Fisheries Research 85 (2007) 306–315

Atlantic cod (Gadus morhua L.) in inner and outer coastal zones ofnorthern Norway display divergent genetic signature at non-neutral loci

Jon-Ivar Westgaard ∗, Svein-Erik Fevolden

Norwegian College of Fishery Science, University of Tromso, Breivika, NO-9037 Tromso, Norway

Received 9 November 2006; received in revised form 21 March 2007; accepted 5 April 2007

Abstract

A large majority of Atlantic cod (Gadus morhua) found all year round in outer parts of fjords and along outer coastal areas in general in northern

Norway have a genetic signature at the pantophysin (Pan I) locus that distinguish them from Norwegian coastal cod (NCC) found further inside

fjords in the same areas. The identity of these fish has been controversial and to examine if they represent a distinct coastal cod group, or are

identical to the migratory North East Arctic cod (NEAC) of the Barents Sea, a comparison was conducted at 10 microsatellite loci for 15 samples

comprising each of the three groups inner coastal NCC, outer coastal cod and NEAC. Spatial analysis of molecular variance revealed that the outer

coastal samples cannot be discriminated from NEAC by means of microsatellite markers, supporting the similarity of the two groups at the Pan I

locus. This implies that a portion of the otherwise migratory NEAC, at least in its premature stage, remains in coastal areas in close contact with

the genetically quite distinct NCC group. Both the coalescent-based simulation approach and the lnRH test for selective sweeps proved two of the

microsatellite loci, GMO 34 and GMO 132 to be non-neutral. Notwithstanding, these two loci, together with the acknowledged non-neutral Pan

I locus, which displayed profound linkage disequilibrium to GMO 34 within NEAC, are as yet the only nuclear markers which unambiguously

discriminate between NEAC and NCC. Although the relative contribution of restricted gene flow versus selection as a causative agent for the

divergence between NEAC and NCC has not been assessed, we believe the magnitude of differentiation at the three loci provides rational for

maintaining NEAC and NCC as separate management units.

© 2007 Elsevier B.V. All rights reserved.

Keywords: Atlantic cod; Microsatellites; Population structure; Selection

1. Introduction

The Atlantic cod (Gadus morhua L.) is one of the most valu-

able commercial fish species in the north Atlantic. As such,

its population structure has attained much focus to ascertain

proper management units. In the NE Atlantic particular atten-

tion has been placed on the genetic discrimination between the

large population of migratory cod in the Barents Sea (Northeast

Arctic cod, NEAC) and the more stationary coastal popula-

tion of cod (Norwegian coastal cod, NCC). This subdivision

has gained support from the much used genetic marker pan-

tophysin (Pan I), synonymous to the GM789 locus of Pogson

et al. (1995) and Syp I of Fevolden and Pogson (1997). This

scnDNA locus possesses two allelic classes, of which Pan IB

prevails in NEAC (≈90% on average) and Pan IA equally pre-

∗ Corresponding author. Tel.: +47 77646796; fax: +47 77646020.

E-mail address: Jon-Ivar.Westgaard@nfh.uit.no (J.-I. Westgaard).

vails in the NCC group (Fevolden and Pogson, 1997; Sarvas

and Fevolden, 2005a). This profound difference between two

groups of cod that occur geographically so close to each other,

in part overlap in distribution, has been observed nowhere else in

the cod’s distributional range; in fact it represents a remarkable

divergence in marine fish in general. Pan I is admittedly not a

neutral marker in cod (Fevolden and Pogson, 1997; Pogson and

Fevolden, 2003; Pogson and Mesa, 2004; Sarvas and Fevolden,

2005a,b), and contemporary selection has been reported to

cause differences in allele frequencies between cohorts, between

sexes, and also to have caused excesses of homozygotes in

a local fjord population of cod (Karlsson and Mork, 2003).

Nevertheless, contemporary selection acting on settling cohorts

from one common and homogeneous spawning population has

been dismissed as a causative agent for the extreme Pan I

allele frequency divergence between NEAC and NCC. Rather,

accumulated effect of selection acting over relatively recent his-

torical time (like since the last ice-age) has been suggested to

evoke the divergence between the two groups (Fevolden and

0165-7836/$ – see front matter © 2007 Elsevier B.V. All rights reserved.

doi:10.1016/j.fishres.2007.04.001

J.-I. Westgaard, S.-E. Fevolden / Fisheries Research 85 (2007) 306–315 307

Pogson, 1997; Pogson and Fevolden, 2003; Sarvas and Fevolden,

2005a,b).

