Population structure of the Dover sole, Solea solea L., in a background of high gene flow

ELSEVIER

Journal of Sea Research 40 (1998) 117–129

Population structure of the Dover sole, Solea solea L., in a background of high gene flow Athanasios Exadactylos, Audrey J. Geffen Ł , John P. Thorpe Port Erin Marine Laboratory, School of Biological Sciences, The University of Liverpool, Port Erin, Isle of Man IM9 6JA, UK Received 7 November 1996; accepted 3 June 1997

Abstract To investigate the genetic population structure of the Dover sole, Solea solea L., allozyme electrophoresis was performed on 303 fish collected from seven locations ranging from Cumbria, Great Britain, to Greece. A total of 22 enzyme systems were analysed, coded by 33 loci. Of these, 27 loci were polymorphic using the P99 criterion. A phenogram using Prevosti’s Distance generated by the Wagner method exhibited a geographic pattern in the clustering of populations. Estimates of Nm (effective number of migrants per generation between populations) were sufficiently high to imply near-panmixia between the North Sea, Bay of Biscay and the Irish Coast populations, indicating a probable movement of migrants through the English Channel. However, despite this high level of gene flow, striking patterns of geographic differentiation were observed at a few loci. Allele frequencies at loci ACOH, EST-I-1, PEP-I-2 exhibited genetic patchiness on both local and range-wide (within the northern and southern European basins) scales. This pattern of genetic patchiness could be the result of localised selection, genetic drift or single-generation sampling effects. Estimates of mean heterozygosity .H/ were inversely related to latitude. Evolutionary processes such as genetic drift and founder effect, and=or selection, may have produced the observed difference in the number of alleles between the basins. Moreover, the absence of isolation by distance provides support for a model of geographic isolation. Such a pattern of genetic patchiness, revealing a slight reduction of genetic variability in the northern European basin, may suggest a population bottleneck, or local reduction in population size. Various physical parameters, especially water temperature during the reproductive period, vary within the range of the species, and may produce or maintain this genetic differentiation. These results indicate the role of both ecological and evolutionary structuring mechanisms in determining the genetic population structure of S. solea.  1998 Elsevier Science B.V. All rights reserved. Keywords: allozyme electrophoresis; gene flow; genetic variability; isolation by distance

1. Introduction The Dover sole, Solea solea L., is the most common member of the Soleidae in European waters. It is common both inshore and offshore, extremely adaptable either in estuarine waters or at sea, although not Ł Corresponding

author. E-mail: [email protected]

found in deep waters (Quero et al., 1986). S. solea commands a consistently high market price (Bond, 1979) sustained by relative scarcity, fine texture and flavour (Wheeler, 1969) and good keeping qualities (Baynes et al., 1993). In recent years, total landings in the northeast Atlantic amounted to an annual average of 35,000 t (Rijnsdorp et al., 1992). In the Mediterranean the landings are about 5 t annually.

1385-1101/98/$19.00  1998 Elsevier Science B.V. All rights reserved. PII S 1 3 8 5 - 1 1 0 1 ( 9 8 ) 0 0 0 1 5 - X

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The characterisation of genetic population structure is vital for the management of ecologically and economically important fish, such as S. solea. In the marine environment gene flow typically occurs through the dispersal of larvae or migration of adults. However, an assumption of high gene flow in species with a high potential for dispersal is not always correct (e.g. Knowlton and Keller, 1986). Therefore, the extent of gene flow will generally determine the genetic heterogeneity of the species, in the absence of localised selection (Altukhov, 1981). These genetic distinctions are important for management plans for harvested species and are used to predict whether a locally depleted population will be successfully repopulated by immigrants (Shaklee, 1983; Utter, 1991). For many marine species, a geographically broad genetic survey of populations provides an efficient and reliable method of determining geographic patterns of population structure (Avise et al., 1990). Many authors report increased genetic differentiation with greater geographical distance between populations (e.g. Larcson et al., 1989; Slatkin, 1993); therefore it is appropriate to consider geographical distance itself when evaluating existing barriers to gene flow. On the other hand, there are numerous examples of population homogeneity among marine fish species (e.g. Grant et al., 1984, 1987). Various studies investigating genetic structure over the geographic range in plaice (Pleuronectes platessa), flounder (Pleuronectes flesus), brill (Scophthalmus rhombus) and turbot (S. maximus) lead to differing conclusions. A high degree of population differentiation has been found in flounder (Galleguillos and Ward, 1982; Berrebi et al., 1985) and plaice (Ward and Beardmore, 1977; Simonsen et al., 1988), but no apparent differentiation in brill and limited differentiation in turbot (Blanquer et al., 1992). These studies suggest that factors other than the dispersal potential during pelagic stages of the species may be important, or have been so in the past. The present work considers all the above parameters and reports a spatial-scale study of the genetic structure of the Dover sole. Since extensive information has already been gathered about the biology, ecology and behaviour of the species (e.g. Koutsikopoulos et al., 1991; Molinero and Flos, 1991; Dorel et al., 1991; Rogers, 1992; Amara et al.,

1993), a study of genetic population structure can now be considered in relation to these factors.

