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Lack of spatial genetic variation in the edible crab (Cancer pagurus) in the Kattegat – Skagerrak area Anette Ungfors, Niall J. McKeown, Paul W. Shaw, and Carl Andre´ Ungfors, A., McKeown, N. J., Shaw, P. W., and Andre´, C. 2009. Lack of spatial genetic variation in the edible crab (Cancer pagurus) in the Kattegat– Skagerrak area. – ICES Journal of Marine Science, 66: 462 – 469.
The stock structure of the edible crab (Cancer pagurus L.) in the Kattegat and Skagerrak was investigated using eight microsatellite DNA loci. Replicate samples, collected 4 – 6 years apart, were derived from the Kattegat (Grove Bank, 578N) and the Skagerrak (Lunneviken, 598N), plus a geographical outgroup sample from the Norwegian Sea (Midsund, 628N). Genetic differentiation among samples, estimated as global FST ¼ 0.002, was significant (p ¼ 0.03) when the statistical test was based on allele frequencies, but not when based on genotype frequencies. Moreover, all single- and multilocus pairwise tests between samples were non-significant. An analysis of molecular variance, AMOVA, did not reveal significant differentiation between spatial (Kattegat vs. Skagerrak) or temporal (2001/2002 vs. 2006/2007) groups of samples. Power analysis suggested that the loci and sample sizes employed conferred a power of .90% of detecting even low (true FST ¼ 0.002) levels of population structure. Low spatial and temporal genetic structure might be explained by either or both of (i) high levels of contemporary gene flow in the area attributable to adult migration or larval dispersal or both factors taken together, and (ii) patterns of historical gene flow persisting among recently founded large populations. Keywords: edible crab, FST, gene flow, genetic differentiation, genetic stock structure, management, microsatellite DNA. Received 4 June 2008; accepted 7 October 2008; advance access publication 18 January 2009. Anette Ungfors and Carl Andre´: Department of Marine Ecology – Tja¨rno¨, University of Gothenburg SE-452, 96 Stro¨mstad, Sweden. N. J. McKeown and P. W. Shaw: School of Biological Sciences, Royal Holloway College, University of London, Egham, Surrey TW20 0EX, UK. Correspondence to A. Ungfors: tel: þ46 526 686 88; fax: þ46 526 686 07; e-mail:
[email protected].
Introduction A prerequisite for the management of commercially exploited fish and shellfish resources is to define how the resource is partitioned spatially (geographically) and temporally, i.e. to identify stock units. As with species and population concepts, there is no universally accepted definition of what constitutes a stock, and there has been a shift towards an adaptive holistic approach. All attempts at stock definition struggle with optimizing the balance between precision and generality, and common words used in definitions are “self-sustaining”, “integrity/sharing”, “spatial/area”, and “temporal/time” (Cadrin et al., 2005). In contrast to species or population concepts, there is an underlying need for applicability to management in the stock definition. The need for conservation of biodiversity or genetic variation within endangered species has resulted in a similar concept: evolutionary significant unit (ESU; reviewed in Fraser and Bernatchez, 2001). However, a basic difference between the management of stocks and ESUs is the aim of maximizing the sustainable yield of the former unit against the evolutionary perspective of the latter. Different methods to investigate the stock unit have been suggested, all of which have strengths and weaknesses, and often reflect the definition chosen by the investigator. The advantage of using a holistic approach is that combining multiple techniques may compensate for weakness in individual methods. For example, morphological or phenotypic markers can be affected by environmental modulation, so separate stocks may be indistinguishable because of similar selection effects (Chaceon quinquedens in the Gulf of Mexico vs. New England;
