Molecular Ecology (2014) 23, 618–636

doi: 10.1111/mec.12628

Regional environmental pressure influences population differentiation in turbot (Scophthalmus maximus) S. G. VANDAMME,*† G. E. MAES,†‡ J. A. M. RAEYMAEKERS,†§ K. COTTENIE,¶** A. K. IMSLAND,††‡‡ B. HELLEMANS,† G. LACROIX,§§ E. MAC AOIDH,¶¶ J . T . M A R T I N S O H N , ¶ ¶ P . M A R TI N E Z , * * * J . R O B B E N S , * R . V I L A S * * * and F . A . M . V O L C K A E R T † *Institute for Agricultural and Fisheries Research (ILVO), Animal Sciences Unit – Fisheries, Ankerstraat 1, B-8400 Ostend, Belgium, †Laboratory of Biodiversity and Evolutionary Genomics, University of Leuven, Charles Deberiotstraat 32, B-3000 Leuven, Belgium, ‡Centre for Sustainable Tropical Fisheries and Aquaculture, School of Marine and Tropical Biology, James Cook University, Townsville, Qld 4811, Australia, §Zoological Institute, University of Basel, Vesalgasse 1, CH-4051 Basel, Switzerland, ¶Laboratory of Aquatic Ecology, Evolution and Conservation, University of Leuven, Charles Deberiotstraat 32, B-3000 Leuven, Belgium, **Department of Integrative Biology, University of Guelph, Guelph, ON, Canada, N1G 2W1, ††Department of Biology, High Technology Centre, University of Bergen, N-5020 Bergen, Norway, ‡‡Akvaplan-niva, Iceland Office, Akralind 4, 201 Kopavogi, Iceland, §§Royal Belgian Institute of Natural Sciences, Operational Directorate Natural Environment, Gulledelle 100, B-1200 Brussels, Belgium, ¶¶Maritime Affairs Unit (G.04) – FISHREG Action, European Commission, Joint Research Centre, TP051 (Bldg. 51), Via Enrico Fermi nr. 2749, I-21027 Ispra, Italy, ***Departamento de Genetica, Facultad de Veterinaria, Universidad de Santiago de Compostela, 27002 Lugo, Spain

Abstract Unravelling the factors shaping the genetic structure of mobile marine species is challenging due to the high potential for gene flow. However, genetic inference can be greatly enhanced by increasing the genomic, geographical or environmental resolution of population genetic studies. Here, we investigated the population structure of turbot (Scophthalmus maximus) by screening 17 random and gene-linked markers in 999 individuals at 290 geographical locations throughout the northeast Atlantic Ocean. A seascape genetics approach with the inclusion of high-resolution oceanographical data was used to quantify the association of genetic variation with spatial, temporal and environmental parameters. Neutral loci identified three subgroups: an Atlantic group, a Baltic Sea group and one on the Irish Shelf. The inclusion of loci putatively under selection suggested an additional break in the North Sea, subdividing southern from northern Atlantic individuals. Environmental and spatial seascape variables correlated marginally with neutral genetic variation, but explained significant proportions (respectively, 8.7% and 10.3%) of adaptive genetic variation. Environmental variables associated with outlier allele frequencies included salinity, temperature, bottom shear stress, dissolved oxygen concentration and depth of the pycnocline. Furthermore, levels of explained adaptive genetic variation differed markedly between basins (3% vs. 12% in the North and Baltic Sea, respectively). We suggest that stable environmental selection pressure contributes to relatively strong local adaptation in the Baltic Sea. Our seascape genetic approach using a large number of sampling locations and associated oceanographical data proved useful for the identification of population units as the basis of management decisions. Keywords: adaptive genetic variation, microsatellite, oceanography, population structure, Scophthalmus maximus, seascape genetics Received 18 February 2013; revision received 2 December 2013; accepted 8 December 2013

Correspondence: Sara G. Vandamme, Fax: +32 59 33 06 29; E-mail: [email protected] © 2013 John Wiley & Sons Ltd

