Submitted 15 January 2020
Accepted 19 April 2020
Published 10 July 2020
Additional Information and
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2020 Nikolic et al.
Creative Commons CC-BY 4.0
Evolutionary history of a Scottish
harbour seal population
Natacha Nikolic1,2, Paul Thompson3, Mark de Bruyn4, Matthias Macé5and
1ARBRE (Reunion Island Biodiversity Research Agency), Saint-Leu, La Réunion
2Génétique Physiologie et Systèmes d’Elevage - UMR1388, INRAE, Castanet Tolosan, France
3Lighthouse Field Station, Sciences School of Biological Sciences, University of Aberdeen, Cromarty,
4School of Life and Environmental Sciences, University of Sydney, Sydney, Australia
5Laboratoire d’Anthropologie Moléculaire et d’Imagerie de Synthèse - UMR 5288, CNRS, Toulouse, France
Efforts to conserve marine mammals are often constrained by uncertainty over their
population history. Here, we examine the evolutionary history of a harbour seal (Phoca
vitulina) population in the Moray Firth, northeast Scotland using genetic tools and
microsatellite markers to explore population change. Previous fine-scale analysis of
UK harbour seal populations revealed three clusters in the UK, with a northeastern
cluster that included our Moray Firth study population. Our analysis revealed that
the Moray Firth cluster is an independent genetic group, with similar levels of genetic
diversity across each of the localities sampled. These samples were used to assess historic
abundance and demographic events in the Moray Firth population. Estimates of current
genetic diversity and effective population size were low, but the results indicated that this
population has remained at broadly similar levels following the population bottleneck
that occurred after post-glacial recolonization of the area.
Subjects Evolutionary Studies, Genetics, Population Biology
Keywords Evolution, Genetic, Seal
Efforts to conserve marine animals are frequently constrained by uncertainty over
historic baselines and the factors driving changes in abundance (Lotze & Worm, 2009).
A variety of techniques have been developed to address this issue, including archaeological
investigations (Rick & Lockwood, 2013), studies based upon historical records or traditional
knowledge (McClenachan, Ferretti & Baum, 2012), and molecular analyses of changes in
genetic diversity (Roman & Palumbi, 2003;ref-101).
Harbour seals (Phoca vitulina) are widely distributed around the North Atlantic and
North Pacific coasts, but studies over recent decades have identified wide variations
in the status of these populations. Abundance in many parts of Europe was severely
reduced by successive outbreaks of Phocine Distemper Virus (PDV), but mortality in
some populations was low (Heide-Jørgensen, Härkönen & Aberg, 1992;Härkönen et al.,
2006). Similarly, longer-term trends in harbour seal abundance show steady increases in
some parts of their range (Jeffries et al., 2003;Aarts et al., 2019), whereas other regions have
How to cite this article Nikolic N, Thompson P, de Bruyn M, Macé M, Chevalet C. 2020. Evolutionary history of a Scottish harbour seal
population. PeerJ 8:e9167 http://doi.org/10.7717/peerj.9167
experienced unexplained declines (Boveng et al., 2003;Lonergan et al., 2007;Hanson et al.,
2015;Thompson et al., 2019).
These changes have resulted in new conservation measures in many areas, the most
significant of which in European waters is the EU Habitats Directive (Baxter, 2001). This
requires EU Member States to develop a NATURA 2000 network of Special Areas of
Conservation (SACs) for a range of key species, including harbour seals (Thompson et
al., 2019). In UK waters, SACs have been established within eleven management units
that reflect the current understanding of genetic structure (Olsen et al., 2017), but there
is uncertainty over the causes underlying different regional trends in abundance and
historic baselines (Matthiopoulos et al., 2014;Thompson et al., 2019). Here, we focus on the
evolutionary history of a population that uses one of these SACs, in the Moray Firth, NE
Scotland. In this area, individual-based studies since 2006 have provided detailed estimates
of contemporary vital rates (Cordes & Thompson, 2014;Matthiopoulos et al., 2014) during a
period where there has been no clear trend in abundance (Thompson et al., 2019). The most
recent counts indicate that the population has declined by around 40% since the mid 1990’s
(Thompson et al., 2019), but direct information on the historic abundance and evolution
over multiple generations is lacking given the lack of comparable survey data prior to this.