As to the distribution of Pan I allele frequencies in fjords and

otherwise near-coastal waters, cod caught well within the fjords

have high frequencies of the Pan IA allele, and thus are suggested

to represent NCC, whereas cod caught in outer parts of the fjords

and along the coast often display high frequencies of the Pan

IB allele (Sarvas and Fevolden, 2005a,b). The identity of the

outer coastal cod displaying high Pan IB frequencies represents a

controversy. At certain seasons they could be NEAC on feeding

or spawning migrations, but their all-year-round presence has

led to speculations whether they represent a separate coastal

variant with Pan I allele frequencies very distinctive from the true

fjord cod, or NEAC which has yet not migrated into the Barents

Sea. Thus, the prime objective of this study was to compare

coastal cod with high Pan IB frequencies with NEAC caught

in the Barents Sea proper and on their major spawning site in

Lofoten. The comparisons are made at a number of microsatellite

markers, which comprise loci within the non-coding regions of

the nuclear DNA and are considered versatile due to their alleged

neutrality (but see below).

The second objective of this study has been a more thor-

ough examination of the contribution of selection in shaping

the genetic divergence between NEAC and NCC. Results from

earlier studies, using different marker classes, have been some-

what contradictory. The general pattern has been that studies

using markers subjected to selection [e.g. blood type E (Møller,

1966, 1968, 1969), haemoblobin (Dahle and Jorstad, 1993) Pan I

(Pogson et al., 1995; Fevolden and Pogson, 1997; Pogson, 2001;

Pogson and Fevolden, 2003; Sarvas and Fevolden, 2005a,b)] has

been able to discriminate between NEAC and NCC, whereas

results from presumed neutral genetic markers have been less

conclusive (mitochondrial cytochrome b sequence, Arnason and

Palsson, 1996, and specific allozymes, Mork and Giaever, 1999).

Nevertheless, a recent comparison of Pan I data with variation

at a number of microsatellite loci in cod from a large geographic

area comprising both NEAC and NCC, revealed very much the

same structuring from the non-neutral and the alleged neutral

markers (Skarstein et al., in press). In the present study, compar-

ison of microsatellite variation in samples that were classified as

true coastal cod (high Pan IA frequencies and taken well within

fjords) or true NEAC (high Pan IB frequencies and taken in

the Barents Sea or on a known spawning site of NEAC) would

enable a test of the neutrality also of the microsatellite markers.

Moreover, Pan I data alone cannot be used to support the long-

standing acknowledgement of fishermen that in several fjords

in northern Norway there exist stationary cod with their own

local spawning sites, quite distinct from the spawning sites of

migratory NEAC (Lofoten and banks off Troms and Finnmark

counties). Thus, the microsatellite data would also allow a pos-

sible discrimination of coastal cod samples that are genetically

homogeneous in terms of Pan I allele frequencies. The neutral-

ity of particularly one of the loci used herein (GMO 132) has

recently been questioned (Karlsson and Mork, 2005; Nielsen et

al., 2006). Whether this would detract it from being a supportive

marker for discriminating between NEAC and NCC, or among

populations within the NCC group, will be discussed.

2. Materials and methods

2.1. Samples

A total of 1107 fish distributed over 15 samples (Table 1)

were selected from the collection of cod samples available at the

Norwegian College of Fishery Science, University of Tromsø.

These samples were all previously analyzed for variation at

the Pan I locus. The geographical area of the samples ranged

from Spitsbergen in the north down to Lofoten in the south

(Fig. 1). The samples were selected according to the prime objec-

tive of the study, namely to compare samples of Atlantic cod

with high Pan IB allele frequencies taken in the outer coastal

areas with alleged NEAC and NCC samples. Thus, the sam-

ples were selected to fit into one of the following three groups:

(1) Samples dominated by the Pan IB allele in coastal areas

of northern Norway. (2) North East Arctic cod (NEAC) sam-

ples from the Barents Sea and Lofoten (Vestfjorden) in the

spawning season of NEAC. (3) Samples dominated by the Pan

IA allele from inner coastal areas of Troms and Finnmark. To

the latter group it was also included one representative of an

outer coastal sample with high Pan IA frequency (AND of

Table 1). Pan IA frequencies are as yet the best criterion avail-

able for an a priori assignment of cod in this area to the coastal

cod group. The samples comprised 0-group cod, juveniles and

adults (Table 1), apart from the outer costal samples dominated

by the Pan IB allele, which consisted exclusively of immature

individuals.

Fig. 1. Map over sampling sites for Atlantic cod included in the study. Sample

abbreviations are as in Table 1.