Weinberg et al., 2003), whereas directional selection may lead to false estimates of stock heterogeneity (e.g. Clupea harengus; Kinsey et al., 1994). Genetic markers are powerful tools for describing population/ stock structure (Utter, 1991; Carvalho and Pitcher, 1995). A major contribution of population genetics to fisheries management has been to define the concept of “stock” in an evolutionarily meaningful way and to promote the use of this concept in management (Ihssen et al., 1981; Allendorf et al., 1987; Carvalho and Hauser, 1994). The extent of gene flow among populations, mediated by the forces of genetic drift and mutation, determines the patterns of variation at selectively neutral genetic loci. Over short timescales, the effect of mutation is expected to be negligible, with genetic population structure being the product of opposing forces of gene flow and genetic drift. Therefore, by characterizing the distribution of genetic variation, population substructuring can be detected and the degree of connectivity among populations estimated (Nesbo et al., 2000; Ruzzante et al., 2000; Hutchinson et al., 2001). In general, marine species are expected to show little population structure over large geographic areas because of the fewer barriers to dispersal via larval drift or adult movements compared with the situation on land (Ward et al., 1994). However, genetic analyses of commercial decapods have revealed complex patterns of genetic differentiation at various geographic scales, even over short distances of 40 –225 km (Weber and Levy, 2000; Weber et al., 2000; Jørstad et al., 2004; Weetman et al., 2007), suggesting
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Lack of spatial genetic variation in the edible crab in the Kattegat–Skagerrak Table 1. Site and date of sampling, sex ratio (number of females F: males M), and size distribution (sex-specific carapace width; mean, standard deviation, and range) in five samples of edible crab, Cancer pagurus. Parameter Sampling area Coordinates Date Sample size Females:males Carapace width (mm) F M
Grove Bank GR01 Grove Bank GR07 Kattegat Kattegat 578060 2100 N 118300 7300 E 26 September 2001 4 July 2007 70 70 66:4 52:18
Lunneviken LU02 Lunneviken LU06 Skagerrak Skagerrak 598030 5000 N 118100 0000 E 27 August 2002 September/October 2006 70 70 51:19 63:7
West Norway NO04 Norwegian Sea 628400 3300 N 068390 7700 E 4 December 2004 69 34:35
162 + 11, 138–186 154 + 17, 131–172
152 + 16, 122 – 183 168 + 16, 126 – 187
152 + 11, 131 –180 154 + 18, 122 –197
168 + 14, 131 –193 172 + 12, 160 –207
that dispersal potential may not be realized. For example, mtDNA analysis of European lobster, Homarus gammarus, revealed significant structuring (FST ¼ 0.078) and distinct genetic clusters across its European distribution range (Triantafyllidis et al., 2005). Similarly, significant differentiation (FST: 0.013 – 0.018) was revealed among samples of Norway lobster, Nephrops norvegicus, from the North Sea, Irish Sea, Portugal, and the Mediterranean (Stamatis et al., 2006, for allozymes; Stamatis et al., 2004, for mtDNA RFLP). Enhanced settling of shrimp larvae within 0 –6 m of their parents, despite a 1–2-week larval dispersal period, indicates one mechanism by which dispersal potential may not be realized (Knowlton and Keller, 1986). Therefore, the genetic structure of marine species is determined by the complex interaction of many factors, including adult mating and prespawning behaviour, larval development time and behaviour, oceanography, and the latter’s seasonal and annual variation (Pringle and Wares, 2007), and must be empirically examined to inform management. The edible crab, Cancer pagurus, is widely distributed in the eastern Atlantic Ocean from northern Norway to northwest Africa (Christiansen, 1969). The total landings in 2004 were 46 280 t, mainly from the UK, Ireland, and France, although landings from more northern areas such as Norway have increased recently (FAO, 2004). Tagging studies from the English Channel (Bennett and Brown, 1983), the Bay of Biscay (Latrouite and Le Foll, 1989), the UK coast of the North Sea (Edwards, 1979), and the Skagerrak and Kattegat (Ungfors et al., 2007) reveal that female edible crabs have the ability to move long distances (100s of km). Laboratory rearing of larvae shows developmental times of at least 50 –80 d at 15 –208C (total number of days at zoea stages I –IV; Nichols et al., 1982), and field surveys indicate that larvae remain in the plankton for 2 –3 months (Eaton et al., 2003). Fecundity is high, ranging from 0.5 to 2.9 million eggs per female (Edwards, 1979; Ungfors, 2007). The aims of this study were to investigate the spatial, genetic stock structure of edible crab in the Kattegat and Skagerrak and to examine the temporal consistency of the structure; a sample from the Norwegian Sea was included as an outgroup comparison. This is the first study of the genetic population structure of edible crab. Genetic variation was analysed in 348 individuals captured at three different locations and on two occasions using eight microsatellite DNA loci. The outcome of the study is discussed in a management perspective.