2. Material and methods 2.1. Sampling protocol Seven populations of adult S. solea were sampled during 1994 and 1995. Three populations were from the Irish Sea, two from the North Sea, one from the Bay of Biscay and one from the Mediterranean (Fig. 1). Sample size varied from 18 to 73 individuals. All fish were collected by trawl and processed fresh. The sample collection dates did not include patterns which would systematically bias the results, such as summer feeding migrations, spawning season or winter offshore migration of adults and juveniles. Sex, standard lengths and body weights were recorded (Table 1), prior to the extraction of tissues. Skeletal muscle from both sides of the body, liver and eyes were used for electrophoresis. To standardise the sampling procedure (Weir, 1990) tissue was taken from close to the tail of the fish for every specimen. All tissues were immediately frozen at 30ºC to 190ºC depending on the facilities available during the extraction and the transportation (liquid nitrogen containers, Polystyrene boxes with dry ice or freezer). Once returned to the laboratory the tissue samples were stored at 75ºC: Allozyme electrophoresis was carried out using standard horizontal starch gel techniques (Richardson et al., 1986; Murphy et al., 1990). The homogenisation buffer used was a mixture of Tris–HCl 1.2 g l 1 , EDTA 0.37 g l 1 , NADP 0.04 g l 1 at pH 6.8 (Pasteur et al., 1985). 2.2. Statistical analysis A total of 22 enzyme systems were assayed (Table 2). Genotypic data were analysed using the computer program BIOSYS-1, Release 1.7 (Swofford and Selander, 1989). The mean number of alleles per locus, the mean observed heterozygosity per locus, the mean expected heterozygosity per locus under random mating .Hunbias / (Nei, 1978), plus the proportion of polymorphic loci (Harris and Hopkinson, 1976), were calculated to assess intra-population

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Fig. 1. Map showing the sample locations of Solea solea adults. See Table 1 for population codes. The total number of individuals sampled are given in parentheses.

Table 1 Sampling information protocol for population genetics of Solea solea (standard errors in brackets) Sample location

Code

Source

Date

N

Mean standard length in cm (S.E.)

54º28N–3º58W, King William Banks=Irish Sea 53º58N–4º58W, Chicken Rock=Irish Sea 53º55N–6º40E, German Bight=North Sea 53º18N–5º47W, Kish Bank=Irish Sea 52º26N–1º15E, Newsombe Bank=North Sea 45º37N–1º46W, L’Ile d’Oleron=Bay of Biscay 40º13N–22º86E, Thermaikos Bay=Aegean Sea

CUM

Commercial trawler hired by Port Erin Marine Lab. Sampling cruise by Port Erin, Research Vessel Roagan Sampling cruise supervised by RIVO-DLO, Netherlands Sampling cruise supervised by DANI, Belfast Commercial trawler hired by MAFF, Lowestoft Commercial trawler hired by CNRS-IFREMER, L’Houmeau Commercial trawler

11=5=95

50

34.52 (3.65)

07=06=94 and 14=02=96 09=29=95

73

29.82 (5.12)

18

25.30 (5.90)

12=09=94 and 22=09=95 09=7=94

18

23.00 (4.92)

43

19.70 (1.60)

22=12=94

48

26.99 (2.40)

15=07=94 and 12=05=94

54

22.40 (2.69)

IOM GER IRL EAN FRA GRE

variation. Hunbias was chosen as a better measure of genetic variation in a sample than a direct count of heterozygosity. Hunbias is considered to be independent of sample size, natural selection and inbreeding (Nei and Roychoudhury, 1974). Departures of genotype frequencies from Hardy– Weinberg expectations were tested using exact tests (Lessios, 1992; Sokal and Rohlf, 1995) analogous to Fisher’s exact test for 2 ð 2 contingency tables.

They were used rather than the more commonly used goodness-of-fit chi-square tests since expected genotype frequencies were often quite small even after pooling of rare alleles. If more than two alleles were present at a locus, all alleles, other than the most common one were pooled. Deficiencies of heterozygotes in each population were estimated for all polymorphic loci using the inbreeding index Fis , and its significance was tested using the equation of Li

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Table 2 Enzyme systems, buffers and number of loci studied Abbreviation of enzyme (substrate)

E.C Number

Tissue

No. of loci

Buffer system

ACP ACOH AAT DDH EST-I (α and β naphthyl acetate) EST-II (4-methylumbelliferyl acetate) FBA GCDH G6PDH GPI GR GAPDH G3PDH IDH LDH MDH MDHP PEP-I (u) PEP-II (Gly-Leu) PGM PGDH SOD

3.1.3.2 4.2.1.3 2.6.1.1 1.8.1.4 3.1.–.–

Liver Liver Muscle=liver Liver Liver

1 1 3 1 2

Tris–Citrate–EDTA, pH 7.0 Tris–Citrate–EDTA, pH 7.0 Tris–Citrate, pH 8.0 Tris–Citrate, pH 8.0 Discontinuous Tris–HCl–Borate, pH 8.2–8.5

3.1.–.–

Liver

2

Tris–Citrate–EDTA, pH 7.0

4.1.2.13 1.1.1.118 1.1.1.49 5.3.1.9 1.6.4.2 1.2.1.12 1.1.1.8 1.1.1.42 1.1.1.27 1.1.1.37 1.1.1.40 3.4.–.–

Muscle Liver Muscle=liver Muscle=liver Liver Muscle Muscle Liver Muscle=eyes Muscle Muscle Liver

