-distances) between all pairs of populations and

the significance levels for these distances are shown in

Table 4. The data in Table 4 confirm the general picture

in Fig. 4 and Table 3. The multivariate distances between

the population centroids of the two ecotypes within both

lakes were all significantly different from 0 (Krankesjon:

mean = 8.24, P < 0.001; Lake Takern: mean = 3.09,

P < 0.001). The stonewort ecotypes from the two lakes

had a lower but still significant distance between their

population centroids (mean = 1.33, P < 0.001) whereas

the multivariate distance between the two source reed

ecotypes was greater (mean = 3.87, P < 0.001).

Parallelism in mating behaviour

Our mating experiments revealed that stonewort isopods

in both lakes were slower in forming couples and had

lower mating probabilities than had the reed isopods

(Fig. 5, Table 2). In contrast to this habitat effect, we did

not find any difference between lakes in neither time to

copulation nor mating probability nor in their interaction

term (Fig. 5, Table 2). Thus, we found a parallel signa-

ture in mating propensity between habitats in both lakes,

but no significant effects of lake history or interaction of

lake · habitat (Table 2). These parallel effects of habitat

across both lakes in influencing mating behaviour

differed from the results on morphological differences,

where the effects of history (lake) and the interaction

term (lake · habitat) were more pronounced (Table 2).

For the mating propensity data, the percentage of

variation explained by the lake term and the lake · hab-

itat terms were low (approximately 0–10.01%; Table 2).

These results show that the isopods from the new

stonewort habitat do not only differ in morphology but

also differ in mating behaviour, as they were clearly more

reluctant to mate (Fig. 5).

Cluster analyses

Based on the molecular, morphological and behavioural

data above, we constructed cluster diagrams (Fig. 6).

These cluster diagrams showed that based on morphol-

ogy or behaviour, one should consider the two stonewort

populations from the two different lakes and the two

reed populations from these two lakes as two indepen-

dent phenotypic groups, in which populations from the

same habitats are always sister groups (Fig. 6). In striking

contrast to this, the cluster diagram based on molecular

data from the AFLP analyses (Fig. 6) grouped populations

among lakes as genetically more distant from one

another than ecotypes within a single lake (Table 1,

Fig. 2). These diagrams confirm the general view that the

new stonewort ecotypes converged to a similar morpho-

logical and behavioural state.

Table 3 Classification matrix from the discriminant analysis show-

ing percentage of correctly predicted classification for each popula-

tion category.

KS KR TS TR

KS 64.4 1.9 24.8 8.9

KR 2 87.9 4 6.1

TS 21.2 11.1 55.6 12.1

TR 16.5 15.5 13.6 54.4

Rows: observed classifications, columns: predicted classifications.

Population categories: Krankesjon Reed (KR), Stonewort (KS),

Takern Reed (TR) and Stonewort (TS). The average percentage of

correct population classifications amounts to 65% and 80% for the

correct ecotype classification.

Table 4 Multivariate phenotypic distances (Mahanalobis distances)

for each population category.

KS KR TS TR

KS 0

KR 8.24*** 0

TS 1.33*** 6.39*** 0

TR 3.04*** 3.87*** 3.09*** 0

Population categories: Krankesjon Reed (KR), Stonewort (KS),

Takern Reed (TR) and Stonewort (TS).

***P < 0.001.

Fig. 5 Parallelism in mating behaviour in Lake Krankesjon and Lake

Takern. Error bars denote 95% confidence limits intervals. Filled

circles (d) connected by solid lines indicate population mean in Lake

Krankesjon and empty squares (h) connected by dashed lines

population mean in Lake Takern. GLM for time to copulation: Lake:

F1,280 = 0.48, P = 0.49; Habitat: F1,280 = 8.41, P = 0.004;

Lake · Habitat: F1,280 = 0.039, P = 0.84. Generalized linear model

(GLZ) for probability of copulation: Lake: v2 (1) = 0.38, P = 0.53;

Habitat: v2 (1) = 19.8, P < 0.001; lake · habitat: v2 (1) = 2.32,

P = 0.13.

