Molecular Ecology (2009) 18 , 3151–3162 doi: 10.1111/j.1365-294X.2009.04253.x
H A L V O R K N U T S E N , * P E R E R I K J O R D E , † H A N N E S A N N Æ S , * A . R U S H O E L Z E L , ‡ O D D A K S E L
B E R G S T A D , * S E R G I O S T E F A N N I , § T O R I L D J O H A N S E N – and N I L S C H R . S T E N S E T H * †
*Institute of Marine Research, Flødevigen, N-4817 His, Norway, † Centre for Ecological and Evolutionary Synthesis (CEES),
Department of Biology, University of Oslo, P.O. Box 1066 Blindern, N-0316 Oslo, Norway, ‡ School of Biological and
Biomedical Sciences, Durham University, South Road, Durham, DH1 3LE, UK, §IMAR ⁄ DOP, Department of Oceanography and Fisheries, University of the Azores, 9901-862 Horta, Azores, Portugal, – Institute of Marine Research, Tromsø, PO Box
6404, N-9294 Tromsø, Norway
ST
Keywords : bathymetric, brosme brosme , habitat boundaries, population structure
Received 19 March 2009; revision received 17 April 2009; accepted 25 April 2009
Genetic structure among natural populations reflects the interplay between multiple evolutionary mechanisms, and gaining insight into the relative significance of these different processes is essential to understand the evolution of biodiversity. Established physical boundaries or vicariances are the most obvious mechanisms restricting gene flow, but in marine ecosystems, cryptic genetic structure evolves in highly mobile species in the absence of obvious boundaries
(e.g. Hoezel 1994; Jørgensen et al.
2005). This has been attributed to local habitat dependence (e.g. Natoli et al.
2005), larval retention (Knutsen et al.
2007a) and
Correspondence: Halvor Knutsen, Fax: +4737059050 ⁄
+4737059001; E-mail: halvor.knutsen@imr.no
2009 Blackwell Publishing Ltd the presence of oceanographical boundaries such as hydrographical fronts (e.g. Naciri et al.
1999; Perez-
Losada et al.
2002).
Demersal (benthic and benthopelagic) marine fish species occur at all depths from the shoreline to the abyss and even in the deepest trenches of the oceans.
Many have immense geographical ranges, but most display more or less species-specific depth ranges (e.g.
Haedrich & Merrett 1988). This means that the geographical distribution area of a depth-limited species may be split up to varying degrees by bathymetric features. Over time this mosaic of habitable subareas of varying quality may facilitate population structuring. In essence, while a shallow ridge may constitute a barrier to dispersal for a very deep-living species, deep troughs or deep ocean basins could have the same restrictive effect on a shallow-living species. Many species appear

3152 H . K N U T S E N E T A L .
to be limited in their migrations and dispersal to certain oceanographical features, such as specific water masses or circulation features including persistent regional and mesoscale gyres (Koslow 1993). Ocean circulation is influenced strongly by the configuration of landmasses and bathymetric features of continental margins, midocean ridges, shallow seamounts and islands at many spatial scales. Hence, bathymetry may act in concert with ocean currents to either prevent or enhance gene flow in demersal deepwater fishes.
A few previous studies of deepwater demersal fish have demonstrated population structure across the
North Atlantic (e.g. Roques et al.
2002; Aboim et al.
2005; Knutsen et al.
2007a). In this study, we focus on the influence of the interplay on population structure, on an ocean-wide scale, between species-specific depth ranges, bathymetry within the distribution area and ocean circulation. We chose the tusk ( Brosme brosme ) as the target species because of its extensive North Atlantic range and its relatively narrow depth range restricted to upper slope and deep continental shelf waters ( 100–1000 m; e.g. Cohen et al.
1990). We sampled tusk across its distribution area at locations comprising varying conditions in terms of opportunities for gene flow though egg and larval drift and migratory intermixing. Genetic variability patterns were analysed by means of newly developed polymorphic microsatellite DNA markers (Knutsen et al.
2007c), and used to study population structure and to test hypotheses about gene flow in this species.
Genetic data in conjunction with information on the physical characteristics of habitats, so-called ‘landscape genetics’, have recently yielded valuable insight into the factors that form and maintain genetic divergence
(Manel et al.
2003; Jørgensen et al.
2005; Kenchington et al.
2006; McCairns & Bernatchez 2008). In the marine environment, ocean currents may also determine gene flow among populations (Galindo et al.
2006). Combining information on species-specific depth range, habitat structure, bathymetric and circulatory features and spatial genetic variability patterns should, therefore, provide a powerful approach for studies on the relative significance of different mechanisms underlying population substructuring in the marine environment.
Understanding these mechanisms will provide transferable insight into the processes that generate biodiversity within and among deep water species, and will be essential for the development of sound management strategies for vulnerable fishery resources (Large et al.
2003; Gordon 2005; Large & Bergstad 2005). Tusk has been exploited commercially for centuries, but at least since the 1970s, abundance appears to have declined significantly (Bergstad & Hareide 1996).
The tusk Brosme brosme (Ascanius 1772) belongs to the family Gadidae (cod fishes) and subfamily Lotinae. It is regarded as a deepwater demersal fish with a depth distribution that varies within its North Atlantic distribution area, but generally ranges from coastal waters to 1000 m on the upper continental slope, on mid-ocean ridges and in deep fjords (Andriyashev
1954; Rahardjo Joenoes 1961; Svetovidov 1986; Cohen et al.
