Journal of Fish Biology (2002) 60, 1459–1474
doi:10.1006/jfbi.2002.2005, available online at http://www.idealibrary.com on
Control region haplotype variation in the central
Mediterranean common sole indicates geographical isolation
and population structuring in Italian stocks
I. G*, S. F*, N. U†, S. T*,
C. P‡  F. T*‡§
*Molecular Genetics for Environmental & Fishery Resources Laboratory, Interdept.
Center for Research in Environmental Sciences, University of Bologna, via T. dall’Ova
55, I-48100 Ravenna, Italy, †Marine Biology Laboratory, I-70123 Bari, Italy and
‡Marine Biology & Fishery Laboratory, University of Bologna, I-60123, Fano, Italy
(Received 7 November 2001, Accepted 25 April 2002)
Nine common sole Solea vulgaris samples from five fishery Management Units (MUs) of the
central Mediterranean exhibited differences in a control region sequence marker. Parsimony
network showed two low-divergent haplotype phylogroups. The former predominated in
samples from the Ligurian, Tyrrhenian, Adriatic and north-western Ionian Seas (MUs 9, 10, 17,
18 and northern part of 19, respectively), whereas the latter was abundant in the south-western
Ionian (southern part of MU 19). The geographical clustering of maternal lineages accounts
for population structuring and indicates geographical isolation of common sole stocks in the
Mediterranean. Several life-history traits of common sole and hydro-geographical features of
the basin might support this pattern of differentiation. Haplotype frequency differences were
detected among samples either from different MUs or within the same MU. This situation
indicates the presence of partially subdivided or nearly panmictic population units, whose
exploitation should be based on appropriate bio-ecological features. The usefulness of control
region sequence marker enhances the routinely use of the genetic stock structure analysis in
low-dispersal demersal marine resources.
2002 The Fisheries Society of the British Isles. Published by Elsevier Science Ltd. All rights reserved.
Key words: control region; fishery genetics; Solea solea.
INTRODUCTION
The priorities of modern fishery management of marine feral bio-resources are
the sustainable exploitation and conservation of stocks, which should warrant
their self-recruitment as well as the maintenance of within-species diversity (i.e.
racial and genetic diversity). Genetic structure analysis of marine fish stocks
based on polymorphic molecular markers can be a powerful tool for the
identification of self-recruiting units and the assessment of relevant population
parameters (Carvalho & Hauser, 1998; Ferguson & Danzmann, 1998; Grant
et al., 1999). Recently, highly polymorphic molecular markers (i.e. a mtDNA
control region fragment and a few microsatellite loci; Tinti et al., 1999; Iyengar
et al., 2000) have been developed specifically for common sole (Dover sole) Solea
vulgaris Quensel. Therefore, these markers can be used to carry out genetic
structure analyses on regional and local stocks of common sole in order to assess
§Author to whom correspondence should be addressed. Tel.: +39 0544 213831/218616; fax: +39 0544
31204; email: [email protected]
1459
0022–1112/02/061459+16 $35.00/0
2002 The Fisheries Society of the British Isles. Published by Elsevier Science Ltd. All rights reserved.
1460
.    .
population structure. Since the population structure of the Italian sole stocks is
still unknown, a population genetic survey was carried using the mtDNA control
region sequence marker.
