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ICES Journal of Marine Science: Journal du Conseil 2006 63(9):1705-1709; doi:10.1016/j.icesjms.2006.07.006
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© 2006 International Council for the Exploration of the Sea

An investigation of the population genetic structure of pollack (Pollachius pollachius) based on microsatellite markers

Grégory Charriera,b,*, Jean-Dominique Durandc, Louis Quinioub and Jean Larocheb

a NOAA Southwest Fisheries Science Center, Santa Cruz Laboratory 110 Shaffer Road, Santa Cruz, CA 95060, USA
b Laboratoire LEMAR (UMR CNRS 6539), Institut Universitaire Européen de la Mer, Place Nicolas Copernic, Technopôle Brest-Iroise 29280 Plouzane, France
c IRD/UR 070, Laboratoire GPIA, Université Montpellier II (UMR CNRS 5171), Station Méditerranéenne de l'Environnement Littoral 1 Quai de la Daurade, 34200 Sète, France

*Correspondence to G. Charrier: NOAA Southwest Fisheries Science Center, Santa Cruz Laboratory, 110 Shaffer Road, Santa Cruz, CA 95060, USA; tel: +1 831 420 3966; fax: +1 831 420 3977. e-mail: gregory.charrier{at}noaa.gov.

In order to explore the population genetic structure of pollack (Pollachius pollachius) along the European coast, of 282 fish sampled from four locations along the Atlantic French coast and from one location off southern Norway were genotyped at six microsatellite loci. The limited genetic differentiation among samples may be due to high levels of larval dispersal, through passive drift with oceanic currents. Alternatively, populations may have diverged too recently for significant genetic differentiation to have become evident. Furthermore, small sample sizes and the limited number of loci may have hampered the detection of genetic structure. Nevertheless, a weak but significant genetic differentiation was detected between samples originating from the western English Channel and the Bay of Biscay.

Keywords: genetic structure, microsatellites, Pollachius pollachius

Received 25 July 2005; accepted 26 July 2006.


   Introduction
Top
 Introduction
Material and methods
 Results
 Discussion
 References
 
Pollack (Pollachius pollachius) are widespread from Norway to Portugal and common all around the coasts of Brittany, the United Kingdom, and Ireland. They tend to live alone or in small dispersed shoals, except during the spawning period, when they group into dense breeding associations on spawning grounds. The species remains close to particular habitats characterized by rocky shores or wrecks (Cohen et al., 1990). The influence of hard substrata on population structure has previously been proposed as a structuring factor of fish populations, as in Mullus species (Mamuris et al., 2001). Therefore, the discontinuity of rocky shores around the coasts of Europe could be a major structuring factor for pollack populations.

Pollack eggs and larvae are pelagic, and this pelagic stage may largely influence patterns of population inter-connectivity. Larval dispersal is notably modulated by the duration of the planktonic phase (Doherty et al., 1995) and the hydroclimatic factors (Wing et al., 1998). Moreover, significant genetic structure that is best explained by fidelity to natal spawning grounds has been demonstrated for cod (Ruzzante et al., 2001). However, no data are available on such a trait in pollack.

Globally, the genetic structure of fish species is shaped by the combined impacts of their ecological/behavioural traits and hydrological factors. Therefore, the main objectives of this initial study of the genetic structure of pollack were (i) to explore the unknown genetic structure of pollack around the European continental coasts using microsatellite markers, and (ii) to consider the various factors that may shape the genetic structure of the species in the area.


   Material and methods
Top
 Introduction
 Material and methods
Results
 Discussion
 References
 
Sampling and DNA extraction
In all, 282 individuals were sampled from five locations: Skarvöyni (Sk, n = 50), Trégastel (Tr, n = 29), Iroise Sea (Is, n = 48), Bay of Audierne (Au1, n = 50; Au2, n = 56), and Cape Breton (Cb, n = 49; Figure 1). In order to reduce the potential risk of sampling mixed population units, only mature adults were targeted (total length >40 cm) during the spawning period (between February and April), when fish gather near or on their spawning grounds. Fin samples were collected immediately after landing or directly on board, and were stored in 95% ethanol. DNA extraction was performed using a standard phenol–chloroform method.


