University of Groningen
Global population structure and demographic history of the grey seal
Klimova, A.; Phillips, C.D. ; Fietz, Katharina
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Molecular Ecology
DOI:
10.1111/mec.12850
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Klimova, A., Phillips, C. D., & Fietz, K. (2014). Global population structure and demographic history of the
grey seal. Molecular Ecology, 23, 3999-4017. DOI: 10.1111/mec.12850
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Molecular Ecology (2014) 23, 3999–4017
doi: 10.1111/mec.12850
Global population structure and demographic history of
the grey seal
A . K L I M O V A , * C . D . P H I L L I P S , † K . F I E T Z , ‡ § M . T . O L S E N , § J . H A R W O O D , ¶ W . A M O S * * and
J. I. HOFFMAN*
*Department of Animal Behaviour, University of Bielefeld, Postfach 100131, 33501, Bielefeld, Germany, †Department of
Biological Sciences and the Museum, Texas Tech University, Lubbock, TX 79409, USA, ‡Marine Evolution and Conservation
group, Centre for Ecological and Evolutionary Studies, University of Groningen, Nijenborgh 7, NL 9747 AG, Groningen, The
Netherlands, §Section for Evolutionary Genomics, Centre for Geogenetics, Natural History Museum of Denmark, University of
Copenhagen, Øster Voldgade 5-7, DK-1350, Copenhagen K, Denmark, ¶Sea Mammal Research Unit, Scottish Oceans Institute,
University of St Andrews, St Andrews, Fife, KY16 8LB, UK, **Department of Zoology, University of Cambridge, Downing
Street, Cambridge, CB2 3EJ, UK
Abstract
Although the grey seal Halichoerus grypus is one of the most familiar and intensively
studied of all pinniped species, its global population structure remains to be elucidated. Little is also known about how the species as a whole may have historically
responded to climate-driven changes in habitat availability and anthropogenic exploitation. We therefore analysed samples from over 1500 individuals collected from 22
colonies spanning the Western and Eastern Atlantic and the Baltic Sea regions, represented by 350 bp of the mitochondrial hypervariable region and up to nine microsatellites. Strong population structure was observed at both types of marker, and highly
asymmetrical patterns of gene flow were also inferred, with the Orkney Islands being
identified as a source of emigrants to other areas in the Eastern Atlantic. The Baltic
and Eastern Atlantic regions were estimated to have diverged a little over 10 000 years
ago, consistent with the last proposed isolation of the Baltic Sea. Approximate Bayesian computation also identified genetic signals consistent with postglacial population
expansion across much of the species range, suggesting that grey seals are highly
responsive to changes in habitat availability.
Keywords: approximate Bayesian computation, asymmetrical gene flow, demographic history,
marine mammal, pinniped, population structure
Received 1 March 2014; revision received 4 June 2014; accepted 25 June 2014
Introduction
A species’ demography is determined by an interaction
between the extrinsic forces generated by its ecology,
including anthropogenic influences, and its intrinsic life
history characteristics. In turn, demographic change is
often reflected in genetic change, both in terms of
genetic diversity and levels of divergence between populations or geographic regions. By combining powerful
new analytical approaches with increasingly large
genetic data sets, this link can now be exploited to infer
Correspondence: Joseph I. Hoffman, Fax: +49 (0)521 1062711;
E-mail: [email protected]
© 2014 John Wiley & Sons Ltd
historical demographic events. Because past demographic trends may inform future response to environmental perturbations, such as anthropogenically driven
climate change, such knowledge is at a particular premium given the predictions of current climate models.
Colonially breeding pinnipeds are interesting in the
context of historical demography for several reasons.
First, they have strong dispersal capabilities yet also
tend to be highly philopatric and site faithful (Pomeroy
et al. 1994, 2000; Hoffman et al. 2006a,b; Hoffman & Forcada 2012). The relationship between these opposing
traits should contribute greatly to population structure,
which will in turn affect the demographic coupling
between breeding colonies and, hence, population
4000 A . K L I M O V A E T A L .
persistence (Olsen et al. 2014). Second, several pinniped
species show evidence of strong demographic responses
to historical changes in the marine environment, either
through rapid population expansion during periods of
increased food or habitat availability (Matthee et al.
2006; Curtis et al. 2009; Dickerson et al. 2010; Phillips
et al. 2011) or via altered migration dynamics (De Bruyn
et al. 2009). Third, many pinnipeds have been heavily
exploited for their meat, oil and/or fur (Bowen & Lidgard 2012). Consequently, contemporary patterns of
genetic diversity may have been shaped by a combination of relatively ancient events, like the last glacial
maximum (Matthee et al. 2006), more recent anthropogenic challenges (Hoffman et al. 2011) and innate life
history characteristics.
The grey seal (Halichoerus grypus) provides an interesting case in point, being widely distributed across
much of the North Atlantic continental shelf, exhibiting
considerable dietary flexibility and breeding in diverse
habitats from pack-ice through land-fast ice to terrestrial colonies (Kovacs & Lydersen 2008). Three main
geographically isolated grey seal populations are currently recognized (Bonner 1981). The Western Atlantic
population is distributed along the northeastern seaboard of the United States and southern Canada. The
Eastern Atlantic population is concentrated around the
coast of the United Kingdom and Ireland but also
includes breeding colonies in Iceland, the Faroe Islands
and along the mainland coast of northern Europe. The
Baltic population is geographically confined to the Baltic Sea (Bonner 1981; Hall 2002; Thompson & H€
ark€
onen
2008). Population size has probably changed a great
deal over time, being estimated at ~20 000 during the
last glacial maximum when continental shelf habitat
was much reduced (Boehme et al. 2012), before expanding around 12 000 years ago as the ice sheets retreated.
More recently, many breeding colonies were extirpated
or heavily reduced by anthropogenic exploitation and
pollution (Lesage & Hammill 2001; H€ark€
onen et al.
2007; Hiby et al. 2007). However, with protection, populations have rebounded strongly to a global size of
approximately half a million and numbers are still
increasing.
Although all three major grey seal populations probably experienced broadly similar histories, the timing
and severity of the component events may have differed considerably. For example, Western Atlantic grey
seals were hunted to population collapse in the 1800s,
later recovering from a few thousand individuals in
the 1960s (Lesage & Hammill 2001) to around 350 000
today. Archaeological evidence indicates that Eastern
Atlantic grey seals were common around 8000–
5500 years ago but gradually declined during the late
Middle Ages when many colonies in the Baltic
disappeared (H€
ark€
onen et al. 2007). Following protection under the Grey Seals (Protection) Act in 1914,
numbers in the UK increased from around 9000 in the
mid-1930s to between 80 and 90 thousand individuals
in 2008 (Lonergan et al. 2011). The Baltic population is
somewhat smaller but has experienced significant
growth in recent years and currently numbers around
40 000 individuals (HELCOM 2013). Grey seals are
thought to have entered the Baltic Sea around 6300–
7000 years ago (Schmolke 2008) and declined by over
90% between 1900 and the 1970s due to a combination
of unrestricted hunting and disease susceptibility
caused by the environmental contaminants polychlorinated biphenyl and dichlorodiphenyl trichloroethane
(Harding & H€
ark€
onen 1999). Following a ban on hunting in 1974 and changes in environmental politics,
the Baltic population has been slowly recovering
(HELCOM 2013).
Previous genetic studies have explored several
aspects of grey seal population structure and historical
demography. Strong philopatry is reflected in the
genetic differentiation between pairs of colonies even
within a geographic region (Allen et al. 1995), although
the pattern appears fluid and may be strongly influenced by directed migration between colonies that have
reached carrying capacity and newly founded or growing colonies (Gaggiotti et al. 2002). On a broader scale,
the Western and Eastern Atlantic are strongly differentiated from one another, while the Eastern Atlantic and
Baltic appear less so, consistent with the latter having
diverged more recently (Boskovic et al. 1996; Graves
et al. 2009; Cammen et al. 2011). The timing of the split
between the Baltic and the Eastern Atlantic grey seal
populations has been estimated variously at 0.35 million
years ago (Boskovic et al. 1996) based on mitochondrial
DNA, and between 10 000 and 1 million years ago
based on microsatellite data (Graves et al. 2009), the
wide range being due to uncertainty over mutation
rates. Both Boskovic et al. (1996) and Graves et al. (2009)
reported unexpectedly high levels of genetic diversity
that argue against an intense bottleneck.