The common sole is a demersal flatfish which inhabits sandy and muddy
bottoms of the continental shelf of the north-eastern Atlantic, Mediterranean
and Black Sea, and whose reproductive and feeding traits are closely related to
depths of 0–200 m (Querò et al., 1986). In the FAO fishery areas N. 27 and 37,
the common sole supports important fisheries (Bauchot, 1987). As a result,
Atlantic and Mediterranean common sole stocks have been heavily exploited
(FAO, 2001). Several studies have aimed at increasing knowledge on the
ecological and behavioural traits of common sole populations, but they have
mainly focused on the north-eastern Atlantic stocks (Horwood, 1993). A few
allozymic surveys (Quignard et al., 1986; Kotoulas et al., 1995; Exadactylos et
al., 1998) were carried out to assess the population structure on several spatial
scales (local, regional and above-regional), detecting a marked geographical
clustering of samples at the above-regional level (i.e. north Atlantic–
Mediterranean samples). At the within-regional geographical scale, Kotoulas
et al. (1995) also observed weak albeit detectable population genetic differences
among the north-eastern Atlantic populations from the Manche Channel,
Bretagne and Bay of Biscay. Similarly, allozymic frequency differences were
detected between western (Ebro–Gulf of Lyons) and eastern (Egypt–Aegean)
populations of the Mediterranean. A linear correlation between genetic and
geographical distances suggested that an isolation-by-distance model might
explain this pattern of differentiation (Kotoulas et al., 1995).
At the regional and local levels, where scientific information is required for
fisheries management, electrophoretic data have produced conflicting results. In
the north Atlantic area, Kotoulas et al. (1995) identified the basic unit of
population structure as a zone (namely, a geographical area with a radius of
c. 100 km, where panmictic or near-panmictic populations gather). Biological
and reproductive observations support the occurrence of panmixia or nearpanmixia at the scale of the zone (Dorel et al., 1991; Koutsikopoulos et al.,
1991). By contrast, the allozymic survey carried out by Exadactylos et al. (1998)
on the stocks from the same area and using a similar number of electrophoretic
loci, indicated high levels of gene flow between samples. This finding supported
an alternative hypothesis of near-panmixia over a larger geographical area
(c. 500 km in radius), because of movement of migrants through the English
Channel between the Irish Coast, North Sea and Bay of Biscay. Together these
surveys show that allozymes can fail in elucidating the population structure of
the common sole at the regional or local geographical scale, even if it cannot be
excluded that differences in the results might be related to temporal changes of
allelic frequencies between sampling years.
MATERIALS AND METHODS
SAMPLING AND DNA EXTRACTION
Nine samples of S. vulgaris (209 individuals) were collected from 1998 to 2000 in five
fishery areas (Management Units, henceforth MUs; GFCM-SAC, 2001) of the centralwestern Mediterranean (Table I and Fig. 1). Common sole samples were obtained from
mt     
1461
T I. Location, year and size of the Solea vulgaris samples
Sample location
Code
Delta Po River
Rimini
Estuary Nerevta River
Lesina
Durres
Gallipoli
Sicilian eastern coast
Salerno Gulf
Livorno
DPO
RMN
NER
LES
DUR
GAL
SIC
SAL
LIV
Management Unit*
Year
17—northern Adriatic Sea
Jun 1998
17—northern Adriatic Sea
Nov 1999
17—northern Adriatic Sea
Feb 2000
17—northern Adriatic Sea
Dec 2000
18—southern Adriatic Sea
Dec 2000
19—western Ionian Sea
Dec 2000
19—western Ionian Sea
Nov 1999
10—south and central Tyrrhenian Sea
Dec 2000
9—Ligurian and north Tyrrhenian Sea Dec 2000
n
21
36
15
19
21
10
24
16
47
*GFCM-SAC (2001).
commercial vessels fishing from 3 to 15 km offshore, except for the LIV sample which was
provided by the Inter-University Center of Marine Biology and Applied Ecology (CIBM,
Livorno). After removal of skin, white muscle tissue (0·2–0·4 g) was sampled from
individuals using a sterile cutter and stored in 80% ethanol at 4 C. Total genomic DNA
was prepared from 25–50 mg of dried muscle tissue according to a saline procedure
(Miller et al., 1988).
CONTROL REGION AMPLIFICATION AND SEQUENCING
The 5 end of the mtDNA control region (c. 370 bp) was amplified and sequenced in 30
specimens using the primers reported for Pleuronectiformes by Lee et al. (1995) and
previously used on S. vulgaris by Tinti et al. (1999). Internal primers were designed to
facilitate amplification and sequencing of the target region using the software Primer3
(Rozen & Skaletsky, 1998): a forward primer SosHDLint 5TCA TTA AAC TAT CTT
CTG TC3 and a reverse primer SosLDLint 5CAC GAT ATC TGT CCC TGA CC3.
