Sparus aurata - Universidad de MÃ¡laga

Aquaculture 230 (2004) 65 – 80 www.elsevier.com/locate/aqua-online

Genetic comparison of wild and cultivated European populations of the gilthead sea bream (Sparus aurata) J.A. Alarcoń a, A. Magoulas b, T. Georgakopoulos b, E. Zouros b,c, M.C. Alvarez a,* a

Department of Cell Biology and Genetics, Faculty of Sciences, University of Ma´laga, Campus Universitario Teatinos, 29071 Ma´laga, Spain b Department of Genetics and Molecular Biotechnology, Institute of Marine Biology of Crete, GR 710 03 Iraklion, Greece c Department of Biology, University of Crete, GR 714 09 Iraklion, Greece Received 7 February 2002; received in revised form 14 January 2003; accepted 7 June 2003

Abstract This study represents the first large-scale population genetic analysis of the marine fish gilthead sea bream (Sparus aurata), one of the most significant species in the South European aquaculture. Six wild and five cultivated sample sets covering the South Atlantic and Mediterranean European area have been screened for allozyme, microsatellite and mitochondrial DNA (mtDNA) variation. Microsatellites showed higher levels of polymorphism than allozymes. The low variability of mtDNA offered no basis for population differentiation. The results reveal levels of variability for S. aurata above those from other sparids. Cultivated populations show a slight decrease of variability related to the wild ones, but not sufficient to document inbreeding depression effects, thus suggesting a fairly proper management. Wild populations reveal a slight degree of differentiation more pronounced with microsatellites than with allozymes, but not apparently associated with geographic or oceanographic factors. The cultivated populations seem to be highly divergent as a result of genetic drift caused by different factors pertaining to their respective histories. With both markers, the two cultivated Spanish sample sets are the most divergent. The high differentiation between cultivated and wild populations from the same area might indicate no evidence for significant genetic flow between them.

* Corresponding author. Tel.: +34-952-131967; fax: +34-952-132000. E-mail address: [email protected] (M.C. Alvarez). 0044-8486/$ - see front matter D 2004 Elsevier B.V. All rights reserved. doi:10.1016/S0044-8486(03)00434-4

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J.A. Alarcoń et al. / Aquaculture 230 (2004) 65–80

This study provides an insight into the population structure of S. aurata, although more questions have arisen that need to be solved. This can be achieved by further screening of small-scaled targeted sample sets in the studied area. D 2004 Elsevier B.V. All rights reserved. Keywords: Sparus aurata; Allozymes; Microsatellites; mtDNA; Aquaculture; Population genetics

1. Introduction The gilthead sea bream Sparus aurata is a marine teleost that is distributed all along the Atlantic and the Mediterranean Sea. It is a protandrous hermaphrodite and highly fecund. It is one of the most important food species in the European aquaculture (Stephanis, 1996). Because so much is known about this species, it is a suitable model for the domestication of other sparid fish. The demand of the sea bream is still high, but production costs are arising and so producing strains of high genetic value is a main priority. Large-scale data on the geographic structure of wild and cultivated S. aurata populations are needed for setting up suitable guidelines for founding and maintaining of cultivated stocks. In general, the effective size of founder populations is conditioned by farming constraints, which results in the use of only a few individuals as broodstock. This practice may lead to the erosion of the genetic diversity of the stocks, thereby compromising industrial performance. Due to the relatively short history of large-scale commercial sea bream culture, phenotypic manifestations of such erosion may not be obvious yet. The main objectives of the study were: (a) to define the genetic structure of sea bream populations in an area covering the South Atlantic and Mediterranean European coasts by measuring the genetic heterogeneity among wild populations and (b) to estimate the degree of potential genetic erosion of reared stocks by comparing their genetic variability with that of geographically close wild stocks. Molecular genetic markers have been used in the last two decades to study the genetic structure of cultivated and wild fish stocks (reviewed in Carvalho and Hauser, 1995; Ferguson, 1994; O’Really and Wright, 1995). However, results from such studies can be critically dependent on the type of the marker used. In this work, three types of complementary genetic markers have been applied: allozymes, microsatellites and mitochondrial DNA (mtDNA). The simplicity and general applicability of allozyme markers made them led to their early use to assay genetic variation in fish (Utter, 1991), including several sparid species from various geographic origins (Sugama and Sumantadinata, 1989; Reina et al., 1994). With the advent of recombinant DNA and PCR techniques, microsatellite analysis emerged due to its greater sensitivity in detecting genetic variability within and between populations (Tautz, 1989; Weber and May, 1989; DeWoody and Avise, 2000). Microsatellites can be sensitive indicators of homozygosity resulting from consanguineous matings and thus suitable for distinguishing slightly differentiated populations. Several microsatellites have been described for S. aurata (Batargias et al., 1999), some of which were used in this study. The utility of the