308 J.-I. Westgaard, S.-E. Fevolden / Fisheries Research 85 (2007) 306–315

Table 1

Locations, sample sizes (n), and microsatellite diversity estimates within samples of Atlantic cod included in the study

Sample name Locality Group Position Date n Ar He Ho Pan IB

ISF Isfjord, Svalbard 2 78◦13′N 14◦39′E December 2001 92 14.17 0.73 0.67 0.78

B05 Bear Island 2 74◦06′N 21◦06′E December 2005 50 13.00 0.69 0.66 0.96

RUS Russia 1 70◦14′N 32◦34′E 1999 48 12.85 0.69 0.69 0.86a

EKK Varanger, Ekkerøy 1 70◦12′N 30◦49′E September 1996 96 14.05 0.73 0.70 0.71

TAN Tanafjord 1 70◦51′N 28◦34′E October 2000 48 14.01 0.72 0.66 0.87

POR Porsangerfjord 1 71◦14′N 25◦12′E September 1997 96 13.85 0.73 0.67 0.83

BAR North Cape 1 71◦14′N 28◦18′E June 1998 48 13.03 0.70 0.68 0.98a

MAS Masøysund 1 71◦00′N 25◦09′E August 1997 48 13.56 0.71 0.70 0.86a

ULL Ullsfjord 1 69◦52′N 19◦58′E October 2000 96 13.27 0.70 0.66 0.81b

VES Vestfjord 2 67◦26′N 12◦04′E March 2002 47 13.72 0.73 0.69 0.84

DOA Dønnesfjord 3 70◦14′N 22◦37′E September 1999 62 14.54 0.77 0.75 0.03

BAL Balsfjord 3 69◦14′N 19◦23′E September 1997 96 14.63 0.78 0.76 0.15a

AND Andenes 3 69◦25′N 15◦49′E January 2006 96 13.83 0.76 0.72 0.38

SOR Sørfjord 3 69◦25′N 19◦37′E September 1996 96 14.71 0.78 0.77 0.20

MAL Malangen 3 69◦18′N 18◦39′E April 2002 88 14.15 0.78 0.75 0.11

Corrected allelic richness (Ar, corrected to n = 82), expected heterozygosity (He) and observed heterozygosity (Ho) are shown as averages over the 10 microsatellite

loci used. Pan IB is the frequency of this allele in the samples. Group 1: outer coastal samples; group 2: NEAC samples; group 3: NCC samples.a Pan I data published in Sarvas and Fevolden (2005a).b Pan I data published in Sarvas and Fevolden (2005b).

2.2. Genetic analyses

DNA was isolated from gill arches according to the method

described in Fevolden and Pogson (1997). The analysis of

the pantophysin locus (Pan I), was conducted according to

Fevolden and Pogson (1997). Ten microsatellite loci were

applied and analyzed by the use of two multiplex reactions

(MP1 and MP2) with five loci each (Table 2). The PCR

reactions consisted of 1× PCR buffer (50 mM KCl, 10 mM

Tris–HCl), 1.5 mM MgCl2, 400 �M dNTPs, 0.1–2.5 �M primer

(Table 2) and 0.5 Units of Taq polymerase. The forward primers

were labeled with fluorescent dye at the 5′ end. The PCR

profile comprised an initial denaturizing step at 95 ◦C for

1 min, followed by 22 (MP1) or 25 (MP2) cycles at 95 ◦C

for 20 s, 56 ◦C for 30 s and 68 ◦C for 1 min. The PCR reac-

tions ended with a final elongation step of 68 ◦C for 10 min.

Separation of the PCR products was performed on an ABI

3100 Genetic Analyzer (Applied Biosystems, Foster City,

USA) and the different alleles were scored using the instru-

ment specific software Genemapper 3.7 (Applied Biosystems).

Each genotype was visually controlled for possible scoring

errors.

2.3. Statistics

2.3.1. Genetic variation within samples

The microsatellite data were analyzed in the software

Microchecker ver. 2.2.3 (Van Oosterhout et al., 2004) in order

to check for possible scoring errors and null alleles. The esti-

mation of basic statistics such as number of alleles, observed

and expected heterozygosities, linkage disequilibrium and test

for possible deviation from Hardy–Weinberg equilibrium were

all estimated using the software Arlequin 3.0 (Excoffier et

al., 2005). The significance levels were adjusted by sequen-

tial Bonferroni corrections (Rice, 1989), when multiple tests

were applied. Mean allelic richness per locus and private allele

richness were calculated using the rarefaction procedure in

the HP-RARE 1.0 software (Kalinowski, 2005) to account for

uneven number of populations and sample sizes (Kalinowski,

2004). The rarefaction sample sizes were set to five populations

and 82 genes and their significance were tested across loci.