1990; Bergstad & Hareide 1996). The highest concentration of adult tusk is found at depth between
100 and 400 m. In the Northwest Atlantic, the species is distributed along the continental shelf from New
Jersey to the Strait of Belle Isle, on the Grand Banks of Newfoundland, and off West Greenland. In the
Northeast Atlantic, it is found off East Greenland
(GR), around Iceland (IS) and the Faroe Islands (FI) and along the European shelf from southern Ireland to the Kola Peninsula and Spitzbergen, including the deeper parts of the North Sea and Barents Sea (Svetovidov 1986). Along the Mid-Atlantic Ridge (MAR), tusk occurs south to the Charlie-Gibbs Fracture Zone
( 52 N) (Hareide & Garnes 2001; Bergstad et al . 2008;
Fossen et al.
2008). Based on observations of planktonic eggs and larvae (Schmidt 1909; Bjørke 1981) and the occurrence of ripe and running adults (Rahardjo
Joenoes 1961; Cohen et al.
1990; Bergstad & Hareide
1996), it has been concluded that spawning is widespread and the species does not seem to aggregate to spawn. In the southern areas, spawning takes place from April to July, while from May to August further north in the Barents Sea (Lukmanov et al.
1985).
The species is long-lived, reaching up to 20 years of age (Cohen et al.
1990), and maturity occurs at an age of 8–10 years (Bergstad & Hareide 1996). Tusk is considered a highly fecund species (Svetovidov 1986;
Cohen et al.
1990). Eggs and larvae are epipelagic (Russels 1976; Bjørke 1981), occurring in the upper 200 m of the water column. Juveniles settle to the bottom after
1–4 months, when they are 5–6 cm long (Cohen et al.
1990). Adults may reach a maximum length of 120 cm and may weigh 30 kg. Little is presently known about the migration and dispersal capacity of the species.
Tusk from different spawning grounds are reported to have different coloration, growth rate, number of vertebrae and fin rays, length distributions and length ⁄ weight relationships (Hareide 1988). It is unknown if these morphologic differences reflect genetic differences among populations or if they represent plastic responses to different environmental conditions.
2009 Blackwell Publishing Ltd

The distribution range of tusk comprises continuous continental shelves and slopes, comparatively shallow ridges connecting islands and continents (the Scotland to Greenland ridge), offshore banks [e.g. Rockall (RA),
Hatton] surrounded by deep channels and the MAR.
As the depth range of demersal life stages of the species
(benthic juveniles and adults) is restricted to depths shallower than 1000 m, deep troughs and ocean basins may create discontinuities in the overall distribution area. The regional surface circulation is dominated by branches of the North Atlantic Current and the
Subpolar Gyre creating a basin-wide cyclonic pattern
(Fig. 1) (e.g. Pickard 1975; Sy et al.
1992; Ha´tu´n et al.
2005). The warm North Atlantic Current flows eastwards across the North Atlantic where it splits into one branch that makes a northward turn and eventually feeds the Irminger Current (west of IS and east of GR) and another that feeds the inflow of warm water to the
Nordic Seas. The large-scale circulation shown in Fig. 1 must be assumed to influence the general drift patterns of epipelagic eggs and larvae, including those of tusk.
Local drift patterns will, however, presumably be strongly affected by smaller-scale patterns, such as mesoscale eddies in the open ocean and bathymetric steering of slope currents.
G E N E T I C S T R U C T U R E I N T U S K 3 1 5 3
Adult tusk were sampled by research vessels and commercial fishing vessels at eight localities across the
North Atlantic (Fig. 1): East GR (two sublocalities,
130 km apart: Table 1); IS (specimens collected on the continental shelf over an 700 km wide area south of
IS and arbitrarily lumped into two subsamples in
Table 1); Canada (CA, on Brown’s Bank, NAFO section
4X); the Rockall Bank; the MAR (three sublocalities spanning 400 km); the FI (two closely situated sublocalities); Storegga (SE) and Tromsøflaket (TF) (three closely situated sublocalities). Sampling took place from
2004 to 2008, as opportunity arose, with most localities being sampled in 2005 or 2006. Size, sex and sexual maturation data were available for five of the eight localities (cf. Table 1). Muscle tissue was collected from fresh or frozen specimens and preserved until DNA extraction in 96 % ethanol at sea, or immediately after thawing in the laboratory.
DNA was extracted from ethanol preserved muscle tissue using the Viogene Inc. extraction kit (Sunnyvale,
CA). Microsatellite DNA fragments (below) were separated using the capillary CEQ 8000 (Beckmann)
Fig. 1 Map identifying locations for each sample site, together with ocean topography and details of water masses of the
North Atlantic Ocean. Note that Rockall
(RA) and Mid-Atlantic Ridge (MAR) are located on sea mountains with deep areas all around (white areas represent >1000 m depth and are beyond the maximum depth range of tusk), whereas Greenland
(GR), Iceland (IS), Faroe Island (FI),
Storegga (SE) and Tromsøflaket (TF) are interconnected with depth <1000 m (greyshaded areas), and are within the recorded depth range of the species.
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3154 H . K N U T S E N E T A L .
