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ICES Journal of Marine Science: Journal du Conseil Advance Access originally published online on January 10, 2008
ICES Journal of Marine Science: Journal du Conseil 2008 65(2):216-225; doi:10.1093/icesjms/fsm187
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© 2008 International Council for the Exploration of the Sea. Published by Oxford Journals. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Evidence for population structuring of blue whiting (Micromesistius poutassou) in the Northeast Atlantic

Anna Was1,2, Elizabeth Gosling1,*, Karen McCrann1 and Jarle Mork3

1 Molecular Ecology Research Group, Department of Life Sciences, Galway-Mayo Institute of Technology, Dublin Road, Galway, Ireland
2 Sea Fisheries Institute Gdynia, Kollataja 1, 81-332 Gdynia, Poland
3 Norwegian University of Science and Technology, Trondhjem Biological Station, N-7493 Trondheim, Norway

* Correspondence to E. Gosling: tel: +353 91 742324; fax: +353 91 758412; e-mail: elizabeth.gosling{at}gmit.ie

Was, A., Gosling, E., McCrann, K., and Mork, J. 2008. Evidence for population structuring of blue whiting (Micromesistius poutassou) in the Northeast Atlantic. – ICES Journal of Marine Science, 65: 216–225.

Many marine fish species are characterized by large population sizes, strong migratory behaviour, high fecundity, and pelagic eggs and larvae that are subject to passive transport by ocean currents, all factors that tend to reduce the rate of development of genetic partitioning among localized populations. The blue whiting (Micromesistius poutassou) is a commercially important gadoid that exhibits all these characteristics, although to date there has been little evidence of genetic heterogeneity except at the latitudinal extremes of its range in the NE Atlantic. Genetic variation was analysed at five microsatellite loci in 16 samples, 14 comprising spawning adults, collected along the continental shelf from 44°N to 60°N, a distance of ~1900 km. Although pairwise FST values were low (0.0–0.040; mean 0.0097), more than 40% of the estimates were significant, with Celtic Sea and Bay of Biscay samples significantly differentiated from samples from the Porcupine Bank, Hebridean Shelf, Sulisker Bank, and Papa Bank. There was also significant differentiation between samples taken in different years on Rockall Bank. Mantel tests revealed no significant isolation by distance. We used a landscape genetics approach, which combines spatial and genetic information, to detect barriers to gene flow. Four zones of lowered gene flow were identified, generally in concordance with hydrographic patterns, fish spawning behaviour, and the simulated transport of larvae in the NE Atlantic Ocean.

Keywords: blue whiting, gene flow, genetic differentiation, landscape genetics, Micromesistius poutassou, microsatellites

Received 5 July 2007; accepted 25 November 2007; advance access publication 10 January 2008.


   Introduction
Top
 Introduction
Material and methods
 Results
 Discussion
 References
 
Many marine fish species are characterized by large population sizes, strong migratory behaviour, high fecundity, and pelagic eggs and larvae that are subject to passive transport by ocean currents, all factors that tend to reduce the rate of development of genetic partitioning among localized populations (Wirgin and Waldman, 2005). For there to be genetic divergence, gene flow must be minimal among populations. In the marine environment, with its apparent lack of physical barriers, it is generally not obvious how genetic differentiation might take place. Consequently, many marine species exhibit weak genetic differentiation with very low FST values, typically <0.01, compared with fresh-water or anadromous species with their strong homing behaviour (Ward et al., 1994). Nevertheless, in many cases, there is discrete subpopulation structure in marine species. Factors that can contribute to the differentiation process are timing, duration, and location of spawning (Begg, 2005), salinity and temperature discontinuities between water bodies (Nielsen et al., 2004; Jørgensen et al., 2005), oceanographic gyres favouring larval retention close to spawning sites (Iles and Sinclair, 1982), or local adaptation (Guinand et al., 2004), and natal homing (herring: McQuinn, 1997; mackerel: Nesbø et al., 2000).