1 1 2 2 1 1 1 1 3 2 2 2

Tris–Citrate, pH 8.0 Tris–Citrate–EDTA, pH 7.0 Tris–Versene–Borate, pH 8.0 Discontinuous Tris–Borate–Citrate, pH 8.2–8.7 Tris–Citrate–EDTA, pH 7.0 Tris–Versene–Borate, pH 8.0 Tris–Citrate, pH 8.0 Tris–Versene–Borate, pH 8.0 Discontinuous Tris–Borate–Citrate, pH 8.2–8.7 Tris–Citrate, pH 8.0 Tris–Versene–Borate, pH 8.0 Discontinuous Tris–HCl–Borate, pH 8.2–8.5

3.4.–.–

Liver

1

Discontinuous Tris–HCl–Borate, pH 8.2–8.5

5.4.2.2 1.1.1.44 1.15.1.1

Muscle Muscle Liver

1 1 1

Discontinuous Tris–Borate–Citrate, pH 8.2–8.7 Tris–Versene–Borate, pH 8.0 Tris–Citrate, pH 8.0

and Horvitz (1953):  2 D Fis2 N .k

1/I d:f: D k.k

1/=2

where N is the sample size for the populations and k is the number of alleles at a locus. For each locus the statistical significance of the inter-population variation in allelic frequencies was calculated by contingency chi-square analysis. All results from chi-square and exact test analyses were corrected for multiple simultaneous tests using the sequential Bonferroni procedure (Rice, 1989). Genetic differentiation among populations was estimated using the Fst estimates and their significance was tested using the equation by Workman and Niswander (1970):  2 D 2N Fst .k

1/I d:f: D .k

1/.s

1/

where N is the total number of individuals sampled across s populations and k is the number of alleles at a locus. Unbiased estimates of F-statistics were calculated using the computer program F-STAT

(Goudet, 1994). Individual multi-locus genotypes were required by FSTAT while BIOSYS-1 accepted genotype frequencies. The amount of gene flow .Nm / was estimated from the estimates by Weir and Cockerham (1984) of Fst . The approximation of Slatkin (1993) Nm D ..1=Fst /

1/=4

was used to estimate the effective migration rate between all pairs of populations and the relationship between Nm and geographic distance between populations was examined. The shortest geographical distance between each pair of populations was estimated by measuring the coastline within the boundaries of general zooplankton distribution in these areas according to FAO maps. Prevosti’s Distance (Wright, 1978) among the various samples was used to estimate genetic distances. A cluster analysis was performed on the matrix of genetic distances generated using the Wagner procedure (Farris, 1972).

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3. Results 3.1. Allele frequencies The allele frequencies of the 33 loci analysed for all seven populations are shown in Appendix A. Nei (1978) and Gorman and Renzi (1979) underlined the need to examine a large number of loci because the reliability (i.e. sampling errors) of summary statistics such as heterozygosity, genetic distance and Fst depends more on this than on the number of individuals screened. From a total of 33 loci, 27 were polymorphic (frequency of the most common allele  0:99/ in most of the populations. The statistical analyses of genetic diversity were carried out for all 27 polymorphic loci (Table 3). Additional alleles at five loci (AAT-2, LDH-2, SOD-1, IDH-2, EST-I-3) were found only in populations from Greece (GRE) and the Bay of Biscay (FRA), whereas the allele 90 at the locus G6PDH-2 was restricted to northern populations, suggesting a greater range of alleles in adult S. solea, in southern Europe. The populations FRA and GRE were pooled to form a southern European basin, which exhibited significantly more alleles than in the northern European basin (Wilcoxon signed-ranked test, p < 0:001). Contingency chi-square analysis for heterogeneity of allele frequencies between samples within basins showed significant differentiation at loci ACOH . 2 D 22:518, d.f. 4, p < 0:001), ESTI-1 . 2 D 32:539, d.f. 4, p < 0:001) and PEP-I-2

121

. 2 D 36:262, d.f. 8, p < 0:001), only within the northern European basin. However, a contingency chi-square analysis for independence of allele frequencies, with pooling of rare alleles across all seven populations indicated significant differences at loci ACOH . 2 D 31:767, d.f. 6, p < 0:001), AAT-1 . 2 D 41:919, d.f. 12, p < 0:001), EST-I-1 . 2 D 35:250, d.f. 6, p < 0:001), G6PDH-2 . 2 D 42:835, d.f. 12, p < 0:001), LDH-2 . 2 D 59:773, d.f. 18, p < 0:001), PEP-I-2 . 2 D 41:291, d.f. 12, p < 0:001) and PGDH . 2 D 65:781, d.f. 18, p < 0:001). Overall, among the seven populations there was extensive heterogeneity. There was no clear geographic trend in the frequencies of the most common allele at most loci. However, the frequency values of the most common allele at the GPI-1 locus increased significantly with latitude, but the cline became non-significant after the sequential Bonferroni correction. 3.2. Conformity to Hardy–Weinberg expectations From 156 exact tests with pooling of rare alleles and sequential Bonferroni correction for multiple tests there was no evidence of significant departure from random mating within the populations studied. However, significant values were observed from the Li and Horvitz (1953) chi-square analysis for deficiency of heterozygotes using the inbreeding index Fis in the Irish Coast population (IRL) at the loci G6PDH-3 and PGDH, the German Bight population

Table 3 Genetic variability at 33 loci in seven populations of adult Solea solea (standard errors in brackets) Population a

CUM IOM GER IRL EAN FRA GRE Total a See

Mean sample size per locus (S.E.)