An alternative approach is to assess historic changes in the genetic-based indicator, effective
population size (Ne). The effective size reflects the abundance and evolutionary history of
the population, and can inform conservation efforts because it affects the degree to which
a population can respond to selection (Berthier et al., 2002).
Analysis of neutral molecular markers such as microsatellites can be used to calculate
Ne and provide an evolutionary perspective to these conservation and management issues
(King et al., 2001). Genetic studies conducted on harbour seals have generally used less
than 15 microsatellite markers for ‘‘Atlantic’’ harbour seals (12 in Olsen et al. (2017); 15 in
Anderse et al. (2011); 7 in Goodman (1997);Goodman (1998)) and less than 20 for ‘‘Pacific’’
harbour seals (5 in Burg et al. (1999), 8 in Curtis, Stewart & Karl (2011), 7 in Dishman
(2011), 20 in Hayes et al. (2006) and 6 in Herreman et al., 2009). We used the markers
developed specifically for harbour seal microsatellites (Allen et al., 1995;Coltman, Bowen
& Wright, 1996;Goodman, 1997) and a mix of pinniped microsatellite markers (Allen et
al., 1995;Buchanan et al., 1998;Davis et al., 2002;Gelatt et al., 2001), as other harbour seal
microsatellite papers (ex. Olsen et al., 2017). We therefore tested 30 markers, which allowed
us to utilise 17 polymorphic microsatellite markers (25 markers minus 8 monomorphic
markers) for genetic analysis of harbour seals. Previous study of genetic structuring of UK
harbour seals suggests that there are two main initial groups consisting of localities in the
northern UK, and the southern UK and mainland Europe (Olsen et al., 2017). However,
Olsen et al. (2017) further divided these groups into geographically distinct genetic clusters,
including the North-East England and Eastern Scotland cluster (cf. Fig. 2 of Olsen et al.,
2017) that includes our Moray Firth study sites (cf: Fig. 1 of Hanson et al., 2015). Here, we
extended the number of microsatellite markers available for harbour seals in the Moray
Firth to estimate historic changes in effective population size in this region.
Using this extended set of microsatellite markers, the objectives of the present study
were to address the following questions: (i) what does current genetic diversity tell us about
Nikolic et al. (2020), PeerJ, DOI 10.7717/peerj.9167 2/29
Figure 1 Mean geographic location of the three areas sampled (Dornoch –13 individuals, Cromarty –
12 individuals and Inverness –68 individuals) from Moray Firth in north-east Scotland.
Full-size DOI: 10.7717/peerj.9167/fig-1
historic changes of harbour seal effective population sizes; and (ii) how might genetic data
contribute to understanding the decline of seals in the north-east of Scotland?
MATERIAL & METHODS
The Moray Firth study population (Fig. 1) contained an estimated 1,653 harbour seals in
1993 (Thompson et al., 1997a) when the samples used in this study were collected. Seals
come ashore at inter-tidal sites throughout the year, with most pups born in three sub-areas
spaced approximately 50 km apart (Dornoch Firth, Cromarty Firth, and Beauly Firth).
One of these sub-areas, in the Dornoch Firth, has been designated as a SAC to protect
harbour seal populations under the EU Habitats Directive (Cordes et al., 2011;Thompson
et al., 2019).
Samples and DNA extraction
Archived blood samples were collected from 93 harbour seals that had been captured,
sampled and released as part of earlier ecological studies (Thompson et al., 1997b;Hall et
al., 2019). Samples were collected between 1992 and 1995 (see Thompson et al., 1997b for
details of capture and sampling methods), and constituted 47 females and 46 males (48
juveniles, 37 adults, 8 sub-adults, data are available at https://doi.org/10.15454/AOZ7JI);
representing approximately 6% of the population. All capture and handling methods were
Nikolic et al. (2020), PeerJ, DOI 10.7717/peerj.9167 3/29
carried out in accordance with the approved guidelines and conducted under licences from
the UK Home Office. Blood samples were collected under Home Office licence issued to the
University of Aberdeen under the Animal (Scientific Procedures) Act 1986 (PPL number
60/01351). These samples were preserved at −20 ◦C and hydrated with PBS (Phosphate
Buffered Saline) before DNA extraction. Genomic DNA was extracted by QIAamp DNA
Blood Mini Kit (QIAGEN) (see the Text S1 for more details).