Until recently, inferences about historical demography required a range of simplifying assumptions such
as constant population size or zero migration. The latest
Bayesian approaches now allow more flexible models to
be fitted in which multiple parameters are estimated
simultaneously (Beaumont & Rannala 2004). However,
most real populations still carry more complicated histories than can be accommodated without simplification. Consequently, it is of interest to compare
alternative algorithms based on both nuclear and uniparentally inherited markers in a species known to have
experienced dynamic demographic population histories.
Applying this rationale to the grey seal, and to provide
© 2014 John Wiley & Sons Ltd
G R E Y S E A L P O P U L A T I O N G E N E T I C S 4001
a global view of this species’ population structure
and demographic history, we sequenced 350 bp of the
mitochondrial hypervariable region 1 (HVR1) to augment published genotype data for nine microsatellites
(Worthington Wilmer et al. 1999; Gaggiotti et al. 2002) in
over 1500 grey seal samples collected from 19 colonies
spanning the Western and Eastern Atlantic Oceans, and
added these to data from three colonies in the Baltic
Sea (Graves et al. 2009).
Materials and methods
Tissue sample collection and DNA extraction
Skin biopsy samples were collected from live grey
seal pups at 19 breeding colonies (see Table 1, Fig. 1
and Fig. S1, Supporting information for details) following Bean et al. (2004). Skin samples were stored
individually in the preservative buffer 20% dimethyl
sulphoxide (DMSO) saturated with salt (Amos &
Hoelzel 1991) and stored at 20 °C. Total genomic
DNA was initially extracted using an adapted Chelex
100 protocol (Walsh et al. 1991), but to improve
sequence quality, this DNA was later purified using a
standard phenol–chloroform
et al. 1989).
procedure
(Sambrook
HVR1 sequencing and microsatellite genotyping of
non-Baltic samples
A 489-bp region of the HVR1 of the mitochondrial
control region was PCR amplified using primers
(L16245 50 -CACCACAGGCACCCAAAG-30 and H54 50 TCCAAATGGCAATGACACC-30 ) designed by Graves
et al. (2009) from the complete grey seal mitochondrial
genome (Arnason et al. 1993). Sequencing was conducted by Alpha Biolabs (http://www.nucleics.com).
Sequences were aligned using BIOEDIT 7.0 (Hall 1999)
and all positions differing from the consensus sequence
were inspected by one of us (WA) to verify base calls
were high quality. All sequences were then trimmed to
the length of the shortest sequence (350 bp). Microsatellite data derive from two published studies (Worthington Wilmer et al. 1999; Gaggiotti et al. 2002), the
samples being genotyped following Allen et al. (1995) at
nine unlinked microsatellite loci: Hg3.6, Hg4.2, Hg6.1,
Hg6.3, Hg8.9, Hg8.10, Hgd.2 (Allen et al. 1995), Pv9 and
Pv11 (Goodman 1997).
Table 1 Numbers of grey seal (Halichoerus grypus) tissue samples for which HVR1 and microsatellite data were analysed. Colonies
are classified according to three ‘regions’ and seven ‘subregions’ as described in the Materials and methods. Data for seals from the
Baltic are from Graves et al. (2009)
Number of samples genotyped at
Region
Subregion
Colony
Mitochondrial DNA
Microsatellites
Western Atlantic
Eastern Atlantic
Nova Scotia
Orkney Islands
Sable Island
Holm of Huip
Holm of Spurness
Calf of Eday
Muckle Greenholm
Rusk Holm
Stroma
Links Ness
Swona
Faray
Copinsay
Corn Holm
Calf of Flotta
Switha
North Rona
Monachs
Isle of May
Farne Islands
Faroe Islands
Stockholm Archipelago
Bay of Bothnia
Estonia
67
144
144
130
129
118
115
89
101
72
57
58
47
41
48
28
54
49
31
39
34
40
1635
28
146
145
130
131
119
115
89
102
73
57
59
47
41
52
32
54
49
33
47
34
50
1633
North West Scotland
Baltic
Eastern Scotland
North East England
North Atlantic
Baltic
© 2014 John Wiley & Sons Ltd
30°0’0"W
40°0’0"W
30°0’0"W
20°0’0"W
20°0’0"W
10°0’0"W
0°0’0"
10°0’0"E
20°0’0"E
10°0’0"W
0
800
0°0’0"
10°0’0"E
20°0’0"E
Bay of
Bothnia
60°0’0"N
50°0’0"W
60°0’0"N
60°0’0"W
Faroe
Islands
1,600 Miles
50°0’0"N
Sable Island
60°0’0"W
50°0’0"W
40°0’0"W
30°0’0"W
20°0’0"W
10°0’0"W
0°0’0"
10°0’0"E
Finland
Stockholm
Archipelago
20°0’0"E
Orkney Islands
60°0’0"N
60°0’0"N
50°0’0"N
Sweden
Norway
Faeroe Islands
Estonia
North Rona
Isle of May
Monachs
Latvia
Estonia
Denmark
Farne Islands
Russia
Belarus
ATL AN T IC OC E A N
Poland
Netherlands
Belgium
50°0’0"N
Lithuania
BALTIC SEA
Isle of Man
United Kingdom
Ireland
Germany
Luxembourg
Czech Republic
Jersey
Ukraine
50°0’0"N
40°0’0"W
70°0’0"N
4002 A . K L I M O V A E T A L .
Slovakia
Moldova
France
0
195
390
780 Miles
Liechtenstein
Switzerland
Italy
40°0’0"W
30°0’0"W
20°0’0"W
10°0’0"W
0°0’0"
Austria
Hungary
Slovenia
C r o a t i aS e r b i a
10°0’0"E
Romania
20°0’0"E
Fig. 1 Map showing the sampling locations of grey seal breeding colonies. The sampling locations of colonies within the Orkney
Islands are shown in Fig. S1 (Supporting information).
Inclusion of data from the Baltic
Additional mitochondrial and microsatellite data were
available for three grey seal breeding colonies from the
Baltic Sea (Graves et al. 2009). The mitochondrial trace
files were scrutinized by two of us (KF and MTO) leading to a number of errors being identified and corrected
(Fietz et al. 2013). These samples were also genotyped
by Graves et al. (2009) for six microsatellite loci, five of
which overlap with our main data set (Hg4.2, Hg6.1,
Hg8.9, Hg8.10 and Pv9). Consequently, all analyses
including samples from the Baltic were restricted to five
microsatellites. Allele frequencies at these loci were harmonized between the two data sets by Graves et al.
(2009), who independently regenotyped 50 samples and
found the two sets of scores to be identical.
Generation of summary statistics
The number of mitochondrial haplotypes, number of
polymorphic sites, haplotype diversity (h) and nucleotide diversity (p) were calculated using DNASP version
5.0 (Rozas & Rozas 1995). Haplotype frequencies were
calculated using ARLEQUIN version 2.0 (Schneider et al.
2000). Using the Bayesian information criterion (BIC) for
model selection implemented in MEGA 5 (Tamura et al.
2011), the best-fit mutational model for our mtDNA data
was determined to be K2 + G(0.55) + I(0.48) and this,
or the most similar model available, was used in subsequent analysis where a substitution model needed to be
specified. GENEPOP (Raymond & Rousset 1995) was used
to test each microsatellite locus for deviations from
Hardy–Weinberg equilibrium, to calculate observed and
expected heterozygosities and to test for linkage disequilibrium. For each test, we set the dememorization number to 10 000, the number of batches to 1000 and the
number of iterations per batch to 10 000.