Mean no. of alleles per locus (S.E.)

Percentage of loci polymorphic b

Mean heterozygosity Observed (S.E.)

Expected c under Hardy–Weinberg equilibrium (S.E.)

50 (0.0) 72 (0.0) 17.9 (0.1) 18 (0.0) 43 (0.0) 48 (0.0) 54 (0.0)

1.8 (0.1) 2.2 (0.2) 1.8 (0.1) 1.7 (0.1) 2.2 (0.1) 2.3 (0.2) 2.4 (0.2)

63.6 72.7 60.6 48.5 75.8 72.7 78.8

0.035 (0.008) 0.071 (0.013) 0.083 (0.017) 0.034 (0.008) 0.085 (0.012) 0.088 (0.013) 0.114 (0.016) 0.073 (0.012)

0.043 (0.011) 0.083 (0.015) 0.107 (0.021) 0.064 (0.015) 0.101 (0.014) 0.108 (0.017) 0.141 (0.019)

Table 1. A locus is considered polymorphic if the frequency of the most common allele does not exceed 0.99. c Unbiased estimate (Nei, 1978). b

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stepwise by a strategy of progressively pooling samples to the south, showed that local gene flow values are, in most cases, higher than values calculated over long geographic distances. Gene flow was highest between geographically proximal populations. As more samples were added, estimates of gene flow decreased and remained constant at comparatively low levels. The mean Nm value gave an estimate of roughly nine migrants exchanged between populations per generation (Table 4). Fig. 2. Mean heterozygosity plotted against latitude for the seven populations of Solea solea. Regression .H D 0:395 0:00627L/ was significant at Þ D 0:05 .r D 0:81, F1;6 D 9:52, p D 0:027/.

(GER) at the MDHP-1 locus and the Cumbrian Coast population (CUM) at the MDHP-2 locus .Fis D 1,  2 > 50, p < 0:001 for up to 20 tests). 3.3. Heterozygosity Estimates of mean heterozygosity .H / were inversely related to latitude (Fig. 2). About 65% of the variation in heterozygosity could be explained by the variation in latitude. Heterozygosity values at loci AAT-1, GPI-1, PEP-II and PGM differed significantly between the northern and southern European basins (ANOVA, F2;5 > 6:7; p < 0:05).

3.5. Genetic distance The Wagner procedure dendogram using Prevosti’s Distance (Fig. 3) indicated that population GRE was most divergent, the Irish Sea populations were closely related, as are the North Sea populations. Population GER was clustered at a midpoint of the tree with population FRA. The goodness of fit statistics measure the degree to which the output reflects the corresponding input distances (Swofford, 1981) and hence can be used to choose between trees generated by different methods. In this case the Wagner procedure tree gave a better fit.

4. Discussion 4.1. Genetic variability within populations

3.4. Population structure The mean value of Wright’s Fst was not statistically significant, indicating little evidence of significant genetic heterogeneity among the seven populations. Wright’s Fst values were significantly greater than zero using the chi-square test of Workman and Niswander (1970) only for the loci LDH-2 and PGDH (Table 4). Relatively high levels of gene flow were indicated from estimates of Weir and Cockerham’s Fst values for each pair of populations (Table 5), despite the significant differences among populations in allele frequencies from the contingency chi-square analyses. These gene flow estimates between pairs of populations were not significantly related to geographic distance (rs D 0:46015 < rs at Þ0 D 0:005/, indicating little isolation by distance across the range sampled. However, patterns of gene flow determined

The observed heterozygote deficiencies may be due to inbreeding or presence of sub-populations. In both processes frequencies of homozygotes tend to increase, by fixing the most common allele in the first case, and when different populations are mixed together, in the second. Other destabilising forces could be the effect of selection on the loci in question, or mutation. Further investigations would be necessary to indicate which process is taking place. For some of these loci significant Fis values may have resulted from the chance occurrence of single very rare homozygotes for rare alleles. Nevertheless, 156 overall exact tests revealed no significant deviation from Hardy–Weinberg equilibrium, hence, there is little evidence to reject random mating within the populations studied. The latitudinal decline in heterozygosity was paralleled by a significant cline in allele frequencies of

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Table 4 Summary of estimates of Wright’s F-statistics and gene flow .Nm / at all 27 polymorphic loci, over the seven populations of Solea solea Locus ACOH AAT-1 AAT-2 AAT-4 DDH-1 EST-I-1 EST-I-3 EST-II-1 EST-II-3 GCDH-2 G6PDH-2 G6PDH-3 GPI-1 GPI-3 GR-1 G3PDH-1 IDH-2 LDH-1 LDH-2 MDH-2 MDHP-1 MDHP-2 PEP-I-2 PEP-II PGM PGDH SOD-1 Mean

Fis 0.1028 0.4020 0.1766 0.0709 0.1578 0.0337 0.3012 0.2003 0.2467 0.2288 0.1791 0.2380 0.0709 0.2311 0.2720 0.1582 0.0588 0.0420 0.0706 0.0373 0.4868 0.2896 0.1897 0.2009 0.0767 0.2962 0.3709 0.1802

Fit 0.1498 0.4180 0.1878 0.1090 0.1714 0.0264 0.3187 0.2082 0.2511 0.2391 0.2047 0.2589 0.0921 0.2478 0.2913 0.1670 0.0418 0.0628 0.1316 0.0166 0.4946 0.2968 0.2089 0.2190 0.0453 0.3225 0.3950 0.2016

Fst

2 (Workman and Niswander, 1970)

d.f.