Marker selection and genotyping
Because the number of available harbour seal microsatellite markers was low, we considered
all potential microsatellites for this species and additionally from other pinnipeds—
Halichoerus grypus, Hydrurga leptonyx (two species belonging to the family Phocidae) and
Odobenus rosmarus rosmarus (Odobenidae). A total of 30 markers were tested Appendix
S1A. Previously some primers were designed using Primer 3 (Rozen & Skaletsky, 2000)
because only the amplified sequences were provided by the authors (ie the primers H12,
HL20, HL16, HL15) Appendix S1B. The primers PVC63 were redefined using Primer 3 to
optimize amplification. We retained 25 loci that amplified reliably (meaning that on 12
individuals tested a minimum of 10 were successfully amplified and visualized on agarose
gel) for harbour seals Appendix S1B, with a minimum of twelve repeat motifs and a GC
percentage of approximately 50%.
All 93 harbour seal samples were successfully genotyped with these 25 microsatellite
markers using fluorescent labelled primers and multiplex PCR pools (Appendices S1A,
S1B). PCR amplification was carried out using an Applied Biosystems 2720 Thermal Cycler
with 10 µl reaction volume containing ∼50 ng DNA, 1.5 mM MgCl2, 1x Promega buffer,
200 µM dNTPs, 0.5 µM each primer, 0.5 U Taq DNA polymerase (Promega). An initial
denaturation step at 94 ◦C for 5 min was followed by 42–45 cycles of 30 s at 94 ◦C, 30 s
at Tm ◦C (annealing temperature), 30 s at 72 ◦C, followed by a final elongation step of 30
min at 72 ◦C. The annealing temperatures were optimised for each locus Appendix S1B.
The PCR products (2 µl) were added to a mixture of deionised formamide and the internal
size standard GENESCAN-400HD Rox (Applied Biosystems) (8 µl), then denatured for
5 min at 95 ◦C. For this mixture (formamide and internal size standard), we prepared a
final volume of 1000 µl with 982.5 µl of formamide and 17.5 µl GENESCAN-400HD Rox.
Individual electropherograms were obtained using an ABI 3730 multi-capillary sequencer.
PCR products were visualised by GeneMapper v4.0 software (Applied Biosystems) (see the
data and profile examples per pool at https://doi.org/10.15454/AOZ7JI). Across the panel of
25 markers, 8 were monomorphic Appendix S1B in both male and female individuals. The
genetic analysis was performed on the overall panel (25 markers) and on the polymorphic
markers (17 markers).
Analysis of diversity and genetic diﬀerentiation
To ensure that the number of loci was sufficient, we calculated the probability of individual
identity (PI; the probability that two individuals in a population have identical genotypes)
for each locus and their combinations with the program GENALEX (Peakall & Smouse,
2006;Peakall & Smouse, 2012). SPOTG (Hoban, Gaggiotti & Bertorelle, 2013) was used to
Nikolic et al. (2020), PeerJ, DOI 10.7717/peerj.9167 4/29
estimate the power of individual assignment, using 1,000 runs, the number of sampled
individuals (93), the mean number of alleles (4), and the FST values found in this study
under a normal allele frequencies model.
Genetic diversity was measured as the mean number of alleles per locus (A) for 25
markers. Excluding the monomorphic markers we measured observed heterozygosity
(Ho), expected heterozygosity (He), and Nei’s (1978) unbiased heterozygosity (H.n.b)
using GENETIX 4.05.2 (Belkhir et al., 1998). Estimates of homozygote and heterozygote
excess that differed significantly from zero (p<0.05) were calculated from the standard
error rates with confidence intervals in Pedant (Johnson & Haydon, 2007). Deviations from
Hardy-Weinberg Equilibrium (HWE) were assessed using polymorphic loci, and exact
tests with p-values and their standard errors were computed using ARLEQUIN version 3.1
(Excoffier, Laval & Schneider, 2005;Excoffier & Lischer, 2010) with permutations (1,000,000
chains and 100,000 steps). Polymorphism information content (PIC) was generated in
Cervus (Kalinowski, Taper & Marshall, 2007). Probability of parentage exclusion (PE1,
single parent (Jamieson & Taylor, 1997); PE2, a second parent given a first parent assigned
(Jamieson, 1994); PE3, a pair of parents (Jamieson & Taylor, 1997)) was estimated per
locus using INEst (Chybicki & Burczyk, 2009). The potential occurrence of null alleles and
scoring errors due to stuttering or large allele dropout in the data set was assessed using the
software MICRO-CHECKER (Oosterhout et al., 2004), and the significance of null allele
frequency (Fnull) was estimated with INEst using the individual inbreeding model with
100,000 iterations (estimates significantly different from zero, p<0.05). The inbreeding
