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Weak large-scale population genetic structure in a
philopatric seabird, the European Shag Phalacrocorax
aristotelis
EMILY J. BARLOW,
1,3
FRANCIS DAUNT,
1
SARAH WANLESS,
1
DAVID A
´LVAREZ,
2
JANE M. REID
3
&
STEPHEN CAVERS
1
*
1
Centre for Ecology & Hydrology, Bush Estate, Penicuik EH26 0QB, UK
2
Departamento de Biologı
´
a de Organismos y Sistemas & Instituto Canta
´brico de Biodiversidad (ICAB), Universidad de
Oviedo, C ⁄Rodrigo Urı
´
as⁄n, 33006-Oviedo, Spain
3
Institute of Biological & Environmental Sciences, School of Biological Sciences, Zoology Building, University of
Aberdeen, Tillydrone Avenue, Aberdeen AB24 2TZ, UK
Quantifying population genetic structure is fundamental to testing hypotheses regarding
gene flow, population divergence and dynamics across large spatial scales. In species with
highly mobile life-history stages, where it is unclear whether such movements translate
into effective dispersal among discrete philopatric breeding populations, this approach
can be particularly effective. We used seven nuclear microsatellite loci and mitochondrial
DNA (ND2) markers to quantify population genetic structure and variation across 20
populations (447 individuals) of one such species, the European Shag, spanning a large
geographical range. Despite high breeding philopatry, rare cross-sea movements and
recognized subspecies, population genetic structure was weak across both microsatellites
and mitochondrial markers. Furthermore, although isolation-by-distance was detected,
microsatellite variation provided no evidence that open sea formed a complete barrier to
effective dispersal. These data suggest that occasional long-distance, cross-sea movements
translate into gene flow across a large spatial scale. Historical factors may also have
shaped contemporary genetic structure: cluster analyses of microsatellite data identified
three groups, comprising colonies at southern, mid- and northern latitudes, and similar
structure was observed at mitochondrial loci. Only one private mitochondrial haplotype
was found among subspecies, suggesting that this current taxonomic subdivision may not
be mirrored by genetic isolation.
Keywords: dispersal, movement, Phalacrocoracidae, phylogeography, population genetics, seabird.
Quantifying the pattern and scale of genetic varia-
tion within and among populations is essential for
understanding relationships between population
dynamics and genetic structure (Avise 2000, Runge
et al. 2007). Such patterns can provide insights
into gene flow across spatial and temporal scales
that direct field measures of dispersal are rarely
able to achieve, and thus improve understanding of
the ecological and genetic dynamics of populations
(Koenig et al. 1996). This is especially true for spe-
cies with substantial known movement capabilities
that may or may not translate into realized gene
flow among breeding populations.
Seabirds are one obvious group of highly mobile
species (Milot et al. 2008). They are able to
disperse large distances, meaning that substantial
gene flow among breeding populations or colonies
might be expected (Friesen et al. 2007, Milot et al.
2008). However, many seabird species exhibit sub-
stantial population genetic structure in terms of
significant divergence among breeding populations
(e.g. Liebers & Helbig 2002, Gómez-Díaz et al.
2009). Two mechanisms in particular may cause
such structure. First, many seabird species show
strong breeding philopatry. This is expected to lead
*Corresponding author.
Email: scav@ceh.ac.uk
ª2011 The Authors
Ibis ª2011 British Ornithologists’ Union
Ibis (2011), 153, 768–778
to genetic differentiation among breeding popula-
tions through drift and ⁄or selection (e.g. Burg &
Croxall 2001, Milot et al. 2008). Secondly, both
historical and contemporary physical processes
may contribute to genetic structuring. For some
seabird species, distributional changes associated
with the Pleistocene ice ages, including population
fragmentation and isolation, have left lasting
imprints on genetic variation among populations
(e.g. Moum & Árnason 2001, Morris-Pocock et al.
2008). Contemporary physical barriers to gene
flow such as land, ice, or for inshore species, open
sea may also promote genetic differentiation (e.g.
Steeves et al. 2003). However, it remains unclear
to what extent both apparent barriers to gene flow
and observed life-history characteristics predict
population genetic structure in some seabird spe-
cies.
The European Shag Phalacrocorax aristotelis
(hereafter Shag) is a colonially breeding seabird
endemic to the rocky coasts of the northeast
Atlantic and Mediterranean (Wanless & Harris
2004). Shags exhibit several traits which suggest
that there may be a relatively high degree of popu-
lation genetic structure across breeding popula-
tions. First, adult Shags show high breeding
philopatry, with c. 99% of adults breeding at the
same colony across years (Potts 1969, Aebischer
1995, Velando & Freire 2002). Secondly, wide
stretches of open sea may impose a physical barrier
to their movements. For example, extensive ring-
ing and recovery effort has shown that no individu-
als ringed in Iceland have been recovered
elsewhere (Petersen 1998), and no adults ringed in
the UK have been recovered on mainland Europe
(Harris & Swann 2002). Finally, three subspecies
of Shag are currently recognized based on plumage
differences, non-overlapping distributions and phe-
nology. The nominate subspecies, P. a. aristotelis,
has a breeding distribution from northern Russia to
the Atlantic coast of Iberia, P. a. desmarestii breeds
within the Mediterranean and P. a. riggenbachi is
found along the coast of Morocco (Cramp & Sim-
mons 1977). Neither of the southern subspecies is
thought to move outside their recognized breeding
range (Fig. 1; Wanless & Harris 2004).