Nm

0.0524 0.0267 0.0136 0.0411 0.0162 0.0582 0.0251 0.0099 0.0059 0.0134 0.0313 0.0275 0.0228 0.0217 0.0264 0.0104 0.0160 0.0217 0.0656 0.0200 0.0152 0.0101 0.0237 0.0227 0.0292 0.0373 0.0384 0.0262

31.754 32.360 16.483 49.813 19.634 35.269 30.421 5.999 7.151 16.241 37.936 33.330 41.450 39.451 31.786 12.605 19.392 13.150 119.261 24.240 9.211 12.241 28.724 27.512 35.390 67.811 23.270 30.572

6 12 12 12 12 6 12 6 12 12 12 12 18 18 12 12 12 6 18 12 6 12 12 12 12 18 6 11.556

4.521 9.113 18.132 5.833 15.182 4.046 9.710 25.003 42.123 18.407 7.737 8.841 10.715 11.271 9.220 23.788 15.375 11.271 3.561 12.250 16.197 24.502 10.299 10.763 8.312 6.452 6.260 9.292

Values of Fst significantly greater than zero corrected by the sequential Bonferroni procedure at Þ 0 D 0:005 are bold. Table 5 Estimates of Prevosti’s Distance (below the diagonal) and estimates of Weir and Cockerham’s Nm (above the diagonal) among the seven populations of Solea solea Population

GRE

GRE FRA IOM CUM IRL EAN GER

0.0460 0.0490 0.0630 0.0590 0.0490 0.0590

FRA

IOM

CUM

IRL

EAN

GER

18.4320

10.6660 26.8440

5.2330 8.7720 16.9250

12.0260 29.6110 ♦ 20.8080

14.2250 142.1300 22.6130 11.3730 62.5760

14.3330 50.7060 9.6540 5.1650 23.8780 22.0600

0.0340 0.0450 0.0430 0.0340 0.0460

0.0300 0.0280 0.0350 0.0510

0.0310 0.0400 0.0480

0.0430 0.0480

0.0500

♦ D negative Fst value, equivalent to an Nm value of infinity.

the most common allele at the locus GPI-1. Associations of allele frequencies with a latitudinal cline correlated, for example, with temperature may result from natural selection acting on allozyme loci, or secondary intergradation of populations previously

differentiated during allopatry (Endler, 1977), but in this case may well result from sampling error, since only 1 of 33 loci gave a significant cline. Nevertheless, the latter became non-significant after the sequential Bonferroni correction for multiple tests.

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Fig. 3. Wagner procedure dendogram using Prevosti’s Distance (Wright, 1978) and goodness of fit statistics between the seven populations of Solea solea. Goodness of fit statistics: Farris (1972) f D 0:045; Prager and Wilson (1976) F D 4:834; % standard deviation (Fitch and Margoliash, 1967)D 5:61; cophenetic correlationD 0:96:

4.2. Differentiation between populations The chi-square contingency analyses indicated a significant heterogeneity in allele frequencies within basins, and between populations. Heterozygosity .H / values also varied significantly between basins, indicating the level of genetic diversity of the species. Average heterozygosity in these S. solea populations was higher than the average for marine teleosts .H D 0:052 š 0:036, Smith and Fujio, 1982) but within the range observed in other flatfish species (Ward and Beardmore, 1977; Galleguillos and Ward, 1982; Blanquer et al., 1992). These findings contradict the assumption that marine organisms capable of extensive dispersal (those that undergo lengthy planktonic larval development) will necessarily demonstrate widespread genetic homogeneity (Scheltema, 1986). S. solea spawn large numbers of pelagic eggs (e.g. Kotoulas et al., 1995); therefore, high levels of gene flow and genetic uniformity could be expected. However, other factors such as temperature and salinity gradients, wind and current patterns or oceanographic fronts may restrict larval dispersal and promote geographic isolation. Furthermore, recruitment of S. solea larvae into the adult population that spawned them or restricted movement of adults within the spawning grounds (Rijnsdorp et al., 1992; Amara et al., 1993) could also serve to reduce

the homogenisation of population allele frequencies between spawning stocks. The observed pattern of genetic patchiness could be the result of localised selection, genetic drift or single-generation sampling effects. Nevertheless, the results indicated a relatively high level of gene flow between the S. solea populations examined. These estimates rely on several assumptions, including random breeding, populations at genetic equilibrium and neutral alleles (Slatkin, 1985a; Waples, 1987; Slatkin and Barton, 1989; Cockerham and Weir, 1993). However, these estimates of gene flow based on F-statistics are averages over a number of populations, so there may be no gene flow at present between the populations. The apparent absence of isolation-by-distance suggests that S. solea may not be at genetic equilibrium. If populations have not reached equilibrium the F-statistics will underestimate the degree of differentiation expected at equilibrium and the estimates of gene flow will overestimate the true levels (Slatkin, 1985b). Waples (1987) also points out that the accuracy of gene flow estimates depends on the stability of the patterns of gene flow. Gene flow values were sufficiently high to imply near-panmixia between the two North Sea populations and FRA and IRL, indicating the possibility of a probable movement of migrants through the English Channel. This result is