Polymerase chain reaction (PCR) was conducted in 25 l reactions containing 20 ng of
template DNA, 1Promega Taq buffer A, 2 m MgCl2, 0·2 m each dNTP (BRL),
0·5  each primer and 0·5 U of Taq (Promega). Thermal cycling consisted of 30 cycles
at 94 C for 30 s, 50 C for 30 s, and 72 C for 60 s. An initial denaturation step (94 C for
3 min) and a final extension holding (72 C for 7 min) were added to the first and last
cycle, respectively. Amplified DNAs were treated with the ExoSAP-IT kit (AmershamPharmacia) and cycle sequenced using the ABIPrism BigDye Terminator Cycle
Sequencing kit (Applied Biosystems). Sequences were resolved on an ABI310 Genetic
Analyser (Applied Biosystems). The nucleotide polymorphism of variant haplotypes was
confirmed by the sequencing of both strands.
SEQUENCE ANALYSIS
Control region haplotypes (GenBank Acc. No. AF431765-AF431812; Fig. 2) were
aligned using the  Multiple Sequence Alignment programme (Thompson et al.,
1997). The within-sample genetic polymorphism was estimated by calculating haplotype
diversity (h) and nucleotide diversity () (Nei, 1987). The relationships between unique
control region haplotypes were described by a parsimony network generated with the
programme  vers. 1.13 (Clement et al., 2000). Distance (neighbour-joining and
), maximum parsimony and maximum likelihood methods were also used throughout for representing haplotype relationships. Phylogenetic analyses were performed
using  2.1 (Kumar et al., 2001),  4.0 (Swofford, 1998) and - 5.0
(Schmidt et al., 2000) programme packages, respectively. The Kimura-2-parameters
distance model (Kimura, 1980) was applied to calculate inter-haplotype genetic distances.
The robustness of internal branches of distance and maximum parsimony trees was
.    .
1462
N
DPO
Ligurian
Sea
RMN
MU17
LIV
Ad
NER
ria
tic
Se
a
MU 9
LES
MU18
DUR
Tyrrhenian Sea
SAL
MU10
GAL
Ionian Sea
SIC
MU19
F. 1. Collecting sites of the nine samples of Solea vulgaris and Management Units of the central
Mediterranean Sea (see Table I).
estimated by bootstrapping (Felsenstein, 1985) with 1000 replicates, whereas that of the
maximum likelihood tree was assessed through quartet puzzling reliability values
(Schmidt et al., 2000).
The analysis of molecular variance (AMOVA) framework (Excoffier et al., 1992)
implemented in the programme Arlequin ver. 2.000 (Schneider et al., 2000) was used to
test the overall genetic heterogeneity of common sole samples. In this statistical method,
a hierarchical ANOVA was carried out based on the partitioning of molecular variability
at arbitrarily defined levels (i.e. from the individual to the group of samples level). In the
present analysis, groups were defined by pooling common sole samples in fishery
management units. Total genetic variation of common sole samples was computed based
on the haplotype frequency distribution analysis [equivalent to an F analysis, Cockerham
(1973)] corrected for inter-haplotype sequence divergence. The fixation indices (),
mt     
Haplotype variable sites
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
A11
A12
A13
A14
A15
A16
A17
A18
A19
A20
A21
A22
A23
A24
A25
A26
A27
A28
A29
A30
A31
A32
A33
A34
A35
B1
B2
B3
B4
B5
B6
B7
B8
B9
B10
B11
B12
B13
CATTCCTCTC
...C......
..........
..........
..........
........C.
........C.
........C.
..........
..........
..........
...C....CT
.......T..
..........
..........
..........
..........
........C.
..........
..........
G.........
..........
..........
..........
..........
........C.
..........
..........
..........
........C.
..C.......
..........
...C......
....T.....
..........
..........
...C....C.