Despite exhibiting traits that have been shown
to promote strong population genetic structure in
other seabird species, Shags display other attributes
that may reduce genetic differentiation among
Figure 1. Distribution of the breeding colonies of European Shag from which samples were taken for this study. Pie charts represent
population level membership (Q) of genetic clusters (K= 3) as estimated using STRUCTURE v.2.3.3. The dotted area indicates the
presence of mitochondrial (mtDNA) haplotypes HM449751–752, the solid-lined area indicates mtDNA haplotype HM449751 only, and
unpatterned areas indicate the location of haplotype HM449750. Colonies included in the mtDNA analysis are marked with an
asterisk. Bold lines along the coastline represent the approximate breeding distribution of the species (after Johnsgard 1993). Colony
abbreviations are defined in Table 1.
ª2011 The Authors
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European Shag population genetics 769
populations. Natal dispersal rates from one colony
on the east coast of the UK have been estimated at
5–10% (Aebischer 1995, E. J. Barlow, F. Daunt,
S. Wanless & J. M. Reid, unpubl. data) and ring
recoveries have demonstrated that juvenile Shags
make occasional long-distance movements from
their natal colony prior to colony recruitment (e.g.
Asturias (Spain) to Den Helder (Netherlands),
2079 km, Álvarez (2009); Foula (Shetland, Scot-
land) to Great Saltee (Ireland), 934 km; Foula
(Shetland, Scotland) to the Farne Islands (England)
505 km; Scilly Isles (England) to France,
c. 200 km, Harris & Swann 2002)). Indeed, 2% of
juveniles ringed in the UK have been recovered in
mainland Europe (Wanless & Harris 2004). In
addition, some breeding populations of Shags exhi-
bit ‘boom and bust’ population dynamics, with
periodic population crashes followed by rapid pop-
ulation growth (Aebischer 1986, Harris & Wanless
1996, Frederiksen et al. 2008). Following crashes,
juvenile Shags have been shown to move c. 38%
further than during non-crash years, averaging over
150 km (Potts 1969). However, as Shags typically
do not breed until aged 3 years (Potts et al. 1980),
it remains unknown to what extent such long-
distance juvenile movements translate into effec-
tive gene flow among breeding populations. Just
one migrant per generation may be sufficient to
erode genetic structure among populations (Mills
& Allendorf 1996), and therefore events that field
observations suggest are relatively uncommon may
be sufficient to homogenize genetic variation
across the species’ range.
In addition to these contemporary determinants
of genetic variation amongst populations, historical
processes such as Pleistocene glaciations have been
shown to substantially influence the magnitude
and distribution of genetic variation in many Euro-
pean species (e.g. Hewitt 2000, Moum & Árnason
2001, Brito 2007). Restriction to refugia and sub-
sequent expansion can leave detectable signatures
in modern populations, even at rapidly evolving
neutral markers (Avise 2000). Species that have
undergone northward expansion from southern
refugia may show reduced variation in northern
populations as a result of successive founder events
(Hewitt 2000). However, the hypothesis that
Shags expanded from southern refugia following
glacial retreat remains untested.
We used seven biparentally inherited micro-
satellite loci (Molecular Ecology Resources Primer
Development Consortium 2010) and maternally
inherited mitochondrial DNA (mtDNA) to quan-
tify genetic variation and differentiation among
Shag populations at a pan-European scale and
thereby to test hypotheses regarding historical and
contemporary gene flow. Specifically, we quantified
the magnitude and pattern of microsatellite and
mtDNA variation among known breeding colonies
spanning two subspecies, used cluster analysis to
infer the presence of genetic groups across the
sampled range and explicitly tested the hypothesis
that open sea acts as a barrier to dispersal. We
discuss observed patterns in the context of a priori
predictions regarding the degree of genetic
structure derived from observed life-history char-
acteristics and dispersal behaviour.
METHODS
Sample collection
DNA samples were obtained from 20 breeding
colonies across the species’ range (Table 1, Fig. 1).
Samples were collected during 2007–2009 except
for 20 individuals sampled on Corsica in 1995. As
our aim was to quantify genetic variation within
and among breeding populations, samples were
collected from breeding adults and chicks. To mini-
mize the possibility of sampling closely related
individuals, samples were taken either from one
chick per nest, or from adults caught on the nest,
but not from both age groups at the same nest. At
three colonies, blood samples were taken; samples
from elsewhere consisted of breast feathers (c. five
per individual) taken from adults or pin feathers
(one to two per individual) taken from chicks
(Table 1). Blood samples were preserved in 2 mM
EDTA or 95% ethanol. All feather samples were
stored dry at room temperature for up to 4 weeks
and then frozen at )20 C.