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mitochondrial DNA (mtDNA), especially the control region (D-loop) as marker has been widely recognized (Avise, 1994; Meyer, 1994). The Single Strand Conformation Polymorphism technique, SSCP, (Orita et al., 1989) can detect any nucleotide difference among individuals and has been adopted for its advantages in terms of cost and time saving, when compared with direct sequencing. We have surveyed wild and cultivated sample sets of S. aurata from Atlantic and Mediterranean origin using the above three methods of molecular analyses, to determine whether it will be possible to design strategies to optimize the genetic resources of this species.

2. Materials and methods 2.1. Sampling A lot of 270 adult S. aurata specimens, based on a total of six sample sets from wild and five sample sets from farms were collected and genetically screened. Individuals averaged 30 cm in size and were collected in 1996– 1997. Geographic locations, sample sizes, and nomenclature are given in Fig. 1. Animals were transported either frozen or refrigerated to laboratories for dissection. Samples of liver, dorsal muscle and eye from each individual were stored frozen for allozyme screening. Muscle samples were kept in 70% ethanol for both microsatellites and mtDNA analyses.

Fig. 1. Geographic origin and size of S. aurata samples: FAW, Oleron Island (n = 40); PAW, Aveiro (n = 50); SAW, Ca´diz (n = 48); SMW, Alicante (n = 51); IMW, Trieste (n = 40); GMW, Mesologgi (n = 50); PAC, Tavira (n = 50); SAC, Ca´diz (n = 50); SMC, Murcia (n = 50); IMC, Trieste (n = 50); GMC, Leros Island (n = 50). The nomenclature of the samples was according to the origin of (a) the country (G = Greece, F = France, I = Italy, P = Portugal and S = Spain); (b) the Atlantic (A) and Mediterranean (M) origin; (c) wild (W)/cultivated (C).

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2.2. Analysis of allozymes Frozen tissues were subjected to no more than three freeze – thaw cycles to obtain cell lysates, which were electrophoresed in horizontal starch gels. Electrophoretic protocols and staining procedures were according to Reina et al. (1994). The 16 enzyme systems screened, the loci detected along with the tissues and buffers used in each case, are listed in Table 1. For loci nomenclature, the locus with the least anodal migration was designed one, the next two and so on. Allele variants were named according to their relative mobilities to the most common allele designated as 100. 2.3. Analysis of microsatellites The procedure of Batargias et al. (1999) was adopted. Of the six microsatellite loci developed for S. aurata in this work, SAGT26, SAGT32 and SAGT41b proved the most suitable for population screening. 2.4. Analysis of mtDNA Mt-DNA was analysed by SSCP following the protocol of Ostellari et al. (1996). Primers, specific for S. aurata (Sa1-L: AGCTAGCGTTCTTCATTTAAACTAT, Sa1-H: ACATATGTGTATTTAAC CCATAACC) were designed that spanned the hypervariable domain I of the control region. Several individuals from each of the different mobility classes (the number depended on the frequency of the respective class in a sample) were sequenced to confirm the SSCP results.

Table 1 List of isozyme systems Enzymes

E.C. No.

Tissue

Buffersa

Loci

Adenilato Kinase, AK Alcohol Dehydrogenase, ADH Diaforase, DIA Endopeptidase, ENDO Esterase, EST Glucose-6-P Dehydrogenase, GPI Glycerol-3-P Dehydrogenase, G3PDH Iditol Dehydrogenase, IDDH Isocitrate Dehydrogenase, IDHP Lactate Dehydrogenase, LDH Malate Dehydrogenase, MDH Malic Enzyme, MEP Mannose-6-P Isomerase, MPI Phosphoglucomutase, PGM 6-Phosphogluconate Dehydrogenase, PGDH Superoxide Dismutase, SOD

2.7.4.3. 1.1.1.1. 1.6.2.2. 3.4.*.*. 3.1.1.*. 5.3.1.9. 1.1.1.8 1.1.1.14. 1.1.1.42. 1.1.1.27. 1.1.1.37. 1.1.1.40. 5.3.1.8. 5.4.2.2. 1.1.1.44. 1.15.1.1.