154 + 17, 78 –185 146 + 13, 127 –167
years (2001/2002 and 2006/2007) to study genetic variation on both a geographic and a temporal scale (Table 1, Figure 1). Genomic DNA was isolated from claw or periopod muscle tissue using the VIOGENE DNA EXTRACTION KIT (protocol Blood and Tissue Genomic Mini). Eight microsatellite DNA loci (Table 2) were amplified by polymerase chain reaction (PCR) on an Eppendorf Mastercycler using C. pagurus-specific primers (McKeown and Shaw, 2008) with modified primer annealing temperatures (TA; Table 2): 30 –33 cycles of 1 min at 958C, 1 min TA, and 1 min at 728C. For the Cpag-3A2 loci, we optimized the PCR using a touch-down procedure, starting with a high and specific TA (558C) for 10 cycles, then a lower, less specific TA (528C) for 25 cycles. Individual locus PCRs incorporated a forward primer labelled with a CY5 tag, permitting visualization on ALFexpressII automated sequencers (Amersham Pharmacia Biotech). Identification of alleles was performed using an ALFwin Fragment Analyser 1.02. To ensure accurate sizing of alleles, a suite of size markers specific to each locus was included
Material and methods Samples of 70 crabs were collected at three geographic locations, from an offshore bank in the Kattegat and coastal locations in the Skagerrak and Norway, and during different
Figure 1. Sampling locations of the edible crab Cancer pagurus: Grove Bank (GR) in the Kattegat, Lunneviken (LU) in the Skagerrak, and Midsund (NO) in the Norwegian Sea.
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Table 2. Annealing temperature (TA), number of alleles, frequency of most common allele (average for five samples), and allele size ranges (base pair, bp) per locus.
Locus Cpag-5D8 Cpag-6C4 Cpag-3A2 Cpag-1B9 Cpag-3D7 Cpag-2A5-2 Cpag-4 Cpag-15 Total
Annealing temperature (TA, 8 C) 54 55 55/52 51 54 58 55 55 –
Frequency of Allele size range the most Number (bp) of alleles common allele 34 0.20 154 –265 8 0.38 166 –194 4 0.52 255 –264 16 0.66 222 –286 8 0.45 169 –217 8 0.54 153 –174 31 0.76 202 –295 8 0.12 124 –145 117 0.45 124 –295
in each run. Two control individuals were run on all gels to ensure consistency among gels. Overall amplification success was 97.7%. Allele sizes were checked for typing errors with Microsatellite toolkit (Excel add-in; Park, 2001) and for potential null alleles using MICRO-CHECKER (van Oosterhout et al., 2004). GENEPOP (Raymond and Rousset, 1995) was used to calculate, for each locus/sample combination, the expected heterozygosity (He; Nei, 1987) and FIS, and to test for deviations from the Hardy – Weinberg equilibrium (HWE). Population structure was quantified using FST (Weir and Cockerham, 1984) calculated in GENEPOP. The null hypotheses of FST ¼ 0 was examined using tests of allelic (genic) and genotypic frequency homogeneity in GENEPOP. Significance levels were adjusted for multiple tests using a simple Bonferroni correction (Rice, 1989). An analysis of molecular variance, AMOVA (ARLEQUIN; Excoffier et al., 2005), was used to analyse the genetic differentiation attributable to temporal and spatial groupings: the two samples in the Kattegat (GR01 and GR07) and the two samples in the Skagerrak (LU01 and LU06) were grouped either by location (Kattegat vs. Skagerrak) or by time (2001/2002 vs. 2006/2007). The statistical power of the applied markers and sample sizes to detect various true levels of divergence was evaluated using the simulation method of Ryman and Palm (2006) implemented in the POWSIM software. The method assesses the sample sizedependent probability of detecting significant heterogeneity, using Fisher’s exact test and traditional Chi-squared, at a
user-inferred degree of divergence defined as FST ¼ 1 2 (1 2 1/ 2 Ne)t, where t is the time since divergence (generations) and Ne the effective population size (Nei, 1987), assuming complete isolation among simulated genetic populations. Simulations were performed for a scenario involving five subpopulations with FST ranging from 0.001 to 0.005, using two different combinations of Ne and t per FST, and sample sizes of 70. The statistical power was estimated as the proportion of statistically significant (p , 0.05) test results. The analysis was also performed for FST ¼ 0, whereby the proportion of significant outcomes is an estimate of the a-error.