DNA extraction, genotyping and
sequencing
DNA was extracted from blood samples using a
DNeasy Blood and Tissue Kit (Qiagen). To isolate
DNA from feathers, approximately 5 mm was cut
from the tip of each shaft, then diced and pro-
cessed using a modification of the DNeasy Kit pro-
tocol. First, samples were incubated for 48 h at
56 C in 180 lL ATL buffer and 20 lL Proteinase
K (20 mg ⁄mL). After incubation, the manufac-
turer’s protocol was followed, with final elution in
ª2011 The Authors
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770 E. J. Barlow et al.
20 lL of buffer, spun through the collection
column twice for a total elution volume of 40 lL.
All samples were genotyped at three dinucleotide
(Phaari02, Phaari05, Phaari06) and four tetra-
nucleotide (Phaari08, Phaari11, Phaari12, Phaari16)
microsatellite loci, as described in Molecular Eco-
logy Resources Primer Development Consortium
(2010). Fragments were visualized via 6% denatur-
ing polyacrylamide gels on a LI-COR 4200 IR2
genotyper. To determine the rate of genotyping
error, a random subset of 10 samples across loci
were genotyped independently at the Natural Envi-
ronment Research Council (NERC) Biomolecular
Analysis Facility (NBAF) at the University of Edin-
burgh. Across this sample, genotyping error rate
was small (£1.4%).
For the mtDNA analyses, a 572-bp fragment of
the ND2 gene was amplified for a randomly
selected subset of samples from 11 colonies
(n= 66, Table 1). Fewer samples were analysed
than for microsatellites, as mtDNA loci show less
variation due to slow mutation rates and smaller
effective population sizes. Although several mito-
chondrial genes were initially tested (ND2, cyto-
chrome band COI; Supporting Information
Table S1), ND2 alone consistently amplified and
showed polymorphism. Amplification was under-
taken by PCR (polymerase chain reaction) using
primers L5143 and H5766 (Sorensen 2003), and a
unique PCR protocol. Reaction mixes were 25 lL
in volume containing: < 1 to 10 ng of genomic
DNA, 0.5 lMof each primer, 1 ·PCR buffer (New
England Biolabs; 10 mMTris–HCl, 50 mMKCl,
1.5 mMMgCl
2
, pH 8.3), 0.1 mMof each dNTP,
0.01% bovine serum albumin (BSA) and 1.0 U Taq
DNA polymerase (New England Biolabs). PCR
conditions were 94 C for 3 min, followed by 40
cycles of 94 C for 1 min, 54 C for 1 min and
72 C for 1 min, with a final extension at 72 C for
10 min, performed in a Hybaid MBS thermocycler.
PCR reaction products were visualized on 1% aga-
rose gels, and sequenced at NBAF.
Genetic diversity, Hardy–Weinberg
proportions and gametic equilibrium
Microsatellites
Each colony was tested for significant departures
from Hardy–Weinberg equilibrium and gametic
equilibrium via log-likelihood ratio G-tests and
randomization procedures using FSTAT v.2.9.3.2
(Goudet 2002). Significance levels were
adjusted for multiple comparisons using the Benja-
mini–Yekutieli (B–Y) modified false discovery rate
(FDR) method (Benjamini & Yekutieli 2001). FDR
methods offer an increase in power and a more
stringent control over type II error than the more
widely used Bonferroni correction (Narum 2006).
Table 1. Sampling colony locations and numbers of individuals (n) included in the microsatellite and mitochondrial (mtDNA) analyses.