Table 1 Sample locations and details for tusk
Position
Sample location
Mid-Atlantic
Ridge
Rockall
Faroe Islands
Storegga
Tromsøflaket
Iceland
Greenland
Canada
Abbreviation
MAR
RA
FI
SE
TF
IS
GR
CA
Latitude
51.17
53.04
54.3
56.0
61.156
61.111
64.185
71.280
71.296
71.331
63.5
63.47
64.188
63.370
42.98
Longitude
)
29.73
)
34.85
) 35.4
)
17.0
)
5.130
) 5.435
5.558
16.599
17.033
16.568
)
16.3
)
23.12
) 36.004
)
38.000
)
66.14
Sampled
4
21
80
84
60
40
99
21
58
21
35
65
48
48
100
Scored*
100
84
98
99
95
97
92
99
Size
Y
Y
N
N
N
Y
Y
Y
⁄ sex†
Sample locations and details for tusk. Positions refer to average latitude and longitude for subsamples of individuals.
*Individuals successfully genotyped at 5 or more microsatellite loci and included in the statistical analyses.
†Availability of individual data on size (length), sex, and sexual maturity (Y = available; N = not available).
Date
July 2004
April 2008
January 2005
April 2005
February 2005
October 2005
May 2006
December 2005
April 2006
June 2006 automated sequencer. We applied Eppendorf 5 Prime
Taq DNA Polymerase for the PCR reactions, using the supplied self-adjusting magnesium 10 · buffer. As a guard against potential genotyping errors, all capillary traces were scored independently by two trained persons, and disagreements were re-analysed to avoid misclassification of alleles and genotypes. Preliminary analyses were based on nine microsatellite DNA loci, using published protocols (Knutsen et al.
2007c). In these preliminary screenings, carried out on a subset of fish from seven localities, two loci (Bbrom8 and
Bbrom11) were found to depart strongly from Hardy–
Weinberg genotype proportions, and the
MICROCHECKER software (Van Oosterhout et al.
2004) reported high frequencies of null alleles for these. These two loci were therefore omitted from further analyses, which were based on seven microsatellite loci (Bbrom1, Bbrom2,
Bbrom5, Bbrom10, Bbrom16, Bbrom18 and Bbrom21:
Table 2). Most of the seven loci were easy to score, but
Bbrom10 was highly polymorphic (cf. Table 2), displayed a moderate amount of PCR stuttering and required extra care when scoring. Several PCR re-runs were performed to ensure consistent genotype scoring for this locus.
Amount of genetic variability was characterized by gene diversity ( H
S within samples; H
T for the total: Nei
& Chesser 1983) and observed number of alleles at each
Table 2 Amount of genetic variability within and among brosme samples at seven microsatellite loci
Locus a
Within total ( H
T
)
Among sample locations
F
ST
P -value
Bbrom1
Bbrom2
Bbrom5
Bbrom10
Bbrom16
Bbrom18
Bbrom21
Average
SD
16
3
5
57
10
6
11
15.4
18.8
0.516
0.022
0.440
0.928
0.208
0.374
0.293
0.397
0.284
0.0024
0.0024
0.0015
0.0013
0.0038
)
0.0016
0.0020
0.0014
0.0016
a is the observed number of alleles and H
T
(Nei & Chesser
1983) is the gene diversity in the total material ( n = 764).
F
ST
(Weir & Cockerham 1984) estimates the level of genetic differentiation among the eight samples, with statistical support indicated by the P -value for the exact test for allele frequency heterogeneity (
GENEPOP v. 4.0.6; Rousset 2008)
0.1655
0.1190
0.2693
0.0046
0.0174
0.6799
0.1888
0.0025
locus. Deviations from Hardy–Weinberg genotype proportions were estimated by F
IS
(Weir & Cockerham
1984) and assessed using the exact probability test in the
GENEPOP software (ver. 4.0: Rousset 2008). To explore specific deviations from the Hardy–Weinberg equilibrium, we also characterized F
IS separately for one allele at a time, by lumping all other alleles at the locus, and calculating the quantity F
IS
= (4 n
11 n
22
) n
12
2
) ⁄
2009 Blackwell Publishing Ltd

[(2 n
11
+ n
12
)(2 n
22
+ n
12
)] (Nei 1987, p. 155, equation
7.15), where n
11 and n
12 are the number of individuals displaying the allele in question in homozygote and heterozygote forms, respectively, and n
22 is the number of individuals lacking the allele altogether. The estimate was evaluated for significance using the chi-square approximation v
2 d.f.=1
= n · F
IS
2
(Nei 1987, p. 156), where n = n
11
+ n
12
+ n
22 is the sample size.
Genetic differences among localities were quantified by F
ST
, using Weir & Cockerham’s (1984) estimator h in all samples and also within pairs of sample localities.
Allele frequency differences among localities were tested using exact tests in the
GENEPOP software (Rousset
2008) with 10 000 dememorizations and batches, using
10 000 iterations per batch. Each locus was tested separately, and a joint P -value over loci was calculated by summarizing twice the negative logarithms of the single-locus P -values, evaluated against the critical chisquare value with 2 · 7 degrees of freedom (i.e. Fisher’s summation procedure).
Because the tusk is a long-lived species, and because all samples comprise multiple adult age classes, the samples should adequately represent the local population. We further expect that different sample years (cf.