Analysis of population structure
Population structure was assessed using hierarchical
analyses of molecular variance (AMOVA) conducted
within ARLEQUIN. Analyses were carried out at two different hierarchical levels: among the three main geographic regions (Western Atlantic, Eastern Atlantic and
Baltic) and among the seven subregions (Orkney
Islands, North West Scotland, Eastern Scotland, North
© 2014 John Wiley & Sons Ltd
G R E Y S E A L P O P U L A T I O N G E N E T I C S 4003
East England, North Atlantic, Nova Scotia and Baltic
Sea). For mitochondrial DNA, pairwise population differences were assessed using Fst (Weir & Cockerham
1984) and Φst (Kimura 1980), the former looking only at
haplotype frequency differences (Excoffier et al. 1992)
while the latter also incorporates haplotype sequence
similarity. For microsatellite data, pairwise population
differences were assessed using Fst (Weir & Cockerham
1984) and Rst (Slatkin 1995), the latter being a microsatellite-specific measure that takes account of the stepwise-mutation process. Statistical significance was
assessed using 10 000 permutations of the data. The
program POPULATIONS version 1.2.31 (Langella 1999) was
used to construct neighbour-joining (NJ) trees for the
mitochondrial data using Φst and for the microsatellite
data using Cavalli-Sforza and Edwards chord distance,
Dc (Cavalli-Sforza & Edwards 1967). The latter measure
has been shown to be most effective in recovering the
correct tree topology from microsatellite data under a
variety of evolutionary scenarios (Takezaki & Nei 1996).
Haplotype network
Haplotype networks are often better suited than phylogenetic trees to depict relationships among populations
because ancestral haplotypes are often extant and
uncertainty over ancestor–descendent relationships can
be indicated by network reticulations. We therefore
visualized relationships among the three geographic
regions based on the mitochondrial DNA sequence data
by constructing a median joining network within
NETWORK version 4.6.1.1. (Bandelt et al. 1999). This program calculates all possible minimum-spanning trees
for the data set and then combines these into a single
minimum spanning network following an algorithm
analogous to that proposed by Excoffier & Smouse
(1994). Inferred intermediate haplotypes are then added
to the network in order to minimize its overall length.
Estimation of migration rates
Migration rates can be estimated at two different timescales. In the short term, immigrants from a different,
genetically distinct, population and their offspring will
tend to carry genotypes that are unlikely in a system
where gene flow is negligible and the population is in
Hardy–Weinberg equilibrium (Faubet et al. 2007). Such
individuals can be identified and used to infer the rates
and directions of contemporary gene flow using the
Bayesian program BAYESASS version 3 (Wilson & Rannala
2003). Parameter values examined are migration rates
(DM), allele frequencies (DA) and inbreeding coefficients
(DF). The user chooses parameter value size changes to
achieve the optimal acceptance rate of between 20%
© 2014 John Wiley & Sons Ltd
and 60% (Wilson & Rannala 2003). After exploring a
range of values, we analysed the seven subregions
(Orkney Islands, North West Scotland, Eastern Scotland,
North East England, North Atlantic, Nova Scotia and
Baltic Sea) using 3 9 107 iterations following a burn-in
of 3 9 106 iterations, DM = 0.1, DA = 0.4 and DF = 0.8.
Each analysis was repeated five times with different initialization seeds. The posterior probability distributions
of the parameters of interest were inferred from samples harvested every 1000 iterations. TRACER version 1.5
(Rambaut & Drummond 2007) was used to monitor
chain mixing and convergence. As high levels of consistency were found among the replicate analyses, data
are presented for the final replicate only.
To explore patterns of historical gene flow, we used
the Bayesian coalescent-based framework implemented
in MIGRATE version 3.5.1 (Beerli & Felsenstein 1999;
Beerli 2006). MIGRATE allows the simultaneous estimation of gene flow (M = m/l, where m = migration rate
and l = mutation rate) between populations and h, a
measure of effective population size (h = 4Nel; where
Ne = effective population size and l = mutation rate).
We used the full migration model, which allows bidirectional migration between populations. Following a
burn-in of 10 000 iterations and ten replicates of a single
long chain, 50 000 genealogies were recorded at a sampling increment of 50. The prior distributions for the
parameters were uniform with h priors bounded
between 0 and 24 and M priors bounded between 0
and 1000. A heating scheme was applied based on four
changes with temperatures 1, 1.5, 3 and 1 9 106 as
recommended by authors. Three additional runs were
performed using results from earlier runs as starting
conditions. All of these runs generated acceptable (i.e.
>200) effective sample sizes (ESSs) for all parameters of
interest. A microsatellite mutation rate of 5 9 104 per
gamete per generation (Weber & Wong 1993) was used
to scale the resulting values of M to m. Modal posterior
parameter estimates are presented as recommended by
the developers (Beerli 2009). The BAYESASS and MIGRATE
analyses were performed on the full data set, as well as
on a reduced data set in which we assessed potential
biases caused by a large Orkney sample size by restricting the Orkney Island sample to 134 individuals, comprising 10–11 samples randomly selected from each of
the breeding colonies.
Bayesian analysis of population divergence
We next used the program IMA2 (Hey 2010) to estimate
divergence dates at the regional level based on mitochondrial data. IMA2 is based on an isolation-withmigration model and estimates the posterior probability
densities using MCMC simulation of six demographic
4004 A . K L I M O V A E T A L .
parameters including population sizes of the extant as
well as the ancestral population (given as q = 4Nel,
where Ne is the effective population size), migration
rates (m = M/l, where M is the migration rate per
generation per gene copy) in both directions, and time
since divergence (t = tl, where t is the time since population splitting). All parameter estimates are scaled by
the mutation rate (l), which was given as 2.75 9 107
substitutions per site per year (Phillips et al. 2009).
IMA2 assumes stable population sizes and a model of
isolation with gene flow, in which a single panmictic
population split into two at some time in the past but
has since remained connected by some degree of gene
flow (Hey & Nielsen 2004, 2007). Initial runs were performed using wide priors (q ≤ 500, m ≤ 50, t ≤ 50) in
order to identify appropriate values for subsequent
parameter estimation. Simulations were then performed
with q = 200, m = 10 and t = 20 using 20 Markov
chains, the recommended heating scheme and 1 9 107
steps with 1 9 106 steps discarded as ‘burn-in’. Genealogies were sampled every 100 steps. Estimates were
generated under the HKY mutation model using an
inheritance scalar of 0.25 for mitochondrial sequence
data. Although IMA2 allows for analysis of more than
two populations, this model assumes that a bifurcating
phylogenetic tree can adequately represent the evolutionary history of the focal taxa. Three independent
runs were performed for each comparison in order to
assess convergence.
Bayesian analysis of large sequence data sets can be
computationally prohibitive, but the coalescent process
is relatively robust with respect to the effect of sample size on tree topology (Felsenstein 2006; Kuhner
2009). Consequently, to expedite analysis of the Eastern Atlantic population, we restricted the number of
individuals used to 213, comprising 11–12 samples
randomly selected from each of the 18 breeding
colonies.
Modelling alternative demographic histories
Finally, we used approximate Bayesian computation
(ABC; Beaumont et al. 2002; Bertorelle et al. 2010) to
explore likely historical changes in population size.
ABC allows the user to broadly define alternative
demographic scenarios, expressed as a stepwise series
of population size changes, then uses summary statistics from the observed and simulated data sets to estimate parameter values and to assess the relative
support for each scenario. ABC was implemented using
the program DIYABC version 1.0.4.46 (Cornuet et al.
2008, 2010). We developed five different demographic
models describing different possible patterns of effective population size change over time (see Table S1 and
Fig. S2, Supporting information for details). Priors for
the timing of events and the magnitude of changes of
Ne were based on known events in the demographic
history of this species, including massive habitat loss
during last glacial maximum (Boehme et al. 2012), the
entry of grey seals into the Baltic Sea (Sommer &
Benecke 2003) and recent anthropogenic exploitation
(Lesage & Hammill 2001; H€
ark€
onen et al. 2007; Hiby
et al. 2007). The first scenario that we evaluated (i) represented the null hypothesis of constant effective population size over time. The alternative scenarios invoked
(ii) population expansion; (iii) population reduction; (iv)
population bottleneck; and (v) population expansion
followed by reduction. The mutation rate for microsatellites was bound between 5 9 104 and 5 9 103 substitutions/generation. For the HVR1 data, we used the
K2P substitution model as determined through model
selection. The substitution rate was uniformly distributed between 8.12 9 107 and 3.8 9 106 substitutions/
site/generation (Phillips et al. 2009; Dickerson et al.