S E A S C A P E G E N E T I C S O F T U R B O T 619

Introduction Population structure is determined by the interaction of homogenizing factors and geographical fragmentation. Knowledge on processes affecting the dispersal of marine organisms is crucial to understand their genetic distribution patterns and to manage effectively their populations (Nielsen et al. 2009b; Manel et al. 2010; Schunter et al. 2011). Most marine species have the capacity to disperse over vast geographical areas, either passively during the planktonic larval phase (White et al. 2010; Selkoe & Toonen 2011) or actively through the migration of juveniles and adults (Gillanders et al. 2003; Pardoe & Marteinsd ottir 2009). For a long time, the general lack of physical barriers in the sea has made humans conclude that the occurrence of local adaptation should be restricted in high gene flow species (Palumbi 1994; Waples 1998; Galindo et al. 2010) due to the homogenizing effects that prevent locally adapted genotypes. In the last few years, however, many studies have illustrated that various mechanisms may explain how population structure evolves in a marine environment. First, as a result of the historical separation of ocean basins and persistent oceanographical constraints, historical (phylogeographical) structure may persist (Vasem€agi 2006; Bierne 2010; Bierne et al. 2011). Second, oceanographical features, such as eddies and fronts, may prevent random mixing and diffusion of pelagic larvae (Galarza et al. 2009; Galindo et al. 2010; White et al. 2010). Third, environmental transitions, such as salinity and temperature gradients, have been associated with genetic divergence, suggesting a level of local adaptation of populations to their native environment (Larmuseau et al. 2009; Limborg et al. 2012; Teacher et al. 2013). Finally, behavioural mechanisms acting at different life stages, for example natal homing, may reduce gene flow (Florin & Franzen 2010). Although marine fish are typically characterized by high levels of gene flow and low levels of differentiation at neutral loci (Waples 1998; DeWoody & Avise 2000; Cuveliers et al. 2012), strong signatures of local adaptation indicate that selection may override the homogenizing effect of gene flow (Bradbury et al. 2013; DeFaveri et al. 2013; Teacher et al. 2013). Concurrent variation in ecologically important traits (e.g. pelagic larval duration, migratory behaviour and spawning time) between populations may also indicate adaptive differentiation, possibly affecting resilience to environmental change and exploitation (Hauser & Carvalho 2008; Teacher et al. 2013). Evidence for temperature-associated adaptive population divergence has been suggested in Atlantic cod Gadus morhua (Bradbury et al. 2010; Star et al. 2011) and herring Clupea harengus (Teacher et al. 2013). Furthermore, cod (Larsen et al. 2012), herring (Limborg © 2013 John Wiley & Sons Ltd

et al. 2012; Teacher et al. 2013) and flounder Platichthys flesus (Larsen et al. 2007) seem to be adapted to local salinity values. At the same time, low levels of neutral genetic divergence were observed amongst these populations. Overall, the evidence for adaptation under high gene flow conditions remains scarce and hence may benefit from complementary case studies for a range of life histories and ecologies. The flatfish turbot (Scophthalmus maximus; Scophthalmidae) offers a fine opportunity to evaluate the effect of life history strategy on the genetic divergence of populations, as different strategies characterize the species across its broad range on the European continental shelves, making local adaption very plausible (Imsland et al. 2001a; Nissling et al. 2006, 2013; van der Hammen et al. 2013). For instance, unlike most other flatfish, turbot has the capacity to survive and reproduce at varying salinities, suggesting different locally adapted optima. Research has found that eggs from the North Sea develop optimally between 20 and 35 psu and do not survive at lower salinities, for example in the Baltic Sea (Karas & Klingsheim 1997). This contrasts with turbot eggs in the Baltic Sea, which develop demersally at salinities as low as 7 psu without any evidence for increased mortality (Nissling et al. 2006, 2013). Furthermore, tagging studies in the Kattegat and Skagerrak have revealed that adult turbot are sedentary (Aneer & Westin 1990; Stottrup et al. 2002), display a relatively strong spawning site fidelity and have restricted movement within a spawning season (Florin & Franzen 2010). This restricted migratory behaviour suggests that the actual movements of a single individual differ strongly from the potential dispersal, providing an opportunity for genetic differentiation based on geographical distance. In addition, the restricted dispersal of turbot might facilitate the evaluation of the effect of oceanographical features on its population structure. Previous studies on turbot illustrated that despite the generally weak spatial structuring indicated by neutral microsatellite loci over large geographical areas (Bouza et al. 2002; Nielsen et al. 2004; Florin & H€ oglund 2007), turbot is predisposed to adaptive population divergence on a small spatial scale. For example, microsatellites isolated from expressed sequence tags (EST) suggest adaptive population divergence in the Baltic-Atlantic transition area (Vilas et al. 2010). Furthermore, inferences from a single candidate gene (haemoglobin) suggest population divergence between Iceland and west Norway on the one hand and southwest Norway, Kattegat and Baltic Sea on the other hand (Imsland et al. 2003). Here, a combination of genetic markers and multivariate techniques is applied to assess how environmental factors influence the genetic variation in turbot at various spatio-temporal scales. In contrast to previous