Muscle Liver Liver Muscle Muscle Muscle Muscle Liver Liver Eye Muscle Muscle Liver Muscle Liver Liver

CTC CTC RID RID CAM RID CTC CTC CTC RID CAM CTC RID RID CTC RID

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

a CTC: Continuous Tris – Citrate, pH 8.0 (McAndrew and Majundar, 1983). CAM: Citrate – Aminopropylmorpholine, pH 6.1 (Clayton and Tretiak, 1972). RID: Citrium – Borate, pH 8.1 (Ridway et al., 1970).


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2.5. Data analysis Genetic variability was estimated by the number of alleles/locus, and the He values. Departure from Hardy– Weinberg expectations and divergence among populations were measured using Wright’s FIS and FST, respectively (Weir and Cockerham, 1984). Departure from the two null hypotheses ( FIS = 0 and FST = 0) was tested by permutation procedures (at least 1000 permutations) of either alleles or individuals. All data were processed with the GENETIX 3.4 software of Belkhir et al. (1998). Bonferroni adjustment of the P-values was used to correct multiple tests (Rice, 1989). With the mtDNA markers no population tests were performed, due to the extremely low variability detected.

Table 2 Allelic frequencies at variable allozymic loci in populations of S. aurata Loci

Alleles

(n) EST *

GPI-2*

IDDH * IDHP *

LDH-2* MDH-2 * MDH-3 * PGM *

PGDH *

SOD-1* SOD-2*

100 110 96 100 80 70 110 100 20 100 65 114 100 150 100 66 100 120 100 133 190 250 100 107 90 100 160 100 75

Samples FAW

PAW

SAW

SMW

IMW

GMW

PAC

SAC

SMC

IMC

GMC

(40)

(50)

(48)

(51)

(40)

(40)

(50)

(50)

(50)

(50)

(50)

0.500 0.500 – 0.637 0.287 0.075 – 1 – 0.718 0.154 0.128 1 – 1 – 1 – 0.637 0.350 – 0.013 0.688 0.313 – 0.962 0.038 1 –

0.540 0.460 – 0.729 0.146 0.125 – 1 – 0.531 0.172 0.297 1 – 1 – 1 – 0.500 0.500 – – 0.667 0.333 – 0.958 0.042 1 –

0.500 0.469 0.031 0.760 0.219 0.021 – 1 – 0.800 0.100 0.100 1 – 1 – 0.990 0.010 0.533 0.467 – – 0.531 0.448 0.021 0.948 0.052 1 –

0.598 0.382 0.020 0.716 0.216 0.069 – 0.980 0.020 0.598 0.108 0.294 0.990 0.010 0.971 0.029 0.990 0.010 0.431 0.569 – – 0.745 0.153 0.102 0.971 0.029 0.990 0.010

0.513 0.487 – 0.667 0.282 0.038 0.013 0.988 0.013 0.688 0.188 0.125 1 – 1 – 1 – 0.563 0.425 0.013 – 0.684 0.316 – 0.962 0.038 1 –

0.438 0.563 – 0.716 0.243 0.041 – 1 – 0.716 0.216 0.068 1 – 1 – 1 – 0.513 0.487 – – 0.730 0.270 – 1 – 1 –

0.542 0.458 – 0.780 0.150 0.070 – 1 – 0.663 0.186 0.151 1 – 1 – 1 – 0.760 0.240 – – 0.685 0.304 0.011 0.880 0.120 1 –

0.440 0.560 – 0.620 0.300 0.070 0.010 1 – 0.667 0.179 0.155 1 – 1 – 1 – 0.580 0.420 – – 0.783 0.217 – 0.950 0.050 1 –

0.570 0.430 – 0.530 0.470 – – 1 – 0.580 0.410 0.010 1 – 1 – 0.840 0.160 0.550 0.400 0.050 – 0.670 0.330 – 1 – 1 –