2010). A generation time of 14 years was assumed
(Thompson & H€
ark€
onen 2008). We used four summary
statistics for microsatellites (mean number of alleles per
locus, mean expected heterozygosity, mean allele size
variance and mean MGW index across loci) and four
summary statistics for the HVR1 (number of haplotypes, number of segregating sites, mean pairwise difference and Tajima’s D). These statistics were chosen on
the basis of their sensitivity to demographic change.
After simulating one million data sets for each scenario, we used a polychotomous logistic regression procedure (Fagundes et al. 2007) to estimate the posterior
probability of each scenario, based on the 1% of simulated data sets for each model producing summary statistics closest to the observed values. The type I error
rate for model selection was estimated as the proportion
of instances where the selected scenario did not exhibit
the highest posterior probability compared with the
competing scenarios for 500 simulated data sets generated under the selected scenario. In a similar way, the
type II error rate was estimated by simulating 500 data
sets for each of four alternative scenarios and calculating the proportion of instances in which the best-supported model was incorrectly selected as the most
probable one (Cornuet et al. 2010). Using the most probable scenario, local linear regression was used to estimate the posterior distributions of parameters.
Specifically, a logit transformation of parameter values
was performed and the 1% closest simulated data sets
to the observed were used for regression and posterior
parameter estimation (Beaumont et al. 2002). Due to
computational limitations, the ABC analysis of the Eastern Atlantic population was run on the reduced subset
of 213 samples described above.
© 2014 John Wiley & Sons Ltd
G R E Y S E A L P O P U L A T I O N G E N E T I C S 4005
Results
We sequenced a 350-bp region of the mitochondrial
HVR1 in over 1500 grey seals from 19 different colonies,
which had previously been genotyped for nine highly
polymorphic microsatellites. Additional published data
were then incorporated from three colonies in the Baltic
(Graves et al. 2009), allowing comparisons to be made
at the levels of three major geographic regions (Western
Atlantic, Eastern Atlantic and Baltic), seven subregions
(Nova Scotia, Orkney Islands, North West Scotland,
Eastern Scotland, North East England, North Atlantic
and Baltic) and 22 colonies (Table 1 and Fig. 1).
The mitochondrial HVR1 was highly informative, containing 94 variable sites of which 57 were parsimony
informative. A total of 167 unique haplotypes were
identified in 1625 grey seal individuals and overall haplotype diversity was high, at 0.941 (Table S2, Supporting
information). Although the most common haplotype
was found in 181 individuals from 18 colonies, there
was little sharing of haplotypes among the three major
geographic regions, none being common to the Western
and Eastern Atlantic, and the latter both sharing a single
haplotype with the Baltic. These patterns are summarized as a minimum spanning network in Fig. 2.
The microsatellite loci were also highly variable, carrying on average 9.8 alleles. Pooling all of the data, six
loci were found to deviate significantly from Hardy–
Weinberg equilibrium, three of which remained significant after table-wide Bonferroni correction for multiple
statistical tests (Table S3, Supporting information). Several
significant deviations were observed after partitioning
the data into three geographic regions, but these were
not consistent across loci, as would be expected if null
alleles were responsible. Colony-level analyses revealed
only six significant deviations following Bonferroni correction, five of which involved different loci (data not
shown). No evidence was found for linkage disequilibrium among the loci.
Population structure
Strong genetic structure was detected at both the mitochondrial HVR1 and microsatellites. Pairwise comparisons at the level of three geographic regions yielded
highly significant Fst values for both types of marker
(Table 2, 0.10–0.28 for mtDNA and 0.11–0.24 for microsatellites, all significant at P < 0.0001). The equivalent
Φst (0.06–0.08) and Rst (0.08–0.13) values were somewhat
smaller but still highly significant (P < 0.0001 for all
comparisons). At the level of seven subregions, the
HVR1 yielded highly significant Fst and Φst values for
all but a handful of pairwise comparisons, with fewer
significant tests obtained using microsatellites (Table 3).
These patterns of differentiation are summarized by
two neighbour-joining trees, one for each marker class
(Fig. 3). Both trees reveal three distinct clades corresponding to colonies from the UK plus the Faroe
Islands, the Baltic and Sable Island, respectively. However, the two trees disagree about which of the regions
are most dissimilar, the mitochondrial DNA favouring
the Western and Eastern Atlantic and nuclear data
favouring the Western Atlantic and Baltic. Logically, the
nuclear picture is probably closer to the truth, given
Fig. 2 A median joining network illustrating phylogenetic relationships among
the grey seal mitochondrial HVR1
haplotypes. Each line joining two circles
corresponds to a single nucleotide substitution, with red circles representing
hypothetical haplotypes that were not
observed in the sample of individuals.
Circle size reflects the relative frequency
of each of the observed haplotypes.
Regions are colour coded as follows:
white = Western Atlantic, grey = Eastern
Atlantic, black = Baltic.
140
50
5
© 2014 John Wiley & Sons Ltd
4006 A . K L I M O V A E T A L .
Table 2 Estimates of genetic differentiation among the three geographic regions; (a) pairwise Fst and Φst values (above and below
the diagonal, respectively) for the mitochondrial HVR1; (b) pairwise Fst and Rst values (above and below the diagonal, respectively)
based on five microsatellite loci
(a) Mitochondrial HVR1
(b) Microsatellites
Region
Western Atlantic
Eastern Atlantic
Baltic
Western Atlantic
Eastern Atlantic
Baltic
Western Atlantic
Eastern Atlantic
Baltic
–
0.08***
0.06***
–
0.08***
0.13***
0.28***
–
0.06***
0.11***
–
0.09***
0.18***
0.10***
–
0.17***
0.24***
–
*P < 0.05; **P < 0.01; ***P < 0.001.
that East Atlantic seals lie between the Baltic and the
West Atlantic. However, one cannot discount unexpected patterns that may have arisen during the demographic upheaval that likely accompanied the retreat of
the ice sheet at the end of the last glacial maximum.
Next, we used AMOVA to assess the proportion of
genetic variation attributable to each level of population
substructure (Table 4). As expected, a greater proportion
of the null variance was explained by among-region as
opposed to among-subregion components, consistent
with population structure being strongest at the regional
level. Among-region and among-subregion variance
components were higher for Φst than Fst at the HVR1
and for Rst than Fst at microsatellites, again consistent
with the fact that the former measures are considered to
be more appropriately matched to the mutation models
of the two marker classes respectively. Finally, mitochondrial Φst explained a marginally greater proportion
of variation both among regions and among subregions
than microsatellite Rst, supporting the impression given
above that levels of paternal gene flow tend to be higher
than maternal gene flow.
Migration rates and directions
Contemporary and historical migration rates among the
seven geographic regions estimated using BAYESASS
(Wilson & Rannala 2003) and MIGRATE (Beerli & Felsenstein 1999; Beerli 2006), respectively, revealed interesting patterns. Estimated rates of gene flow out of the
Orkney Islands into other subregions within the East
Atlantic are almost an order of magnitude higher than
all other rates and are the only ones that are statistically
different from zero (Table 5). Although much less
pronounced, there is also a suggestion that the Baltic is
relatively isolated, experiencing low rates of immigration and emigration, and that Nova Scotia behaves in
the opposite way compared with the Orkney Islands,
with low rates of emigration but moderate levels of
immigration.