Information on population structure is an essential prerequisite for the conservation and rational management of marine fisheries (Carvalho and Hauser, 1994; Ward and Grewe, 1994). Although many different classes of molecular markers have been used to detect genetic structure in marine fish (Cadrin et al., 2005), highly polymorphic microsatellite loci have proved to be the most informative in uncovering structure that was not apparent using less variable markers (Wenburg et al., 1998; Ruzzante et al., 1999; Shaw et al., 1999; Lundy et al., 2000; Lage et al., 2001; Mattiangeli et al., 2002; Knutsen et al., 2003; Taylor et al., 2003; Carlsson et al., 2004; Jørgensen et al., 2005). Consequently, microsatellites are the tool of choice in genetic stock identification.

The blue whiting (Micromesistius poutassou) is a pelagic species that is very important to Irish and international fleets. Landings in the NE Atlantic over the past 6 years have more than doubled, with a record catch of 2.3 million tonnes in 2003 (ICES, 2004). Most of the catch is reduced to fishmeal, although there have been recent efforts to increase the amount of the catch suitable for human consumption. The stock is classified as having full reproductive capacity, but the current exploitation rate is not sustainable (ICES, 2004).

The geographic distribution of the species extends along the continental shelf in the NE Atlantic from the Canary Islands to Spitzbergen (26°N–82°N), with smaller populations in the NW Atlantic and the Mediterranean Sea (Zilanov, 1968, 1980; Bailey, 1982). Adults reach maturation at 2–7 years old, and undertake long annual migrations from feeding grounds to spawning grounds, and back again (Bailey, 1982). Between March and April each year, most NE Atlantic blue whiting aggregate to spawn in the region around the Porcupine Bank, west of Ireland and Scotland (Monstad et al., 1995). Spawning takes place at depths of ~200–400 m and, by the time the hatched larvae are >5 mm, they are concentrated in the upper 60 m of the water column (Coombs et al., 1981). Most larvae drift north towards feeding grounds in the Norwegian Sea (Monstad, 1990), and spent fish return north in May and June (Bailey, 1982). Larvae move south in much smaller numbers to feeding areas in the Bay of Biscay (Carrera et al., 2001), an area considered to be a nursery ground, fish from the region, and from resident populations to the west and southwest of Ireland, contributing to the Porcupine Bank spawning aggregation (Bailey, 1982). The extent of mixing on the spawning grounds is unknown, but morphometric and meristic data, and information on parasite load suggest that there are two stocks within the main spawning component, a northern stock that spawns north of the Porcupine Bank, and a southern one that spawns to the south of the Bank and along the continental slope (Isaev and Seliverstov, 1991).

The first genetic study on blue whiting was carried out by Mork and Giaever (1995), who used two polymorphic allozyme loci, PGM* and IDHP-2*, to compare 65 samples from the eastern Atlantic Ocean and Mediterranean Sea. Samples from the Barents Sea were genetically distinct from all other samples, a finding later corroborated by Giaever and Stien (1998). More recently, Ryan et al. (2005), using one minisatellite and five microsatellite loci, analysed 11 samples of blue whiting from the Barents Sea, the Northeast Atlantic, and Mediterranean Sea. Their results indicated genetically differentiated populations at the latitudinal extremes of the range (Barents Sea and Mediterranean). In addition, they found evidence of temporal variation between samples collected in 1992 and 1998 from the Hebridean Shelf. ICES treats blue whiting in the NE Atlantic as a single stock, because it has so far proven impossible to define an unambiguous border between populations (ICES, 2004).