0.430 0.570 – 0.860 0.130 0.010 – 1 – 0.469 0.306 0.224 1 – 0.940 0.060 1 – 0.640 0.360 – – 0.653 0.347 – 0.990 0.010 1 –

0.780 0.220 – 0.670 0.310 0.020 – 1 – 0.745 0.214 0.041 1 – 1 – 1 – 0.690 0.310 – – 0.640 0.360 – 0.870 0.130 1 –


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3. Results Data obtained from allozymes, microsatellites and mtDNA markers were analyzed to determine the following: (1) Evidence of changes in the genetic variability and structure of farmed stocks versus wild stocks of S. aurata. (2) The differentiation pattern of wild and cultivated populations from the Southern European area. 3.1. Genetic variability and structure of wild and farmed stocks of S. aurata Allozymic screening revealed 23 loci from 16 enzymes (Table 1). The allelic frequencies of the 11 polymorphic loci are presented in Table 2. EST*, GPI-2*, IDHP*, PGM*, PGDH* and SOD-1* were variable in the majority of sample sets while the remaining loci were rarely polymorphic. The number of alleles/locus (Table 2) ranged from 2 (IDDH*, MDH-2*, MDH-3*, SOD-1*) to 4 (GPI-2* and PGM*). Considering all sample sets together, five more alleles were detected in wild fish than in cultivated fish, (Est96, Iddh20.Ldh-2150.Pgm250 and Sod-275), although some appeared in very low frequencies. When the numbers of alleles of wild sample sets were compared with their respective cultivated ones, they were higher in wild populations in seven comparisons and lower in four. The differences were not significant ( P>0.05). The allozymic He values (Table 3) were used to compare diversity between wild and cultivated stocks from the same location. Values from wild fish were higher in 19 out of 30 loci, but the difference is not statistically significant ( P>0.05). The mean He values from all wild and all cultivated populations were not t-test significant ( P>0.05). Table 3 Genetic variability values obtained from allozyme and microsatellite data from S. aurata populations Loci

EST * GPI-2* IDDH* IDHP* LDH-2* MDH-2* MDH-3* PGM* PGDH* SOD-1* SOD-2* SA32 SA41b SA26

Samples

He He He He He He He He He He He He # alle/2N He # alle/2N He # alle/2N

FAW

PAW

SAW

SMW

IMW

GMW

PAC

SAC

SMC

IMC

GMC

0.500 0.505 – 0.444 – – – 0.471 0.430 0.072 – 0.915 0.27 0.911 0.21 0.786 0.22

0.497 0.431 – 0.600 – – – 0.500 0.444 0.080 – 0.895 0.18 0.901 0.21 0.830 0.19

0.529 0.374 – 0.340 – – 0.021 0.498 0.517 0.099 – 0.924 0.18 0.929 0.20 0.817 0.08

0.496 0.437 0.038 0.544 0.019 0.057 0.019 0.491 0.411 0.057 0.019 0.862 0.21 0.911 0.22 0.841 0.17

0.500 0.474 0.025 0.477 – – – 0.503 0.432 0.072 – 0.912 0.25 0.911 0.27 0.820 0.18

0.492 0.426 – 0.436 – – – 0.500 0.394 – – 0.911 0.26 0.938 0.30 0.868 0.20

0.497 0.364 – 0.503 – – – 0.365 0.438 0.211 – 0.903 0.16 0.827 0.17 0.728 0.11

0.493 0.521 – 0.500 – – – 0.487 0.340 0.097 – 0.936 0.19 0.820 0.14 0.833 0.16

0.490 0.498 – 0.495 – – 0.269 0.535 0.442 – – 0.844 0.15 0.888 0.14 0.846 0.12

0.490 0.243 – 0.636 – 0.113 – 0.461 0.453 0.020 – 0.889 0.16 0.887 0.16 0.882 0.13

0.343 0.455 – 0.398 – – – 0.428 0.461 0.226 – 0.905 0.17 0.890 0.14 0.883 0.13

For the microsatellites, the normalized values of the number of alleles (#allele/2N) are shown. He: Expected heterozygosity.