Table 3 Estimates of genetic differentiation among the seven subregions; (a) pairwise Fst and Φst values (above and below the diagonal, respectively) for the mitochondrial HVR1; (b) pairwise Fst and Rst values (above and below the diagonal, respectively) based on
five microsatellite loci
(a) Mitochondrial
HVR1
(b) Microsatellites
Subregion
Nova
Scotia
Orkney
Islands
North West
Scotland
Eastern
Scotland
North East
England
North
Atlantic
Baltic
Nova Scotia
Orkney Islands
North West Scotland
Eastern Scotland
North East England
North Atlantic
Baltic
Nova Scotia
Orkney Islands
North West Scotland
Eastern Scotland
North East England
North Atlantic
Baltic
–
0.08***
0.07***
0.13***
0.12***
0.09***
0.06***
–
0.11***
0.10***
0.07***
0.09***
0.13***
0.24***
0.28***
–
0.01*
0.04***
0.04***
0.03**
0.06***
0.08***
–
0.01
0.01
0.01
0.02
0.17***
0.28***
0.01
–
0.05***
0.05***
0.02*
0.06***
0.07***
0.01
–
0.01
0.02
0.01
0.16***
0.41***
0.05***
0.06***
–
0.01
0.10***
0.10***
0.07***
0.01
0.01
–
0.03
0.01
0.17***
0.35***
0.02**
0.03**
0.01
–
0.09***
0.09***
0.06***
0.01
0.01
0.02
–
0.04
0.09***
0.26***
0.01
0.01
0.13***
0.07***
–
0.07***
0.09***
0.01
0.01
0.01
0.01
–
0.15***
0.19***
0.10***
0.07***
0.15***
0.11***
0.06***
–
0.13***
0.09***
0.07***
0.08***
0.05***
0.08***
–
*P < 0.05; **P < 0.01; ***P < 0.001.
© 2014 John Wiley & Sons Ltd
G R E Y S E A L P O P U L A T I O N G E N E T I C S 4007
Fig. 3 Neighbour-joining
trees
constructed for (a) the mitochondrial HVR1
and (b) microsatellites using the genetic
distance measures Φst and Dc, respectively (See ‘Materials and methods’ for
details).
(a) Mitochondrial DNA
eI
sla
nd
na
Ro
Ca
fE
da
y
say
of S
Corn Holm
ess
purn
Holm
rth
No
lf o
nes
ay
of M
Isle
Farnes
M
Ho
S lm of Huip
Co troma
rn
Ho
lm
Ca
Far
G
ss
Ne
ks
Lin Spurness
Holm of
(b) Microsatellites
le
k
uc
Isle of May
pin
Stroma
es
Faro hs
c
na olm
o
M sk h
u
R
m
ol
nh
e
re
Co
ia
Calf of Flotta
0.03
othn
Faray
of B
olm
Stockh onia
Est
Bay
Sw North R
ith
ona
a
bl
lf o
f Ed
a
CopinsaFyaray y
Sa
a
lm
on
nho
Sw
ree
le G
k
c
Mu
Rusk holm
Stroma
Ho
Lin lm of
Hui
ks
p
Ne
ss
Faro
ta
es
a
nia
oth
of B
lot
fF
lf o
Swith
Ca
Bay
M on a c h s
Estonia
lm
ho
ck
o
St
Sa
ble
Isla
nd
0.04
Longer-term estimates of gene flow tend to be somewhat smaller than the contemporary estimates (Table 6),
but in general are strongly correlated with them (Mantel’s r = 0.49, n = 42, P = 0.004) indicating a broadly
consistent signal over time. As with contemporary rates,
the Orkney Islands appear to be the largest source of
emigration, while the Baltic appears relatively isolated.
Interestingly, BAYESASS and MIGRATE reveal subtle but
plausible differences as well as similarities. While contemporary emigration from the Orkney Islands is both
© 2014 John Wiley & Sons Ltd
very high and largely dominated by movement to other
colonies in the East Atlantic, the historical pattern suggests that local emigration is exceeded by that to the
West Atlantic and Baltic. Similarly, the strongly asymmetrical pattern inferred for recent gene flow out of
and into Nova Scotia is not apparent in the deeper
history.
Elsewhere, it has been shown that Bayesian migration
rate estimates can be sensitive to differences in
sample size (Faubet et al. 2007). To test whether the
4008 A . K L I M O V A E T A L .
Table 4 Results of analyses of molecular variance (AMOVA) based on (a, b) the mitochondrial HVR1 sequence data; and (c, d) microsatellites, with the data set being partitioned into 3 geographic regions and 7 subregions respectively
(a) Mitochondrial
DNA (using Fst)
Partition
Source of variation
3 regions
Among regions
Among colonies within
Within colonies
Among subregions
Among colonies within
Within colonies
Among regions
Among colonies within
Within colonies
Among subregions
Among colonies within
Within colonies
7 subregions
(b) Mitochondrial
DNA (using Φst)
3 regions
7 subregions
(c) Microsatellites
(using Fst)
subregions
regions
subregions
Variance
% of total
variance
F
P
2
19
1603
6
15
1603
2
19
1603
6
15
1603
0.03
0.01
0.46
0.02
0
0.46
0.48
0.02
2.17
0.26
0.01
2.22
6.08
0.72
93.2
4.66
0.02
95.32
17.9
0.7
81.4
10.6
0.25
89.1
0.061
0.008
0.068
0.047
0.000
0.047
0.179
0.008
0.186
0.106
0.003
0.109
<0.001
<0.001
<0.001
<0.001
0.45
<0.001
<0.001
<0.001
<0.001
<0.001
0.14
<0.001
Partition
Source of variation
Sum of squares
Variance
% of total
variance
F
P
3 regions
Among regions
Among colonies within regions
Among individuals within colonies
Within individuals
Among subregions
Among colonies within subregions
Among individuals within colonies
Within individuals
Among regions
Among colonies within regions
Among individuals within colonies
Within individuals
Among subregions
Among colonies within subregions
Among individuals within colonies
Within individuals
102.8
58.0
2872.3
2792.5
117.1
43.8
2872.5
2792.7
8908.4
2069.6
123965.9
126709.5
9138.1
1839.9
123965.9
126709.5
0.17
0.01
0.04
1.80
0.08
0.01
0.04
1.80
15.4
1.19
0.31
81.8
6.80
0.25
0.31
81.8
8.5
0.4
1.9
89.2
4.2
0.3
2.0
93.4
15.8
0.2
0.3
84.3
7.7
0.3
0.4
92.4
0.085
0.004
0.021
0.108
0.042
0.003
0.021
0.066
0.158
0.002
0.004
0.157
0.077
0.003
0.003
0.076
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.025
0.627
<0.001
<0.001
0.008
0.619
0.003
7 subregions
(d) Microsatellites
(using Rst)
regions
d.f.
3 regions
7 subregions
disproportionately large sample size of the Orkney
Islands could have affected our results, we therefore
repeated the BAYESASS and MIGRATE analyses after restricting the Orkney Island sample to a total of 134 individuals selected at random from within each of the breeding
colonies. The overall pattern obtained was very similar,
with estimated migration rates out of the Orkney Islands
being somewhat lower than for the full data set but still
significantly different from zero (Tables S4 and S5,
Supporting information). This suggests that the strongly
asymmetric pattern of gene flow out of the Orkney
Islands is not an artefact of unequal sample sizes.