620 S . G . V A N D A M M E E T A L . studies on turbot, our sampling scheme covered nearly the entire distribution range of turbot, representing an open system experiencing multidirectional migration between many combinations of connected populations and across several environmental gradients. The marker panel allowed for the assessment of both the dynamics of gene flow and selection. We address the following questions: (i) What is the global population structure of the highly vagile turbot over a densely sampled geographical area, using random and gene-linked markers? (ii) What is the proportion of the observed genetic variation attributable to demographic (neutral) or selective (adaptive) processes, enabling the pinpointing of footprints of selection? and (iii) To which degree neutral and adaptive population differentiation correlates with spatial, environmental or temporal variation? For this purpose, key environmental parameters were collected at every sampling site. Our results show that neutral loci mainly identify populations at a large scale (Baltic – Irish Sea), while loci putatively under selection identify an additional break within the North Sea. The adaptive genetic variation is significantly associated with seascape variables, suggesting that a stable environmental structure contributes to local adaptation in the Baltic Sea.

from genomic libraries, seven were previously characterized in Coughlan et al. (1996), Estoup et al. (1998) and Iyengar et al. (2000) (Sma3-8INRA, Sma3-12INRA, Sma3-129INRA, Sma4-14INRA, Sma5-111INRA, SmA1152INRA, Sma1-125INRA). The remaining 14 markers are EST (E code)-derived microsatellites described in Bouza et al. (2008) (SmaUSC-E1, SmaUSC-E2, SmaUSCE4, SmaUSC-E5, SmaUSC-E7, SmaUSC-E8, SmaUSC-E10, SmaUSC-E21, SmaUSC-E26, SmaUSC-E28, SmaUSC-E32, SmaUSC-E36, SmaUSC-E40, SmaUSC-E41). These EST loci were chosen based on their fragment length and type of repeat motif, so that they could be combined in the multiplex PCRs. Microsatellite markers were combined into three multiplex reactions; two of the PCRs used a touchdown protocol. Details on the PCR conditions of microsatellite markers are presented in the Supporting information (Table S1, Supporting information). The allele sizes were determined using an internal lane size standard (250 LIZ) and the GENEMAPPER v.4.0 software package (Applied Biosystems). Furthermore, the TANDEM v.1.07 software package was used for automated allele binning (Matschiner & Salzburger 2009). Approximately 15% of all samples were regenotyped to check for reproducibility.

Quality of genotyping and summary statistics Materials and methods Sampling A total of 999 turbot samples were collected during research surveys or sampling onboard commercial vessels at 290 locations across the northeast Atlantic Ocean between 2006 and 2010 (Table 1, Fig. 1). Fin tissue samples were collected and preserved in 96% ethanol for genetic analyses. However, individuals collected in 2006 and 2007 were stored in a solution of TNES urea (see Estoup et al. 1998 for details). Temporal replicates were available for seven sampling locations within the Belt Sea, North Sea, Irish and Celtic Seas and Bay of Biscay (Table 1). For a comprehensive population genetic analysis, these newly collected samples were supplemented with additional samples from previous studies to cover almost the entire distributional and environmental range of turbot (see Supporting information and Table 1).