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The range of variability and the allelic frequencies of the three microsatellite loci from wild and cultivated sample sets are presented in Fig. 2. The distributions of allelic frequencies show a bimodal pattern, commonly observed in animal species (reviewed in O’Connell and Wright, 1997). In general, wild and cultivated populations share most alleles, although the locus SA41 presents rare alleles only in wild populations. Interest-

Fig. 2. Allelic distribution of three microsatellite loci (SA32, SA41b and SA26) in wild and cultivated European populations of S. aurata. Abcisae represent the number of base-pairs of the corresponding alleles. In ordinates are represented the allele frequencies.

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ingly, the locus SA32 shows in the SMC sample three rare alleles with an odd number of base pairs (169, 171 and 173, respectively). Data from microsatellite variability (Table 3) reveal that the number of alleles/locus ranged from 7 to 16 for SA26, from 15 to 19 for SA32 and from 14 to 24 for SA41b. Given this high variability, the observed number of alleles may be strongly affected by the sample size so that the number of observed alleles was normalised by sample size. The data in Table 3 and Fig. 2 show that wild sample sets contain more alleles than the farmed ones. When wild and their corresponding cultivated populations were pairwise compared (three loci and five localities), the wild sample sets show a higher number of alleles ( P < 0.05) in 13 out of 15 comparisons. Furthermore, the mean number of alleles for the 3 loci is higher in the wild sample sets (0.211) than in the farmed ones (0.149), which is statistically significant ( P < 0.01). When He values (Table 5) from wild and cultivated sample sets were pairwise compared, they were higher in wild than in cultivated ones in 9 out of 15 cases, which is not significant ( P>0.05). The results of mitochondrial DNA revealed only five mobility classes or mitotypes (Table 4). Class A is the most common and some samples, such as GMC, IMC, PAC and GMW appeared to be monomorphic. There are however two cultivated sample sets, SAC and SMC, with relatively high frequencies of alleles that were not found in wild populations. The low variability obtained from this region was inadequate to estimate parameters of variability. Hardy –Weinberg equilibrium for allozyme and microsatellite loci is shown in Table 5. Most of the allozyme FIS values in wild populations were negative, although only rarely significantly different from zero, thus suggesting a slight excess of heterozygosity. In cultivated sample sets some significant values were found, mostly involving the SMC sample. The average FIS values for all loci in wild populations seem to be in equilibrium and the same holds true for the cultivated ones, except for SMC, which has a FIS = 0.307 ( P < 0.01). The microsatellite data suggest that, all wild and cultivated sample sets are in equilibrium, although some loci show negative FIS values. An exception is the SMC sample that shows a significant negative FIS average values ( P < 0.05), indicating an excess of heterozygotes.

Table 4 Frequencies of mtDNA haplotypes (A, B, C, D and E) in European samples of S. aurata

Wild

Cultivated

FAW PAW SAW SMW IMW GMW PAC SAC SMC IMC GMC

A

B

C

D

E

38 44 48 47 39 40 45 41 28 50 54

– – – – – – – – 22 – –

– – – – – – – 9 – – –

– – – – 1 – – – – – –

2 – – – – – – – – – –

Table 5 Genetic structure of S. aurata populations based on FIS values obtained from allozymes and microsatellites Loci

Samples FAW

PAW

SAW

SMW

IMW

GMW

PAC

SAC

SMC

IMC

GMC

0.187 0.072 – 0.089 – – – 0.049 0.023 0.026 – 0.013

0.117 0.100 – 0.026 – – – 0.110 0.111 0.032 – 0.036

0.170 0.062 – 0.030 – – 0.000 0.060 0.118 0.376 – 0.046

0.021 0.066 0.010 0.180 0.000 0.020 0.000 0.511** 0.031 0.020 0.000 0.049

0.089 0.309 0.000 0.120 – – – 0.081 0.327 0.026 – 0.026

0.054 0.064 – 0.040 – – – 0.322** 0.191 – – 0.069

0.003 0.076 – 0.097 – – – 0.023 0.179 0.063 – 0.032

0.278 0.011 – 0.249 – – – 0.387** 0.370 0.042 – 0.083

0.234 0.640** – 0.363** – – 0.031 0.525** 0.393** – – 0.307**

0.071 0.188 – 0.046 – 0.300 – 0.055 0.070 0.000 – 0.057

0.077 0.261 – 0.034 – – – 0.121 0.205* 0.213 – 0.071

0.042 0.204 0.046 0.043

0.400 0.314 0.094 0.274

0.174 0.026 0.027 0.077

0.276 0.105 0.091 0.157

0.566 0.068 0.320 0.319

0.038 0.026 0.010 0.000

0.074 0.132 0.126 0.055

0.102 0.056 0.092 0.025

0.126** 0.048 0.094 0.055*

0.057 0.131 0.077 0.037

0.068 0.059 0.109 0.033


EST * GPI-2* IDDH* IDHP* LDH-2* MDH-2* MDH-3* PGM* PGDH* SOD-2* SOD-1* All allozyme loci SA32 SA41b SA26 All microsatellite loci

* P < 0.05 after Bonferroni correction. ** P < 0.01 after Bonferroni correction.