..........
...C......
...C......
.........T
......C...
.....T....
..........
.T........
..........
...C......
.........T
TCCTTACATT
..........
..........
..........
..........
.T........
..........
..........
..........
.........C
.T........
..........
.T........
..........
..........
..........
..........
..........
..........
....C.....
..........
......G...
.....C....
........C.
.......T..
..........
..........
.......T..
..........
.......T..
..........
..........
........C.
..........
.T........
..........
C.........
..TC......
..........
..........
..........
..........
..........
..........
..A.......
..T.......
..........
..........
TGCTGAGTCA
..........
.A....CC..
......CC..
.......C..
..........
..........
......C...
........T.
..........
..........
..........
..........
....C.....
.........G
......CC.G
..........
........T.
.A........
.........G
.....G....
..........
......C...
..........
..........
......C..G
......C..G
......C...
..........
..........
..........
..........
..........
..........
C.........
C..C......
C..C......
C..C......
C..C.....G
C.TC.....G
C..C..C...
C..C......
C..C......
C..C.....G
C.........
C..C......
...C.....G
C..C......
1463
DPO RMN NER LES DUR GAL SIC SAL LIV
TTT
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
A..
...
...
...
...
...
..T
...
...
...
...
...
..C
...
...
.C.
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
π
h
7
2
1
2
3
1
1
1
1
1
1
18
1
3
1
10
8
18
6
3
1
2
1
1
1
1
3
1
1
2
1
2
2
1
1
2
1
10
3
1
1
1
1
21
1
1
2
1
3
1
1
1
1
6
1
1
1
1
2
2
1
1
1
1
1
1
1
1
2
1
1
6
1
1
1
2
2
1
1
1
1
1
1
1
0.006 0.006 0.003 0.005 0.002 0.004 0.011 0.004 0.005
0.876 0.748 0.562 0.795 0.271 0.644 0.931 0.625 0.790
F. 2. Nucleotide variation and frequencies of the control region haplotypes in the Solea vulgaris
samples. . , identical bases. Haplotype (h) and nucleotide () diversity of samples are also given.
analogue to the Cockerham F estimates of genetic variation, were calculated to assess the
genetic divergence of overall and between paired common sole samples. The statistical
significance of the total and pairwise fixation indices was estimated by comparing the
observed distribution with a null distribution generated by 10 000 permutations, in which
individuals were randomly re-distributed into samples. The exact test of sample
differentiation (Raymond & Rousset, 1995) based on haplotype frequencies was also
1464
.    .
applied as implemented in the Arlequin ver. 2.000 software (Markov chain length: 10 000
steps). The significance threshold of pairwise comparisons (P<0·05) was adjusted with
the sequential Bonferroni correction for multiple simultaneous comparisons (Rice, 1989).
Pairwise st values were used to calculate co-ancestry coefficients of divergence
between samples (Reynolds et al., 1983), which are assumed to be proportional to the
divergence time between samples. An UPGMA dendrogram illustrating the pattern
of differentiation among samples was constructed using the matrix of co-ancestry
coefficients applying the method implemented in the  2.1 programme (Kumar et al.,
2001).
To infer the demographic history of common sole populations, a mismatch distribution of pairwise nucleotide differences among control region haplotypes was compared
with the expectations of a sudden-expansion model (Rogers & Harpending, 1992; Rogers,
1995) using the  ver 3.5 software (Rozas & Rozas, 1997). According to Rogers
(1995), the initial parameters 0 (which estimates population size before expansion, N0),
1 (which estimates the present population size, N1) and (which estimates the time
elapsed between 0 and 1) were calculated under the approximation of 1<`, that
closely represents the case in which 1 is merely large (Rogers & Harpending, 1992).
RESULTS
The sequence variation of the 283-bp control region fragment in 209 individuals of S. vulgaris showed 33 variable sites that defined 48 haplotypes (Fig. 2).