Parallelism and contingency during divergence 9

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Discussion

In studies of parallel phenotypic divergence, it is impor-

tant to not only understand the relative magnitudes of

shared selective pressures in similar environments but

also to quantify the effects of unique histories (Langer-

hans & DeWitt, 2004; Langerhans et al., 2006). Historical

contingency is one way genetic constraints and historical

(ancestral) conditions may influence the direction and

even the final outcomes of phenotypic evolution (Lan-

gerhans et al., 2006). By focusing their study efforts on

cases of parallel divergence, biologists could quantify the

relative importance of initial conditions, selection and

the combination of these factors during adaptive diver-

gence (Schluter et al., 2004). Observed cases of parallel

evolution can be viewed as natural experiments, in

which identical outcomes under similar selective pres-

sures can be used as a null hypothesis (Reznick et al.,

1996; Losos et al., 1998; Huey et al., 2000; Schluter et al.,

2004). Any significant deviation from the expected

outcome of a perfect parallel evolutionary change can

then be interpreted as historical effects and further

investigated, for instance, by using comparative

phylogenetic methods (Langerhans et al., 2006) or by

incorporating historical, ecological and geographical

information about the populations (Lee, 1999; Taylor &

McPhail, 2000).

The mtDNA haplotype network (Fig. 2) shows four

distinct genetic clusters corresponding to each lake, with

rather mixed patterns of reed and stonewort haplotype

frequencies. The two main lake haplotypes are separated

by seven mutations, except for two reed haplotypes

present in Krankesjon which are closely related to

Takern haplotypes (Fig. 2). The other two lakes show

similar patterns and, although it is clear that gene flow

has recently occurred between these lakes, the molec-

ular data suggests that the stonewort populations have

evolved independently. It is therefore highly unlikely

that the different stonewort populations share a single

origin, otherwise the molecular analyses would have led

to a haplotype network where stonewort populations

would tend to group together. In addition, the AMOVAAMOVA

revealed a higher level of differentiation among lakes

compared to within lakes. Thus, both the AFLP and

mtDNA molecular data suggest that the different stone-

wort populations have emerged independently, rather

than emerged in one of the lakes and then subsequently

spread to the others. There is however the possibility

that recent gene flow between ecotypes of the same

lakes might have rendered the stonewort ecotypes

genetically closer to the reed ecotypes in each lake even

if the stonewort ecotypes share a common origin, as

discussed in other parallel evolution cases (Taylor &

McPhail, 2000). However, this scenario would predict

the coexistence in each lake of two distinct common

haplotypes shared by both ecotypes, reflecting the

admixture of one already established population and

one invasive population. This is not the case in the three

lakes where both ecotypes were sequenced. Thus, the

molecular analyses conducted here suggest that the

stonewort populations have emerged in situ in each lake,

and are inconsistent with a single origin of all these

stonewort populations.

These new molecular data are thus consistent with the

previous suggestion that the isopod ecotypes from the

different habitats represent a true case of parallel evolu-

tion (Hargeby et al., 2004), but with the caveat that we

cannot exclude the possibility the stonewort ecotypes

evolved elsewhere and then independently invaded the

two current lakes. However, independent ecological data

do indeed suggest that at least some phenotypic diver-

gence has taken place in situ in each lake (Hargeby et al.,

2004). For instance, in a previous study, stonewort

isopods that were sampled early in the colonization of the

new habitat were not phenotypically different from reed

individuals, but field sampling studies over time revealed

that the newly established stonewort populations subse-

quently became smaller and brighter in pigmentation

(Hargeby et al., 2004). Thus, some phenotypic divergence

has clearly taken place in the two lakes and this system is

therefore likely to be a case of rapid parallel phenotypic

evolution. However, the conclusions drawn in this study

Fig. 6 Cluster analyses for both ecotypes of Lake Krankesjon and Lake Takern based on (a) molecular amplified fragment length

polymorphism, (b) morphological (length, width at segment 1, 4 and 7, colour, saturation and brightness) and (c) behavioural data

(time to copulation and probability of copulation).

10 F. EROUKHMANOFF ET AL.

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should not be generalized to other lakes without caution,

because the role of either selection or history during this

parallel evolution event could be idiosyncratic to the two

lakes studied. More empirical work remains to be done in

other lakes to specifically address the general role of

selection and history during parallel phenotypic evolu-

tion in A. aquaticus.