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3.2. Genetic differentiation Table 6 shows hierarchical analysis of genetic heterogeneity with FST values derived from three sets of comparisons. When sample sets from wild are pairwise compared (Table 6a), the highest FST values appeared with allozymes in the SMW/SAW and GMW/FAW pairs. The FST value for SAW/PAW is also statistically significant. The rest of comparisons showed low FST values, indicating that the involved populations were homogeneous. Microsatellites showed higher FST values, but the overall conclusion was similar to the one obtained from allozymes—the two Spanish populations SAW and SMW were highly divergent and FAW, PAW and IMW were homogeneously grouped. To further evaluate the inter- and intra-population components of variability, the overall FST values were obtained for allozymes (0.031) and for microsatellites (0.036). The FST analysis of cultivated sample sets (Table 6b) shows that all samples were highly heterogeneous and that the SMC population had the highest FST values by both analyses. To assess possible links between cultivated and geographically related wild samples, further FST comparison analyses was performed with both markers (Table 6c). Each Table 6 Pairwise estimates of FST values between samples of S. aurata (a) Comparison between wild samples Allozymes

PAW SAW SMW IMW GMW

Microsatellites

SAW

SMW

IMW

GMW

FAW

SAW

SMW

IMW

0.023*

0.006 0.040**

0.006 0.004 0.017

0.016 0.013 0.024* 0.005

0.015 0.041** 0.036** 0.012 0.021** 0.011 0.010 0.040** 0.045** 0.039** 0.047** 0.029** 0.016* 0.025** 0.018** 0.009 0.013* 0.004 0.001 0.020**

(b) Comparison between cultivated samples Allozymes

PAC SAC SMC IMC

Microsatellites

SAC

SMC

IMC

0.021*

0.064** 0.036**

0.021* 0.028* 0.029** 0.085** 0.042** 0.039** 0.029** 0.055** 0.085** 0.025** 0.015** 0.064** 0.050** 0.058** 0.057** 0.081** 0.011*

GMC

SAC

SMC

IMC

(c) Comparison between each wild and its geographically related cultivated sample Allozymes Microsatellites PAW – PAC SAW – SAC SMW – SMC IMW – IMC GMW – GMC

0.027** 0.027** 0.065** 0.021* 0.061**

0.028** 0.044** 0.069** 0.018** 0.028**

* P < 0.05 after Bonferroni correction. ** P < 0.01 after Bonferroni correction.

GMC

GMW

FAW


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sample pair was highly heterogeneous for both markers, indicating an absence of close genetic relatedness. Considering that the sampling area covers the Atlantic –Mediterranean break, and given the differentiation patterns reported between Atlantic and Mediterranean populations in marine fish (reviewed in Borsa et al., 1997), suitable tests were performed to check the situation in S aurata. The comparison of Atlantic (FAW, PAW, SAW) and Mediterranean (SMW, IMW, GMW) sample sets, produced a mean FST value of 0.004 ( P < 0.05) for allozymes and a mean FST value of 0.010 ( P < 0.01) for microsatellites, thus indicating that both groups seem to be heterogeneous after microsatellites but not after allozymes.