The aim of the present study was to focus sampling effort on the main spawning area of blue whiting in the Northeast Atlantic, and to analyse spatial and temporal genetic structure of spawning adults collected in 2003, 2004, and 2005 during their peak spawning period. We used three of the most polymorphic microsatellite loci developed for blue whiting, plus two loci isolated from walleye pollock (Theragra chalcogramma) and one from whiting (Merlangius merlangus) that cross-amplify with blue whiting. Four of these loci were the same as used by Ryan et al. (2005). We explored spatial genetic patterns in our data, using a landscape genetics approach (Manel et al., 2003), an amalgamation of molecular population genetics and landscape ecology that has only recently been applied to marine organisms (Jørgenson et al., 2005; Kenchington et al., 2006).


   Material and methods
Top
 Introduction
 Material and methods
Results
 Discussion
 References
 
Sample collections
In all, 15 samples of blue whiting (~50 fish per location) were collected using a pelagic trawl along the continental shelf of the NE Atlantic (51–60°N 4–17°W; total straight-line distance 1200 km) during the spring spawning period March–April 2003, 2004, and 2005 (Figure 1, Table 1). We focused sampling on mature fish, to reduce the potential risk of sampling genetically mixed populations. An additional sample (16BB) was collected from the Bay of Biscay (44°N 11°W) in December 2004, outside the spring spawning period. Maturity staging (Borge et al., 2002) and age estimation based on otoliths (Power et al., 2006) was carried out onboard the fishing vessel. Age analysis was performed to test whether temporal samples were composed proportionally of the same fish, i.e. that they are not made up of different cohorts in different years. Fish older than 2 years and at gonad stages 5 (maturing/mature), 6 (spawning/running), and 7 (spent) were considered as mature fish, those younger than this or at gonad stages 1–4 being classified as immature.


Figure 1
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  		Figure 1. Sampling locations for spawning periods March–April 2003 (circles), 2004 (triangles), and 2005 (squares). PpB, Papa Bank; SB, Sulisker Bank; HS, Hebridean Shelf; RB, Rockall Bank; PB, Porcupine Bank; CS, Celtic Sea; BB, Bay of Biscay. The depth contours for 100, 200, 500, and 1000 m are indicated together with the main oceanic currents and gyres (arrows).

 


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  		Table 1. Details of blue whiting (Micromesistius poutassou) samples.

 
Microsatellite analysis
The standard phenol–chloroform method (Sambrook et al., 1989) was used to extract DNA from frozen muscle tissue, or from otoliths (sample 16BB), and DNA was then diluted to a concentration of 30–100 ng µl–1. We used three of the most polymorphic microsatellite loci (MpouBW7, MpouBW8, and MpouBW13) developed for blue whiting (Moran et al., 1999), plus two loci isolated from walleye pollock (Tch6 and Tch10; O’Reilly et al., 2000) and one (MmerUEAW01) from whiting (Rico et al., 1997) that cross-amplify with blue whiting. The six loci were amplified in 10 µl volumes containing 1 µl DNA, x10 reaction buffer A (Promega), 0.5 U Taq DNA polymerase (Promega), 0.2 mM each of dNTP and 2–2.5 mM MgCl2. Primer concentration varied from 0.15 µM for loci MpouBW7, MpouBW8, and MpouBW13 to 0.2 µM for loci Tch6, Tch610, and MmerUEAW01. One primer of each pair was end-labelled with fluorescent dye (700IRD or 800IRD Li-COR). The thermocycling regime consisted of an initial denaturation step at 95°C for 3 min, followed by 30 cycles of denaturating at 95°C for 30 s, annealing at 53°C (Tch6, Tch10, and MmerUEAW01), or 55°C (MpouBW7, MpouBW8, and MpouBW13) for 30 s, extension at 72°C for 30 s, and a final extension at 72°C for 5 min.

Amplification products were separated on 6% polyacrylamide gels using a Li-COR 4300 automated sequencer. PCR samples were diluted 1:10–1:30 with distilled water, and mixed 1:1 with a loading buffer (formamide and bromophenol blue). Allele size was calibrated using a molecular weight ladder (Li-COR). To ensure reliability of allele scoring, three reference samples were run on each gel.