The parsimony network (Fig. 3) revealed two haplotype phylogroups (A and B)
separated only by two T↔C transitions at the positions 163 and 178, that
co-occurred in most haplotypes (Fig. 2). An intermediate position was shown by
haplotype B10. Within groups, differentiation between haplotypes was low, with
most separated by single base changes. Both haplotype groups showed star-like
phylogenies, with A1 and B1 as the ancestral haplotypes. In the phylogenetic
analysis, the separation of phylogroups was shown only by the neighbour-joining
(Fig. 4) and UPGMA trees, but it was never supported by bootstrapping. In the
maximum parsimony and maximum likelihood trees, haplotypes never clustered
according to phylogroups (data not shown). The weak phylogenetic support to
the separation of lineages, however, can be related to the small differences
between the within-group and between-group average sequence divergences
(DK2P within-groupA =0·010; DK2P within-groupB =0·011; DK2P between-group =0·017).
A clear geographical distribution of the mitochondrial lineages was evident
(Fig. 2). Common sole samples from the Adriatic Sea (DPO, RMN, NER, LES,
DUR) together with GAL and SAL showed only haplotypes of lineage A. This
lineage also predominated in the LIV sample (93·6%) but was poorly represented
in the SIC sample (12·5%). Within lineage A, the most common haplotype A1
was shared by all northern samples. Several high-frequency haplotypes were
shared by the north-western Adriatic and northern Ionian samples (A4, A5, A7,
A9). Haplotypes of the lineage B were abundant in the SIC sample (87·5%), but
rarely occurred in the LIV sample (6·4%). Haplotype and nucleotide diversity
values were generally high in all samples, except for the samples from eastern
Adriatic coasts (Fig. 2). The hypothesis of panmixia among populations was
rejected with high statistical significance by the results of the AMOVA analysis
(Table II), carried out by grouping common sole samples by fishery management
units. A relevant proportion of the overall molecular variation was related to the
subdivision of populations within fishery MUs (16·84%; P<0·001). In addition,
mt     
1465
§ A23, A28
A30
A8
A18
A26
§
A6
A9, A10, A14,
A17, A19, A22,
A24, A25, A29,
A31, A32,A34
A4
*
A7
A5
‡
‡
*
A3
A27
A16
A11
A15
A20
A1
A13
A2
2
A21
3
A33
B1
B10
B7, B8
B13
3
B6
A12
2
A35
3
2
B11
B9
B2
B4
B3
B5
B12
F. 3. Parsimony network of the Solea vulgaris control region haplotypes. All haplotypes differ by single
substitutions except where shown.
a remarkable though not significant percentage of molecular variance was
detected among MUs (6·22%). The pairwise st values were calculated to
identify samples that might account for deviation from panmixia (Table III).
Pairwise comparisons involving the SIC sample showed highly significant sts
(P<0·001), which were still significant after the application of the Bonferroni
sequential correction. Most of the sts shown by DUR and LIV samples were
positive though low and were significant at different levels: DUR-DPO, DURLES (P<0·001); DUR-NER, DUR-GAL, DUR-LIV, LIV-RMN (0·001<P<
0·01); LIV-DPO, LIV-LES (0·01<P<0·05). After the application of the
Bonferroni sequential correction only the former two comparisons were still
significant. All the other st values were not significantly different from zero.
Moreover, it is remarkable that negative sts were observed in the pairwise
comparisons among samples from the north Adriatic and north Ionian Seas as
well as among geographically near samples collected in different years (e.g.
DPO-RMN). Almost identical results were obtained applying the exact test of
population differentiation (Table III).
.    .
1466
A3
A4
A16
A26
A27
A8
A28
A5
A23
A25
A30
A11
A13
A6
A7
A9
A18
52
A15
A20
A19
A17
A10
A22
A34
A1
A21
A31
A32
A14
A29
A2
A12
A24
A33
51
A35
B10
53 B4
B5
B12
B9
B2
B6
58
B13
B3
65
B11
B1
B7
B8
0.002
F. 4. Neighbour-joining control region tree for Solea vulgaris. The bootstrap value is shown for nodes
found in >50% of 1000 replicates.
mt     
1467
T II. AMOVA analysis of Solea vulgaris samples from five Management Units
Source of
variation
d.f.