nColony Geographic location Abbreviation Subspecies Latitude Longitude Year Microsatellites mtDNA
1 Hornøya Norway HN aristotelis 7023¢N3108¢E 2009 14 5
2 Anda Norway AD aristotelis 6903¢N1510¢E 2009 9 0
3 Røst Norway RS aristotelis 6726¢N1154¢E 2009 28 4
4 Sklinna Norway SK aristotelis 6512¢N1100¢E 2009 15 0
5 Melstein Norway MS aristotelis 6357¢N0932¢E 2009 13 0
6 Kjøer Norway KJ aristotelis 5853¢N0526¢E 2009 18 5
7 Flatey Iceland FT aristotelis 6522¢N2254¢W 2007 20 3
8 Sku
´voy Faroe Islands SV aristotelis 6146¢N0649¢W 2009 5 5
9 Canna Scotland CA aristotelis 5730¢N0633¢W 2008 16 0
10 Badbea Scotland BD aristotelis 5890¢N0333¢W 2008 44 0
11 Bullers of Buchan Scotland BB aristotelis 5725¢N0149¢W 2009 51 0
12 Isle of May Scotland IM aristotelis 5611¢N0233¢W 2008 27 20
13 Staple Island England ST aristotelis 5537¢N0137¢W 2009 41 0
14 Lambay Ireland LB aristotelis 5329¢N0601¢W 2009 15 5
15 Ireland’s Eye Ireland IE aristotelis 5324¢N0603¢W 2009 6 0
16 I
ˆle de Be
´niguet France IB aristotelis 4850¢N0301¢W 2009 10 4
17 Asturias Spain AS aristotelis 4333¢N0659¢W 2009 24 0
18 Vizcaya Spain VZ aristotelis 4326¢N0256¢W 2009 15 2
19 Galicia Spain GC aristotelis 4213¢N0854¢W 2009 56 4
20 Corsica France CS desmarestii 4222¢N0832¢E 1995 20 9
Grand Total 447 66
ª2011 The Authors
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European Shag population genetics 771
Adjusted Pvalues were calculated using the
‘p.adjust’ function in the Rlibrary ‘stats’. The
presence of null alleles was tested for using MICRO-
CHECKER (van Oosterhout et al. 2004). Genetic vari-
ation for each locus–colony combination was quan-
tified using numbers of alleles, genetic diversity
(Nei 1973) and the presence of private alleles using
GENEPOP v.4.0 (Rousset 2008), and allelic richness
(Petit et al. 1998) using FSTAT v.2.9.3.2 (Goudet
2002). To aid detection of any postglacial coloniza-
tion patterns, the correlation between latitude and
genetic diversity was quantified using Rv.2.11.1 (R
Core Development Team 2008).
mtDNA
ND2 sequences were aligned using CODONCODE
ALIGNER v.3.5.4 (CodonCode Corporation) and
compared against those of other species for which
fragments had previously been verified as mtDNA.
No insertions ⁄deletions were found. The number
of haplotypes was calculated in DNASP v.4.10
(Rozas et al. 2003).
Population genetic structure
Genetic differentiation among colonies was quanti-
fied using estimates of both F
ST
(Weir & Cockerham
1984) and R
ST
(Slatkin 1995). F
ST
generally esti-
mates genetic differentiation more accurately than
R
ST
when limited numbers of loci and individuals are
genotyped, especially when populations are weakly
structured (Balloux & Goudet 2002). However,
there are some cases where R
ST
is superior to F
ST
,
such as when the loci used follow the stepwise
mutation model (SMM) exactly (Balloux & Lugon-
Moulin 2002). The significance of F
ST
was tested
using a G-test and 1000 randomizations in FSTAT
v.2.9.3.2 (Goudet 2002). R
ST
values and their signifi-
cance were estimated using RSTCALC v.2.2 (Goodman
1997). D
EST
, an unbiased estimator of divergence
(Jost 2008), was also calculated using SMOGD (Craw-
ford 2009), as estimates of F
ST
can be misleading if,
for example, populations with different levels of
genetic variation are compared (Jost 2008).
A Bayesian clustering analysis, implemented in
STRUCTURE v.2.3.3 (Pritchard et al. 2000), was used
to infer genetic structure. Ten replicate runs were
performed for values K= 1–10, where Kis the
number of independent genetic clusters. The
admixture model was used with the LOCPRIOR set-
ting, which takes into account sample location,
and is expected to perform better than previous
STRUCTURE algorithms when genetic structure is
weak or when the number of loci is small (< 20;
Hubisz et al. 2009). Correlated allele frequencies
were assumed (Pritchard et al. 2000). Exploratory
runs showed that a burn-in of 500 000 followed
by 1 000 000 iterations was sufficient to achieve
stable estimates. The vector specifying the degree
of admixture between each subpopulation (alpha)
was inferred from the data, the parameter of the
distribution of allele frequencies (lambda) was set
to one and the prior for F
ST
was specified with
mean 0.01 and standard deviation 0.05. The sensi-
tivity of the final result to specific prior assump-
tions of alpha and independence of allelic
frequencies was also tested. The optimal number
of clusters for our data was estimated by examin-
ing log-likelihood given the number of clusters
(lnP(X|K)) (Pritchard et al. 2000), and by examin-
ing the second-order rate of change of lnP(X|K)
(DK) (Evanno et al. 2005). The results of all runs
were summarized in CLUMPP v.1.1.1 (Jakobsson &
Rosenberg 2007) using the Greedy algorithm with
random input order and 10 000 permutations to
align the 10 replicate runs from STRUCTURE and the
G’ pairwise matrix similarity statistic. Results were
visualized using DISTRUCT v.1.1 (Rosenberg 2004).
The correlation between genetic and geo-
graphical distances was calculated to test for isola-
tion-by-distance. As Shags are virtually never seen
out of sight of land and rarely cross land in flight
(Harris & Swann 2002), coastal distances may best
reflect the true dispersal path between colonies.