Table 1), and possible differences in the age composition of samples, should have little influence on allele frequency differences among samples. While we cannot explicitly test for effect of sample year on genetic differentiation, because each locality was sampled in a single year only, we can indirectly check for temporal instability as a potential confounding factor (e.g. Palumbi
2003), using data on individual length as a proxy for age. The effect of age differences (i.e. length differences) on genetic differentiation was tested for by calculating genetic differences a ij
(Rousset 2000) within pairs of individuals ( ij ), averaged over the seven loci for the five localities for which the length data were available (cf.
Table 1). We used the measure a ij as the dependent variable in a generalized additive model (GAM): a ij s ð d ij
Þ þ c ij
þ s ð d ij
Þ : c ij
; where d ij is the measured length difference between two individuals i and j (accounting for temporal effects) and c i j is a categorical variable indicating whether the two individuals were caught at the same place or not, defined at the sublocality level (accounting for spatial effects).
s () are smoothing spline functions with degrees of freedom chosen by cross-validation, and allowing for the expected nonlinear effects of age differences when generations overlap (cf. Jorde & Ryman 1996). The last term represents interactions among the two explanatory variables and is included in the model to account for potential effects of size differences among sample locali-
2009 Blackwell Publishing Ltd
G E N E T I C S T R U C T U R E I N T U S K 3 1 5 5 ties. The model was fitted to data from the localities where length data were available, using the gam library in the
R statistical package (R Development Core Team
2006). Tests for significant effects on genetic differentiation were based on random pertubations (repeated 5000 times) of individual covariates to generate null-distributions, and compared with the original estimates.
P -values for the tests were calculated as the proportion of permutations that resulted in estimates that were smaller than the original estimate, if negative, or larger than the original, if positive.
Spatial patterns in the genetic structure were evaluated from estimates of F
ST
(Weir & Cockerham 1984) between sample pairs. First, linearized pairwise estimates, F
ST
⁄ (1
) F
ST
), were regressed against log-transformed geographical (Euclidean) distance between samples to test for potential isolation-by-distance effects
(cf. Rousset 1997). The correlation was tested by a simple Mantel test (for this and subsequent Mantel tests, we used the
ZT software, ver. 1.1, from Bonnet & Van de Peer 2002). Second, the pairwise F
ST values were used to study and visualize spatial genetic patterns using the
BARRIER software (ver. 2.2; Manni et al.
2004).
This latter method links geographical coordinates to each sample and provides a Delauney triangulation that is interlinked with genetic differentiation h among samples. Then, Monmonier’s maximum distance algorithm was used to identify ‘barriers’ to gene flow among samples. We performed separate tests for each locus and also one test including all loci to ensure that observed patterns were consistent and not due to a single locus only. Three barriers were supported by the data. The test applying all loci denotes the rank of importance of these barriers, whereas the number of loci supporting a given barrier is provided by the single locus tests.
We then expanded the isolation-by-distance analysis by substituting Euclidian distance by: (i) the minimum downstream distances between sites along the path of the predominant surface currents (denoted as ‘current distance’); and (ii) by the minimum distances between sites along the most likely migration corridor of benthic juveniles and adults (denoted as ‘habitat distance’). The rationale behind approach (i) is that if gene flow is facilitated by passive drift of pelagic eggs and larvae, we expected genetic differentiation to correlate with distance along the axis of the predominant drift route, i.e.
along the ocean currents. Alternatively, option (ii) hypothesizes that if genes are exchanged primarily by migration of benthic juveniles and adults, then the path of gene flow should be along the slopes within the depth range of the species. ‘Habitat distances’ were measured along a belt running perpendicular to the slopes defined by the 100–1000 m isobaths (grey-shaded areas in Fig. 1). Rather than moving in straight lines

3156 H . K N U T S E N E T A L .
among sites, the fish would be expected to migrate within its preferred habitat and not cross deep troughs or ocean basins. ‘Current distances’ were measured between localities following approximately the major currents (arrows) depicted in Fig. 1. Two of our localities, RA and the MAR, are bathymetrically separated from the remaining distribution area, and we arbitrarily assigned a large distance of 9000 km between them and each of the other localities. This value was chosen to be larger than the greatest measured habitat distance (i.e.
8700 km, between CA and TF). We tested the two hypotheses for gene flow, viz .
(i) passive larval drift with oceanic currents and (ii) active migration along habitable corridors, by correlation between the two distance measures and genetic differentiation,
F
ST
⁄ (1 ) F
ST
), using Mantel tests. For these and subsequent tests, we used untransformed distances (cf. Rousset 1997) because the hypothesized gene flow paths were essentially one dimensional, either when considering the path along the ocean currents or the narrow habitat range (cf. Fig. 1). Because ‘current distances’ and ‘habitat distances’ are obviously correlated with
Euclidian distances, we finally removed the effect of the latter and tested the residual effects of ‘current distance’ and ‘habitat distance’ on F
ST
⁄ (1 ) F
ST
), using partial
Mantel tests.
In total, 764 specimens of tusk ( Brosme brosme ) were successfully scored at five or more of the seven microsatellite loci (Table 1). This excludes 20 specimens that were scored at less than five loci (cf. sampled and scored columns in Table 1), presumably due to poor
DNA quality as several loci were affected, and which were omitted from further consideration.