Sum of
squares
Variance
components
Percentage
of variation
Fixation
indices
P
Among MUs
Among samples within MUs
Within samples
Total
4
4
200
208
28·594
3·834
149·450
194·051
0·06043 Va
0·16356 Vb
0·74725 Vc
0·97123
6·22
16·84
76·94
ct =0·062
sc =0·180
st =0·231
NS
***
***
***P<0·001.
The UPGMA tree (Fig. 5), constructed to illustrate the pattern of genetic
divergence between common sole samples using a matrix of co-ancestry coefficients (Reynolds et al., 1983; data not shown), showed the separation of the
common sole sample of south-western Ionian from all the other samples as well
as the low divergence of the south-eastern Adriatic sample (DUR) and of the
Ligurian–Tyrrhenian stock (LIV and SAL) from the north-western Adriatic and
north Ionian samples.
The distribution of the pairwise number of differences in the control region
haplotypes fitted an expansion model (Rogers & Harpending, 1992) well in all
comparisons, showing a smooth wave predicted for a population that had
undergone a demographic expansion (Fig. 6).
DISCUSSION
In S. vulgaris, the identification of population units and the definition of
population boundaries are of high priority in the management of sustainable
fisheries in the Mediterranean area (Caddy & Oliver, 1996). These issues assume
international importance if the common sole stocks are shared by fishing fleets of
different countries (e.g. the Adriatic stocks which are shared by Italian, Croatian,
Slovenian, Albanian and Montenegrin–Yugoslavian trawlers; Adriamed,
2000, 2001). The genetic stock structure analysis carried out on the central
Mediterranean common sole samples has revealed remarkable haplotype heterogeneity among stocks at various geographical levels.
The greatest divergence was between the SIC sample and all the other samples
based on the occurrence of two control region lineages. Although lineages
appeared to be weakly divergent, the high frequency in the south-western Ionian
sample of an evolutionary mtDNA lineage, which was lacking or poorly
represented in central-north Mediterranean stocks, accounted for a marked
genetic differentiation of this sample. This divergence, which was clearly shown
by high and significant pairwise st values and by the partition of molecular
variance, seems to be most likely related to historical events, rather than to
present-day disruptions in gene flow. Although common sole samples were
collected over a time period of 2 years, this finding supports the lack of high rates
of migrants between populations inhabiting the southern part of the central
Mediterranean Sea and those from northern basins (i.e. Adriatic, Ligurian and
Tyrrhenian Seas). This north-to-south regional pattern of differentiation might
NS
NS
NS
***
NS
***
NS
NS
NS
NS
NS
***
NS
NS
0·007
0·008
0·003
NS
NS
NS
NS
***
NS
**
NER
RMN
NS, not significant; **0·05<P<0·01; ***P<0·001.
DPO
RMN
NER
LES
DUR
GAL
SIC
SAL
LIV
DPO
**
NS
***
NS
NS
0·015
0·011
0·001
LES
NS
***
NS
NS
0·116***
0·035
0·036
0·078***
DUR
***
NS
NS
0·049
0·022
0·024
0·030
0·134
GAL
***
***
0·438***
0·448***
0·454***
0·447***
0·493***
0·423***
SIC
NS
0·042
0·011
0·035
0·019
0·013
0·045
0·430***
SAL
0·064
0·027
0·022
0·055
0·035
0·047
0·430***
0·007
LIV
T III. Pairwise st values (above the diagonal) and significance of the exact test of population differentiation (below the diagonal)
between common sole samples
mt     
1469
LES
GAL
RMN Northern Adriatic, western Ionian (north)
DPO
NER
DUR Southern Adriatic
SAL
LIV
SIC
Tyrrhenian, Ligurian
Western Ionian (south)
F. 5. UPGMA dendrogram illustrating the genetic relationships among Solea vulgaris samples based on
Reynolds et al.’s (1983) co-ancestry coefficients.