Molecular analyses and microsatellite genotyping Total genomic DNA was extracted using the Nucleospin Tissue Extraction Kit according to the manufacturer’s guidelines (Macherey-Nagel GmBH, D€ uren, Germany). Samples were genotyped at 21 microsatellite loci on the automated capillary sequencer ABI 3130 AVANT (Applied Biosystems). Of these 21 loci sourced

Individuals were classified into 29 spatio-temporal samples according to ICES fisheries subdivisions (Table 1, Fig. 1). For every sample, MICRO-CHECKER v.2.2.3 (van Oosterhout et al. 2004) was used to check for potential technical problems such as null alleles, stuttering and large allele dropout. FSTAT v.2.9.3 software (Goudet 1995) was used to estimate the amount of genetic variation within samples as allelic richness (according to El Mousadik & Petit 1996), the number of alleles and observed and expected heterozygosity. Deviation from Hardy–Weinberg equilibrium was tested per locus and sample using the exact test (Guo & Thompson 1992) implemented in GENEPOP v.4.1 (Raymond & Rousset 1995). Statistical significance was tested with 1000 permutations and adjusted using sequential Bonferroni correction to correct for multiple testing (Rice 1989).

Outlier analyses The 29 spatio-temporal samples were reduced to 20 spatial samples genotyped for all loci by pooling the temporal replicates (see Table 1). Two tests were applied to identify loci that showed divergent patterns of differentiation compared with neutral expectations and therefore potentially affected by selection. First, we tested our data set for outliers using the FDIST FST outlier method described by Beaumont and Nichols (1996), © 2013 John Wiley & Sons Ltd

© 2013 John Wiley & Sons Ltd

Bay of Biscay

British Isles

English Channel

North Sea

North Atlantic

Transition Area

Baltic Sea

Geographical region

VIIf VIIf VIIg VIIa VIIa VIIa VIIb-VIa VIIIb

Bristol Channel

Bristol Channel

Southeast Ireland Irish Sea Irish Sea Irish Sea West Ireland Bay of Biscay

VIIe

Western English Channel VIIf

VIId

Eastern English Channel

Bristol Channel

VIId

Eastern English Channel

IVc

Southern North Sea

 Aland Sea Estonian Coast Gotland Island Arkona Sea Belt Sea Belt Sea Kattegat West Coast of Norway Iceland IVb IVb IVb IVc

29 32–28 28 24 IIIc IIIc IIIa IVa Va2

Sample location

German Bight Central North Sea Central North Sea Southern North Sea

ICES rectangle

SEI IRS06 IRS07 IRS09 WIR BOB07

BCH10

BCH09

BCH07

WEC

EEC09

EEC07

SNS09

ENS CNS10 CNS07 SNS07

ALD EST GOT ARK BEL10 BEL09 KAT NNS ICE

Sample ID

51.6 53.5 53.6 53.6 54.6 45.2

50.8

51.4

50.7

50.0

50.5

50.4

51.7

55.5 54.1 54.1 52.5

60.1 59.2 57.2 54.8 54.5 55.3 56.5 62.0 63.2

Latitude

6.0 53.0 5.0 5.0 9.0 1.8

5.5

4.7

5.5

2.8

1.1

0.6

2.2

6.7 2.1 2.1 1.9

19.3 23.2 18.7 13.8 11.2 11.0 11.3 4.0 21.1

Longitude

Mean position

2009 2006 2007 2009 2009 2007

2010

2009

2007

2010

2009

2007

2009

2003 2010 2008 2010 2010 2009 2009 1997 1998 –2010 2010 2010 2007 2007

Year

90 21 20 82 26 25

43

20

16

16

51

29

32

53 14 48 18

46 48 45 24 39 26 15 45 43

Sample size

Northern Atlantic Northern Atlantic Northern Atlantic Northeastern Atlantic Northeastern Atlantic Northeastern Atlantic Northeastern Atlantic Northeastern Atlantic Northeastern Atlantic Northeastern Atlantic Northeastern Atlantic Irish Shelf Irish Shelf Irish Shelf Irish Shelf Irish Shelf Northeastern Atlantic