Data analysis
Allelic distribution, observed (HO) and unbiased expected (HE) heterozygosity estimates (Nei, 1978) for 16 populations were computed for each locus individually, and as a multilocus estimate using GENETIX 4.01 (Belkhir et al., 2000). Conformance to Hardy–Weinberg equilibrium (HWE) was assessed using the procedure of Guo and Thompson (1992) in GENEPOP 3.4 (Raymond and Rousset, 1995), with specified Markov chain parameters of 10 000 dememorization steps, followed by 100 batches of 5000 iterations per batch for all loci and populations. Tests for linkage disequilibrium between pairs of loci were also performed using the same program. Single and multilocus FIS (indicating heterozygote deficiency/excess) were estimated (Weir and Cockerham, 1984). Population differentiation was analysed using global and pairwise FST (Weir and Cockerham, 1984), and Fisher’s exact tests of genotypic and genic distributions (Raymond and Rousset, 1995) between pairs of populations (single and multilocus). The significance of all FIS and FST estimates was tested with 45 000 permutations using FSTAT 2.9.3.2 [EC] (Goudet, 2001). To correct for multiple pairwise comparisons, the False Discovery Rate procedure of Benjamini and Yekutieli (2001) was applied. This method accommodates large numbers of potentially dependent tests while balancing risks of Type I and Type II error, and is a good alternative to the very conservative Bonferroni correction (Rice, 1989), which is effective in reducing Type I, but not Type II errors (Narum, 2006).

Where observed genotype frequencies deviated significantly from HWE expectations, the method of Brookfield (1996), as implemented in MICRO-CHECKER 2.2.0 (Van Oosterhout et al., 2004), was used to estimate null allele frequencies.

Isolation by distance, the correlation between genetic and geographic distance, was tested by regressing FST/1–FST against the natural logarithm of the geographic (shortest waterway) distance between pairs of samples (Rousset, 1997), and the significance of the association was estimated using a Mantel test with 1000 permutations, as implemented in GENEPOP.

The program BARRIER 2.2 (Manni et al., 2004) was used to present a graphic representation of genetic discontinuity between samples. The geographical coordinates for each sample were connected by Delauney triangulation, resulting in a network connecting all samples. Genetic distances (we used FST estimates) between neighbouring samples were calculated, and Monmonier’s maximum distance algorithm was used to identify barriers. Starting at the edge of the network with the largest FST value between two samples, a boundary line was drawn perpendicular to the edges of the network to the next largest FST value. This step was repeated until the growing boundary met another boundary, or reached the edge of the network (Manel et al., 2003; Manni et al., 2004). The analysis was performed for each locus separately, and for the total locus set. The user sets the number of barriers to be identified, and we decided to use only barriers that were supported by significant FST estimates (p < 0.0093; Benjamini and Yekutieli, 2001).


   Results
Top
 Introduction
 Material and methods
 Results
Discussion
 References
 
Genetic diversity and HWE
A total of 755 fish was analysed from 16 locations. Owing to a large number of alleles (59, many of them rare), and the presence of null alleles indicated by MICRO-CHECKER (frequency 0.14–0.40), the Tch6 locus was omitted from further analysis. The remaining five microsatellite loci were highly polymorphic, with 16 (MpouBW7) to 21 alleles per locus, and a similar level of polymorphism across samples (Table 2). The mean number of alleles per locus ranged from 9.3 to 15.0. In each sample, the frequency of the most common allele was no higher than 0.5, except for locus Tch10, for which the frequency ranged from 0.011 to 0.72. Of the 96 alleles detected at the five loci, 14 were private, with frequencies no higher than 0.02. Four of the five loci had similar high levels of gene diversity (0.76–0.89), but Tch10 had a lower and variable gene diversity level across samples, ranging from 0.48 to 0.79 (Table 2). Mean multilocus HO was similar across all samples (0.70–0.82; Table 2). Significant departures (heterozygote deficits) from HWE were observed for all loci and in all samples; 16/80 tests and 4/80 tests were significant at p < 0.05 and p < 0.01, respectively. Multilocus FIS estimates were significant in just two cases (Table 2).