4. Discussion 4.1. Genetic variability and population structure of wild and farmed stocks The purpose of this study was to obtain a general view of the population genetics of the gilthead sea bream in Southern Europe. The farming of this species has started more than 20 years ago and is still in expansion, but no attempts have been undertaken to assess the genetic status of both wild and cultivated stocks of this species. Our work is unusual in that it represents one of a few instances, whereby wild and cultivated sample sets of a fish species were simultaneously studied for allozymic, microsatellite and mtDNA polymorphisms. Genetic variability is an important attribute of the species under domestication, since those with higher levels of variation are most likely to present high additive genetic variance for productive traits. Wild populations represent the primary source of genetic variability for aquacultured stocks. In this study the average He values obtained for S. aurata from allozyme and microsatellite loci are 0.108 and 0.845, respectively (Table 3). Higher variability detected with microsatellites appears common (e.g., Hughes and Queller, 1993; Taniguchi and Perez-Enriquez, 2000), presumably due to higher mutation rates and/or by the lower selective pressure of microsatellites versus allozymes (O’Connell and Wright, 1997) or finally by the underestimation of variability with allozymes. A previous analysis of allozymes in S. aurata using sample sets from an area close to that of the present study reported a H = 0.040 (Reina et al., 1994), less than half of the H values in this study (Table 4). The He values were 2 to 10 times higher in S. aurata (manuscript in preparation) than those from 12 other sparids species of the same geographic areas and screened for the same allozymes. Likewise, comparison of the He values of S. aurata with those from a large representation of marine species gave a mean value of He = 0.064 for allozymes (Ward et al., 1994) and a mean value of He = 0.79 (DeWoody and Avise, 2000) for microsatellites. The reasons for the high variability are unknown, but this observation suggests a favourable combination of molecular, demographic and evolutionary factors in this species, able to maintain high levels of variation, which might be one of the reasons for its success in aquaculture. The partitioning of variability of wild populations seen after F-statistics comparisons with both types of markers shows that most of genetic variation is within populations. This

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information should be taken into account in the management policy of this species, since genetic variability is presumably beneficial for domestication and genetic improvement. A major concern in the aquaculture of sea bream is the genetic variation of the farmed stocks regarding the influence of the culture practice and/or the biology of the species itself. However, for the stocks under study sea bream farmers have not released records on either the constitution or maintenance of broodstocks. Some farms buy their fry from wholesale suppliers while other farmers produce their own fry using breeding stock that is not maintained under any guidelines. In most cases the cultivated populations are supplemented with wild fish taken from a neibourghing area. As a result, the history, the origin, the size and the sex ratio composition of the breeding stocks are most of the time not suitably controlled. In species where sexes are separated and unchanged through life, breeders regularly add new individuals of the most vulnerable sex. Due to the protandric hermaphroditism shown by S. aurata, the farmer can rely on the same individuals to act initially as males and later as females. As a result, everything else being equal, the effective population size of S. aurata breeding stocks would be smaller than that of non-hermaphrodite species. This aspect, coupled with the putative small numbers of founders derived from wild, could lead to inbreeding depression. A more precise assessment of the genetic variability in farmed stocks can be made were the magnitude of the genetic variation of wild populations made available. This is almost a requirement when the number of generations of the farmed stock is small and there has not been enough time for the variation to be reduced to a detectable level. But the differences in genetic variability between wild and farmed stocks seen by allozymes are not significant (Tables 2 and 4, top). A significant reduction in the number of microsatellite alleles was detected in farmed versus sample sets, although there was no significant difference in the He values (Table 4, bottom). The loss of alleles with low frequency and the slight decrease in variability in cultivated samples may indicate bottleneck and/or inbreeding effects, but inbreeding depression has not been observed. Opposite to the trend of loosing alleles, in the SMC population three unique alleles have been detected (Fig. 2), which might also be a sign of bottleneck effect. The low variation in the screened mtDNA heterogeneity fragment (Table 3) was a bit surprising. These results were unexpected because: (a) previous mtDNA analyses of the cytochrome b region have shown normal amounts of variation in S. aurata (Magoulas et al., 1995), and (b) the greater polymorphism detected in the same control region of mtDNA of other sparid species (Ostellari et al., 1996; Tabata and Taniguchi, 2000). The possibility of a technical artefact was rejected after suitable tests. Such limited variability in mtDNA has been reported for this region in salmonids (Bernatchez and Danzmann, 1993) and other vertebrates (Baker et al., 1994; Walker et al., 1998) and has been justified by unusual low evolutionary rates due to increased, not well-defined, functional constraints (Crochet and Desmarais, 2000). However, we did find that the SAC and SMC cultivated populations from Spain contained a rare allele in mtDNA at high frequency (Table 3). This pattern might be expected from a subdivided population, where each subpopulation has small effective population size and where one allele in low frequency, may drift to higher frequencies. The FIS values obtained from allozyme and microsatellite loci in wild and cultured populations (Table 5) indicate that they are in Hardy – Weinberg equilibrium. Only the