Baltic Sea Baltic Sea Baltic Sea Baltic Sea Baltic Sea Baltic Sea Baltic Sea Northern Atlantic Northern Atlantic

Genetic cluster

0.673 0.630 0.625 0.678 0.672 0.652

0.662

0.675

0.644

0.649

0.674

0.647

0.666

0.655 0.655 0.649 0.621

0.632 0.649 0.640 0.598 0.654 0.667 0.642 0.626 0.662

He

4.88 4.61 4.67 4.87 4.81 4.83

4.75

4.97

4.50

4.86

4.91

4.66

4.78

4.81 4.66 4.68 4.59

4.43 4.41 4.57 4.03 4.55 5.00 4.83 4.38 4.85

AR

and and and and and and and and and

A A A A A A A A A

N and A N N N and A N and A N

N and A

N and A

N

N and A

N and A

N and A

N and A

N and A N and A N N

N N N N N N N N N

Neutral or Adaptive

Table 1 Individual samples of turbot are clustered according to ICES fisheries rectangles. For each of these pooled locations information includes sampling ID, latitude, longitude, year of sampling, number of samples and whether samples were genotyped with neutral markers and/or putative adaptive microsatellites (N/A, respectively). Estimates of genetic diversity are based on neutral loci expressed in expected heterozygosity (He) and allelic richness (AR). Also shown for each sample is the most likely of four clusters as inferred from STRUCTURE analysis based on the full marker set (see Supporting information)

S E A S C A P E G E N E T I C S O F T U R B O T 621

N and A 4.87 0.668

N and A 4.82 0.668

N and A 4.81 0.680

implemented in the LOSITAN software (Antao et al. 2008). We used 105 iterations and assumed 50 demes (varying the input parameters did not change the results). Runs were performed using the two possible mutation models: the stepwise-mutation model and the infinite allele model. To minimize the risk of detecting false positives, we compared our results to outputs from a different, commonly applied method, the Bayesian approach of Beaumont & Balding (2004) as implemented in the BAYESCAN v.2.1 program (Foll & Gaggiotti 2008). We used the default Markov chain Monte Carlo (MCMC) parameters, varied the prior odds between 3 and 10 in favour of a model without selection. Correcting for multiple testing, the program computes q-values based on the posterior probability for each locus, and we considered loci with q < 0.1 as significant outliers. Both outlier tests were conducted on all samples. The tests were repeated on a number of subsets including samples from the Baltic and North Sea, the Irish shelf and samples from the Iberian coast. Please check the Supporting information for the pairwise comparisons between geographical locations.

Geographical structure of neutral and adaptive genetic variation

19 2000

Samples used for the seascape genetic analyses are represented by a bold sample ID.

POR IXa Portugal

North and northwest Spain Portuguese Coast

42.6

8.8

27 NWS VIIc

Bay of Biscay

43.7

7.4

2000

Northeastern Atlantic Northeastern Atlantic Northeastern Atlantic 18 2009 VIIIb

Sample location

BOB09

45.2

1.8

Year Latitude

Longitude

Sample size ICES rectangle Geographical region

Table 1 Continued

Sample ID

Mean position

Genetic cluster

He

AR

Neutral or Adaptive

622 S . G . V A N D A M M E E T A L .

Two methods were applied to assess the current distribution of genetic variation in turbot. Genetic differentiation between the 29 spatio-temporal samples was estimated by global and pairwise FST (using Weir & Cockerham 1984 statistics) using FSTAT v.2.9.3. The Bayesian model-based clustering STRUCTURE v.2.3.3. program (Pritchard et al. 2000) was used to infer the number of genetically homogeneous groups. Considering the high levels of gene flow in turbot, we used the admixture model with the spatio-temporal origin as prior information, allowing for better performance for data with weak structure (Hubisz et al. 2009). For each simulation of K (1–10), 10 independent replicates were run. In total, 104 runs were used as burn-in, followed by 105 MCMC iterations. The most likely number of clusters was selected by choosing K with the largest log-likelihood according to Evanno et al. (2005) implemented in the STRUCTURE HARVESTER v.0.6.92 Web application (Earl & vonHoldt 2012). Assignment proportions to specific clusters per population were plotted following Mac Aoidh et al. (2013). More information can also be found in the supplementary information.