We found strong evidence for parallel convergence

towards a similar phenotypic state in the novel stone-

wort habitat in both lakes. This strong convergence was

documented for both morphology and mating behav-

iour. Although the initial conditions in the source reed

habitat are certainly not identical in Takern and

Krankesjon, the environmental conditions in the novel

stonewort habitat are likely to be similar between the

two lakes (Hargeby et al., 2007). For instance, the

stonewort habitat in both lakes forms dense stands that

cover most of the bottom area outside the reed belts

and which are mainly constituted of one stonewort

species (C. tomentosa) (A. Hargeby & F. Eroukhmanoff,

unpublished data). Whereas there were significant

differences between isopods from the source reed

habitat, reflected by the significant effects of history

(i.e. lake and lake · habitat interactions, Table 2), the

isopods from the stonewort habitat in both lakes were

remarkably similar in both morphology and mating

behaviour. This was also revealed by the shorter

multivariate phenotypic distance between the two

stonewort populations than the distance between the

two reed populations.

The source reed ecotypes differed in length and

pigmentation (Fig. 3), which both contributed to the

significant historical effects of lake or lake · habitat

(Table 2). However, we found remarkably similar phe-

notypic parallelism in mating behaviour, with almost

no historical effects (Table 2; Fig. 5). The strong effect

of habitat, but no significant effects of lake or the

lake · habitat terms (Table 2), suggests that for mating

behaviours, the historical legacy from the source reed

habitat is less pronounced than for morphological

traits. The relatively low percentage of variance

explained by lake histories both for morphological

and behavioural traits (Table 2) indicates that parallel

divergence has been relatively little constrained by

historical factors, unique to the two reed populations in

Lake Takern and Lake Krankesjon. We should also add

that the term lake in these analyses, which should not

narrowly be interpreted as a simple nonadaptive

historical legacy. Rather, the lake-term is likely to

reflect some adaptive ecological differentiation between

the two reed populations in the different lakes,

reflecting, e.g. differences in light regimes or vegetation

within the different reed habitats.

This rather unexpected finding could be explained by

two mutually nonexclusive alternatives. First, divergent

selection might have been so strong so that it has wiped

out the historical signatures that we expected to find.

Alternatively, parallel divergence might largely have

resulted from selection on pre-existing standing genetic

variation (Barrett & Schluter, 2008) perhaps through a

process of phenotype sorting (Rice & Pfennig, 2007). Our

results show that the isopods in the source habitat in

both lakes are phenotypically more variable, which is

clearly consistent with a pronounced role for standing

variation driving rapid parallel divergence in both lakes

(Barrett & Schluter, 2008). Rice & Pfennig (2007)

suggested two tests to distinguish phenotype sorting

from in situ changes. In our system, they would translate

as: (i) phenotypes from the stonewort lying within the

range of the reed phenotypes and (ii) decreased pheno-

typic variation in the stonewort ecotypes. Both these

predictions are fulfilled in both lakes. Thus, certain

phenotypes could have increased in frequency through

sorting as the animals colonized the novel stonewort

habitat in both lakes. Rice & Pfennig (2007) also point

out that other mechanisms like stabilizing selection could

be responsible for reduced variation in new habitats.

However, the rapidity of the observed divergence is

clearly consistent with phenotype sorting (Rice & Pfen-

nig, 2007). We suggest that this mechanism could also

potentially explain the strong parallel signatures docu-

mented in this study and the rapid phenotypic changes

(Hargeby et al., 2004; this study). In the future, the use of

genetic markers to determine whether lake history can

account for most of the observed divergence between

ecotypes could confirm this hypothesis (Rice & Pfennig,

2007).

Two possible mechanisms can explain the strong

parallel changes in mating propensity in the two lakes.

First, divergent selection on size or pigmentation could

have caused correlated responses in mating behaviours.

Alternatively, novel selective pressures in the new

stonewort habitat may have selected directly for altered

mating behaviours. Qualitative and quantitative differ-

ences in predation regimes between habitats could affect

risk-prone behaviours, including mating behaviours.

Visually hunting predators (fish) are more prevalent in

the new stonewort habitat than in the reed habitat

(Wagner & Hansson, 1998), and this could have selected

for more risk-averse behaviours in that habitat (Dill et al.,

1999; Bernal et al., 2007), resulting in observed lower

mating propensity (Fig. 5).