2.3.2. Differentiation among samples

Pair-wise differences among the samples were estimated with

the FST analogous θ unbiased estimator (Weir and Cockerham,

Table 2

Characterization of the microsatellite markers used for Atlantic cod in this study

Locus Multiplex [Primer] Size range (bp) Na He Ho Reference

GMO8 1 0.10 �M 110–314 53 0.93 0.90 Miller et al. (2000)

GMO 19 1 0.15 �M 124–228 26 0.92 0.87 Miller et al. (2000)

GMO35 1 0.20 �M 110–140 11 0.83 0.75 Miller et al. (2000)

GMO37 1 2.50 �M 204–308 21 0.84 0.83 Miller et al. (2000)

TCH11 1 0.30 �M 118–226 27 0.93 0.89 O’Reilly et al. (2000)

GMO2 2 0.25 �M 96–150 27 0.84 0.86 Brooker et al. (1994)

GMO3 2 0.10 �M 158–208 13 0.19 0.19 Miller et al. (2000)

GMO34 2 0.10 �M 80–116 10 0.29 0.28 Miller et al. (2000)

GMO132 2 0.30 �M 97–171 34 0.57 0.59 Brooker et al. (1994)

TCH13 2 0.30 �M 72–174 48 0.92 0.89 O’Reilly et al. (2000)

Na: number of alleles; He and Ho: expected and observed heterozygosity.

J.-I. Westgaard, S.-E. Fevolden / Fisheries Research 85 (2007) 306–315 309

1984), assuming the infinite allele model (IAM). Tests for

population differentiation were performed as an unbiased esti-

mate of the P-value of a log-likelihood (G) based exact test

(Goudet et al., 1996).

Assignment tests were performed in the software Geneclass2

(Piry et al., 2004) using a Bayesian approach based on Rannala

and Mountain (1997), implemented in the software (assignment

treshhold, 0.05). For the visualization of the relationship among

samples a principal component analysis (PCA) was performed

in GenAlEx 6 (Peakall and Smouse, 2006), a software package

for Microsoft Excel.

2.3.3. Hierarchical structure

In order to define a possible hierarchical structure among

the samples, we used a SAMOVA (spatial analysis of molecu-

lar variance) approach (Dupanloup et al., 2002), using the IAM

based estimator FCT. This approach is an extension of the anal-

ysis of molecular variance (AMOVA) (Excoffier et al., 1992)

in that it incorporates the geographical sampling site into the

genetic data and seeks to define groups of populations that are

geographically homogenous and genetically maximally differ-

entiated from each other. Thus, the software searches for the

configuration of groups with the largest FCT values (variance

among groups) associated with them. This was done in the soft-

ware SAMOVA 1.0 (Dupanloup et al., 2002). Using hierarchical

structure as defined by the SAMOVA a two-tailed permutation

test for differences between the groups of samples for observed

and expected heterozygosity (Ho and He) was performed by

means of the software Fstat 2.9.3 (Goudet, 2001).

2.3.4. Marker neutrality

Two approaches were pursued for testing the microsatel-

lite loci against neutral expectations. First, we applied the

coalescent-based method simulation approach of Beaumont

and Nichols (1996) implemented in the software FDIST2

(http://www.rubic. reading.ac.uk/∼mab/software/fdist2.zip).

This software calculates FST and heterozygosity according to

the method described by Cockerham and Weir (1993). The

expected FST is then calculated from the data as the average

among loci weighted by their heterozygosity. Coalescent

simulations were performed using samples of the same size and

number as the data (i.e. number of populations in the analysis

and the median sample size of all populations), assuming an

island model with 100 islands. Fifty thousand independent loci

were generated using the infinite allele (IAM) mutation model.

From this procedure we get a simulated distribution of FST

values conditional on heterozygosity under a neutral model.

This distribution is then compared to the observed FST values

in order to identify potential outlier loci.

For the second approach we used the lnRH statistic of the

“Schlotterer test”, which is designed for detecting selective

sweeps at hitch-hiking microsatellite loci (Schlotterer, 2002;

Kauer et al., 2003). This statistics calculates the relative vari-

ance in gene diversity within loci, between populations, and

seeks to identify loci that differ in variability from the reminder

of the genome by calculating the ratio of gene diversities in two

populations. Because it is the ratio of genetic variability of two

populations that is calculated, all loci have equal expectations of

variability irrespective of locus-specific mutation rates (Kauer

et al., 2003). Both tests were performed upon pooling the sam-

ples into two groups, one comprising outer coastal samples and

known NEAC samples, while the other group comprised NCC

samples, thus allowing a direct comparison between the two

groups.