The amount of genetic variability in the combined tusk samples ranged from very low at locus Bbrom2, segregating for three alleles (one of them rare) with a gene diversity of only H
T
= 0.022, to very high at Bbrom10 (57 alleles, including eight singletons; H
T
= 0.928) (Table 2).
Tests for random association of alleles within loci revealed that most population samples conformed to
Hardy–Weinberg genotype expectations (Table 3). Of the 53 single-locus tests, eight tests came out as departures at the 5 % confidence level (no correction for multiple tests), with P -values ranging from barely significant ( P = 0.0493) to highly significant ( P = 0.0001).
There was no obvious pattern to these departures from
Hardy–Weinberg genotype proportions, and F
IS estimates and P -values varied markedly among loci within samples as well as among samples at the same locus
(cf. Table 3). Closer inspection of the eight significant tests’ results revealed that several depended strongly on the presence of a single deviating individual or genotype in the sample. At Bbrom1, the weak significance in sample FI ( P = 0.0493) disappeared completely
( P = 0.308) when a single heterozygote individual, segregating for two rare alleles (315 and 327), was temporary removed from the Hardy–Weinberg test. Likewise, single heterozygous individuals were responsible for the significant test results at Bbrom16 in the MAR sample and contribute substantially to the significant results at loci Bbrom1 and Bbrom16 in the TF sample (the
P -values increased from <0.001 to 0.04 when removing a single genotype from each locus). Hence, many of the significant tests’ results reported in Table 3 appear to depend on the presence of a single deviating individual or genotype in the sample, and do not seem to
Table 3 Estimated departures from Hardy–Weinberg genotype proportions ( F
IS
) and exact probability tests for random assortment of alleles within loci and samples (using GENEPOP v. 4.0.6; Rousset 2008)
Estimated F
IS
Sample
MAR
RA
FI
SE
TF
IS
GR
CA
Average
Bbrom1
)
)
0.001
0.078
0.094*
) 0.003
0.281**
)
0.117
0.012
)
0.203
)
0.002*
Bbrom2 na na na
) 0.005
)
0.022
)
0.005
) 0.017
)
0.005
)
0.011
Bbrom5
0.089
0.103*
)
0.015
) 0.104
0.153
)
0.076
) 0.028
)
0.080
0.005
Bbrom10
0.065
0.063***
0.074
0.128*
0.175***
0.022
0.078
0.007
0.077***
Bbrom16
0.106*
0.150
0.030
) 0.082
0.161**
)
0.061
0.114
)
0.054
0.046*
Bbrom18
) 0.050
)
0.058
0.114
) 0.054
0.076
0.049
) 0.003
)
0.052
0.0028
Bbrom21
) 0.019
)
0.095
0.030
0.070
0.069
)
0.073
) 0.059
)
0.076
)
0.019
Average
0.033
0.011***
0.061
0.018
0.160***
)
0.030
0.025
)
0.061
0.027***
Fisher’s procedure was used to summarize single-locus P -values for the average over loci. See Table 1 for sample abbreviations.
na, not applicable (Bbrom2 was fixed or nearly so in samples MAR, RA and FI).
Note : No corrections for multiple tests. * P < 0.05;
** P < 0.01; *** P < 0.001.
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reflect a common trend or pattern, such as population mixture (Wahlund effect). A possible exception is locus
Bbrom10, which displayed a notable excess of homozygotes in samples RA, SE and TF. Calculating F
IS separately for each allele at the Bbrom10 locus, treating all other alleles in turn as a single composite allele, the chisquare tests were significant for three alleles in sample
RA, for four alleles in SE and for seven alleles in TF
(data not shown). This suggests a general homozygote excess at Bbrom10.
The level of genetic variability among loci had no apparent effect on genetic differentiation as measured by F
ST
(Table 2). Averaging over all seven loci, the amount of genetic differentiation among sample sites was small but statistically significant ( F
ST
= 0.0014; Table 2). The joint null hypothesis of no allele frequency differences among localities was rejected as highly improbable ( P < 0.0025), although only two loci (Bbrom 10 and Bbrom16) were significant when each locus was tested separately (cf.
Table 2). Exploring the robustness of these findings to potential genotyping errors, we temporarily removed problematic genotypes identified during the Hardy–
Weinberg test mentioned earlier [altogether 14 genotypes at loci Bbrom1 (2 genotypes), Bbrom16 (2) and Bbrom10
(10) were removed for this exercise]. The changes in F
ST and P -values resulting from omitting these genotypes were negligible, and actually led to a slight increase in
F
ST among samples, with correspondingly lower P values (data not shown). Also, entirely omitting locus
-
Bbrom10, which appeared to segregate for null alleles
(mentioned earlier), had little effect on the average F
ST and raised the estimate slightly, from a mean of 0.0014
(including Bbrom10) to 0.0015 (excluding Bbrom10). This small difference is consistent with Chapuis & Estoup’s
(2007) finding that null alleles are not expected to bias estimates of F
ST when the parametric F
ST is small (implying high gene flow). Hence, the observed genetic differentiation among tusk samples appeared robust to potential genotyping problems and null alleles, and we therefore retained all genotypes and loci in the following analyses.