(a)
0.3
0.3
0.2
0.2
(b)
m = 2.50
Frequency
m = 2.73
0.1
0.1
0
0
0
10
20
30
(c)
0.2
0
10
20
30
(d)
0.3
0.2
m = 2.98
0.1
m = 3.37
0.1
0
0
0
10
20
30
0
Pairwise differences
10
20
30
F. 6. Distribution of pairwise differences (——, expected; ········, observed) in the Solea vulgaris
populations (m, observed mean of the pairwise sequences differences). Samples were pooled
according to the dendrogram of Fig. 5. (a) northern Adriatic–western Ionian (Samples: DPO,
RMN, NER, LES, GAL ) (Lineage A); (b) southern Adriatic (Sample: DUR) (Lineage A);
(c) Tyrrhenian–Ligurian (Samples: SAL, LIV) (Lineages A and B), (d) western Ionian (Sample:
SIC) (Lineages A and B).
overlap the genetic divergence observed between western (Ebro delta–Gulf of
Lyons) and eastern populations (north Aegean–Egypt) of the Mediterranean Sea
(Kotoulas et al., 1995). Moreover, the low intra-regional genetic divergence
detected between the two eastern Mediterranean samples by allozyme analysis
indicates a high gene flow rate between northern and southern populations
inhabiting this part of the basin (Kotoulas et al., 1995). The SIC sample may
represent the western-most layer of this group of panmictic or near-panmictic
south-eastern Mediterranean populations. The geographical clustering of
1470
.    .
multiple related mtDNA haplotypes (i.e. phylogroups) in Mediterranean populations of S. vulgaris might be related to the Quaternary oscillations of the sea level
(McCullach & De Deckker, 1989). Past geological events and present
hydrographical patterns of the central Mediterranean may have promoted the
development and maintenance of differentiation in S. vulgaris Mediterranean
populations (Exadactylos et al., 1998). The demographic history traits and the
star-like phylogeny shown by mitochondrial lineages may indicate that such
populations could be very old and have undergone a sudden expansion; they may
presently be in a drift-migration equilibrium. For comparison, the relationship
between the origin of geographical differentiation of mtDNA lineages and
the hydro-geographical features of the sub-basins has already suggested for
Mediterranean populations of the small pelagic species Engraulis encrasicolus L.
(Magoulas et al., 1996; Grant et al., 1999).
Several life history traits of the common sole might speak in favour of high
rates of gene flow among populations inhabiting more or less adjacent areas (e.g.
wide and continuous distribution, high fecundity, wide temperature tolerance,
long planktonic larval stages). In addition, a recent study on the tracking of
plaice Pleuronectes platessa L. in the North Sea, carried out using long-term
electronic tagging, revealed that adult individuals can visit more than one
spawning area within a single spawning season and travelling >900 km (Metcalfe
& Arnold, 1997). On the other hand, several factors might retard gene-flow,
including the occurrence of hydro-geographical barriers, the homing behaviour
of spawners and the larval retention in the nursery feeding areas (Horwood,
1993). In the northern Adriatic Sea, tagging experiments carried out on common
sole using the traditional mark-and-recapture procedure, showed that all individuals were re-captured within the sub-basin (Pagotto et al., 1979). Local
currents, eddies and marked differences of oceanographic features of this
sub-basin with respect to those of southern Adriatic and Ionian Sea (Artegiani
et al., 1997) may prevent a high rate of exchange of adult spawners and the
mixing of planktonic larval stages from nursery areas of adjacent basins
(Magoulas et al., 1996).
The present survey also revealed lower, though remarkable, levels of haplotype
frequency differentiation among samples within the mitochondrial lineage A.
Although the most common haplotype A1 was shared by all samples from the
central-northern Mediterranean, the haplotype structure of the Tyrrhenian
samples (LIV and SAL) appeared to be sufficiently differentiated to allow
for their geographical grouping. A similar separation was also observed within
the common sole stock from the Adriatic Sea (MUs 17 and 18), since the most
south-eastern Adriatic sample (DUR) clustered separately from both the other
Adriatic samples and the adjacent north-western Ionian GAL sample (MU
19).