Bayesian analysis of divergence times and effective
population sizes
We next used the program IMA2 (Hey 2010) to estimate
divergence dates and effective population sizes from
our mitochondrial data. Although we were primarily
interested in deriving parameters relating to the divergence of the Baltic region from the Eastern Atlantic, we
also analysed divergence between the Western and
Eastern Atlantic. The resulting marginal posterior probability distributions of divergence time and effective
population size parameters show clear peaks bounded
within the prior distribution in all pairwise comparisons
(Fig. 4). Parameter estimates together with 95% highest
probability density intervals are also given in Table S6
(Supporting information). Figure 4a shows clear support for the Baltic having diverged from the Eastern
Atlantic a little over 10 000 years ago, which is consistent with geological records pertaining to the opening
of the Baltic Sea. The posterior probability distribution
for the divergence time of the Western and Eastern
Atlantic is somewhat flatter but indicates a more
ancient divergence, which probably took place around
30 000 years ago. We also obtained smaller Ne estimates
for ancestral than contemporary populations (Fig. 4b),
© 2014 John Wiley & Sons Ltd
© 2014 John Wiley & Sons Ltd
(0–0.028)
(0–0.022)
(0–0.017)
(0–0.018)
(0–0.014)
(0.257–0.322)
0.290
–
0.010
0.008
0.006
0.006
0.005
(0–0.007)
(0–0.005)
(0–0.002)
(0–0.002)
(0–0.002)
(0–0.001)
–
0.002
0.002
0.001
0.001
0.001
0.000
0.282
0.012
–
0.008
0.007
0.008
0.006
(0–0.022)
(0–0.021)
(0–0.023)
(0–0.016)
(0.244–0.319)
(0–0.034)
Eastern Scotland
0.261
0.014
0.020
–
0.009
0.009
0.008
(0–0.026)
(0–0.024)
(0–0.023)
(0.212–0.310)
(0–0.039)
(0–0.051)
North East England
0.261
0.020
0.014
0.010
–
0.010
0.009
(0–0.028)
(0–0.026)
(0.205–0.317)
(0–0.056)
(0–0.042)
(0–0.028)
North Atlantic
0.022
0.017
0.014
0.013
0.013
–
0.010
(0–0.029)
(0–0.059)
(0–0.048)
(0–0.04)
(0–0.037)
(0–0.037)
Nova Scotia
0.007
0.008
0.005
0.004
0.004
0.003
–
Baltic
(0–0.017)
(0–0.020)
(0–0.014)
(0–0.011)
(0–0.012)
(0–0.009)
Orkney Islands
North West Scotland
Eastern Scotland
North East England
North Atlantic
Nova Scotia
Baltic
(0–0.012)
(0–0.009)
(0–0.014)
(0–0.016)
(0–0.009)
(0.010–0.040)
0.025
–
0.004
0.001
0.004
0.006
0.001
–
0.006
0.005
0.004
0.006
0.004
0.005
(0–0.015)
(0–0.013)
(0–0.013)
(0–0.017)
(0–0.012)
(0–0.013)
North West Scotland
Orkney Islands
0.016
0.005
–
0.004
0.004
0.004
0.003
(0–0.012)
(0–0.015)
(0–0.012)
(0–0.010)
(0.004–0.030)
(0–0.016)
Eastern Scotland
0.011
0.005
0.003
–
0.004
0.003
0.003
(0–0.014)
(0–0.011)
(0–0.011)
(0–0.020)
(0–0.014)
(0–0.010)
North East England
0.009
0.005
0.003
0.003
–
0.004
0.003
(0–0.012)
(0–0.011)
(0–0.019)
(0–0.016)
(0–0.010)
(0–0.010)
North Atlantic
0.011
0.006
0.003
0.005
0.003
–
0.007
(0–0.017)
(0–0.020)
(0–0.015)
(0–0.011)
(0–0.013)
(0–0.011)
Nova Scotia
0.019
0.003
0.003
0.003
0.003
0.007
–
Baltic
(0.008–0.030)
(0–0.011)
(0–0.011)
(0–0.010)
(0–0.010)
(0–0.017)
Table 6 Modal estimates of historical gene flow among seven subregions calculated using five microsatellites within the program MIGRATE (Beerli & Felsenstein 1999; Beerli
2006). Results are given as m, the proportion of migrants per generation, from each of the subregions on the left (row headings) into the subregions on the right (column headings). 95% confidence intervals are given in parentheses
Orkney Islands
North West Scotland
Eastern Scotland
North East England
North Atlantic
Nova Scotia
Baltic
North West Scotland
Orkney Islands
Table 5 Rates of recent gene flow among seven subregions calculated using five microsatellites within the program BAYESASS (Wilson & Rannala 2003). Results are given as m,
the proportion of migrants per generation, from each of the subregions on the left (row headings) into the subregions on the right (column headings). 95% confidence intervals
are given in parentheses
G R E Y S E A L P O P U L A T I O N G E N E T I C S 4009
4010 A . K L I M O V A E T A L .
(a)
(b)
scenarios: (i) constant population size; (ii) population
expansion; (iii) population reduction; (iv) population
bottleneck; and (v) population expansion followed by
reduction (see Fig. S2, Supporting information for further details). The posterior probability of each competing scenario was evaluated using a polychotomous
logistic regression on the 1% of simulated data sets closest to the observed. The population expansion model
was clearly the most probable for the Baltic and Eastern
Atlantic, but results obtained for the Western Atlantic
were less clear-cut, with a 68.3% probability being
recovered in favour of the expansion model. We next
evaluated the power of the model choice procedure by
calculating type I and II error rates for the best-supported model. In all cases, type I error ranged from
16.8% to 19.2% and type II error rates were <10% (Table
S7, Supporting information). For each population, we
estimated the marginal posterior probability density of
each parameter based on expansion scenarios using the
1% closest simulated data sets from the observed data
set. Posterior estimates of expansion time and contemporary Ne were similar among regions, although preexpansion Ne appears to have been largest for the Baltic
region (Fig. 5 and Table 7).
Discussion
Fig. 4 Marginal posterior probability distributions obtained in
pairwise IMa2 analyses between the Eastern Atlantic and Baltic
and between the Western and Eastern Atlantic (see ‘Materials
and methods’ for details). (a) Divergence times in years, (b)
Effective population sizes. ‘Ancestral 1’ corresponds to Ne
prior to the divergence of the Western and Eastern Atlantic,
and ‘Ancestral 2’ corresponds to Ne prior to the divergence of
the Baltic from the Eastern Atlantic. Note that two Ne estimates
are available for the Eastern Atlantic because this region features in two pairwise comparisons (‘Eastern Atlantic 1’ and
‘Eastern Atlantic 2’ correspond to analyses involving the Baltic
and Western Atlantic, respectively).
consistent with range-wide historical population expansion. Interestingly, the numerically smallest contemporary population in the Baltic region recovered the
largest estimate of Ne.
Approximate Bayesian Computation (ABC) analysis
Finally, we analysed the combined mitochondrial and
microsatellite data from each of the three main grey seal
regions within an ABC framework to evaluate relative
statistical support for the following five demographic
We conducted a comprehensive survey of the population structure and historical demography of a widely
distributed pinniped species, the grey seal. Strong
genetic structuring was found in both the mitochondrial
and nuclear data sets, which becomes progressively
weaker over finer geographic scales. Signatures of population expansion were detected for all three regions
using ABC. Migration rate estimates revealed added
complexity, with a possible key role for the Orkney
Islands, both historically and currently, while the Baltic
appears relatively isolated.
The literature contains a wealth of increasingly
sophisticated statistical tools for inferring demographic
parameters from genetic data. We have deployed a
number of these to analyse a large data set from the
grey seal while endeavouring to minimize the overlap.
Thus, we aimed to infer current population substructure, current and historical migration rates and historical changes in population size using both nuclear and
mitochondrial data. While a reasonably cohesive picture
emerges, it is also noticeable that many simplifying
assumptions still need to be made because no one program yet estimates all parameters simultaneously. Thus,
IMA2 assumes constant population sizes, something that
historical records and other genetic analyses show to be
false. Equally, to estimate recent patterns of gene flow
requires an assumption of approximate equilibrium in
© 2014 John Wiley & Sons Ltd
G R E Y S E A L P O P U L A T I O N G E N E T I C S 4011
(a)
the period immediately before. Moreover, the observed
patterns could potentially be affected by unsampled
‘ghost’ populations in other parts of the Eastern Atlantic range such as the Wadden Sea, Denmark, Norway
and Iceland. For these reasons, although we expect the
overall patterns to be genuine, no one result should be
taken entirely at face value.
Population structure
(b)
(c)
Fig. 5 Posterior density curves of model parameters based
on 10 000 accepted values from 1 9 106 iterations of the population expansion model (see ‘Materials and methods’ for details)
shown separately for grey seals from the Western Atlantic (full
lines), Eastern Atlantic (dashed lines) and Baltic (dotted lines).