The results of GAM modelling of genetic differences among pairs of individual fish (Table 4) confirmed that spatial location has a significant ( P = 0.023) impact on genetic differentiation. Individuals from the same sublocalities were on average less different genetically, as indicated by a negative estimate, than those caught at different locations or sublocations. In contrast, differ-
2009 Blackwell Publishing Ltd
G E N E T I C S T R U C T U R E I N T U S K 3 1 5 7
Table 4 Results of GAM model testing for potentially confounding effects on genetic differentiation among individual tusk from MAR, RA, IS, GR and CA and their sublocalities (cf.
Table 1)
Source
Length difference ( d )
Co-located ( c )
Length difference: co-located
Estimate
)
0.000087
)
0.016357
0.000742
P -value
0.459
0.023
0.128
The model is based on pairwise genetic differences ( a ij
, calculated from seven microsatellite loci) as the response and the potential explanatory variables (covariates) are the difference in body lengths within pairs and whether or not the individuals were sampled at the same sublocality or not
(co-located). The interaction term (bottom row) is also included.
P -values were calculated from 5000 random perturbations of individual covariates.
ences in length (and presumably age) had little or no effect on genetic differences among individuals in the five samples where size data were available (samples
MAR, IS, GR, CA and RA). Hence, the observed spatial genetic differentiation does not appear to be an artefact of confounding size (or age)-related factors in this study.
Inspection of F
ST
-estimates between pairs of samples
(Table 5) revealed that the central distribution area
(sample localities FI, SE, IS and GR) appears genetically homogeneous or nearly so, as all the estimates between samples from this area were below zero, and only one test (out of 42) appeared significant at the 5 % level in single-locus tests. For other sample pairs, F
ST
-estimates were mostly positive and several estimates were significant at the 5 % confidence level or better. Tusk from
CA, MAR and RA were clearly the most genetically differentiated as judged by the average F
ST
-estimates for each sample (Table 5, bottom row). Based on the pairwise F
ST
-values, the
BARRIER software accordingly placed the most significant ‘barriers’ to gene flow around these three samples (Fig. 2). There was little or no evidence for further barriers to gene flow in this analysis.
Overall, there was a tendency for increased genetic differentiation, F
ST
⁄ (1 ) F
ST
), with geographical (i.e.
Euclidean) distance, although the relationship was not significant, neither for log-transformed ( r = 0.404,
P = 0.096, Mantel test) nor for untransformed distances
( r = 0.399, P = 0.120; Table 6). Habitat distance correlated more strongly ( r = 0.547) with genetic differentiation than either Euclidean or current distance
( r = 0.148). Habitat distance was statistically significant, and remained so also after accounting for the effect of
Euclidean distance (partial Mantel test, P = 0.030;
Table 6). Inspection of the residual plots (Fig. 3),

3158 H . K N U T S E N E T A L .
Table 5 Genetic differentiation within pairs of tusk samples
Sample MAR RA FI SE TF IS GR CA
MAR
RA
FI
SE
TF
IS
GR
CA
Average
—
0.0050
0.0008
0.0012
0.0008
0.0051
0.0001
0.0069
0.0028
3*
—
0.0018
)
0.0008
0.0030
0.0022
)
0.0006
0.0090
0.0028
0
2*
—
)
0.0014
0.0021
) 0.0008
)
0.0020
0.0020
0.0004
1
0
1
—
)
0.0011
) 0.0005
)
0.0028
0.0024
)
0.0004
1*
1
2*
0
—
0.0016
)
0.0011
0.0036
0.0013
2**
0
1*
0
1
—
)
0.0002
0.0010
0.0012
0
0
1
0
0
0
—
0.0042
)
0.0003
1**
3***
1*
1
2**
1
1*
—
0.0042
Below diagonal: estimated F
ST within each sample pair, averaged over seven microsatellite loci. Above diagonal: significance level
(* P < 0.05; ** P <0.01; *** P < 0.001) for the exact test of allele frequency heterogeneity jointly over loci (using Fisher’s summation procedure); the numbers refer to the number of loci that came out significant at the 5 % level in the single-locus tests (no adjustment for multiple tests). The bottom row gives the average of the pairwise F
ST for each sample. See Table 1 for sample abbreviations.
CA
3
6
GR
MAR
5
6
IS
5
RA
FI
7
SE
TF
Fig. 2
BARRIER
-inferred gene flow patterns among brosme sample localities based on seven microsatellite loci. Numbers denote the number of loci supporting the respective barrier
(red bars), and thickness of the red barriers indicate their rank of importance. Sample abbreviations are given in Table 1.
Table 6 Correlations ( r ) of geographical (Euclidean) distance, minimum downstream distance (‘current’) and minimum continuous habitat distance (‘habitat’: setting isolated patches to
9000 km apart) with estimated F
ST
⁄ (1
)
F
ST
) values between pairs of sample localities
Distance measure
Euclidean
Current
Habitat
Current | Euclidean
Habitat | Euclidean r
0.399
0.148
0.547
) 0.119
0.476
P -value
0.120
0.311
0.030
0.380
0.030
Simple (upper three rows) and partial (lower two rows)
Mantel tests were performed with the
ZT software (ver. 1.1;
Bonnet & Van de Peer 2002), using the exact permutation method when calculating P -values.
depicting the remainder of F
ST
⁄ (1 ) F
ST
) after subtracting the effect of Euclidean distance, indicated that the effect of habitat distance on genetic differentiation resulted largely from several high estimates at a habitat
‘distance’ of 9000 km (cf. Fig. 3b). All these refer to sample pairs that included the RA and MAR samples
(identified by triangular symbols in the figure). In contrast, the residuals were more scattered for current distance (Fig. 3a), which displayed an average negative trend ( r = ) 0.119; Table 6).