A. Exadactylos et al. / Journal of Sea Research 40 (1998) 117–129

in agreement with tagging experiments carried out by Greer Walker and Emerson (1990). Kotoulas et al. (1995) studied the genetic structure of S. solea in mixed juvenile and adult populations over several spatial scales, and concluded that the strongest result was an east to west pattern of population differentiation, while a north to south pattern was also significant. Biological data indicate nearpanmixia, for example, transport of eggs and larvae of S. solea from the offshore spawning areas to the inshore estuarine nurseries by diffusive mechanisms (Koutsikopoulos et al., 1991), offshore movements towards spawning grounds (Greer Walker and Emerson, 1990), mixing in spawning grounds of adults originating from adjacent nursery grounds (Koutsikopoulos and Lacroix, 1992; Rijnsdorp et al., 1992) or random dispersion mechanisms resulting in nursery grounds containing juveniles from different spawning grounds (Marchand, 1991; Koutsikopoulos and Lacroix, 1992). 4.3. Differentiation within basins The geographic clustering of the populations in the Wagner procedure dendogram agree with the conclusions of many Mediterranean biogeographers. McCullach and De Deckker (1989) have hypothesised that the history of the Mediterranean, combined with the present hydrographic patterns, might have promoted and maintained the differentiation of Mediterranean populations. Since the Pleistocene the history of the Mediterranean can be seen as a succession of glacial and interglacial periods with associated regressions and transgressions (Blanc, 1968). It is possible that during one of the regressions the Mediterranean and Atlantic populations of S. solea separated. It would be interesting to be able to date the time of separation and relate it to geological events. Unfortunately, because of the small number of populations sampled across the total distribution of the species, especially in the western Mediterranean, we were unable to calibrate the molecular clock and thus relate the genetic distance to evolutionary time (Thorpe, 1982). Besides the possible contribution of the history and hydrographic barriers, evolutionary processes such as genetic drift and founder effect, and=or selection, may have produced the observed genetic

125

differentiation between the northern and southern European basins. Various physical parameters, especially water temperature during the reproductive period, vary within the range of the species and may produce or maintain genetic differentiation. A pattern emerged from the comparison between basins and their estimates of genetic variability, revealing a slight reduction of the latter in the northern European basin, possibly resulting from, for example, a population bottleneck, or local reduction in population size. The absence of isolation by distance provided backing for a model of geographic isolation. The analysis of more samples would help to complete the picture and possibly give a better understanding of the local and wide-range structuring of S. solea.

Acknowledgements We thank A.D. Rijnsdorp and his colleagues at RIVO-DLO, F. Lagardere, J.P. Lagardere and crew at La Rochelle, France, D.J. Symonds, B. Harley and B. Turner from the CEFAS laboratory at Lowestoft, England, P. Newton and his colleagues at DANI, Belfast, and the crew of our research vessel Roagan for providing us with the samples. We also thank R.D.M. Nash, A. Hill, S.M. Lynch, G. Kotoulas, P. Panagiotaki, E.R. Daka for advice and support. A. Exadactylos acknowledges the financial support of the Greek Scholarship Foundation throughout the course of the research programme. Appendix A. Allele frequencies for Solea solea at seven locations Locus

Population CUM

IOM

GER

IRL

EAN

FRA

GRE

50 1.000

72 1.000

18 1.000

18 1.000

43 1.000

48 1.000

54 1.000

ACOH: .N/ 50 100 1.000 120 0.000

72 1.000 0.000

18 0.917 0.083

18 1.000 0.000

43 0.988 0.012

48 1.000 0.000

54 0.926 0.074

AAT-1: .N/ 100 110 120

72 0.965 0.000 0.035

18 1.000 0.000 0.000

18 1.000 0.000 0.000

43 0.953 0.012 0.035

48 0.948 0.000 0.052

54 0.917 0.074 0.009

ACP: .N/ 100

50 1.000 0.000 0.000

126

A. Exadactylos et al. / Journal of Sea Research 40 (1998) 117–129

Appendix A (continued)

Appendix A (continued)