Results are shown for (a) contemporary population size, (b)
expansion time, and (c) Pre-expansion population size.
© 2014 John Wiley & Sons Ltd
Bearing in mind the provisos above, there is a strong
consensus that the Baltic, Western Atlantic and Eastern
Atlantic show a high degree of genetic differentiation at
both markers. This is expected given the geographic
and ‘anthropogenic’ barriers that have largely isolated
the Baltic population from the remainder of the species’
distribution and the large geographic separation
between the Western and Eastern Atlantic. However,
there is a slight contradiction. Based on the two preferred measures, Φst and Rst, the most dissimilar population pairs are the Western and Eastern Atlantic
according to mtDNA data, and the Baltic and Western
Atlantic according to nuclear data. It is unclear whether
this discrepancy is genuine or arises out of statistical
noise. If it is genuine, then the contrasting patterns
could arise in either of two ways. If the Atlantic populations were ancestrally comparatively small, this could
have allowed more rapid divergence within the Atlantic
at mitochondrial markers due to their lower effective
population size. Alternatively, if the Baltic became more
isolated and gene flow reduced across the Atlantic,
the faster mutation rate of microsatellites would allow
the Baltic population to diverge faster, particularly from
the more geographically distant Western Atlantic.
Below the level of the three major regions, we again
find contrasting patterns between nuclear and mitochondrial markers. This is seen in Table 3, where pairwise comparisons not involving the Baltic or Nova
Scotia in the Western Atlantic never reveal significant
differences using microsatellites but almost invariably
do show differentiation using mitochondrial DNA. This
pattern probably stems from the greater fidelity to pupping sites shown by adult female seals, many of whom
return to breed time and again within a few tens of
metres of where they bred in previous seasons (Pomeroy et al. 2000). In contrast, males, not being tied to
newborn pups, have the potential to visit more than
one breeding colony during a single breeding season.
Indeed, this is made easier by the way the timing of the
breeding season varies around the UK. How many
males do this remains to be determined, although radioand satellite-tagging studies suggest it may be fairly
common (Thompson et al. 1996; McConnell et al. 1999;
Jessop et al. 2013). Having said this, male-mediated
4012 A . K L I M O V A E T A L .
Table 7 Prior uniform distributions, mean, median, mode, quantiles and estimates of bias and precision (MRB = mean relative bias,
RMSE = root mean square error) for the posteriors of parameters calculated from simulations of population expansion. N1, current
effective population size; N2, effective population size before expansion; T1, time from expansion (generations ago). See Fig. S2 and
Table S7 (Supporting information) for details
Posterior
Region
Parameter
Prior
Mean
Western Atlantic
N1
N2
T1
N1
N2
T1
N1
N2
T1
1–1.5 9 105
100–10 000
1–5000
1–1.5 9 105
100–10000
1–5000
1–1.5 9 105
100–10 000
1–5000
5.15
4.84
1.68
3.92
4.87
1.94
4.68
6.10
2.05
Eastern Atlantic
Baltic
9
9
9
9
9
9
9
9
9
Median
104
103
103
104
103
103
104
103
103
gene flow cannot be very high, at least in certain areas
of the species distribution, because previous studies
have identified highly significant microsatellite allele
frequency differences between UK colonies (Allen et al.
1995) and in our current data set, AMOVA identifies a
small but highly significant proportion of the total variation attributable to between subregion differences.
Migration rates
Our estimates of recent and historical gene flow are
intriguing in that they both identify the Orkney Islands
as playing a pivotal role in regional grey seal demography. High levels of recent gene flow out of the Orkney
Islands have already been reported (Gaggiotti et al.
2002) and seem to reflect a general expansion of the
entire grey seal population in this area. Once a colony
reaches its local carrying capacity, groups of emigrants
leave to seek less crowded breeding areas, preferring
nearer sites but certainly being capable of moving much
further away. Overall, therefore, our results suggest that
many colonies in the Orkney Islands may be at, or close
to, carrying capacity and this both encourages emigration and discourages immigration. Although colonies
elsewhere in the Eastern Atlantic may also be approaching or have reached their carrying capacity, it is interesting that the Orkney Islands are the sole subregion
that reveals a strong signal of emigration, mostly to
other sites within the Eastern Atlantic, although with
weak evidence of perhaps indirect movement as far as
Sable Island via colonies in southern Greenland (Rosing-Asvid et al. 2010). Although not undertaking seasonal migrations such as ringed seals (Pusa hispida),
harp seals (Pagophilus groenlandicus) and hooded seals
(Cystophora cristata), long-distance movements of individual grey seals within the Eastern Atlantic have been
3.62
4.68
1.23
2.62
4.72
1.69
2.97
6.34
1.74
9
9
9
9
9
9
9
9
9
104
103
103
104
103
103
104
103
103
Mode
1.27
4.03
3.67
1.58
4.25
8.70
1.20
7.71
1.77
9
9
9
9
9
9
9
9
9
104
103
102
104
103
101
104
103
102
5%
95%
6.37 9 103
1.23 9 102
9.67 9 102
7.71 9 103
7.74 9 102
1.02 9 102
8.10 9 103
1.73 9 103
1.39 9 102
1.36
4.46
9.20
1.19
9.28
4.59
1.32
9.60
4.70
9
9
9
9
9
9
9
9
9
105
103
103
105
103
103
105
103
103
MRB
RMSE
0.99
0.74
2.77
0.68
0.64
1.52
0.88
0.79
7.39
3.27
3.26
28.38
2.95
2.01
6.93
3.43
2.46
99.18
documented by tagging studies (Thompson et al. 1996;
McConnell et al. 1999; Jessop et al. 2013) and, over time,
stepping-stone-like movements between the Western
and Eastern Atlantic could potentially occur via colonies in southern Greenland (Rosing-Asvid et al. 2010)
and Iceland (Hauksson 2007a,b).
Over the longer term, the Orkney Islands also appear
to be important with all emigration values from this
area being greater than any other values (Table 6), even
for analyses based on the reduced data set (Table S6,
Supporting information). However, while contemporary
emigration from the Orkney Islands is both very high
and largely dominated by movement to other colonies
in the Eastern Atlantic, the historical pattern suggests
that local emigration was more restricted in the Eastern
Atlantic, mainly involving neighbouring colonies at
North Rona, the Monachs and the Isle of May. As elsewhere, we must not forget the possibility that contemporary and historic estimates could be slightly distorted
by the much larger sample size of Orkney Island individuals. Nevertheless, the zooarchaeological record of
seals is particularly rich in the Orkney Islands relative
to other parts of the British Isles (Fairnell 2003), hinting
that the Orkney Islands is probably an area that has
been particularly suited to this species for a long time.
Consequently, it appears that, whenever conditions
allow, seals in this region have been able to fill available breeding sites and this has generated a reliable
source of emigrants who leave to seed other areas.
Divergence times
It is thought that a transient dispersal corridor connecting the Baltic and North Seas formed around
10 000 years ago and closed during the formation of
Ancylus Lake around 1000 years later (Sommer &
© 2014 John Wiley & Sons Ltd
G R E Y S E A L P O P U L A T I O N G E N E T I C S 4013
Benecke 2003). Previous genetic studies provide rather
broad estimates of the divergence time of the Baltic and
Eastern Atlantic grey seal regions, ranging between
11 000 and 0.35 million years ago depending on data
type, mutation rate assumptions and the method of estimation (Boskovic et al. 1996; Graves et al. 2009). Boskovic et al. (1996) interpreted their upper estimate (0.35
million years ago) as meaning that an ancient, unknown
event may have driven the separation of the Baltic
seals, but also cautioned that their mutation rate
assumption could be incorrect. Our estimate of 7000–
30 000 years ago is far closer to the previous lower estimate (Graves et al. 2009) and includes the narrow time
window during which the dispersal corridor was
known to be open. This provides support for the
hypothesis that the opening and subsequent closing of
the Baltic Sea was the primary factor promoting the
genetic divergence of the Baltic grey seal. Also,
although capable of moving long distances, an additional factor leading to increased divergence between
Eastern Atlantic and Baltic sea populations could be the
loss of intermediate haul-out sites, and hence dispersal
corridors, caused by anthropogenic disturbance, population size reductions and local extinctions of grey seal
populations starting the Middle Ages in the Netherlands, Germany, Denmark and western Sweden.