Therefore, geographical distances were measured
as the coastline distance between pairs of colonies,
with cross-sea distances measured between land
masses at the closest points. For comparison,
Euclidean distances between colonies were also
measured. The significance of the correlation co-
efficient between population pairs was estimated
using a Mantel test (Mantel 1967). A partial Man-
tel test was used to determine whether open sea
represented a barrier to dispersal, by controlling
for those colonies in regions separated by sea. All
tests were based on 10 000 randomizations and
implemented in IBDWS (Jensen et al. 2005).
Microsatellite data were used to test for evi-
dence of local inbreeding (F
IS
; El Mousadik & Petit
1996) using FSTAT v.2.9.3.2 (Goudet 2002). Recent
population size reductions were tested for using
BOTTLENECK v.1.2.02 (Cornuet & Luikart 1997).
When a population bottleneck occurs, both allelic
diversity and heterozygosity decrease, but the
ª2011 The Authors
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772 E. J. Barlow et al.
number of alleles decreases more quickly than het-
erozygosity due to the loss of rare alleles, leading
to a situation where heterozygosity is higher than
expected for a given number of alleles. The distri-
bution of allele frequencies is also affected, shifting
towards larger proportions of low-frequency
alleles. The analysis was repeated under two mod-
els of mutation, as the exact mutation model
underlying the evolution of the microsatellites
used in this study was unknown: first, using the
SMM, where each mutation creates a novel allele
either by adding or deleting a single repeated unit;
and secondly, using the two phase model (TPM;
70% SMM), which is analogous to the SMM but
allows a proportion of larger mutation events (Sel-
koe & Toonen 2006). A total of 10 000 simulation
replicates were used. The significance of heterozy-
gosity excess was evaluated using the Wilcoxon
signed-rank test (Piry et al. 1999).
RESULTS
Genetic diversity, Hardy–Weinberg
proportions and gametic equilibrium
Microsatellites
In total, 447 Shags from 20 different breeding col-
onies were genotyped at seven polymorphic loci
(Table 1, Fig. 1, Supporting Information Table S4).
There was no strong evidence of departure from
gametic equilibrium between any pair of loci at
any of the breeding colonies (P> 0.01, the
adjusted critical value), or from Hardy–Weinberg
expectations after B–Y FDR correction for multi-
ple comparisons (P> 0.01, the adjusted critical
value; Supporting Information Table S3). There
was evidence for a low frequency of null alleles at
three loci (Phaari02, Phaari05 and Phaari06,
P£0.15). The total number of alleles detected per
locus ranged from two (Phaari05 and Phaari06) to
18 (Phaari11), averaging 6.6 ± 3.4 over all loci
across populations (Table S3). There were consis-
tently high levels of genetic diversity (mean
expected heterozygosity ranged from 0.46 to 0.76)
and consistently low levels of allelic richness (A;
mean ranged from 2.58 to 3.75) for all locus–
colony combinations (Table 2). Four colonies pos-
sessed private alleles (Table 2). There was no cor-
relation between latitude and allelic richness
(r=)0.37, P= 0.1) but a significant negative
correlation between latitude and observed
heterozygosity (r=)0.49, P= 0.02).
mtDNA
Only low levels of genetic variation were observed
in the ND2 gene. Two variable sites were found
among 66 sequences, which defined three haplo-
types (Supporting Information Tables S1 and S5):
one present in all populations at latitudes greater
than 440¢N, one present in five sequences from the
Corsican population only, and one shared between
Corsica and the Spanish colonies at Vizcaya and
Galicia. Sequences were submitted to GenBank
(accession numbers HM449750–HM449752).
Population genetic structure
Pairwise F
ST
values ranged from 0.006 to 0.169
with a global F
ST
of 0.055 (P£0.001; 95% boot-
strap confidence intervals: 0.039–0.076), R
ST
ran-
ged from 0.003 to 0.255, with a global estimate of
0.069 (P£0.001; 95% bootstrap confidence inter-
vals: 0.051–0.086), and the overall value of D
EST
was 0.066 (P£0.001; 95% bootstrap confidence
intervals: 0.049–0.091; Supporting Information
Table S2). These values indicate weak, although
Table 2. Genetic variation at seven microsatellite loci for 20
European Shag breeding colonies.