Spatial, environmental and temporal correlation analyses Environmental variables. Detailed environmental data were available for the Baltic and North Sea representing © 2013 John Wiley & Sons Ltd

S E A S C A P E G E N E T I C S O F T U R B O T 623

0 125 250

500

750

1000 Kilometers

31

Va 30 IVa

29 llla

Vla

lllb

IVb lllc

Vllb

Vlla

27 25

32

28 26

24

IVc

Vllf

Vllg

Vlle

Vlld

Vlla Vllb Vllc

lXa

Fig. 1 Individual sampling locations of turbot. Samples used for the seascape genetic analyses are indicated with black circles; additional samples for the population genetic analyses are represented with light grey squares. Seas are labelled according to the ICES fishing rectangles.

170 unique sites; turbot samples collected specifically in these basins were retained for the following analysis (Table 1). For each individual (N = 488), we calculated average values of six habitat and three hydrodynamic variables collected between 1980 and 2004. These values were selected because (i) they are associated with known strong environmental gradients between the Baltic and North Sea (Poulsen et al. 2011; Limborg et al. 2012; Teacher et al. 2013) or because (ii) these variables influence demographic behaviour of marine species (e.g. Galarza et al. 2009; Galindo et al. 2010). All data were extracted from the 10-km resolution ECOSMO model (Schrum et al. 2003) and downloaded from the WGOOFE ICES working group website (groupsites.ices.dk/sites/wgoofe). Habitat variables included temperature of the sea surface and sea bottom (SST and SBT, respectively), salinity of the surface and bottom waters (SSS and SBS, respectively), oxygen concentration (O2, mL/L) and primary production (PP, expressed as g C/m2/day). The three hydrodynamic variables included bottom shear stress (BSS, m2/s2), depth of © 2013 John Wiley & Sons Ltd

pycnocline (PYC, m) and a density-based stratification index (STRAT, kg/m3) (for details see Supporting information). Annual estimates of environmental data may differ substantially from population-specific seasons potentially affecting divergent selection; thus, we calculated different aggregated values for some variables. Due to the variation in seasonal water temperature, a climatological mean value was included for the summer season (July–September). As temperature has a large effect on other associated hydrodynamic variables, a summer average was calculated for oxygen concentration, depth of pycnocline and stratification. Mean net primary productivity was estimated for spring and summer (April–September), a period that includes the main spawning time for turbot and the occurrence of juvenile life stages (Jones 1972; Nissling et al. 2006; ICES 2012a). These data were linked with the individual sampling sites by overlaying a global map of sampling sites and abiotic variables in ARCGIS v.10. First, a vector grid was constructed using the ET GEO WIZARD v.10.2 tool for