Figure 1
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  		Figure 1 Sampling locations of Pollachius pollachius: Skarvöyni (Sk), Trégastel (Tr), Iroise Sea (Is), Bay of Audierne (Au1-2), and Cape Breton (Cb).

 
Microsatellite amplification and screening
Among the microsatellites isolated from gadids, six loci were efficiently amplified in pollack: four of Theragra chalcogramma (Tch8, Tch14, Tch18, and Tch20; O'Reilly et al., 2000), one of Micromesistius poutassou (MpouBW13; Moran et al., 1999), and one of Gadus morhua (Gmo02; Brooker et al., 1994). These six loci were used for the present study.

Polymerase chain reactions (PCR) were performed in volumes of 10 µl containing 1 µl of DNA, 1x reaction buffer (Uptima), 0.074 mM dNTPs mix, 0.05 µM primer F end-labelled with IRDye 800 (Li-Cor) and 0.4 µM primer R, 0.25 U Taq Uptitherm DNA polymerase (Uptima), and between 1.5 and 2.5 mM MgCl2 depending upon the locus. PCR amplifications were performed in a Geneamp PCR System 9700 (Perkin Elmer). In order to increase the stringency of microsatellite amplification, a touchdown procedure adapted from O'Reilly et al. (2000) was incorporated in the thermal cycling regime: (94°C for 3 min, Tm + 4°C for 2 min, 72°C for 1 min) x1; (94°C for 30 s, Tm + 3°C for 30 s [–1°C per cycle], 72°C for 1 min) x13; (94°C for 30 s, Tm – 9°C for 30 s, 72°C for 1 min) x11 or 16 according to each locus; (72°C for 5 min) x1. Loading buffer (5 µl) was added to the PCR products, and 1 µl was loaded on 8% denaturing polyacrylamide gels of 25 cm with 1% TBE Buffer. Gels were run on an automated sequencer IR2 (Li-Cor) using Eseq software (Li-Cor) for 1–3 h at 1500 V and 50°C. Gels were then analysed with Gene Profiler (Scananalytics). Of the 282 fish sampled, 268 were successfully screened on these six loci. For each locus, between 10% and 50% of fish were genotyped 2–3 times to limit allele mis-scoring.

Data analysis
Allelic diversity and observed and unbiased expected heterozygosities were computed for each location with POP100GENE v.1.1.02 (Piry and Bouget, 1999). Departure from Hardy–Weinberg equilibrium (heterozygote deficiency or excess) was calculated for each locus and each location with GENEPOP v.3.4 (Raymond and Rousset, 1995) according to Weir and Cockerham's FIS (Weir and Cockerham, 1984), and was tested using the Markov chain method (10 000 dememorization steps, 1000 batches, and 3000 iterations) to obtain unbiased estimates of the exact p value. Linkage disequilibrium was tested using the Markov chain method (10 000 dememorization steps, 1000 batches, and 3000 iterations) and Fisher's exact test (Haldane, 1954).

Genetic differentiation between samples was assessed using FST and exact tests of genic differentiation (Raymond and Rousset, 1995). Global and pairwise FST were estimated using Weir and Cockerham's {theta} (Weir and Cockerham, 1984), with GENETIX v.4.05 (Belkhir et al., 2004). The significance of {theta} was tested using 5000 permutations. Global and pairwise exact tests of genic differentiation were calculated using GENEPOP v.3.4. Allelic frequencies were compared for all pairs of locations using the Markov chain method (10 000 dememorization steps, 1000 batches, 3000 iterations) and Fisher's exact test under the null hypothesis that allelic distribution is identical among samples. Sequential Bonferroni correction (Rice, 1989) for multiple comparisons was applied when necessary.

A spatial analysis of molecular variance (SAMOVA) based on {Phi}-statistics was performed with SAMOVA v.1.0 (Dupanloup et al., 2002) to investigate the distribution of genetic diversity over the whole data set. Significance of {Phi}-statistics was tested by 100 000 permutations. The software SAMOVA v.1.0 implements a method to define groups of populations that are geographically homogeneous and maximally differentiated: the groupings that maximized {Phi}CT values (among group variance) and minimized {Phi}SC values (among populations within group variance) were assumed to be the most probable geographic subdivisions. In addition, a principal components analysis (PCA) of genetic data was performed with PCA-GEN v.1.2 (Goudet, 1999), to examine the relationships among populations.