Results MICRO-CHECKER indicated possible null allele frequencies in the 2001 sample from Grove Bank at locus Cpag-4, and in the 2006 sample from Lunneviken at loci Cpag-5D8 and Cpag-3A2, of 7, 5, and 7– 11%, respectively. Genotype frequencies for those loci were adjusted, and original and adjusted genotypes were used for calculations of FST for population comparisons. Of the 40 comparisons of locus-specific FIS, six indicated significant deviation from HWE, and of those, two remained significant after Bonferroni correction for multiple comparisons (Table 3). Overall genetic differentiation among samples was low (global FST ¼ 0.0019) and not significantly different from zero (p ¼ 0.27) using genotypic data, but significant (p ¼ 0.03) using allelic (genic) frequency data. Individual locus FST values ranged between 20.0049 and 0.0088, and all associated tests of global differentiation were non-significant after correction for multiple tests (Table 4). Pairwise population comparisons revealed no significant difference in allele frequencies (Figure 2) between the five samples, with multilocus FST ranging from 20.0008 to 0.0038 (Table 5). Similar results were obtained for analyses adjusted for null alleles [global multilocus FST ¼ 0.0020, p ¼ 0.25; global single locus FST ranging from 20.0048 to 0.0088 (all p . 0.05); pairwise multilocus comparisons FST ¼ 20.0008 to 0.0040, p . 0.05]. The results from the AMOVA indicated neither significant spatial (Kattegat vs. Skagerrak; Table 6) nor temporal structure (2001/2002 vs. 2006/2007; Table 6). However, the AMOVA, which uses allele frequency data, indicated a difference between samples within groupings, in accordance with the genic analysis above (Table 4). Similarly, the pairwise FST shows a significant
Table 3. Expected heterozygosity, He, and deviation from HWE, FIS, for eight loci in five samples of edible crab, and the number of private alleles per sample. GR01 Locus Cpag-5D8 Cpag-6C4 Cpag-3A2 Cpag-1B9 Cpag-3D7 Cpag-2A5-2 Cpag-4 Cpag-15 Number of private alleles
He 0.91 0.69 0.57 0.48 0.64 0.52 0.95 0.44 3
FIS 20.02 0.03 20.04 20.02 20.11 0.12 0.14* 0.07
GR07 He 0.91 0.72 0.57 0.56 0.68 0.49 0.94 0.46 2
FIS 20.00 20.03 20.11 0.06 20.10 20.05 0.03 0.01
Emboldened FIS values differ significantly from zero (Exact test, p , 0.05). *Values remaining significant after Bonferroni correction, a ¼ 0.05/40 ¼ 0.00125.
LU02 He 0.88 0.73 0.58 0.59 0.66 0.51 0.94 0.44 1
FIS 0.08 0.02 20.20 0.05 0.03 20.04 20.01 20.01
LU06 He 0.90 0.70 0.58 0.45 0.69 0.53 0.94 0.35 3
FIS 0.11 20.17 0.21 0.07 0.15 20.20 0.08* 0.06
NO05 He 0.90 0.69 0.59 0.50 0.65 0.57 0.95 0.33 6
FIS 0.04 20.00 20.04 20.03 0.10 20.19 0.10 20.04
Lack of spatial genetic variation in the edible crab in the Kattegat–Skagerrak Table 4. Genetic differentiation among edible crab samples, FST, for single loci and all eight loci combined. Locus Cpag-5D8 Cpag-6C4 Cpag-3A2 Cpag-1B9 Cpag-3D7 Cpag-2A5-2 Cpag-4 Cpag-15 All loci
FST 0.0021 0.0023 20.0049 0.0088 0.0057 20.0018 0.0005 0.0031 0.0019
p genotypic 0.220 0.151 0.994 0.164 0.459 0.700 0.290 0.153 0.272
p genic 0.061 0.117 0.994 0.050 0.347 0.668 0.215 0.042 0.030
Statistical significance (probability, p) is calculated using both genotypic (genotype frequencies) and genic (allele frequencies) data. Emboldened values significant at p , 0.05: Bonferroni correction, a ¼ 0.05/8 ¼ 0.00625.