coefficient FIS was estimated from polymorphic loci with 10,000 bootstraps by GENETIX
software. FIS measures the decrease in heterozygosity due to inbreeding, assortative mating,
or selection. Genotyping error rate per allele, E1 referring to allelic dropout rate and E2
to the false allele rate, and the 95% confidence interval (CI) with 10,000 permutations,
were evaluated with maximum likehihood from 10 individuals’ random replicates by
marker based on He computed in Pedant. The number of repeated genotypes (Nrep) and
the percentage (%) of the total number of individuals genotyped for each loci were also
Our findings were consistent with the previous study identifying Moray Firth sites
as part of one genetic cluster (Olsen et al., 2017). Thus, we also investigated whether
external migrations (e.g., gene flow from another cluster) were possible because grouping
several different populations could skew our analyses of evolutionary history. Genetic
differentiation was visualized by Factorial Correspondence Analysis (FCA) in GENETIX
using the polymorphic loci (n=17). Pairwise Wright’s F-statistics FST (Weir & Cockerham,
1984) and their levels of significance were assessed based on 10,000 permutations
using ARLEQUIN 3.1 (Excoffier, Laval & Schneider, 2005), considering geographical
location per individual. Number of migrants (Nm) was estimated through the frequency
of private alleles with GENEPOP (Raymond & Rousset, 1995;Rousset, 2008) online
(http://genepop.curtin.edu.au). We estimated only the level of migration rates (m)
(superior or inferior to 0.1, and not the values of gene flow due to uncertainty with a
weak genetic structure) using the Bayesian multi-locus genotyping approach implemented
in BAYESASS 3.0.4 (Wilson & Rannala, 2003) with different random number seed values
Nikolic et al. (2020), PeerJ, DOI 10.7717/peerj.9167 5/29
(S) and sampling frequency (n), 10,000,000 iterations, and a burn-in period of 1,000,000. To
detect immigration into the Moray Firth, we used Rannala & Mountain’s (1997) estimate
and ran Monte-Carlo simulations with 10,000 repetitions implemented in GENECLASS2
software (Piry et al., 2004), considering geographical location per individual.
Finally, we used a population assignment test to check for sex-biased dispersal using the
software GENALEX. This method produces an Assignment Index correction (AIc) for each
sex following the method of Mossman & Waser (1999). Negative AIc values characterize
individuals with a higher probability of being migrants (high dispersion) and positive
values characterize individuals with a lower probability of being migrants. Mean AIc values
were compared for each sex with a non-parametric Mann–Whitney U-test using R version
3.0.2 software. Moreover, hierarchical Analysis of Molecular Variance (AMOVA) analyses
were performed, with 9,999 permutations, for each sex on their own, and for the combined
male and female data set to determine whether genetic variation was similar for males and
STRUCTURE 2.3 (Pritchard, Stephens & Donnelly, 2000) was used to identify population
structure and individual admixture coefficients. Five independent runs were performed
on STRUCTURE for each assumed number of population(s) K=1–6 under an admixture
model. All runs were executed with 50,000 burn-in periods and 200,000 MCMC
(Markov chain Monte Carlo) repetitions, using the three regions defined earlier as prior
information. STRUCTURE HARVESTER 0.6.94 (Earl Dent & VonHoldt Bridgett, 2012)
was used to visualise results and assess K, the number of genetic populations that best
fit the data, based on Maximum Likelihood (Evanno & Goudet, 2005). Structure Selector
(http://lmme.qdio.ac.cn/StructureSelector/) was also used to process the Puechmaille
(2016) approach on uneven sampling and to also estimate the best K. Plots for optimal K
was performed with CLUMPAK (Kopelman et al., 2015).
In addition, a discriminant analysis of principal components (DAPC, Jombart, Devillard
& Balloux, 2010) was performed using R package ADEGENET (Jombart, 2008;Jombart
& Ahmed, 2011). DAPC is a multivariate analysis that integrates principal component
analysis (PCA) with discriminant analysis to summarize genetic differentiation between
groups (Jombart, 2008). Sampling location was used as prior. While STRUCTURE forms
genetic clusters of individuals by minimizing departure from Hardy–Weinberg and linkage
disequilibria, DAPC maximizes genetic separation among groups and minimizes variation
within groups (Jombart, Devillard & Balloux, 2010) which may constitute a more accurate
approach for species exhibiting potentially high gene flow (Bailleul et al., 2018).