Finally, correlation between pairwise geographic distances and pairwise linearized FST (FST/(1 – FST)) was tested by permuting the Mantel's test (Mantel, 1967) 5000 times in GENETIX v.4.05.


   Results
Top
 Introduction
 Material and methods
 Results
Discussion
 References
 
Genetic variability within populations
Levels of polymorphism varied between loci, with the total number of alleles per locus ranging from 8 (MpouBW13 and Tch18) to 41 (Gmo02). Mean allelic diversity per sample ranged from 4.33 (MpouBW13) to 29.00 (Gmo02), and mean observed heterozygosity ranged from 0.445 (MpouBW13) to 0.957 (Gmo02). Mean multi-locus allelic diversity and observed heterozygosity were similar in the different samples, ranging respectively from 11.67 (Cb) to 13.00 (Au1), and from 0.678 (Sk) to 0.741 (Tr). Significant departures from Hardy–Weinberg equilibrium were observed for some loci in Tr (MpouBW13, Tch14, and Tch18), Is (Gmo02, Tch8, Tch18, and Tch20), Cb (Tch8), and Sk (Tch14 and Tch20) samples. Only the FIS value observed at the Tch20 locus in Sk remained significant after sequential Bonferroni correction. However, it is widely considered that above five comparisons, the Bonferroni method becomes highly conservative and therefore unsuitable (Altman, 1991). Only Is and Sk samples showed multi-locus heterozygote deficiency even after sequential Bonferroni correction. No evidence of linkage disequilibrium was observed between locus pairs, and consequently each locus was considered as independent from the others.

Genetic differentiation
Over the whole data set, the global estimation of {theta} (Weir and Cockerham, 1984) indicated a low level of genetic structure in pollack ({theta} = 0.001). However, significant multi-locus genic differentiation was detected among samples (p value = 0.040). Neither significant genetic differentiation at any single locus, nor significant heterogeneity of allelic frequencies was observed after sequential Bonferroni correction.

Pairwise comparisons of samples underlined a limited genetic structure (Table 1), because all pairwise tests ({theta} and exact tests) were not significant after sequential Bonferroni correction. However, as observed previously, the Bonferroni method becomes very conservative when more than five comparisons are considered.


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  		Table 1 Estimation of FST ({theta}) (above diagonal) and exact tests of genetic differentiation (below diagonal) for each pair of locations. Pairwise {theta} have been calculated using the method of Weir and Cockerham (1984), and have been tested using 5000 permutations (*p < 0.05). Pairwise exact tests have been tested using the Markov chain method with 3000 iterations (bold value indicates significant p value). All values became non-significant after sequential Bonferroni correction.

 
The best partitioning of the genetic diversity by SAMOVA was obtained when samples were divided into four units ({Phi}CT = 0.0043, p = 0.049): (i) Bay of Biscay (Cb, Au1, and Au2), (ii) Iroise Sea (Is), (iii) western English Channel (Tr), and (iv) southern Norwegian coast (Sk). The PCA was congruent with the SAMOVA result. The three first axes explained 39.61%, 18.62%, and 17.16% of the total variance, respectively, representing a large proportion of the genetic variance over the whole data set. Globally, the same four units assessed by SAMOVA were discriminated by these three first axes of the PCA.

Finally, the Mantel test between pairwise linearized FST and geographical distances did not show a significant correlation (Z = 65.98, p = 0.105), suggesting a lack of isolation by distance among pollack samples.


   Discussion
Top
 Introduction
 Material and methods
 Results
 Discussion
References
 
The level of genetic differentiation detected between pairs of pollack samples was relatively limited ({theta} ≤ 0.008), but rather similar to those observed in gadid fish of the Northeast Atlantic (Lundy et al., 1999; Hutchinson et al., 2001; Ryan et al., 2005).