3. Results

3.1. Microsatellite DNA variation within the collected

samples

Average heterozygosity in the populations over the 10

microsatellite loci varied from 0.69 to 0.78 (mean 0.73)

(Table 1). Departure from Hardy–Weinberg equilibrium was

found in 5 out of 150 cases, which is within range of what

can be expected by chance. The expected heterozygosity across

the loci varied between 0.19 and 0.93 (Table 2). The number of

sampled alleles for each microsatellite loci ranged from 10 to 53

with an average of 27. Tests for linkage disequilibrium among

the microsatellite loci revealed 5 out of 825 possible signifi-

cant interactions, i.e. far less than expected by chance. However,

including the Pan I locus in the linkage disequilibrium analy-

ses displayed significant interactions between this locus and the

microsatellite GMO 34 in 10 of 11 samples with high Pan IB

frequencies (that is, the a priori alleged NEAC samples plus the

outer coastal samples), affecting the power of the pair-wise FST

statistics. For samples with high Pan IA frequencies (alleged

NCC) no such interactions were found.

3.2. Relationship among the different samples

The pair-wise comparison between samples, based on FST

estimates, provided no significant differences between the outer

coastal samples and the known NEAC samples (Table 3). More-

over, the SAMOVA gave the largest FCT value (0.0104, P < 0.01)

in a two-group configuration where one group comprised outer

coastal samples together with known NEAC samples and the

other group comprised NCC samples. For further statistical anal-

yses, therefore, the outer coastal samples and known NEAC

samples were pooled into one joint NEAC group, whereas the

high frequency Pan IA samples were still considered the NCC

group. Cases of significant FST values were largely found in pair-

wise comparison between NEAC samples, and NCC samples

(Table 3). However, the significance was highly influenced by

the two loci GMO 34 and GMO 132. Removing these loci elim-

inated all but one significant FST value (between samples VES

and MAL; FST = 0.005, P = 0.001). Within the NCC group two

(out of 10 possible) significant FST values were found, compared

to none within the now redefined NEAC group (Table 3).

The two-tailed permutation test of heterozygosity with the

two-group configuration from the SAMOVA revealed signifi-

cant differences between the NEAC and NCC group at the 0.1%

significance level (He = 0.712 and 0.773, respectively). The over-

all assignment success were low (11%), which is in accordance

with the overall low levels of differentiation among samples.

310 J.-I. Westgaard, S.-E. Fevolden / Fisheries Research 85 (2007) 306–315T

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The PCA plot (Fig. 2) illustrates clearly the clustering of sam-

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also visualizes the higher variance among samples associated

with the NCC group.

3.3. Marker neutrality and mutational impact on the

observed structure

A plot of FST against heterozygosity for each of the 10

microsatellite loci superimposed on the distribution expected

when FST equals 0.01 is shown in Fig. 3. The upper and lower

lines enclose an envelope for the 95% confidence interval under

the IAM. The plot shows a poor fit for the FST estimates under

both mutational models were most loci fall along or below the

lower line. In contrast the locus GMO 34 falls well above the

upper CI, while GMO 132 is located slightly above the upper

line, characterizing both as significant outliers.

The “Schlotterer” test based on the variance in gene diversity

between the pooled samples, lnRH, still holds GMO 34 as an

outlier whereas GMO 132 falls within the 95% CI (Fig. 4). The

direct comparison of allele frequency distribution at the two loci

in the two groups clearly shows reduced variability in the NEAC

group compared to the NCC group (Fig. 5).

4. Discussion

4.1. The identity of near coastal samples with high Pan IB

frequencies

One essential finding of this study is that samples domi-

nated by the Pan IB allele in coastal areas of northern Norway

most probably comprise NEAC, and do not represent a diver-

gent group of coastal cod. The panel of 10 microsatellite markers

revealed no differences between the high frequent Pan IB coastal

samples and samples known to consist of NEAC on the basis of

their Pan I allele frequencies. In conformity with Pan I allele

frequency patterns, two of the microsatellite loci (GMO 34 and

GMO 132) clearly discriminated between outer coastal samples

and samples from the inner parts of fjords. The discriminant

contribution of GMO 34 is, however, weakened by its linkage

disequilibrium with Pan I. The eight remaining, and assumedly

neutral loci failed to discriminate between inner and outer coastal

samples.