Locus

Locus

Population

Population

CUM

IOM

GER

IRL

EAN

FRA

GRE

CUM

IOM

GER

IRL

EAN

FRA

GRE

AAT-2: .N/ 80 100 120

50 0.000 0.990 0.010

72 0.000 0.972 0.028

18 0.000 0.972 0.028

18 0.000 1.000 0.000

43 0.000 1.000 0.000

48 0.010 0.958 0.031

54 0.000 1.000 0.000

G6PDH-3: .N/ 50 80 0.130 100 0.870 120 0.000

72 0.028 0.944 0.028

18 0.083 0.917 0.000

18 0.000 0.944 0.056

43 0.047 0.953 0.000

48 0.000 0.990 0.010

54 0.074 0.917 0.009

AAT-4: .N/ 80 100 120

50 0.030 0.970 0.000

72 0.014 0.958 0.028

18 0.056 0.861 0.083

18 0.028 0.917 0.056

43 0.023 0.953 0.023

48 0.031 0.938 0.031

54 0.130 0.796 0.074

GPI-1: .N/ 80 100 115 120

50 0.010 0.970 0.020 0.000

72 0.007 0.944 0.014 0.035

18 0.000 0.917 0.000 0.083

18 0.028 0.944 0.000 0.028

43 0.035 0.965 0.000 0.000

48 0.010 0.917 0.031 0.042

54 0.046 0.843 0.065 0.046

DDH-1: .N/ 90 100 110

50 0.000 0.960 0.040

72 0.042 0.917 0.042

18 0.000 0.833 0.167

18 0.028 0.944 0.028

43 0.070 0.872 0.058

48 0.063 0.865 0.073

54 0.065 0.889 0.046

GPI-3: .N/ 90 100 110 120

50 0.010 0.930 0.040 0.020

72 0.083 0.826 0.049 0.042

18 0.028 0.778 0.000 0.194

18 0.056 0.806 0.056 0.083

43 0.000 0.930 0.023 0.047

48 0.052 0.885 0.042 0.021

54 0.056 0.880 0.028 0.037

EST-I-1: .N/ 50 98 0.000 100 1.000

72 0.000 1.000

18 0.139 0.861

18 0.000 1.000

43 0.093 0.907

48 0.115 0.885

54 0.037 0.963

GR-1: .N/ 90 100 110

50 0.010 0.980 0.010

72 0.021 0.965 0.014

16 0.000 0.969 0.031

18 0.000 1.000 0.000

43 0.000 0.907 0.093

48 0.031 0.896 0.073

54 0.074 0.898 0.028

EST-I-3: .N/ 50 90 0.000 100 0.980 110 0.020

72 0.000 0.965 0.035

18 0.000 1.000 0.000

18 0.000 0.917 0.083

43 0.000 0.977 0.023

48 0.021 0.958 0.021

54 0.056 0.880 0.065

GAPDH-1: .N/ 50 100 1.000

72 1.000

18 1.000

18 1.000

43 1.000

48 1.000

54 1.000

EST-II-1: .N/ 50 95 0.040 100 0.960

72 0.042 0.958

18 0.083 0.917

18 0.000 1.000

43 0.081 0.919

48 0.042 0.958

54 0.074 0.926

G3PDH-1: .N/ 50 85 0.000 100 0.960 115 0.040

72 0.007 0.889 0.104

18 0.028 0.861 0.111

18 0.028 0.889 0.083

43 0.070 0.884 0.047

48 0.031 0.906 0.063

54 0.037 0.889 0.074

EST-II-3: .N/ 50 95 0.010 100 0.990 105 0.000

72 0.028 0.972 0.000

18 0.000 0.972 0.028

18 0.000 1.000 0.000

43 0.012 0.977 0.012

48 0.031 0.969 0.000

54 0.028 0.963 0.009

IDH-2: .N/ 80 100 120

50 0.010 0.990 0.000

72 0.035 0.965 0.000

18 0.083 0.917 0.000

18 0.028 0.972 0.000

43 0.023 0.977 0.000

48 0.052 0.948 0.000

54 0.065 0.907 0.028

FBA-1: .N/ 50 100 1.000

72 1.000

18 1.000

18 1.000

43 1.000

48 1.000

54 1.000

LDH-1: .N/ 50 100 0.990 110 0.010

72 0.938 0.063

18 1.000 0.000

18 0.972 0.028

43 0.930 0.070

48 1.000 0.000

54 0.963 0.037

GCDH-2: .N/ 50 80 0.000 100 0.990 120 0.010

72 0.028 0.951 0.021

18 0.028 0.972 0.000

18 0.000 1.000 0.000

43 0.012 0.930 0.058

48 0.042 0.948 0.010

54 0.000 0.972 0.028

18 0.000 1.000 0.000

43 0.058 0.919 0.023

48 0.000 0.948 0.052

54 0.000 0.917 0.083

LDH-2: .N/ 70 90 100 110

50 0.000 0.000 1.000 0.000

72 0.000 0.007 0.979 0.014

18 0.000 0.000 0.917 0.083

18 0.000 0.000 0.972 0.028

43 0.000 0.035 0.895 0.070

48 0.021 0.000 0.948 0.031

54 0.028 0.019 0.787 0.167

G6PDH-2: .N/ 50 90 0.000 100 1.000 110 0.000

72 0.035 0.965 0.000

18 0.000 1.000 0.000

LDH-4: .N/ 50 100 1.000

72 1.000

18 1.000

18 1.000

43 1.000

48 1.000

54 1.000

A. Exadactylos et al. / Journal of Sea Research 40 (1998) 117–129

127

Appendix A (continued)