Isolation by distance was tested using a Mantel test between genetic (Euclidean
Edwards’ distance) and geographic distances with 10,000 resamplings between individuals
and regions using ADEGENET, ADE4 (Thioulouse et al., 1997) and GRDEVICES (R
Development Core Team and contributors worldwide) R packages.
Eﬀective population sizes and evolutionary history
The average mutation rate on the set of loci (µ), ancestral time (Tf ), and present and
ancestral effective size (No and Na) were estimated for the seals using MSVAR (Beaumont,
Nikolic et al. (2020), PeerJ, DOI 10.7717/peerj.9167 6/29
1999), running 80,000 MCMC chains and 20,000 iterations between chains. This method
is suited to microsatellite data that are assumed to be evolving by a stepwise mutation
model (SMM), sampled from a population that has varied in size. When compared with
other classic coalescence models (TM3 and DIYABC) aimed at estimating the present and
ancestral effective sizes, MSVAR was most efficient for small populations (Nikolic et al.,
2009). Because we used this model only to get preliminary results for the seal mutation rate,
and computation times are very long (here more than 3 days), this analysis was run only
with the polymorphic panel (17 markers). MSVAR estimates the effective population size
for the present and ancestral effective sizes but does not infer fluctuation of effective size
between them. We applied Gelman & Rubin, 1992’s (1992) test to monitor convergence of
To check for historic population declines in the seals, we used BOTTLENECK software
(Piry, Luikart & Cornuet, 1999) with 10,000 iterations and we applied three tests—the sign
test, the standardized differences test (Cornuet & Luikart, 1997), and the Wilcoxon sign-
rank test (Luikart et al., 1997) to analyze the presence of heterozygote excess resulting from
perturbation of allele frequencies. We applied the Wilcoxon signed-rank test specifically
as it does not require a large number of polymorphic loci which are scarce in a population
with low variability (Han et al., 2010). Inferences from heterozygosity excess or deficiency
tests are heavily influenced by the mutational model (Busch, Waser & DeWoody, 2007).
Here, we used the Stepwise Mutation model as it is thought to be a more appropriate model
for use with microsatellites (Nikolic & Chevalet, 2014b).
Bayesian methods using coalescence theory and MCMC sampling to estimate posterior
distributions of demographic parameters and history, seem more robust to certain
violations of mutation model assumptions (Girod et al., 2011) and bottleneck duration
(Peery et al., 2012). Hence, to estimate historic fluctuations of effective population size
from microsatellite markers, we used the algorithm and method VarEff (Chevalet & Nikolic,
2010;Nikolic & Chevalet, 2014b) programming in R package. The model assumes a stepwise
mutation model for microsatellites and makes use of an approximate likelihood of data
based on theoretical results. It is then implemented in a MCMC framework which simulates
past demography by sampling step functions. The model is freely available as an R package
size/). Results were based on MCMC chains including 10,000 dememorization steps, a
total length of 1,000,000 and the extraction of 10,000 uncorrelated states as suggested by
previous analysis (Nikolic & Chevalet, 2014b). Results are provided in normalized scales ˆ
Nµfor population size (4* effective size*mutation rate) and T =gµfor time (number
of generations *mutation rate), or in the natural scale, effective size Ne and generation
number gprovided the mutation rate µis known. For harbour seals, we set µ=0.00015
and used the generation time of 8.75 years based upon the value used in Swart, Reijnders
& Van Delden (1996).
VarEff offers several functions to characterize the posterior distribution of effective
size at several times in the past: arithmetic and harmonic means, median and mode of
the distribution, detailed distribution at specified times, with the quantiles and standard
deviation. In addition, the results of VarEff allow the posterior distribution of the Time
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to the Most Recent Common Ancestor (TMRCA) of two random alleles to be recovered,
which provides complementary information on the occurrence and time of past bottlenecks,
since peaks in this distribution indicate times when coalescence events likely occurred,
and intervals between peaks may indicate periods when bottlenecks occurred. Depending
on the intensity and shape of the peak and the value of effective size (obtained with the
previous functions), information on the bottleneck event(s) can be recovered. Futhermore,
because migration can mimic the effects of bottlenecks (Nikolic & Chevalet, 2014b), we
repeated the same analysis discarding the immigrants that were detected (2 individuals).