This weak genetic structure of pollack suggests high gene flow between spawning units. Indeed, the marine environment is commonly considered as very dispersive and, therefore, low levels of genetic structure have been observed in marine species generally (Ward et al., 1994). The relative fragmentation of the particular habitat of pollack (hard seabed and wrecks) does not appear as a major structuring factor, contrary to observations for other marine fish species (Mamuris et al., 2001). In addition, the geographical distances along European coasts do not shape significantly the genetic structure of pollack populations, as suggested by the Mantel test.

High gene flow may be induced at a large scale, from the Bay of Biscay to the Norwegian coast, by oceanic currents. Notably, passive dispersal of pelagic eggs and larvae may be ensured by the general north–northeastward drift of the North Atlantic Current and some possible advection by the Shelf Edge Current flowing from Northwest Africa to Norway along the edge of the continental shelf (Pingree and Le Cann, 1989), and by the branching of the North Atlantic Drift southwards into the North Sea along the Norwegian coast (Turrell, 1992).

Alternatively, the limited genetic structure observed for pollack could be explained by the recent origin of populations, so preventing significant genetic drift yet. This assumption may be particularly true with the large effective population sizes commonly observed in marine fish. Ferguson (1994) noticed that many populations of freshwater and anadromous fish in the northern hemisphere only established 10 000–15 000 years ago, after the Last Glacial Maximum, and that this period would not have been long enough in most cases to allow significant neutral genetic differentiation to happen. This can be generalized to many marine species, because a large part of northern seas was occupied by ice sheets during the Pleistocene ice ages (Hewitt, 2000).

In addition, small sample sizes and the limited number of microsatellites may have hampered the detection of weak population differentiation for pollack. Therefore, any conclusions about the limited genetic structure in this species should be tempered with caution, and this initial study on pollack genetics warrants further investigation by increasing sample sizes and/or adding new loci to the data set. In particular, the development of species-specific microsatellites may be useful in circumventing amplification artefacts and null alleles, so limiting departures from the Hardy–Weinberg equilibrium. Furthermore, transcribed loci such as the pantophysin gene could be very informative in detecting population structure, as observed for cod (Fevolden and Pogson, 1997; Jónsdóttir et al., 2002).

Despite the generally low level of differentiation observed among pairwise population comparisons, partitioning of the genetic diversity with SAMOVA and PCA produced congruent results and suggested the existence of four units that may be at least partially differentiated: (i) Bay of Biscay, (ii) Iroise Sea, (iii) western English Channel, and (iv) southern Norwegian coast. However, the status of the Iroise Sea and the southern Norwegian coast samples should be considered with caution because of their significant departure from Hardy–Weinberg equilibrium.

The weak genetic structure between pollack from the Bay of Biscay and from the western English Channel may be associated with the presence of the Ushant front between stratified Atlantic waters and well-mixed western Channel waters (Pingree et al., 1978). This thermal front appears with increasing temperature in spring and lasts until the autumnal temperature decrease, and may hamper larval dispersal between the two areas, so furthering genetic differentiation. Further, such a limited dispersal of larvae across the Ushant front should be associated also with a limited mixing of adults between spawning units to maintain restricted gene flow between populations. Therefore, the weak genetic differentiation depicted between samples from the western English Channel and the Bay of Biscay may result from the combination of hydrological factors and ecological/behavioural traits of pollack.

The pattern of population structure revealed in this small study needs further investigation; notably, a wider range of locations and the interannual stability should be tested. Moreover, more data are necessary to evaluate the possibility of homing behaviour in pollack, and to test the hypotheses of natal homing (Iles and Sinclair, 1982) and learned homing (McQuinn, 1997). Knowledge of the genetic structure and dispersal patterns among marine populations constitutes a crucial step in setting up effective management boundaries and in designing marine protected area (MPA) networks (Botsford et al., 2003).


   Acknowledgements
 
We thank R. Floch, V. Howden, J. Léost, G. Morandeau, E. Oudin, A. Rivier, and G. Stéphan for helping to collect our samples. We also thank F. Bonhomme and the GPIA laboratory (UPR CNRS 9060) for technical help, and P.F. Straub and J. Marchand for useful corrections. Finally, we are grateful to S. Coombs for his assistance, and to two anonymous reviewers for their comments and suggestions on how to improve the manuscript. The research was supported by the Région Bretagne.


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Top
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 Material and methods
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 Discussion
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