Colony N
A
APH
E
H
O
F
IS
HN 7.29 2.95 0 0.548 0.575 0.059
AD 7.57 3.35 1 0.609 0.662 0.098
RS 8.00 3.22 0 0.597 0.650 0.090
MS 8.14 3.30 1 0.733 0.684 )0.050
SK 6.86 3.38 0 0.699 0.676 )0.001
KJ 3.14 2.58 0 0.495 0.484 0.107
FT 6.57 3.27 0 0.617 0.623 0.043
SV 5.57 2.90 0 0.590 0.537 )0.061
CA 4.57 3.06 0 0.654 0.601 )0.019
BD 8.71 3.38 0 0.671 0.661 )0.041
BB 6.86 3.61 0 0.635 0.700 0.139
IM 7.29 3.62 0 0.655 0.686 0.093
ST 6.29 3.39 0 0.719 0.667 0.005
LB 3.86 2.67 0 0.464 0.460 0.085
IE 4.71 2.68 0 0.509 0.505 0.029
IB 4.71 3.07 0 0.757 0.604 )0.202
VZ 6.86 3.37 0 0.673 0.669 0.034
AS 9.00 3.75 0 0.732 0.735 0.026
GC 8.43 3.43 2 0.740 0.688 )0.067
CS 6.86 3.51 4 0.642 0.644 0.031
Colony abbreviations are defined in Table 1. N
A
, average num-
ber of alleles; A, allelic richness; P, number of private alleles;
H
E
, average expected heterozygosity; H
O,
average observed
heterozygosity; F
IS,
inbreeding coefficient. Bold values indicate
values that are significant after correction for multiple compari-
sons using the Benjamini–Yekutieli modified false discovery
rate method (Benjamini & Yekutieli 2001, Narum 2006).
ª2011 The Authors
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European Shag population genetics 773
statistically significant, genetic structure across
colonies (Wright 1978).
Cluster analysis showed low variance in ln
P(X|K) across replicate runs, and visual inspection
of time series plots of the likelihood and the esti-
mated parameters confirmed convergence. Evalua-
tion of ln P(X|K), DKand Qfor different values of
Kstrongly supported the K= 3 model for our data
(Pritchard et al. 2007, Supporting Information
Fig. S1). Changing the assumptions of an equal
alpha for all populations and correlated allele fre-
quencies did not change this conclusion. At K=3
the proportion of membership (average Q; Figs 1
and 2) for 18 of the 20 colonies to one of the clus-
ters varied from 0.769 to 0.905. The colonies at
Flatey, Île de Béniguet, Vizcaya and Asturias were
admixed with the strongest membership propor-
tion to a single cluster: 0.570, 0.492, 0.560 and
0.631, respectively (Fig. 2). The three genetic clus-
ters corresponded well with the geographical loca-
tions of the colonies. The first comprised colonies
with latitudes at, or greater than, 573¢N (average
Q= 0.739), with the exception of Flatey. The sec-
ond comprised colonies at latitudes of less than
573¢N but greater than 4333¢N (average
Q= 0.890), with the exception of Île de Béniguet.
The third comprised the most southerly colonies
at latitudes of 4333¢N or less, with the exception
of Vizcaya and Asturias (average Q= 0.859).
Genetic distance showed a significant, positive
relationship with Euclidean (r= 0.34, one-sided
P= 0.004 from 1000 randomizations) and coastal
distance (r= 0.52, one-sided P= 0.001 from 1000
randomizations). Partial Mantel tests demonstrated
that this relationship did not alter after controlling
for colonies separated by open sea (r= 0.52, one-
sided P= 0.001 from 1000 randomizations). The
r
2
for the reduced major axis regression of coastal
distances vs. genetic distances was 0.27.
Significant heterozygosity excess was detected
at two colonies (Melstein and Sklinna) at the 1%
level (Wilcoxon signed-rank test, P> 0.02;
Table 2) under the SMM and TPM. Mean F
IS
ran-
ged from )0.202 to 0.139 across locus–colony
combinations, and multi-locus tests revealed a het-
erozygote excess at five of the 20 colonies analysed
after B–Y FDR correction for multiple compari-
sons (P< 0.001; Badbea, Canna, Skúvoy, Île de
Béniguet and Galicia; Table 2), indicative of a pop-
ulation bottleneck or outbreeding. Two popula-
tions (Skúvoy and Île de Béniguet) exhibited allele
frequency distributions that showed a shift towards
larger proportions of low-frequency alleles.
DISCUSSION
Quantifying the pattern and magnitude of genetic
variation and structure within and among breeding
populations can provide major insights into the
extent of contemporary and historical gene flow
across populations. This in turn can provide insight
into ecological and evolutionary dynamics that
cannot be discerned easily from field observations
(Koenig et al. 1996, Avise 2000, Runge et al.
2007). We quantified nuclear and mtDNA varia-
tion and differentiation among breeding popula-
tions of Shags, a species that shows high breeding
philopatry but also evidence of high mobility prior
to colony recruitment. These analyses revealed
weak but statistically significant genetic structure
among populations at a Europe-wide scale. Cluster
analyses indicated the presence of three genetic
groups arranged by latitude and there was no evi-
dence that open sea acted as a barrier to effective
dispersal. These patterns suggest the influence of
both contemporary and historical gene flow and
may be explained by processes occurring at both
colony and range-wide scales.
Colony-level processes
The rate of breeding dispersal by adult Shags is
considerably lower, and covers substantially shorter
distances, than natal dispersal by juveniles (Potts
Figure 2. Proportional membership (Q) of European Shags to genetic clusters (K) for K= 3 as estimated using STRUCTURE v.2.3.3.