The samples from the outer coastal areas were dominated

by immature individuals of a size suggesting the fish were 2–5

years old, according to mean length at age in NEAC (Nakken

and Raknes, 1987). Two different scenarios may have caused the

presence of high frequent Pan IB cod in the coastal zone. One

is that immature NEAC performs feeding migrations southward

to the North-Norwegian coast. It is well known that immature

NEAC performs such migrations in association with the spawn-

ing of capelin in the spring (Hjort, 1914; Eggvin, 1938; Hylen,

1961). However, the sampling time for the outer coastal samples

in this study spans from June to October, which implies that

NEAC migration would not be limited only to the spring. The

alternative scenario, therefore, is that some of the 0-group NEAC

J.-I. Westgaard, S.-E. Fevolden / Fisheries Research 85 (2007) 306–315 311

Fig. 2. PCA plot showing all 15 samples of Atlantic cod included in the study. The dashed line surrounds NCC samples, the solid line surrounds NEAC samples

including outer coastal samples with high Pan IB frequencies. Sample abbreviations are as in Table 1.

Fig. 3. Plot of FST as a function of heterozygosity in the various microsatellite

loci used in the study of Atlantic cod. The upper and lower lines indicate 0.975

and 0.025 quantiles. Solid points represent the different microsatellite loci, only

GMO 34 and GMO 132 are denoted.

Fig. 4. Plot of FST against standardized lnRH estimates for 10 microsatellites

between the NEAC and NCC group of Atlantic cod. Dashed lines indicate the

95% CI (−1.96, +1.96). Solid points represent the different microsatellite loci,

only GMO 34 and GMO 132 are denoted.

do not drift into the Barents Sea their first year of life, which

is the common understanding (Robichaud and Rose, 2004, and

references therein), causing the fish to remain in coastal areas,

including outer and middle parts of fjords, for a prolonged time

during their immature phase. Studies have shown that the disper-

sal of eggs and 0-year-old NEAC is highly dependent on currents

and wind induced mixing in the upper water masses, which may

vary from year to year, and that distribution of 1-year-old cod

most probably is determined by the extent of 0-year-old drift dur-

ing the preceding autumn (Sundby, 1983; Nakken and Raknes,

1987; Vikebo et al., 2005). Also, the distribution of larvae and

pelagic juveniles is dependent upon their vertical position in the

water. Larvae and juveniles drifting close to the surface are more

likely to end up in the central or western Barents Sea, whereas

those lower in the water column are more likely to end up close

to the Norwegian coast or the eastern Barents Sea (Vikebo et al.,

2005). Such buoyancy mechanisms, together with the fact that

NCC fry settle at shallower depths and earlier in the season than

NEAC (Løken et al., 1994; Nostvik and Pedersen, 1999), might

also contribute to prevent NEAC eggs and larvae from mixing

with NCC eggs and larvae. So, whereas the data herein infer that

the predominantly immature cod steadily found in outer coastal

regions of northern Norway cannot be genetically distinguished

from NEAC, it remains obscure whether they represent migra-

tory fish or fish that has yet not migrated up into the Barents

Sea.

4.2. The role of selection in shaping population structure

The genetic divergence found among samples in this study

was mainly displayed upon comparing samples between the

NEAC and the NCC group, the former group redefined to include

also coastal cod with high Pan IB frequencies. Within each of

these groups no profound divergence was found. Moreover, the

divergences that were observed were almost exclusively caused

by two loci, GMO 34 and GMO 132, especially pronounced

when the pooled samples of NEAC and NCC were compared.

312 J.-I. Westgaard, S.-E. Fevolden / Fisheries Research 85 (2007) 306–315

Fig. 5. Allele frequency distribution for the loci GMO 34 (a and c) and GMO 132 (b and d) for the NCC group (a and b) and NEAC group (c and d) of Atlantic cod.

When testing the microsatellite loci against neutral expectations

for the two groups of samples, both GMO 34 and GMO 132 were

detected as outlier loci in the coalescent-based simulation test

(Beaumont and Nichols, 1996). However, the smaller number of

samples and loci included, compared to what is recommended

(Beaumont and Nichols, 1996) reduced the power of this test.

The “Schlotterer” test (Kauer et al., 2003) revealed only GMO

34 as an outlier locus. The narrower distribution in allele fre-

quencies in the NEAC compared to the NCC group (Fig. 5),

together with the significant reduction in heterozygosity in the

NEAC group, may be indicative of selective sweeps at both of

these loci. Additional support for selection at GMO 34 is given

by the profound linkage disequilibrium between this locus and

the Pan I locus, which is acknowledged to be under selectional

constraints (Fevolden and Pogson, 1997; Karlsson and Mork,

2003; Pogson and Fevolden, 2003; Pogson and Mesa, 2004;

Case et al., 2005; Sarvas and Fevolden, 2005a,b).