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cultivated SMC shows a significant excess of heterozygotes (Table 4), which can be attributed to a mixed origin during founding and to subsequent bottlenecks in following generations. 4.2. Genetic structuring FST values in wild populations (Table 6a) reveal few comparisons different with allozymes, while significant differences are revealed with microsatellites in all cases. With both markers SAW population is almost the most divergent, followed by SMW and GMW. The lack of agreement between the two markers in levels of differentiation has been widely documented in fish and has been justified either by the intrinsic level of polymorphism of each marker (Estoup and Angers, 1998) and/or to by the variable sensitivity of the markers to the selection process. (Pogson et al., 1995; Schmidt and Rand, 1999). It can be concluded that both markers suggest some structuring pattern that cannot be associated with geographic and/or oceanographic known factors. FST values obtained with both markers (Table 6a) are clearly below the average value ( FST = 0.062) obtained from a group of marine species (Ward et al., 1994). In fish, negative correlation has been demonstrated between FST values and dispersal capability (Waples, 1987). According to this, S. aurata should present high dispersal capability presumably due to the absence of physical or ecological barriers to adults or planktonic larval stages. Consequently high exchange of migrants among subpopulations might be produced, thus allowing large effective subpopulation sizes and low structuring (Gyllensten, 1985; Ward et al., 1994). Although our microsatellite FST results (Table 6a) indicate that the Atlantic and Mediterranean sample sets were heterogeneous, this finding may be misleading because microsatellite comparisons in wild stocks indicate that all sample sets are different between them, so that definitive conclusions cannot be drawn for S. aurata. The various patterns found in fish around this transition area have been interpreted in the light of different dispersal mechanisms and historical biogeographic factors of each species (reviewed in Borsa et al., 1997; Naciri et al., 1999). All previous considerations lead us to conclude that the South Atlantic and Mediterranean populations of S. aurata present some sort of structuring pattern, although lower than the average of marine species, that cannot be associated with geographic and/or oceanographic known factors. This ‘‘undefined’’ structuring sharply contrasts with the high geographic differentiation detected in other sparid species which are very close to S. aurata, in terms of biological and ecological traits. The actual reasons of such structuration are unknown, although they might be associated with fluctuations in the effective population size and/or bottlenecks and expansions, possibly combined with differences in gene flow rates. Cultivated sample sets (Table 6b) shows a strong heterogeneity between all farms, especially in the SMC sample. Larger divergence among hatchery stocks compared with that of wild populations has other precedents (Taniguchi and Perez-Enriquez, 2000) and has been justified by small population size effects. Due to the unavailability of records from the farms in this study, we cannot trace the origins of the significant differences we have identified, however their current structure might be due to: (i) founder effects in the initiation of stocks, (ii) the establishment of each farm at different times with different

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number of specimens from wild and/or cultured origin and (iii) the maintenance regime varying among farms. All the above factors seem to be especially acute for the SMC stock. General principles of Genetics suggest that the level of genetic differentiation found in this study might increase over time due to small effective population sizes in many farms and the restricted gene flow among them. The comparison of the FST values (Table 6c) between cultivated and wild populations shows that in every case the values are significantly different. This indicates than in spite of the reported periodical incorporation of some individuals from neighbouring wild sources, the reduction in the effective size can account for a high differentiation in cultivated stocks. Large scale approaches to assess the genetic structures of wild and cultivated populations, such as that we have reported here for S. aurata, are most suitable to provide insights about its evolutionary history and its potentiality as source of variation for renewing cultivated stocks. In this study, interesting data have been disclosed regarding the genetic status of wild and cultivated populations of S. aurata, however, several questions have been arisen that need a more extensive survey in terms of designing a micro-scale geographic sampling and checking fish from different ecosystems in order to answer more specific questions.

Acknowledgements The authors are very grateful to the ‘‘CUPIMAR’’ (Spain), to Dr. A. Garcia (IEO, Spain) and to J.S. Bruant (FMD, France) for providing samples and to Prof. T. Patarnello (University of Padova, Italy) for providing samples and performing the nucleotide sequence of mtDNA. This work has been funded by the EC GrantAIR3-CT94-1926.

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