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  		Table 2. Summary of genetic variation in blue whiting at five microsatelite loci at 16 locations.

 
MICRO-CHECKER analysis indicated that heterozygote deficits were likely attributable to the presence of null alleles. The second method of Brookfield (1996), as implemented in MICRO-CHECKER, was used to calculate the expected frequency of null alleles per locus, which ranged from 0.12 to 0.40. None of the loci showed evidence of upper allele dropout and/or stuttering during PCR amplification.

Genotype data were also tested for linkage disequilibrium, but no significant disequilibrium was detected for any locus pair in any of the populations after correction using the B–Y (Benjamini and Yekutieli, 2001) method.

Intra-population differentiation
Fish were divided into immature and mature groups from samples exhibiting different proportions of each category 9PB(03), 3RB(03), 6RB(03): ~50% mature fish in each, 12PB(03): 80%, 14CS(03): 28% (Table 1), and exact tests of genic and genotypic proportions were performed for each locus in each population and for combined loci in each population. No significant difference in either genic or genotypic proportion was observed between immature and mature fish.

Interpopulation differentiation
Results from pairwise multilocus FST estimates and exact tests of genic and genotypic proportions for five loci after MICRO-CHECKER correction indicated that the Rockall Bank sample 3RB(03) was genetically differentiated from all other samples, except three samples collected in 2003, 6RB(03), 9PB(03), and 1PpB(03). FST estimates indicated that Rockall Bank 4RB(04), 5RB(05), and 6RB (03) were genetically similar to each other and to all other samples apart from those farther south in the Celtic Sea and Bay of Biscay, in addition to 9PB(03) and 2SB(03) in the case of 5RB(05). Exact tests showed that these three samples also differed significantly from 2SB.

The Celtic Sea sample 15CS(04) was differentiated from all other samples, including 14CS(03). The latter, located on the western edge of the Celtic Sea area, was also differentiated from all but two samples from the Porcupine Bank, 11PB(05) and 12PB(03), and one from the Hebridean Shelf, 7HS (04), but results from exact tests showed both Celtic Sea samples to be significantly different from all other samples in this study (Table 3).


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  		Table 3. Probability values for Fisher’s exact tests of genic differentiation (below the diagonal) and pairwise FST values (above the diagonal; Weir and Cockerham, 1984) for five loci.

 
Samples located along the continental shelf, excluding the Celtic Sea samples, appear to constitute a genetically homogenous group. However, the Bay of Biscay sample, although significantly different from the majority of samples, did not differ from some of the Porcupine Bank, Celtic Sea, or more geographically distant samples [11PB(05), 12PB(03), 13PB(03), 14CS (03) 1PpB, 2SB], although exact tests showed that 16BB was significantly differentiated from all but the 2SB sample (Table 3).

Using pairwise FST values in BARRIER, four areas of restricted gene flow were identified based on combined data from the five loci (Figure 2). The first barrier separated the Bay of Biscay sample 16BB from the other samples. The second barrier separated the Celtic Sea sample 15CS(04), and the third barrier isolated 14CS(03) from the rest. The fourth barrier separated one of the Rockall Bank samples 3RB(03) from the rest of the samples on Rockall Bank and along the continental shelf. All barriers were supported by significant multilocus FST estimates using the B–Y method.


Figure 2
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  		Figure 2. Areas of restricted gene flow identified by BARRIER using the Monmonier algorithm. Thin lines, Delaunay triangulation; thick lines (I, II, III, and IV), main barriers to gene flow.

 
Regression of pairwise FST/(1–FST) values against ln geographical distance for five loci showed no significant correlation (p = 0.07). Therefore, there was no support for an isolation-by-distance model of gene flow, where individuals mate at random within a defined area but are constrained from mating with other more distant members through limitations on their dispersal (Sokal and Wartenberg, 1983).