Differences in the mtDNA haplotype structure suggest preliminary important
fishery management issues for the exploitation of local Italian common sole
stocks. Based on the great genetic divergence of the SIC sample, the common
sole stock exploited in the southern part of MU 19 (western Ionian Sea) would
require an independent management action based on appropriate biological,
demographic and recruitment parameters, being reproductively isolated from the
northern stocks. Within the northern stocks, the lack of differences between
mt     
1471
haplotype structures of Ligurian and Tyrrhenian samples support a common
management action for common sole stocks of the MUs 9 and 10. The genetic
differentiation detected within the Adriatic common soles in contrast accounts
for a re-definition of the common sole fishery guidelines in this area. In the
Adriatic Sea, c. 30 cephalopod, decapod and fish species are actually exploited as
shared stocks by fishing fleets of coastal states (Adriamed, 2000, 2001; Mannini
et al., 2001). The present analysis suggests that in the Adriatic Sea two
near-panmictic populations of common sole exist. The former population
would inhabit the entire MU 17 (northern Adriatic Sea), the western part of
the MU 18 (southern Adriatic Sea), and the adjacent north Ionian continental
waters of the MU 19 (western Ionian Sea). The second unit seems to be
spread along the Albanian coasts (eastern part of the MU 18). The hydrogeographical features of this semi-enclosed basin might support the overall
pattern of differentiation of the Adriatic common soles. The central-northern
Adriatic Sea has a high geographical homogeneity, with an extended continental shelf and eutrophic shallow-waters. The southern Adriatic in contrast is
characterized by narrow continental shelves and a marked, steep continental
slope (1200 m deep; Adriamed, 2001). For S. vulgaris this deep canyon could
represent a significant geographical barrier. Different actions for fishery
management should be proposed for the Adriatic common sole stock, which
appears to be shared in the northern area (MU 17) but subdivided in the
southern part (MU 18).
The small size of some samples, the lack of data on spawning and the 2 year
collecting period, however, does limit this study. Thus, the genetics of central
Mediterranean stocks needs to be confirmed by further analyses performed on
larger and repeated samples at the same locations. In addition, mtDNA data
should be integrated by those obtained from other types of markers such as
microsatellites, which may be useful to detect subtle divergence levels in marine
populations (Carvalho & Hauser, 1998; Lundy et al., 2000). Nevertheless, the
present survey demonstrated that the 283 bp control region mtDNA fragment is
a powerful marker that can be used to assess evolutionary histories and
population units among populations of S. vulgaris on a large geographical scale.
The high nucleotide variation of this marker is similar to that observed in the
homologous gene region of several actinopterygian fish species (Meyer, 1994;
Lee et al., 1995; Faber & Stepien, 1997). The 5 end mtDNA control region has
proved to be useful in revealing genetic relationships in flatfish species either at
the between-species level (Lee et al., 1995; Tinti et al., 1999) or at the
within-species level, when different geographical samples were compared (Jones
& Quattro, 1999). The developed sequence marker could also be used to assess
population genetic structure in closely related sole species of the Atlantic and
Mediterranean Sea (i.e. the Senegalese sole Solea senegalensis Kaup and the
Adriatic sole Solea impar Bennett; Querò et al., 1986).
We thank S. De Ranieri (Centro Interuniversitario Biologia Marina e Ecologia
Applicata ‘ G. Bacci ’, Livorno) and C. Di Nunno for providing common sole samples
from Livorno and eastern coast of Sicily, respectively. We are grateful to C. Cristoni
for the helpful comments to the manuscript. This work was partially financed by a
Progetto Giovani Ricercatori e.f. 1998 grant given by the University of Bologna to FT.
1472
.    .
We thank the two anonymous referees and D. Ruzzante for the improvement of the
manuscript.
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