Effective population sizes
Counterintuitively, the numerically smaller Baltic grey
seal region recovered a larger estimate of Ne than either
the Western or Eastern Atlantic regions. This is consistent, however, with previous findings by Graves et al.
(2009) as well as with the mitochondrial network diagram (Fig. 2). Here, one can see the origin of the signal
of population expansion, with a few common haplotypes surrounded by many closely related but rare satellite haplotypes. It is also clear why the Baltic appears to
be the larger population because the Baltic haplotype
network covers a wider range, at least in this two
dimensional representation, and shows affinities with
both the Western and the Eastern Atlantic. This might
indicate that the Baltic population in some sense carries
a more ancestral signature, although a similar pattern
could plausibly arise through drift alone, with the two
Atlantic populations both having lost one of their two
deepest branches. Differences in social group structure
and breeding behaviour might also potentially contribute towards the observed differences in effective population size. Grey seals in the British Isles tend to breed
in colonies, whereas Baltic grey seals breed on ice or
land, depending on the prevailing environmental conditions (Jussi et al. 2008). The more dynamic nature of the
Baltic breeding habitat may therefore facilitate greater
© 2014 John Wiley & Sons Ltd
admixture, which would tend to inflate Ne relative to
the more static, colonial structure of British grey seals.
Historical demographic patterns
Trends in historical population size were studied using
several different methods applied to the mitochondrial,
nuclear and combined data sets. The dominant feature
supported in all cases was a period of recent population
expansion, with historical population sizes consistently
appearing smaller than modern sizes. This is seen most
intuitively in the simple network diagram, in which
small numbers of common haplotypes are surrounded
by haloes of rare haplotypes differing by a single substitution, the classical signal of population expansion. Similarly, although no single model is strongly supported
for the Western Atlantic, overall ABC consistently
favours population expansion over both the simple constant population size model and more complicated
models featuring either recent bottlenecks or periods of
expansion and contraction.
Modal estimates of expansion times from the ABC
analysis range from approximately 87–367 generations
ago. To convert generations to time, we assume a generation time of 14 years (Thompson & H€
ark€
onen 2008),
implying that expansion occurred around 1218–
5380 years ago. However, generation length is usually
appreciably shorter during population expansion than
when a population is at carrying capacity (Harwood &
Prime 1978). As our estimate was based on an expanding population, it is likely to be an underestimate,
implying that the likely date range for expansion is
appreciably older.
The confidence intervals on all date ranges are inevitably large. We chose HVRI and microsatellites because
their high mutation rates afford good resolution for
recovering the branching pattern of lineages within
each population. However, regardless of the accuracy
by which the actual lineages are reconstructed, the sparsity of deeper nodes and the stochasticity inherent in
lineage sorting means that the older regions of any
given gene tree are consistent with a broad range of
demographic scenarios. Consequently, while the expansion times estimated for each of the regions are broadly
consistent, we cannot say with confidence that they do
not differ.
With the above provisos, the observed pattern of
range-wide population expansion is consistent with the
increase in habitat availability that would have occurred
as the ice sheets retreated after the last glacial maximum
(LGM). At the peak of the LGM, the grey seal’s range in
the western North Atlantic appears to have been
restricted to the continental shelf edge between around
40 and 45°N and around the Flemish Cap, whereas on
4014 A . K L I M O V A E T A L .
the eastern side, only the coast of the Bay of Biscay and
the northeastern coast of the Iberian Peninsula would
have been suitable (Boehme et al. 2012). It has been estimated that the total amount of habitat available for grey
seals globally could have been as little as 3% of today’s
value (Boehme et al. 2012). Moreover, regional primary
productivity would probably also have been severely
depleted (Pyenson & Lindberg 2011).
Finally, genetic studies have explored the population
structure of several other Northern Hemisphere pinniped species (e.g. Stanley et al. 1996; Andersen et al.
1998; Goodman 1998; Hoffman et al. 2006a,b, 2009; Martinez-Bakker et al. 2013), raising the question of whether
other species show evidence of parallel demographic
responses due to changes in their shared environment.
Unfortunately, few of the other studies tested for historical demographic patterns and, of those that did, even
fewer provide putative dates for historical expansions.
Comparisons among studies are also hampered by differences in genetic markers used, the analytical
approaches and the assumptions made about mutation
rate and generation time. Nevertheless, our results are
broadly in line with findings from northern fur seals,
which appear to have undergone population expansion
around 11 000 years ago (Dickerson et al. 2010) and harbour (Westlake & O’Corry-Crowe 2002) and hooded
seals (Coltman et al. 2007), which both show signals of
recent population expansion interpreted as being consistent with postglacial expansion.
Conclusion
We elucidated the population structure and historical
demography of grey seals globally. The overall pattern
that emerges is one of strong population structure coupled with range-wide historical population expansion.
Highly asymmetrical patterns of gene flow were also
inferred, with the Orkney Islands being identified as a
source of migrants for many other populations. Taken
together, these findings suggest that grey seals are
highly responsive to changes in habitat availability and
that the Orkney Islands may play a pivotal role in local
population dynamics.
Acknowledgements
We are grateful to Phil Harrison for assisting with the collection of grey seal tissue samples from the Orkney Islands. We
are also indebted to Jeff Graves for sharing genetic data for the
Baltic grey seal populations. Samples were collected and
retained under permits issued by the Department for Environment, Food and Rural Affairs (DEFRA). JIH was funded by a
Marie Curie FP7-Reintegration-Grant within the 7th European
Community Framework Programme (PCIG-GA-2011-303618).
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J.I.H., W.A. and J.H. designed the research; J.I.H., W.A.
and J.H. performed the research; J.H. contributed
reagents/analytic tools; A.K., J.I.H., C.D.P., K.F. and
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© 2014 John Wiley & Sons Ltd
G R E Y S E A L P O P U L A T I O N G E N E T I C S 4017
Data accessibility
Mitochondrial DNA sequences are available from GenBank (Accession nos KJ769328–KJ769461). Raw microsatellite genotypes and complete data set of the
mitochondrial control region sequences are available via
Dryad Digital Repository (doi:10.5061/dryad.r232 g).
Supporting information
Additional supporting information may be found in the online version of this article.
Table S1 Prior distributions for demographic parameters in
the Approximate Bayesian Computation (ABC) analysis.
Table S2 Summary statics for the mitochondrial HVR1 dataset
broken down by region, sub-region and colony.
Table S3 Summary of the microsatellite loci used in this study,
including the number of alleles (A) per locus, allelic richness
(Ar), expected heterozygosity (HE), observed heterozygosity
(HO) and results of an exact test for Hardy-Weinberg equilibrium (P-values without Bonferroni correction).
© 2014 John Wiley & Sons Ltd
Table S4 Rates of recent gene flow among seven sub-regions
calculated using five microsatellites within the program BAYESASS (Wilson & Rannala 2003;) using a reduced dataset of 134
Orkney Island samples (See Materials and methods for details).
Table S5 Modal estimates of historical gene flow among seven
sub–regions calculated using five microsatellites within the
program MIGRATE (Beerli 2006; Beerli & Felsenstein 1999) using
a reduced dataset of 134 Orkney Island samples (See Materials
and methods for details).
Table S6 IMA2 estimates and 95% highest probability density
(HPD) intervals for various demographic parameters in pairwise comparisons between the Baltic and Eastern Atlantic and
between the Western and Eastern Atlantic (see Materials and
methods for details).
Table S7 Posterior probabilities of competing scenarios in the
approximate Bayesian computation (ABC) analysis, including
error rates for the top ranked scenarios for each region.
Fig. S1 Sampling locations of 13 grey seal colonies within the
Orkney Islands.
Fig. S2 Five alternative demographic scenarios evaluated in the
grey seal using Approximate Bayesian Computation (ABC).
See Materials and methods for details.