We detected weak but statistically significant genetic subdivision among tusk samples drawn from across the
North Atlantic. In the analysis of genetic differentiation among individual fish, sample locality had a significant effect on genetic differentiation, whereas size differences (and presumably age differences) did not. This suggested that the observed genetic heterogeneity in this study reflected spatial differentiation, not temporal variation or ‘noise’. The observed genetic structure also appeared robust to potential genotyping errors, in the sense that exclusion of statistically deviating (i.e. ‘problematic’) genotypes or loci (Bbrom10) had little or no effect on estimates of genetic differentiation. These are important considerations when dealing with species that have low levels of genetic differentiation, as confounding factors assume a relatively greater importance for such species (Waples 1998).
Our results expand on earlier findings of Johansen &
Nævdal (1995), who in trans-Atlantic comparisons detected significant allele frequency differences at the haemoglobin locus, but not at isozyme loci. The existence of population structure in tusk has previously been suspected from observations of consistent differences in age and size distributions, and size-at-age patterns, among fishing areas of the North-East Atlantic
(Bergstad & Hareide 1996). So far, other methods such as tagging experiments (Sigurdsson et al.
2006) or trace element analyses (Swan et al.
2006) that might have provided independent information on population structure have not been applied in this species.
2009 Blackwell Publishing Ltd

(a) (b)
G E N E T I C S T R U C T U R E I N T U S K 3 1 5 9
Fig. 3 Residual amounts of genetic differentiation, F
ST
⁄ (1
)
F
ST
), among sample pairs after subtracting the amount ascribed to geographical (Euclidean) distance among samples, as calculated from linear regression (intercept
)
3.055e-04, slope
8.428e-07). (a) Residual effect of ‘current distance’ among sample sites; (b) Residual effect of ‘habitat distance’, encoding discontinuous sites (Rockall and the Mid-
Atlantic Ridge: triangular symbols) by
9000 km. See Table 6 for test results.
Current distance (km) Habitat distance (km)
The observed level of genetic differentiation in tusk is comparable in magnitude with that observed for another North Atlantic deepwater fish with a similarly extensive distributional range (the Greenland halibut:
Knutsen et al.
2007b). But the level appears to be somewhat lower than that in Atlantic cod (O’Leary et al.
2007; Pampoulie et al.
2008) and redfish (Roques et al.
2002). The patterns of differentiation may, however, differ among species in this region. Other North
Atlantic species typically display an isolation-bydistance effect, i.e. a more or less linear increase of genetic differentiation with (logarithmic) geographical distance (Atlantic cod), distance measured along the path of major ocean currents (Greenland halibut) or by depth ( Sebastes mentella ). In tusk, differentiation was not significantly related to these simple distance measures.
Instead, genetic differentiation was more strongly related to distance within habitable areas (essentially defined by the bathymetric range of the species). This was demonstrated clearly by the samples from RA and the MAR, which were comparatively isolated with respect to bathymetry. Our data thus indicated a genetic structure in tusk with modest divergence over large distances (up to more than 5000 km) combined with comparatively strong divergence over shorter distances in some areas (e.g. RA). The different patterns observed at different geographical scales suggest independent evolutionary forces shaping population structure in the tusk.
The prevalence of a planktonic life stage of most marine species indicates an adaptation to dispersal (e.g. Bonhomme & Planes 2000), and gene flow among sites connected by ocean currents should be expected (e.g.
Palumbi 2003). The ocean currents in the North Atlantic represent a possible means for long-distance larval transport for all species with a pelagic life stage.
Roques et al.
(2002) found that the deepwater redfish
( S.
mentella ) was largely genetically homogenous throughout the ‘Panoceanic’ region from Labrador to the Faroe Island above 500 m, and ascribed the apparent lack of genetic differentiation in this area to larval drift by ocean currents. Our sample localities GR, IS, FI,
SE and perhaps TF are all situated within this region and tusk larvae produced here may be transported with the same ocean currents as the redfish, although differences among species may pertain regarding position of spawning grounds and timing of spawning in relation to currents. In pioneer mapping exercises, Schmidt
(1909) and Ehrenbaum (1909) described the widespread occurrence of tusk eggs and larvae in European shelf waters, and off Scandinavia tusk eggs occur in fjords and shelf waters (Bjørke 1981 and references therein).
Spawning areas appear to be found throughout the distribution area. However, no information exists on the drift patterns and distances or mesoscale features, such as retention areas, that could be used to analyse potential for gene flow by egg and larval transport. Nonetheless, larval drift is a possible explanation for the apparent absence of genetic differentiation that we observed among sample localities in the central North
Atlantic, perhaps with the exception of RA and the
MAR. In this context, it was surprising to find no effect of ‘current distance’ on genetic differentiation in this study (but such an effect could exist at a different geographical scale).
Migration of demersal juveniles and adults among sites could represent an additional means of gene flow, at least in areas interconnected by potential migration corridors of suitable depth. There is no indication that tusk conducts extensive migrations once it has attained a demersal mode of life (Andriyashev 1954; Svetovidov
1986; Cohen et al.