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difference, without multiple test correction, between sampling occasions within the Lunneviken location (Table 5), indicating some level of temporal genetic heterogeneity. The statistical power, the probability of rejecting the null hypothesis (H0) when it is false, with our sampling design is .93% for FST of 0.002 (Table 7). An expected FST of zero estimates a to be 0.04 –0.05, indicating expected levels of type I error.
Discussion Spatial and temporal genetic differentiation of edible-crab samples was low across 1300 km of coastal waters in the area investigated. This conclusion is supported by (i) low global multilocus FST of 0.002, and no significant single-locus estimates; (ii) non-significant pairwise FST estimates between individual
Figure 2. Allele frequencies at eight microsatellite loci in five samples of edible crab Cancer pagurus (sample codes as in Table 1).
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Table 5. Pairwise FST (below diagonal, eight-locus combined value) and p (above diagonal) between samples based on (top panel) genotype frequencies (¼genotypic test; no insignificant pairwise comparisons), and (bottom panel) allele frequencies (¼genic test). GR01 GR01 GR07 LU02 LU06 NO
0.0033 0.0019 0.0012 0.0010
GR01 GR07 LU02 LU06 NO
0.0033 0.0019 0.0012 0.0010
GR07 0.43 20.0008 0.0031 0.0026 0.28 –0.0008 0.0031 0.0026
LU02 0.64 0.76 0.0038 0.0035 0.38 0.57 0.0038 0.0035
LU06 0.08 0.36 0.13
NO 0.49 0.13 0.31 0.82
0.0005 0.06 0.13 0.02
0.25 0.05 0.16 0.50
0.0005
Emboldened values significant at p , 0.05: none remains significant after Bonferroni correction, a ¼ 0.05/10 ¼ 0.005.
samples; and (iii) no genetic differentiation among groups in AMOVA. Furthermore, simulation analysis indicated that the combination of microsatellite loci and sample sizes employed conferred a high degree of statistical power (.93%). Together, these results provide strong evidence that there is little, if any, genetic differentiation among samples of adult crabs taken from the basin of the Kattegat at 578N and the Norwegian Sea at 628N. Lack of spatial genetic differentiation may be explained by high gene flow over large areas. Female edible crabs may migrate more than 100 km in their lifetime, and the pelagic larvae have the
potential to disperse over long distances. Female migrations are thought to be directed against prevailing surface currents to compensate for larval dispersal (Bennett and Brown, 1983), which could promote genetically distinct local populations through larval philopatry, but this may not be the case in all areas. Mark-recapture experiments in the Kattegat and Skagerrak (Ungfors et al., 2007) demonstrated a large proportion of long southerly migrations by females against the coastal northward surface current, but also long migrations both north and south from some locations. Tagging experiments off south and southwest Norway revealed similar movement patterns (Gundersen, 1977; Karlsson and Christiansen, 1996), but also a more limited movement pattern inshore in the deep fjord system farther north at the Midsund sample area (Woll, 1981, 1995). There is little evidence of a return movement or natal homing behaviour for edible crab, as described for other species in relation to spawning (Vannini and Cannicci, 1995; Thorrold et al., 2001; Hauser et al., 2006), but some observations indicate return movements of crabs (Robinson et al., 2003; Ungfors et al., 2007). The results of this study do not, however, support a hypothesis of compensatory adult migration against oceanographic currents promoting population structure in the area. Instead, the lack of genetic structure observed indicates a high degree of genetic mixing over a large area attributable to adult or larval movement, or both factors together. Alternatively, low genetic differentiation among samples may reflect persistence of patterns of historical gene flow among recently founded large populations, i.e. no present gene flow but populations remaining genetically similar through having had
Table 6. AMOVA results of (top panel) grouping according to location, i.e. Kattegat (GR01 and GR07) vs. Skagerrak (LU02 and LU06), and (bottom panel) grouping according to sampling period, i.e. 2001/2002 (GR01 and LU02) vs. 2006/2007 (GR07 and LU06). Source of variation Among locations Among samples within locations Within samples Total
d.f. 1 2 556 559
Sum of squares 1.770 6.607 1 176.021 1 184.398
Variance components 20.0054 0.0084 2.115 2.118
Percentage of variation 20.26 0.40 99.86 100
p-value 1.000 0.024 0.051 –
Among periods Among samples within periods Within samples Total
1 2 556 559
2.359 6.018 1 176.021 1 184.398
20.0023 0.0064 2.1151 2.1192
20.11 0.30 99.81 100
0.686 0.021 0.068 –
NO not included.