Each vertical bar represents a single individual and individuals are ordered by geographical sampling location. Shading corresponds
to genetic clusters. Colony abbreviations are defined in Table 1.
ª2011 The Authors
Ibis ª2011 British Ornithologists’ Union
774 E. J. Barlow et al.
1969, Aebischer 1995, Velando & Freire 2002).
Given the weak population genetic differentiation
observed among populations at large geographical
scales, it seems likely that occasional long-distance,
cross-sea movements occur and translate into effec-
tive gene flow. Juvenile dispersal is therefore likely
to be primarily responsible for the weak structure
observed across the distribution. Indeed, partial
Mantel tests concurred with results from ringing
recoveries of juveniles from the UK that open sea
does not form a complete physical barrier to Shag
movements. Similar levels of differentiation have
been observed in two other, closely related species
(Great Cormorant P. carbo, Goostrey et al. 1998,
Double-crested Cormorant P. auritus, Wait et al.
2003), both of which have been observed to disperse
more widely, both coastally and inland, than the
Shag (Cramp & Simmons 1977).
The amount and distance of gene flow across
colonies may also be influenced by the population
dynamics of Shag (Aebischer 1986, Harris &
Wanless 1996, Frederiksen et al. 2008). During a
population crash, the reduction in effective
population size may be sufficient to create bottle-
necks detectable as heterozygote excess (due to
more rapid reduction in allelic diversity than
heterozygosity). This would be compounded if
migration from other colonies also occurred (due
to admixture ⁄outbreeding), with the additional
consequence that differentiation between source
and destination colonies would be reduced even if
the spatial scales were large (Nei et al. 1975, Pru-
ett & Winker 2005). Thus both declining and
recently recovered populations might be expected
to show heterozygote excesses. Of those popula-
tions we studied, the colony at Île de Béniguet
showed highly significant heterozygote excess and
had recently expanded from just three pairs in
1992 to 190 pairs in 2009 (Monnat & Pasquet
2004, P. Yésou pers. comm.). Here, substantial
genetic admixture (Fig. 2) may reflect an influx of
migrant individuals originating from populations
both north and south of the colony. Other colonies
which showed evidence of heterozygote excess
have all declined in recent years and may have
experienced population bottlenecks (Wanless &
Harris 2004).
Range-wide patterns
Inferring the number of genetic clusters within a
dataset can be problematic when population struc-
ture is weak (Pritchard et al. 2000, 2007, Evanno
et al. 2005, Hubisz et al. 2009). However, our
genetic clustering analyses revealed strong support
for the presence of three distinct genetic clusters
(K= 3). The distribution of these clusters across
the range appears to reflect colony latitude rather
than being defined by potential open sea barriers,
providing further support for cross-sea movements.
For example, the coastally connected colonies of
Canna and Badbea in Scotland cluster together
with colonies located further north, whilst other
Scottish populations cluster with more southerly
colonies. This may be due to recent long-distance,
cross-sea movements or the effect of local
population dynamics, such that founder effects or
bottlenecks have created differences among geo-
graphically proximate populations; most likely, it is
a combination of the two. However, genetic dis-
tance was more strongly correlated with coastal
distance than with Euclidean distance. Although
there have been growing efforts to incorporate
landscape data into genetic studies of natural pop-
ulations (McRae 2006, Jacquiéry et al. 2011 and
references therein), few studies have considered
landscape resistance to dispersal in avian species,
presumably stemming from the assumption that
their high vagility leads to few physical barriers to
movement being encountered. Our data demon-
strate the importance of landscape features in
shaping patterns of genetic variation in the Shag
(Jacquiéry et al. 2011).
Patterns of population genetic structure in
nuclear and mtDNA data were widely concordant,
although the magnitude of population genetic vari-
ation was greater at nuclear markers than mtDNA.
Both sets of markers independently assigned a
genetic split between the Spanish and Corsican
populations in the southern part of the breeding
range, and those populations in the north (Fig. 1).
Fontaine et al. (2010) found a similar split for Har-
bour Porpoise Phocoena phocoena around the Bay
of Biscay and attributed this to the species tracking
the retreat of cold-water prey as a result of warm-
ing waters. Interestingly, the admixed colonies at
Île de Béniguet and Vizcaya were located on either
side of this split (Fig. 2). Congruent patterns
between independent markers often reflect com-
mon historical events (Avise 2000) and can be an
indication of range expansion into areas previously
unoccupied (Brito 2007). Friesen et al. (2007)
suggested that range expansion may be one of
the factors reducing genetic structure in seabird
ª2011 The Authors
Ibis ª2011 British Ornithologists’ Union
European Shag population genetics 775
populations. Several studies have found evidence
for post-Pleistocene range expansions in seabirds
(e.g. Moum & Árnason 2001, Morris-Pocock et al.