   Discussion
Top
 Introduction
 Material and methods
 Results
 Discussion
References
 
Genetic diversity
Four of the loci analysed in our study (MpouBW7, MpouBW8, MpouBW13, MmerUEAW01) were also used in an earlier study of blue whiting in the NE Atlantic by Ryan et al. (2005). We also sampled three areas, Porcupine Bank, Hebridean Shelf, and Celtic Sea, but not the exact locations that were sampled by Ryan et al. (2005). Unfortunately, it was not possible to compare our results with theirs directly, because allele sizes were not given in their paper nor were we in a position to do a cross-calibration exercise. For the loci common to the two studies, numbers of alleles were approximately the same for three of the four loci. However, for MpouBW8, we observed only 21 alleles compared with their 31, most of the additional alleles (not present in our study) occurring in their Porcupine Bank and Hebridean Shelf 1992 samples. Identical values of multilocus HO (0.81) were observed in the two studies.

Departures from HWE
A number of significant departures from HWE was observed, caused by a deficit of heterozygotes, but these were spread across loci in the different samples (Table 2). This phenomenon is well documented in marine fish (O'Connell and Wright, 1997; Karlsson and Mork, 2005). Although factors such as inbreeding, the Wahlund effect, or selection can explain such deficits, they are very often an artefact of the PCR amplification process. For example, deficits can arise when alleles (referred to as null alleles) fail to amplify because of base substitutions or deletions in PCR priming sites flanking microsatellite arrays. Null alleles are a common problem with microsatellite loci, and can lead to high, observed deficits of heterozygotes (Dakin and Avise, 2004). Deficits may also be due to preferential amplification of small alleles (Wattier et al., 1998), or slippage during PCR amplification (Shinde et al., 2003). MICRO-CHECKER tested for these three scenarios and found that 8 of the 16 significant departures from HWE were attributable to null alleles. Ryan et al. (2005) also found evidence of null alleles at four of their loci, three of which are common to our study, and for two of these (MpouBW9 and MpouBW13), null alleles were indicated in most of their samples. However, null alleles at MpouBW13 were only indicated in one of our samples, and were not detected at all using the same locus in samples of pollack (Pollachius pollachius; Charrier et al., 2006) or whiting (Charrier et al., 2007) from the NE Atlantic.

Population differentiation
FST values for multilocus pairwise comparisons were low (range 0.0–0.040; mean 0.0097), but consistent with estimates for other gadoids (cod, Gadus morhua: Bentzen et al., 1996; Ruzzante et al., 2000; hake, Merluccius merluccius: Lundy et al., 2000; walleye pollock: Olsen et al., 2002; pollack: Charrier et al., 2006; whiting: Charrier et al., 2007). Despite the low values of FST, 49 of the 120 comparisons (41%) in our study were significant. A greater number of significant values were obtained from exact tests of genic (69/120; 58%) proportions, reflecting the enhanced power of exact tests to detect significant population structuring (Balloux and Lugon-Moulin, 2002). We do not know why our values are somewhat higher than those reported by Ryan et al. (2005) for blue whiting in the NE Atlantic (0.0–0.012). One reason could be that the Tch10 locus (FST estimates 0.0–0.13; mean 0.024), which Ryan et al. (2005) did not use, contributed substantially to our multilocus FST value. Moreover, MpouBW13 (FST 0.0–0.075; mean 0.009), for which Ryan et al. (2005) reported null alleles, showed no evidence of null alleles in our study, or indeed in the two studies of Charrier et al. (2006, 2007), and was an important locus for showing genetic differentiation, particularly between 14CS, 15CS, and 16BB samples.