Genetic polymorphism and panel of markers
Of the 25 microsatellite loci, eight loci were monomorphic for both sexes (PVC63,
PVC74,SGPV3,PVC26,PVC29,GS1,Hl-16, and OrrFCB23) and one marker Hl-20
was monomorphic in males Appendix S1B. These markers were hence excluded from
the analyses resulting in a final panel of 17 polymorphic markers (Table 1). Our final
genotyping data contained only 3.2% missing data for 93 individuals and 17 polymorphic
The probability of identity (PI) values ranged from 0.023 to 0.927 and the probability of
exclusion (E1, E2, E3) from 0.001 to 0.921 (Table 1). A total of 9 markers had a probability of
identity in the higher range at 0.5 (Table 1). The analysis based on the cumulated probability
of individual identity (PI), an indication of the statistical power of marker loci, revealed
that 10 to 17 polymorphic markers were sufficient to carry out the population genetics
analysis of harbour seals in the Moray Firth (Fig. 2). SPOTG estimated that 93 individuals
and 17 microsatellite markers could detect power of individual assignment >57% and
connectivity >87%. However, the theoretical study by Nikolic & Chevalet (2014b) shows
that the number of markers to assess accurate effective population size must be higher than
the basic diversity analyses. It was therefore important to have more than 10 markers for
this study to reduce the variance of the estimators around the effective size values.
The total number of alleles for the remaining 17 loci ranged between 2 and 14 (Table 1),
while the mean number of alleles was 3.7 (Table 1). The global mean of observed
heterozygosity was 0.384 for the 17 polymorphic microsatellites. Estimates of homozygote
and heterozygote excess were not significant except OrrFCB24 with a heterozygote excess
Genotyping error rates and associated 95% confidence intervals were very close to zero
for all loci except HL20 and OrrFCB1 (Table 1). Most loci were close to HW equilibrium
(P>0.05, Bonferroni correction applied) (Table 1) except SGPV11, SGPV17,PVC78 and
OrrFCB24 (Table 1).The locus SGPV17 has been suspected to be X-linked in pinniped
species (Coltman, Bowen & Wright, 1996;Gemmell et al., 1997;Pastor et al., 2004), although
this remains equivocal (Herreman, Blundell & Ben-David, 2008). Such a linkage would
imply that males are homozygous for this marker. In the present study, we identified 3
alleles (153, 155 and 161) at SGPV17 with similar allelic frequencies in males and females.
Heterozygote frequencies were 14/46 in males and 24/47 in females suggesting no X-linkage
Nikolic et al. (2020), PeerJ, DOI 10.7717/peerj.9167 8/29
Table 1 Summary statistics of the 17 microsatellite markers selected for Harbour seals (Phoca vitulina). Sample sizes per locus (S). Number of alleles (A). Expected
(He), unbiased Nei’s95 expected (H.n.b) and observed (HO) heterozygosity. Hardy–Weinberg equilibrium (HWE) p-values (P) with the standard error in parentheses.
Polymorphism information content (PIC). Probability of identity (PI). Probability of parentage exclusion (PE1, single parent; PE2, a second parent given a first parent as-
signed; PE3, a pair of parents). Null allele frequency (Fnull). Number of repeated genotypes (Nrep and percentage (%) of the total number of individuals genotyped for
each loci). Genotyping error rate per allele, E1 referring to allelic dropout rate and E2 to the false allele rate, and the 95% confidence interval (CI). Significant values are
highlighted in bold (P<0.05) for heterozygote excess.