Table 7. POW– SIM analysis of the statistical power of rejecting the null hypothesis H0 (no differentiation) when false. Expected FST 0.0010 0.0010 0.0020 0.0020 0.0020 0.0020 0.0050 0.0050 0.0000 0.0000
Average FST 0.0010 0.0010 0.0020 0.0020 0.0020 0.0020 0.0050 0.0050 0.0000 0.0000
x 2-test 0.57 0.54 0.96 0.95 0.96 0.96 1.00 1.00 0.040 0.040
Fisher’s test 0.54 0.52 0.94 0.93 0.94 0.94 1.00 1.00 0.050 0.048
Ne 1 000 10 000 1 000 10 000 1 000 10 000 1 000 10 000 1 000 10 000
Generations (t) 2 20 4 40 4 40 10 100 0 0
Runs* 1 000 1 000 1 000 1 000 10 000 10 000 1 000 1 000 1 000 1 000
Simulations of different expected levels of FST below, equal to and above the observed overall FST of 0.0020, based on calculation of effective population size (Ne) and generations (t) using the Nei (1987) formula FST ¼ 1 2 (1 2 1/2 Ne)t. Proportion of significant Chi-squared and Fisher’s Exact tests after 1000 or 10 000 runs is the power to detect given population differences (FST) with the sampling design in the present study. The proportion of significant tests with no simulated genetic drift (FST ¼ 0, t ¼ 0) estimates a (type I error). *The proportion of significant tests (power) is calculated from 1000 runs per combination. For the FST value (0.0020) observed in our study, 10 000 runs (longer calculation time) were also used to further estimate the power.
Lack of spatial genetic variation in the edible crab in the Kattegat–Skagerrak insufficient time to diverge from a homogeneous founding population. The Pleistocene ice ages (1.8 million years ago to 10 000 years ago), and in particular the last glacial maximum (LGM) 21 000 years ago, had a lasting impact on spatial genetic differentiation of both marine invertebrates (Atlantic assemblages of Macoma balthica; Luttikhuizen et al., 2003) and algae (Fucus serratus; Coyer et al., 2003). During the Pleistocene, northern Europe was repeatedly covered by ice sheets, and the present marine circulation system in the Skagerrak was established only 8000 years ago when the present eastern North Sea coastlines were formed (Gyllencreutz et al., 2006). The edible-crab populations in the Kattegat, the Skagerrak, and mid-Norway could be colonists from areas south of the British Isles or possibly from a refuge in the northern North Sea. The time since colonization may have been insufficient for genetic differences to accumulate. This is especially true for large populations that are less subject to stochastic allele frequency changes, and the edible crab has the potential to form such populations because of its high density and fecundity. Lack of migration-drift equilibrium stemming from recent postglacial population establishment has been suggested to explain patterns of genetic diversity in the Norway lobster (Stamatis et al., 2004) and the European lobster (Triantafyllidis et al., 2005) too. Environmental modelling of cod, Gadus morhua, distributions during the LGM (Bigg et al., 2007) suggests that refugial populations off Northwest Europe were probably responsible for recolonization of North Sea areas; a similar history is possible for the edible crab. It is unlikely that the lack of significant genetic differentiation stems from a type II error, i.e. that the signal for differentiation could not be detected by the experimental design. Our analysis indicated a statistical power of .93%, suggesting that our design should detect any significant genetic differentiation equivalent to FST ¼ 0.002, if it is present. Our observation of low levels of genetic differentiation within the edible-crab population is not unexpected: many marine species display similarly low differentiation, although structuring of this differentiation is often species-specific and may be affected by sampling of non-breeding demes (Nesbo et al., 2000; Ruzzante et al., 2006). In decapod crustaceans, for example, patterns of no differentiation (Chilean hairy crab, Cancer setosus; Gomez-Uchida et al., 2003), low differentiation with no geographical structuring (blue crab, Callinectes sapidus; McMillen-Jackson and Bert, 2004), or low differentiation structured into broad regional