References

Locus

Altukhov, Y.P., 1981. The stock concept from the viewpoint of population genetics. Can. J. Fish. Aquat. Sci. 38, 1523–1538. Amara, R., Lagardere, F., Desaunay, Y., 1993. Seasonal distribution and duration of the planktonic stage of Dover sole, Solea solea, larvae in the Bay of Biscay: a hypothesis. J. Fish Biol. 43 (Suppl. A), 17–30. Avise, J.C., Nelson, W.S., Arnold, J., Koehn, R.K., Williams, G.C., Thorsteinsson, V., 1990. The evolutionary genetic status of Icelandic eels. Evolution 44, 1254–1262. Baynes, S.M., Howell, B.R., Beard, T.W., 1993. A review of egg production by captive sole, Solea solea L. Aquacult. Fish. Manage. 24, 171–180. Berrebi, P., Vianet, R., Agnese, J.-F., Quignard, J.-P., Pasteur, N., 1985. Variabilite´ ge´ne´tique et morphologique de quelques populations de flets: Platichthys flesus des Coˆtes Me´diterrane´ennes et Atlantiques Francaises. Biochem. Syst. Ecol. 13, 55–61. Blanc, J.J., 1968. Sedimentary geology of the Mediterranean Sea. Oceanogr. Mar. Biol. Annu. Rev. 6, 377–454. Blanquer, A., Alayse, J.-P., Berrada-Rkhami, O., Berrebi, P., 1992. Allozyme variation in turbot (Psetta maxima) and brill (Scophthalmus rhombus) (Osteichthyes, Pleuronectiformes, Scophthalmidae) throughout their range in Europe. J. Fish Biol. 41, 725–736. Bond, C.E., 1979. Biology of Fishes. Oregon State University, Corvallis, 514 pp. Cockerham, C.C., Weir, B.S., 1993. Estimation of gene flow from F-statistics. Evolution 47, 855–863. Dorel, D., Koutsikopoulos, C., Desaunay, Y., Marchand, J., 1991. Seasonal distribution of young sole (Solea solea (L.)) in the nursery ground of Bay of Vilaine (Northern Bay of Biscay). Neth. J. Sea Res. 27, 297–306. Endler, J.A., 1977. Geographic Variation, Speciation and Clines. Princeton University Press, Massachusetts, 246 pp. Farris, J.S., 1972. Estimating phylogenetic trees from distance matrices. Am. Nat. 106, 645–668. Fitch, W.M., Margoliash, E., 1967. Construction of phylogenetic trees. Science 155, 279–284. Galleguillos, R.A., Ward, R.D., 1982. Genetic and morphological divergence between populations of the flatfish Platichthys flesus (L.) (Pleuronectidae). Biol. J. Linn. Soc. 17, 395–408. Gorman, G., Renzi, J., 1979. Genetic distance and heterozygosity estimates in electrophoretic studies: effects of sample size. Copeia 1979, 242–249. Goudet, J., 1994. FSTAT, a program for IBM PC compatibles to calculate Weir and Cockerham’s (1984) estimators of F-statistics. Universite´ de Lausanne, Dorigny, 15 pp. Grant, W.S., Teel, D.J., Kobayashi, T., Schmitt, C., 1984. Biochemical population genetics of Pacific halibut (Hippoglossus stenolepis) and comparison with Atlantic halibut (H. hippoglossus). Can. J. Fish. Aquat. Sci. 41, 1083–1088. Grant, W.S., Leslie, R.W., Becker, I.I., 1987. Genetic stock structure of the southern African hakes Merluccius capensis and M. paradoxus. Mar. Ecol. Prog. Ser. 41, 9–20. Greer Walker, M., Emerson, L., 1990. The seasonal migration of

Population IOM

GER

IRL

EAN

FRA

GRE

MDH-1: .N/ 50 100 1.000

CUM

72 1.000

18 1.000

18 1.000

43 1.000

48 1.000

54 1.000

MDH-2: .N/ 50 80 0.020 100 0.970 120 0.010

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18 0.000 1.000 0.000

18 0.056 0.944 0.000

43 0.035 0.907 0.058

48 0.010 0.938 0.052

54 0.028 0.898 0.074

MDHP-1: .N/ 50 90 0.040 100 0.960

72 0.042 0.958

18 0.056 0.944

18 0.083 0.917

43 0.116 0.884

48 0.094 0.906

54 0.037 0.963

MDHP-2: .N/ 50 90 0.020 100 0.980 105 0.000

72 0.063 0.917 0.021

18 0.056 0.944 0.000

18 0.083 0.917 0.000

43 0.047 0.919 0.035

48 0.083 0.896 0.021

54 0.056 0.898 0.046

PEP-I-1: .N/ 50 100 1.000

72 1.000

18 1.000

18 1.000

43 1.000

48 1.000

54 1.000

PEP-I-2: .N/ 50 95 0.010 100 0.990 105 0.000

72 0.021 0.972 0.007

18 0.000 0.889 0.111

18 0.028 0.917 0.056

43 0.081 0.919 0.000

48 0.031 0.927 0.042

54 0.065 0.935 0.000

PEP-II: .N/ 95 100 105

50 0.000 0.990 0.010

72 0.035 0.944 0.021

18 0.000 1.000 0.000

18 0.000 0.972 0.028

43 0.047 0.953 0.000

48 0.010 0.948 0.094

54 0.046 0.926 0.028

PGM: .N/ 90 100 115

50 0.020 0.980 0.000

72 0.021 0.979 0.000

18 0.028 0.944 0.028

18 0.000 1.000 0.000

43 0.012 0.942 0.047

48 0.010 0.906 0.083

54 0.037 0.880 0.083

PGDH: .N/ 60 80 100 110

50 0.000 0.110 0.850 0.040

72 0.000 0.049 0.799 0.153

18 0.028 0.222 0.722 0.028

18 0.000 0.056 0.944 0.000

43 0.012 0.105 0.849 0.035

48 0.021 0.125 0.729 0.125

54 0.028 0.000 0.741 0.231

SOD-1: .N/ 50 80 0.000 100 1.000

72 0.000 1.000

18 0.000 1.000

18 0.000 1.000

43 0.000 1.000

48 0.000 1.000

54 0.046 0.954

CUM D Cumbria; IOM D Isle of Man; GER D German Bight; IRL D Ireland; EAN D East Anglia; FRA D France; GRE D Greece.

128

A. Exadactylos et al. / Journal of Sea Research 40 (1998) 117–129

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