624 S . G . V A N D A M M E E T A L . ARCGIS. This polygon vector grid covers the entire area of the model data with a cell size as fine grained as the model, which reaches approximately 10 9 10 km2. After the establishment of this extra layer, the nearest environmental value was linked to the coordinates of each individual sampling location. In cases where model data were missing for a particular site (for example in the case of inshore samples), the value of the closest cell was extracted without extrapolation. Seascape analysis of neutral and adaptive variation. Genetic differentiation affected by spatial, temporal or environmental variability or a combination thereof was estimated by partitioning the genetic variation into a spatial (S), temporal (T) and environmental (E) component. This allows for the investigation of independent and collinear effects. For instance, the proportion of genetic variation attributable to spatial autocorrelation of environmental data can be quantified, each explaining the genetic variation indicative for selection (rather than demography). Individuals with missing genotypes were removed from the data set for this analysis (max. number of individuals is 466). The multilocus genetic data were coded as relative allele frequencies (individuals in rows, alleles in columns) using the ADEGENET package in R (Jombart et al. 2010). For each individual, frequencies are assigned a value 1 for a homozygote and 0.5 for a diploid heterozygote. No scaling was applied on these allele frequencies as this can drastically change the results (Jombart et al. 2009). The first group of explanatory variables was the nine environmental variables calculated using the ECOSMO model (see above). Second, a matrix of the sampling years was constructed using presence/absence to test for temporal stability. Lastly, geography was modelled with distance-based Moran’s eigenvector maps (MEM) along with latitude and longitude, which might reflect historical trends in recolonization or range expansion (Gaggiotti et al. 2009; Gavilanez & Stevens 2012). For our analysis, we used the same method to generate axes as for the principal coordinate analysis of neighbour matrices (PCNM) (Borcard & Legendre 2002). A distance matrix between individuals was truncated above a threshold equal to the minimum distance required to form a network joining all sample points together (i.e. a minimum-spanning tree). Distances above the threshold were reassigned to four times the threshold. This threshold offers a reasonable balance between resolving fine and coarse-scale spatial structure (Borcard & Legendre 2002). However, only positive eigenvectors were retained for further analysis based on Moran’s I. Depending on the geographical area under investigation, we constructed a different matrix with separate MEM variables (see below). Together with these MEM,

latitude and longitude represent the explanatory matrix space. We tested the null hypothesis that each set of explanatory variables (S, T or E) does not explain genetic variation, separately for neutral and outlier loci. First, we performed a global analysis that involved all the individuals from the Baltic and North Sea. Furthermore, we also conducted variance partitioning on regional subsets to assess patterns at smaller scales: (i) North Sea samples and the transition zone and (ii) Baltic Sea and transition zone samples (see Table 1). Variance components were estimated and tested for significance using 103 random permutations of the data. After calculating and testing the genetic variation explained by environmental, spatial and temporal data, we applied a canonical redundancy analysis (RDA). As with typical partial regressions, partial RDA can be conducted on residuals from another set of explanatory variables allowing us to control for variables such as spatial structure (Borcard et al. 2011). RDA and associated analyses were performed in R v.2.13 with the VEGAN package (Oksanen 2011). In the cases where the abovementioned RDA analysis was significant, we subsequently applied forward selection, as this corrects for highly inflated type I errors and overestimated amounts of explained variation. Forward selection adds suitable variables one at a time until the adjusted R2 is reached. Additional variables do not significantly improve the model (Sharma et al. 2012). This reduced panel of explanatory variables was used to recalculate the total proportion of genetic variation in the variance partitioning. The function used to perform the forward selection was implemented in the PACKFOR package in R (Dray et al. 2007).

Results Genotype quality and summary statistics Genotyping problems were observed at locus SmaUSCE5, and MICRO-CHECKER analysis indicated that two loci (Sma4-14INRA and SmaUSC-E1) might be affected by null alleles or stuttering. Stuttering was identified by estimating the average null-allele frequency using FREENA; as such these loci were omitted from the analysis. Locus SmaUSC-E2 was almost completely fixed (number of alleles = 2), and due to its low informative status, it was not included in any statistical analysis. Observed Hardy–Weinberg disequilibria could not be specifically associated with one locus or population specifically, and hence, all 17 remaining loci were retained for further analysis. Details on genetic diversity indices are provided in the Supporting information and Table 1. © 2013 John Wiley & Sons Ltd

S E A S C A P E G E N E T I C S O F T U R B O T 625

Outlier analyses Applying the FDIST method, we detected three loci, SmaI-152INRA, SmaUSC-E4 and SmaUSC-E7, potentially influenced by directional selection in a global analysis. All three loci exceeded the 99% confidence limit of neutral expectations, even when corrected for multiple testing (FDR lower than 10%). BAYESCAN analysis was consistent with the identification of both SmaUSC-E4 and SmaUSC-E7 as potential outliers according to the prior odds favouring the neutral model of 3 and 10, respectively, and using a q

Regional environmental pressure influences population differentiation in turbot (Scophthalmus maximus).

Unravelling the factors shaping the genetic structure of mobile marine species is challenging due to the high potential for gene flow. However, geneti...
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