We used a novel method to analyse spatial genetic variation. This approach, landscape genetics, requires two key steps: detection of genetic discontinuities and the correlation of such discontinuities with geographical and environmental features, e.g. salinity, temperature, ocean currents (Manel et al., 2003; Guillot et al., 2005). BARRIER identified four zones of lowered gene flow (Figure 2): barrier I isolated the Bay of Biscay sample from the rest of the samples, barriers II and III isolated the 15CS(04) and 14CS(03), respectively, from the rest, and barrier IV separated Rockall Bank sample 3RB(03) from other samples on Rockall Bank and along the continental shelf.

These barriers may be barriers to dispersal that are determined by current circulation in the NE Atlantic. The North Atlantic Current, originating from the Gulf Stream, comes from the west and meets the continental slope to the west of Ireland in the region of the Porcupine Bank. There, it splits into two major branches, one flowing north towards the Norwegian Sea, and one flowing south towards the Bay of Biscay. Larval drift is an important factor in recruitment, and several studies have used numerical circulation and transport models to simulate dispersion of blue whiting larvae in the eastern North Atlantic (Svendsen et al., 1996; Bartsch and Coombs, 1997; Skogen et al., 1999). In the last study, simulations run over a period of 137 days at a fixed depth of 30 m found that eggs and larvae of blue whiting that were deposited north of the Porcupine Bank, followed a northerly drift trajectory, whereas those deposited south of that area drifted farther south. On the basis of the modelled drift patterns for 20 years (1976–1995), Skogen et al. (1999) found a separation "line" at 54.5°N between north- and south-moving larvae, which they suggested might be the dividing line between a northern and a southern stock, with the northern component spawning north of the Porcupine Bank and the southern component to the south. Over the 20 years examined, the line separating north- and south-moving particles varied by as much as 200 km, indicating possible mixing between putative stocks (Skogen et al., 1999). None of the models incorporated behavioural characteristics of the post-larvae, e.g. shoaling and vertical migration, factors that lessen the influence of passive dispersal.

The combined information from hydrography and particle tracking led us to expect that eggs and larvae released near the Porcupine Bank spawning site would follow a northerly drift trajectory towards the Norwegian Sea. If adults tend to return to the same general area that they inhabited as larvae, it would be expected that samples collected along this route would not be significantly differentiated. This agrees with the absence of genetic structure reported for other gadoids sampled along the western margins of the British Isles (Lundy et al., 2000; Charrier et al., 2006, 2007). What was unexpected was the significant differentiation between one of the Rockall Bank samples 3RB(03) and most of the samples in our study. We are not convinced that this is evidence of true genetic structuring on Rockall Bank, because the other three samples on the Bank were genetically similar and similar also to all other samples collected on the Porcupine Bank, Hebridean Shelf, and Sulisker and Papa Banks. Instead, we believe that the significant differentiation is due to temporal variability in sample composition, supported by the absence of differentiation from 6RB(03), collected in the same year. Interannual differentiation can be due to variance in reproductive success (Ruzzante et al., 1996) or survival (Cushing, 1990), or movement of fish from different spawning assemblages into and out of the sampling area (Lundy et al., 2000). Lack of data preclude us testing the first two, and genetic homogeneity of samples in the region, accompanied by an absence of significant deficits of heterozygotes (indicative of a Wahlund effect), argues against the last possibility. Also, Iceland is an unlikely source of spawning adults, because blue whiting off Iceland are not genetically different from Porcupine Bank or Hebridean Shelf populations (Ryan et al., 2005). Therefore, the reason(s) for temporal variation remain inconclusive, which highlights the need for more rigorous sampling to clarify population structure on a temporal scale on the Rockall Bank. In contrast to Rockall Bank, there was no evidence of interannual variation between Hebridean Shelf (n = 2) or Porcupine Bank (n = 4) samples. Ryan et al. (2005) reported significant heterogeneity between Hebridean Shelf samples collected in 1992 and 1998, but were unclear as to the cause.