Differences among natural populations in allele frequencies

at loci subjected to selection and not in neutral loci are consis-

tent with selection modifying frequencies at selected loci at the

same time that gene flow homogenize variation at neutral sites

(Berry and Kreitman, 1993; Veuille et al., 1998; Schmidt et al.,

2000). Loci subjected to selection should, however, not automat-

ically be disqualified as useful genetic markers since they have

the ability to respond to population sub-structuring much faster

than purely neutral markers (e.g. Carvalho and Hauser, 1998;

Pogson and Fevolden, 2003). Even very weak selection (e.g.

s < 0.01) will drive gene frequencies much more quickly than

genetic drift (Gavrilets, 2003), making weakly selected markers

particularly useful when examining differences among popu-

lations with a short evolutionary history. In a recent paper by

Conover et al. (2006), the statement was made that for marine

fishes selection is a potent evolutionary force capable of sus-

taining adaptive divergence on contemporary time scales, not

detectable by FST estimates of neutral markers. Limitations in

using selected markers, however, exist, e.g. in estimating coa-

lescent times and historical gene flow. As to microsatellites,

previous studies have documented that this class of markers may

have a functional role themselves (Kashi and Soller, 1999; Li et

al., 2002), or are linked to regions of the genome which are

subjected to selection (Selkoe and Toonen, 2006). In a recent

study of Atlantic cod, microsatellites (many of the same as used

herein) were used to compare samples from the Baltic Sea, North

Sea, Barents Sea, and Newfoundland (Nielsen et al., 2006). The

authors reported the strongest indication for selection at GMO

132 compared to GMO 34 in the present study, illustrating that

the selectional constraints on a microsatellite locus may vary

with environmental conditions.

4.3. Peculiarities of Atlantic cod in northern Norway

An array of biological evidence, plus claims from local fish-

ermen during centuries, supports the existence of two divergent

populations of cod in northern Norway and the adjacent Barents

Sea. Life-history characteristics, habitat and migration patterns

(Bergstad et al., 1987; Jakobsen, 1987; Berg and Albert, 2003),

growth and maturity, otoliths (Rollefsen, 1934) and morpho-

metrics (Løken et al., 1994), all validate the NEAC and NCC

subdivision, although some of those traits are admittedly plas-

tic. Much of the pattern of divergence observed at present might

have been shaped during the last 10,000 years, i.e. after the last

ice age. Although this study does not provide information rela-

J.-I. Westgaard, S.-E. Fevolden / Fisheries Research 85 (2007) 306–315 313

tive to the time scale at which divergence at the microsatellites

operates, the striking habitat differences and thereby different

selection pressure experienced by NEAC and NCC (open ocean

compared to fjords), may have caused divergence at such short

evolutionary time for non-neutral loci like GMO 132 and GMO

34.

Differences in Pan I allele frequencies of Atlantic cod

between adjacent fjords in northern Norway, and even within

fjords, have previously been suggested to be a result of vary-

ing degree of intermingling between NCC and NEAC (Sarvas

and Fevolden, 2005a). When divergent Pan IA frequencies are

observed among fjord samples that are in Hardy–Weinberg

equilibrium, the existence of separate fjord populations could

be a causative agent (Fevolden and Pogson, 1997; Sarvas and

Fevolden, 2005a; Skarstein et al., in press). There are, however,

samples of alleged NCC in northern Norway with intermedi-

ate Pan I allele frequencies displaying both significant deficits

(caused by intermingling of NEAC and NCC?) and excesses of

heterozygotes (e.g. Sarvas and Fevolden, 2005a,b; own unpub-

lished data). Although the latter observations are not more than

what could be expected by chance, the phenomenon is over-

represented in one specific fjord and has, therefore, become

the subject of a separate study addressing both selection and

interbreeding effects (to be published elsewhere).

The isolation-by-distance pattern reported for Atlantic cod

using microsatellites (Skarstein et al., in press) can be interpreted

as a stepping-stone model of gene flow between neighboring

populations of NCC; thus, the relative strength of migration

will vary in response to geographical distance. In the present

study, NCC samples were selected in order to be as homoge-

nous as possible for high Pan IA frequencies to ensure that the

samples comprise NCC. This enabled a test of the potential of

microsatellites to distinguish among NCC samples where Pan I

allele frequencies could not. Significant differentiation in pair-

wise comparisons of samples was, however, only found in three

cases (Table 3). The one NCC sample from outer coastal regions

(AND, Fig. 1), differed somewhat from the other NCC samples

in its Pan I allele frequencies (Table 1), yet showed no clear evi-

dence of being divergent from the other NCC samples on basis

of the microsatellite data (Table 3). The PCA plot (Fig. 2) clearly

clusters the AND sample with the remaining NCC samples.