1990), but no studies using e.g.
tagging or other means to study migration have
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3160 H . K N U T S E N E T A L .
been possible for practical reasons (but see Sigurdsson et al.
2006). We note that the four or five sample sites showing little or no genetic differentiation are all connected by shelves or slopes that are above 1000 m deep
(the grey-shaded areas in Fig. 1) and should be accessible to tusk migrating along the seabed.
An alternative, or additional, explanation for low levels of genetic differentiation over widely separated areas is limited opportunity for divergence because of recent colonization. The last glaciation had a major impact on marine life, especially in the NW Atlantic
(Wares & Cunningham 2001). The species may have become locally extinct during this period, followed by postglacial re-colonization sometime during the last few thousand years. Divergence since re-colonization could be slow if effective population sizes are large, limiting genetic drift and speed of differentiation, and further contributing to this historical factor. If gene flow is low, more highly mutable loci, such as Bbrom10, are expected to be less divergent than less mutable loci
(Nichols & Freeman 2004; Ryman & Leimar 2008).
There are widely divergent levels of genetic variation
( H
T
) among the seven microsatellite loci studied in this study (cf. Table 2), indicating very different mutation rates among loci (RoyChoudhury & Stephens 2007).
Despite this, there is little variation in F
ST among loci
(cf. Table 2) and the predictions for low gene flow are thus not observed. This weakens the hypothesis for postglacial re-colonization and low gene flow as an alternative to high gene flow for explaining low levels of genetic differentiation in tusk.
Despite opportunities for gene flow at larval and adult life stages, we found that the tusk is not panmictic over its entire range but display a pattern with localized genetic differentiation. This differentiation is apparently dictated by habitat structure reflecting bathymetric features. Outside the interconnected shallow slope and shelf areas, a likely isolating mechanism in tusk is limited dispersal of adult fish across very deep waters.
Tusk has neither been reported below 1000 m, nor in midwater, and deeper troughs or ocean basins could constitute effective migration barriers to this demersal species. The sample sites at RA and the MAR are surrounded by such deep areas (cf. Fig. 1), and limited adult dispersal is a likely contributing factor for the partial genetic isolation of these localities.
Genetic differences can, however, only persist if intermixing and gene flow are restricted at both adult and earlier life stages. A plausible explanation for observed genetic differentiation is that eggs and larvae from some spawning areas are retained in gyres and eddies surrounding banks or islands, such as in the RA
(Bartsch & Coombs 1997), and perhaps the FI (retention described around FI by Hansen 1992). Similar processes may limit advection away from the Canadian shelf and the MAR. Retention of pelagic larvae represents an additional means for limitation of gene flow in tusk, as for other marine species (e.g. Bailey et al.
1997; Taylor
& Hellberg 2003).
The finding that bathymetric barriers shape the population structure in tusk has implications for understanding other deep-sea species also. Most organisms inhabiting the ocean have restricted depth ranges, and the presence of deep basins (and perhaps also shallow waters) between habitable areas represents a potential structuring force that has previously been given little attention.
This structuring appears despite a prolonged pelagic larval phase and indicates that larval drift may be an ineffective means for gene flow. This could be caused by bathymetric forcing of ocean currents, creating retention zones with limited transport of larvae, or that survival of larvae is poor over deep waters that may be low in nutrition. The observed pattern of genetic differentiation in tusk underscores the importance of habitat boundaries and cryptic population structure in marine species
(Palumbi 2004; Bradbury & Bentzen 2007; McCairns &
Bernatchez 2008). Because the processes studied in this work are quite general, there is transferable insight of relevance to other explored deep-sea fishes which should not a priory be assumed to be panmictic. Instead, management strategies should take population structure into account, also for species that presently lack adequate data for assessing such structure.
This work was funded by the Norwegian Research Council and the Norwegian Ministry of Fishery and Coastal Affairs.
Primer development was supported by MAR-ECO (http:// www.mar-eco.no), a field project under the Census of Marine
Life programme. Sergio Stefanni is a postdoctoral fellow funded by FCT (Foundation for Science and Technology, Portugal, ref. SFRH ⁄ BPD ⁄ 14981 ⁄ 2004). IMAR ⁄ DOP is funded through the pluri-annual and programmatic funding schemes of FCT (Portugal) and DRCT (Azores, Portugal) as Research
Unit No. 531 and Associate Laboratory No.9. We are grateful to fishermen, colleagues and projects for provision of tissues samples: K. Helle, L. Harris, K. Kristinsson, vessels of the Norwegian ‘reference fleet’, K. Lorgen on M ⁄ S Torita, MAR-ECO, and Francis Neat, MAR-LAB, UK. We thank participants of
Høines for producing Fig. 1. Three anonymous referees and the Associate Editor provided valuable comments on an earlier version of this manuscript.
2009 Blackwell Publishing Ltd

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All co-authors share an interest in understanding the mechanisms that drive population structure in Marine Organisms.
This paper resulted from interactions among all co-authors, and within a European Science Foundation project DEECON
(www.imr.no/deecon) with the primary aim of examining focal mechanisms that promote and maintain the genetic population structure of tusk in the North Atlantic. Such information will provide management with useful information in the context of sustainable use of marine resources.
2009 Blackwell Publishing Ltd