Again, considering current movement and results from particle-tracking studies, we expected that samples along the south-flowing current from the Porcupine Bank would be genetically differentiated from those in the northward flow. This was indeed the case, because the Celtic Sea and Bay of Biscay samples were genetically differentiated from most of the samples collected along the northerly flow. Charrier et al. (2007) also reported significant differentiation between Bay of Biscay whiting and samples collected farther north. In addition, however, we observed significant genetic heterogeneity between the two Celtic Sea samples, and between each of these and the Bay of Biscay sample (Table 3, Figure 2). The latter sample contained only immature blue whiting 1–2 years old, and several authors have previously found that most blue whiting caught in the Bay of Biscay throughout the year are <25 cm and mainly immature (Bailey, 1982, and references therein). It is generally assumed that juveniles move from the Bay of Biscay nursery area to areas farther south, but as yet this has been neither confirmed nor refuted. Results from a recent analysis of larval core width increment patterns in fish from the same 16BB sample (Brophy and King, 2007) indicated that Bay of Biscay fish move to the southern edge of the Porcupine Bank region to spawn, and contribute little if any to aggregations in the middle and northern parts of the spawning area.

Regarding the Celtic Sea, concentrations of large blue whiting (30–33 cm) were reported in the area from August 1963 to January 1964, prompting Bailey (1982) to suggest the possibility of a self-contained stock in the Celtic Sea. Although this may explain the genetic discontinuity between the Celtic Sea and Bay of Biscay samples, it does not explain the significant differentiation between the two Celtic Sea samples. Examination of Table 1 indicates that the samples contain more or less the same proportions of fish, assuming that that 1-year-old fish in 14CS (03) will be 2-year-olds in 15CS(04). Our two samples were collected from approximately the same locations as the two 1997 Celtic Sea samples in Ryan et al. (2005). Those authors found no evidence that their Celtic Sea samples were genetically different from their Hebridean Shelf and Porcupine Bank samples, although if they had used a less conservative correction than Bonferroni, this would not have been the case. Sample 14CS, situated closer to the Porcupine Bank than 15CS, was not significantly differentiated (FST estimates) from some of the Porcupine and Hebridean Shelf samples, which might suggest that 14CS included some mature fish from those areas. A significant positive multilocus FIS value, one of the only two significant estimates observed in this study, would lend support to this suggestion (Table 2). In contrast, 15CS was significantly differentiated from all other samples in this study. Whatever the reason for interannual variation within the Celtic Sea, there is strong evidence of significant genetic structuring between the Celtic Sea and the other regions sampled in this study. Overall, it is likely that ocean currents are not the only mechanism driving genetic structuring in blue whiting.

In conclusion, we observed significant population structuring in NE Atlantic blue whiting, despite the potential for high gene flow through larval drift, high fecundity, presumably large effective population sizes, and the propensity of the species to undertake extensive migrations for feeding and spawning. At present, blue whiting in the NE Atlantic are assessed as a single stock (ICES, 2004). However, growing evidence, from this study and others (Giaever and Stien, 1998; Ryan et al., 2005; Brophy and King, 2007), suggests that such an assumption is no longer tenable. Treating the blue whiting fishery as a single management unit could introduce bias into assessment, resulting in inaccurate estimates of fishing effort, fishing mortality, and spawning–stock biomass (Bailey, 1997), and could lead to the possible elimination of some subpopulations (Carvalho and Hauser, 1994).


   Acknowledgements
 
We thank the staff and crew of the RV "Johan Hjort" Norwegian Blue Whiting Acoustic Survey and the BO "Cornide de Saavedra", for blue whiting samples, Gavin Power and Stephano Mariani for useful discussion, and Deirdre Brophy for otolith samples from the Bay of Biscay. Funding was provided by the Department of Education, Ireland, under the Technological Sector Research Programme: Strand III Core Research Strengths Enhancement (2004–2007).


   References
Top
 Introduction
 Material and methods
 Results
 Discussion
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