2008). The presence of both a unique haplotype
and 50% of all private alleles within the Corsican
population, in addition to the negative relationship
between observed heterozygosity and increasing
latitude, suggest that Mediterranean Shag popula-
tions may have acted as source populations for
postglacial colonization. However, the sampling
coverage throughout the Mediterranean, and the
signature itself, were not strong enough to pinpoint
the number and location of such refugia. Analysis
of range-wide structure with much higher resolu-
tion mtDNA sequence data is needed to provide
the robust phylogeographical framework that
would allow such hypotheses to be tested.
Despite significant F
ST
values between Corsica
and all other populations, the Corsican population
of P. a. desmarestii clusters together with Atlantic
Spanish populations of P. a. aristotelis. Rasmussen
(1994) found that allozyme frequencies were
similar across seven South American populations
of Imperial Shags P. atriceps, despite significant
morphological differentiation. Indeed, few studies
of genetic variation, and of mtDNA phylogeogra-
phy in particular, have revealed relationships that
are concordant with subspecific designations across
taxa (Avise 2000, Zink 2004, Peucker et al. 2009).
The Corsican population may represent the
extreme end of a cline in variation between
P. a. aristotelis in the west and populations of
P. a. desmarestii to the east. However, samples
from across the range of P. a. desmarestii are
needed to test this hypothesis.
We are indebted to everyone who provided samples or
facilitated sampling at all colonies, most particularly: M.
Newell (Isle of May), R. Duncan and C. Jones (Bullers
of Buchan), R. Sellers and M. Oksien (Badbea), R. L.
Swann (North Sutor & Canna), The National Trust and
C. Redfern (Staple Island), O. Merne (Ireland’s Eye), S.
Newton (Lambay), B. Olsen (Skúvoy), A. Petersen (Fla-
tey), R. Barrett (Hornøya), S. Christensen-Dalsgaard
(Anda), T. Anker-Nilssen (Røst), G. Bangjord (Melstein),
S-H. Lorentsen (Sklinna), A. Follestad (Kjøer), P. Yésou,
B. Cadiou and J. Nisser (Île de Béniguet), A. Velando
(Galicia), J. Hidalgo (Vizcaya), J-C. Thibault (Corsica).
We also thank Morten Frederiksen and two anoymous
referees for helpful comments on an earlier version of
the manuscript. E.J.B was supported by NERC. All sam-
ples were collected under appropriate licences in accor-
dance with national legal, ethical and welfare
regulations.
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Received 15 November 2010;
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Associate Editor: Morten Frederiksen.
SUPPORTING INFORMATION
Additional Supporting Information may be found
in the online version of this article:
Figure S1. Inference of the number of genetic
clusters (K) estimated using STRUCTURE. Both ln
P(X|K) (the likelihood of the data given K; black
circles) and DK(the standardized second-order rate
of change of ln P(X|K); open circles) are plotted as
a function of K.
Table S1. Variable sites in the mitochondrial
ND2 gene of 66 European Shags. Haplotypes
shared by multiple individuals have the sample size
in parentheses. Haplotype HM449750 was found
at IM (n= 11), LB (n= 5), SV (n= 6), FT
(n= 2), HN (n= 5), RS (n= 4), KJ (n= 5) and IB
(n= 4). Haplotype HM449751 was found at VZ
(n= 2), GC (n= 4) and CS (n= 5). Haplotype
HM449752 was found only in CS (n= 5).
Sequence identity to HM449750 is indicated by a
‘.’. Colony abbreviations are defined in Table 1.
The following mitochondrial primer pairs (Soren-
sen 2003) were also tested, but no variation was
detected: L7036 (COI) & H7548 (COI), L7525
(COI) & H8121 (COI) and L14996 (cyt b) and
H15646 (cyt b).
Table S2. Estimates of genetic differentiation
for European Shags from 20 breeding colonies
across the species range. Pairwise F
ST
values are
below, and R
ST
and D
EST
values (in parentheses)
are above the diagonal. Bold values indicate values
that are significantly different from zero at the 5%
nominal level. Colony abbreviations are defined in
Table 1. Estimates are based upon microsatellite
data.
Table S3. Summary of genetic variation at seven
microsatellite loci scored from 20 European Shag
populations: number of alleles per locus (N
A
),
observed heterozygosity (H
O
) and expected
heterozygosity (H
E
). Bold values indicate depar-
tures from Hardy–Weinberg equilibrium after cor-
rection for multiple comparisons using the
Benjamini–Yekutieli modified false discovery rate
method (Benjamini & Yekutieli 2001, Narum
2006).
Table S4. Raw microsatellite genotype data for
447 individual European Shags from 20 breeding
populations.
Table S5. Raw mitochondrial DNA sequence
data for 66 individual European Shags.
Please note: Wiley-Blackwell are not responsible
for the content or functionality of any supporting
materials supplied by the authors. Any queries
(other than missing material) should be directed to
the corresponding author for the article.
ª2011 The Authors
Ibis ª2011 British Ornithologists’ Union
778 E. J. Barlow et al.