The types and number of predators differ between the

different habitats (Hargeby et al., 2004, 2005), and these

ecological habitat differences have presumably driven

divergent natural selection in a similar phenotypic

direction in both lakes. The overall ecology of the

isopod predators is similar between Lake Takern and

Lake Krankesjon since the same species of fish and

predatory invertebrates are shared between these two

lakes (Rask & Hiisivuori, 1985; Wagner & Hansson,

1998). Whereas the stonewort habitat is similar and

consists of the same Chara-species (see above), the

source reed habitat differs somewhat between the lakes

Parallelism and contingency during divergence 11

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(A. Hargeby & F. Eroukhmanoff, unpublished data). For

instance, in Lake Krankesjon, the water depth in the

reed is shallower and the reed stands are also mixed

with stands of Great fen-sedge (Cladium mariscus). In

Lake Takern, on the other hand, the reed stands are

more extensive, the water depth is more pronounced

and the reed stands are mixed with Lesser Bulrush

(Typha angustifolia). These and other possible differences

between the reed habitat in Lake Takern and Lake

Krankesjon could potentially explain the phenotypic

differences between the reed ecotypes of the two lakes

(Fig. 3). Although the isopods converged towards a

phenotypically similar stonewort ecotype in both lakes

(Figs 3 and 5), the starting conditions and the source

phenotypes differed, demonstrating an effect of history

in affecting the phenotypic direction in each lake. The

results in this study show that the parallel phenotypic

changes have only a minor historical signature but in

some cases there is evidence for an interaction between

history and selection (‘unique diversification’, cf. the

scenarios outlined in Fig. 1c,d).

Parallelism in morphology and mating behaviours has

resulted in pronounced phenotypic clusters in these

isopods (Fig. 6) with strong ecotype correspondence

across lakes. In light of these parallel changes in pheno-

typic traits, it will be of interest to investigate if there is

also evidence for parallelism in sexual isolation, as

previously demonstrated in limnetic and benthic ecotypes

of sticklebacks (Gasterosteus aculeatus) (McPhail, 1994;

Schluter, 2000; Boughman et al., 2005). Mating experi-

ments involving males and females from the different

habitats and lakes indicate some degree of incipient

sexual isolation between ecotypes (F. Eroukhmanoff,

A. Hargeby & E. I. Svensson, unpublished data) as shown

in a recent microcosm experiment on size-assortative

mating (Hargeby & Erlandsson, 2006).

Repeated and convergent phenotypic outcomes have

been observed in the fossil record (Gould, 1989; Vermeij,

2006) as well as in studies of extant organisms invading

novel but similar environments (Reznick et al., 1996;

Losos et al., 1998; Huey et al., 2000). However, these

well-documented cases of parallel and convergent

changes certainly do not exclude some additional role

of historical contingency, and contingencies could also

influence the course of evolutionary trajectories, espe-

cially in the early stages of divergence (Schluter, 1996).

While selection has clearly been shown to produce

similar and repeated outcomes in similar environments,

the results in this and other recent studies (Huey et al.,

2000; Langerhans & DeWitt, 2004; Langerhans et al.,

2006) also show that historical contingencies can influ-

ence the finer details of evolutionary trajectories and

restrict the number of possible phenotypic outcomes.

There is clearly a need for more studies on the relative

roles of selection and historical contingency, and how

these factors interact during the early stages of parallel

phenotypic divergence.

Acknowledgments

The authors are grateful to S. Kuchta for his help with

the interpretation of the molecular data and to

A. Runemark, S. Ibanez, R. Hardling., R. Svanback and

T. Gosden for constructive criticisms on the first draft of

this manuscript, to M. Brydegaard Sørensen for his work

on the picture analysis software (Picstats) and to field

assistants S. Guechot, M. von Post, K. Karlsson who

participated in this project during 2005–2007. This study

was financially supported by The Ecole Normale Superi-

eure (ENS) and The Royal Physiographic Society in Lund

(KFS) to FE and the Swedish Research Council (VR) and

The Swedish Council for Environment, Agriculture, and

Spatial Planning (FORMAS) to EIS.

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Supporting information

Additional Supporting Information may be found in the

online version of this article:

Appendix S1 mtDNA sequences.

Please note: Wiley-Blackwell are not responsible for

the content or functionality of any supporting materials

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material) should be directed to the corresponding author

for the article.

Received 17 October 2008; revised 1 February 2009; accepted 5 February

2009

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