The amount of differentiation among samples in the NCC

group could be expected to be higher than in the NEAC group

at non-neutral loci due to the more heterogeneous environment

this group is exposed to (due to, e.g. open versus sill-closed

fjords; Berg and Albert, 2003; Lupus, 2006). Considering the

much smaller population sizes of possible fjord populations

(total number of NCC recruits ≪ 1/10 that of NEAC recruits,

before the decline of the former started, cf. ICES, 2006), this

might also apply to the neutral loci due to the effect of genetic

drift. The examination of nucleotide sequences within the Pan

IA allele comprising six coastal samples of NCC, from a slightly

larger geographic area than the present NCC samples, revealed

significant heterogeneity in the frequency of neutral sites among

different fjord populations (Pogson and Fevolden, 2003). The

homogeneity at neutral microsatellites observed herein is thus

not consistent with the isolation suggested by analysis of neu-

tral sites at the Pan I locus and could suggest the occurrence of

gene flow in this area. The apparent incongruity with the data of

Pogson and Fevolden (2003) may have alternative explanations.

The two marker classes may be different in their evolutionary

history due to different mutational mechanisms, i.e. point muta-

tions versus insertions/deletions of repeat units, and degree of

selective neutrality. Moreover, microsatellites, because of their

evolutionary properties, are subjected to homoplasy, that is two

alleles with the same size are identical by state (IBS) but not

by descent (IBD) (Estoup et al., 2002). In a study compris-

ing 14 microsatellite loci in walleye pollock (O’Reilly et al.,

2004), the authors suggested homoplasy as a possible reason for

the highly polymorphic microsatellites having reduced ability

to detect population structure compared to less variable loci in

a weakly structured marine species.

As for the joint NEAC group, which displayed even stronger

homogeneity among samples (Fig. 3), migratory behavior com-

bined with the absence of barriers for gene flow and large

effective population sizes (average of 600 mill 3-year recruits

from 1946 to 2006; ICES, 2006) contributes to minimize possi-

ble divergent mechanisms (e.g. genetic drift).

5. Conclusion

In conclusion, the findings of the present study clearly states

that the Atlantic cod found all year round in outer coastal regions

of northern Norway cannot be genetically discriminated from

NEAC and is not proven to represent a divergent coastal cod

population. Moreover, the data herein, together with those of

Nielsen et al. (2006), strongly suggest that the microsatellite

loci GMO 34 and GMO 132 show clear evidence of being non-

neutral. Rather than dismissing them, therefore, as population

markers, we consider them valuable supporting tools for dis-

criminating between NEAC and NCC, due to their capability

of revealing population differences on shorter evolutionary time

scales than purely neutral markers, as e.g. the eight remaining

microsatellites assayed herein. Thus, the present data support the

suggestion of Pogson and Fevolden (2003) that the differences

detected between NEAC and NCC is driven by diversifying

selection acting in the recent past (and at present) that is not

reflected in the slower evolution of neutral loci. Moreover, like

for Pan I, we believe that the significant divergence between

NEAC and NCC at the two non-neutral microsatellite loci (FST

values comparing pooled NEAC and NCC samples were 0.105

and 0.033 for GMO 34 and GMO 132, respectively) is a result of

accumulated effects of ongoing selection over generations. The

arguments for extensive gene flow between NEAC and NCC

provided by the homogeneous structure displayed by the neu-

tral microsatellites we believe could be caused by these loci

not being sensitive enough to detect restricted gene flow over

such short evolutionary periods, e.g. since the last ice age. Thus,

we consider the population differences found at the non-neutral

loci as strong support for the historical/biological claim that

NEAC and NCC represent separate populations. We acknowl-

edge the shortcomings of exact data on the quantification of

possible gene flow between the two stocks, and possible geno-

typic/environmental interactions (cf. Case et al., 2006), but to

314 J.-I. Westgaard, S.-E. Fevolden / Fisheries Research 85 (2007) 306–315

ascertain a management policy aimed at conserving biological

diversity we recommend that the separate management of the

two units should be upheld.

Acknowledgements

This work was supported by grants 127357/120 and

153343/120 from the Norwegian Research Council to S.-E.

Fevolden. We thank J. Wolkers for her technical assistance in

analyzing the samples.

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