groups (northern shrimp, Pandalus borealis—allozymes, Drengstig et al., 2000, and RAPD, Martinez et al., 2006) have all been demonstrated. Random sampling of biological populations is usually constrained in time and space (Waples, 1998). We sampled the Grove Bank and Lunneviken locations twice more than 4 –6 years (1 generation) to examine whether temporal effects, e.g. seasonal behaviour, had an impact on the outcome. No genetic differentiation was seen between temporal or spatial groups, or in pairwise FST analyses. However, AMOVAs among samples within locations/periods indicated some differentiation. This can possibly be explained by some temporal instability in gene frequencies signalling sweepstake recruitment processes (Hedgecock, 1994), i.e. a temporal change in genetic composition attributable to small effective population size and random genetic drift. The edible crab is highly fecund, and random, skewed reproductive success among females may result in short-term temporal instability in population allele frequencies.
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Management In Sweden and internationally, suggestions are raised and research programmes launched for investigations into the potential for local management, e.g. the Swedish programme SUCOZOMA (Sustainable Coastal Zone Management). Laikre et al. (2005) discussed three types of genetic population structure of fish in the Baltic Sea: distinct, continuous, and no differentiation, respectively. They conclude that for no differentiation, local over-harvest and even extinction of local populations may have a less serious effect on total genetic variation than the two other genetic population structures, as long as the remaining effective population size was large enough to resist the effect of genetic drift. However, ecological impacts could be serious. Several studies show concordant patterns of genetic population structure across different selectively neutral markers (Larsson et al., 2007; Addison et al., 2008), but comparisons between neutral and coding genes are rare in the marine environment (Conover et al., 2006; Zane, 2007). Recently, Hemmer-Hansen et al. (2007) showed little heterogeneity of neutral markers in European flounder, Platichthys flesus, in the North Atlantic, including the Baltic Sea, but sharp structuring of the heat protein gene Hsc70 population frequencies over short distances. Therefore, despite high gene flow, local adaptation and hence population/stock genetic differentiation may be more widespread in the marine environment than formerly indicated from neutral-marker data alone. The area we investigated is in the marine North Atlantic region, with no obvious environmental gradients in salinity or temperature that might be driving selection. Therefore, local management of the edible crab should be considered, whereby stakeholders take a precautionary approach such as implementing size restrictions and not fishing below a certain local biomass. Local over-harvest may not have a severe impact on the total genetic pool, as long as extensive fisheries do not affect all local management areas simultaneously, and as long as corridors exist for genetic connectivity between areas. The Kattegat and Skagerrak landings of edible crab are made by Swedish, Danish, and Norwegian fishers, but different regulations are used within these areas: different minimum landing sizes (MLS) are legislated along the Norwegian coast (Anon, 2004b) but no MLS is in use in Swedish (Anon, 2004a) or Danish waters within these areas (Anon, 1998). If the exploitation rate increases in these basins, international negotiations on catch or effort limits may be necessary.
Acknowledgements We thank Astrid Woll, Møreforskning, for edible-crab sampling at Midsund, Norway, and the Swedish fishers Patrik Ingemarsson and Per-Go¨sta Martinsson for providing Swedish samples. Financial support was provided by the Helge Axson Johnsons Foundation, KVVS, the Orvar and Gertrud Nybelins Foundation, the European Fisheries Fund (EFF), and the Swedish Research Council FORMAS.
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