Genotyping error rate
Locus S A He H.n.b Ho P PIC PI PE1 PE2 PE3 Fnull Nrep(%) E1
(CI 95%) E2
SGPv9 85 3 0.277 0.279 0.329 0.254 (0.00038) 0.242 0.558 0.038 0.123 0.205 0.023 10 (12) 0.00 (0.00–0.41) 0.00 (0.00–0.07)
SGPV11 92 3 0.275 0.276 0.174 0.002 (0.00004) 0.240 0.561 0.038 0.122 0.203 0.107 10 (11) 0.00 (−0.00–0.78) 0.00 (0.00–0.07)
SGPV17 92 3 0.409 0.411 0.413 0.019 (0.00013) 0.345 0.413 0.084 0.184 0.292 0.039 10 (11) 0.00 (−0.00–0.20) 0.00 (−0.00–0.07)
SGPV10 90 2 0.231 0.232 0.267 0.352 (0.00047) 0.204 0.618 0.027 0.102 0.174 0.026 10 (11) 0.00 (0.00–0.40) 0.00 (−0.00–0.07)
SGPV16 74 14 0.886 0.892 0.919 0.659 (0.00033) 0.876 0.023 0.628 0.772 0.921 0.013 10 (14) 0.00 (0.00–0.08) 0.00 (−0.00–0.07)
PVC19 93 2 0.350 0.352 0.344 1.000 (0.00000) 0.289 0.484 0.061 0.144 0.229 0.039 10 (11) 0.00 (−0.00–0.19) 0.00 (0.00–0.07)
PVC30 82 4 0.493 0.496 0.476 0.767 (0.00037) 0.388 0.362 0.122 0.204 0.310 0.039 10 (12) 0.00 (−0.00–0.21) 0.00 (0.00–0.07)
PVC78 93 4 0.243 0.245 0.204 0.003 (0.00005) 0.219 0.597 0.030 0.113 0.193 0.058 10 (11) 0.00 (−0.00–0.19) 0.00 (0.00–0.07)
GS7 93 2 0.157 0.158 0.172 1.000 (0.00000) 0.145 0.723 0.012 0.072 0.129 0.033 10 (11) 0.00 (−0.00–0.71) 0.00 (0.00–0.07)
GS2 91 3 0.173 0.174 0.165 0.557 (0.00049) 0.163 0.694 0.015 0.086 0.156 0.047 10 (11) 0.00 (−0.00–0.25) 0.00 (0.00–0.07)
GS3 93 4 0.560 0.563 0.570 0.579 (0.00048) 0.491 0.262 0.158 0.294 0.442 0.022 10 (11) 0.00 (−0.00–0.16) 0.00 (0.00–0.07)
H12 93 5 0.614 0.617 0.613 0.901 (0.00025) 0.550 0.213 0.194 0.345 0.504 0.029 10 (11) 0.00 (−0.00–0.09) 0.00 (0.00–0.07)
HL20 90 2 0.043 0.044 0.044 1.000 (0.00000) 0.043 0.916 0.001 0.021 0.041 0.050 9 (10) 0.70 (−0.03-1.81) 0.00 (−0.00–0.14)
HL15 93 3 0.242 0.243 0.237 0.707 (0.00043) 0.215 0.601 0.029 0.109 0.186 0.042 10 (11) 0.00 (−0.00–0.76) 0.00 (0.00–0.07)
OrrFCB2 90 4 0.547 0.550 0.633 0.070 (0.00022) 0.489 0.263 0.151 0.296 0.449 0.016 10 (11) 0.00 (−0.00–0.11) 0.00 (−0.00–0.07)
OrrFCB1 79 2 0.013 0.013 0.013 1.000 (0.00000) 0.012 0.927 0.001 0.019 0.037 0.055 10 (13) 0.70 (−0.03-1.81) 0.00 (0.00–0.12)
OrrFCB24 90 3 0.510 0.513 0.956 0.000 (0.00000) 0.391 0.359 0.130 0.202 0.303 0.009 9 (10) 0.00 (0.00–0.08) 0.00 (−0.00–0.08)
Mean 89 3.7 0.354 0.356 0.384 0.522 (0.000) 0.312
Nikolic et al. (2020), PeerJ, DOI 10.7717/peerj.9167 9/29
Figure 2 Probability of Identity for each Locus (PI) and for Increasing Combinations (PIsibs) with the
17 polymorphic microsatellite markers genotyped on harbour seals in the Moray Firth.
Full-size DOI: 10.7717/peerj.9167/fig-2
for this locus. Analysis of sex-biased dispersal using the marker SGPV17 showed there were
no significant differences in the mean AIc with the polymorphic panel (Mann–Whitney
U-test, p-value = 0.78). This result indicates no sex-biased dispersal. Thus, we concluded
that we have no reason to exclude the marker SGPV17 in the polymorphic panel on the
basis of linkage.
MICROCHECKER estimated that only SGPV11 showed evidence for a null allele but
the test by permutations with INest revealed that the result was not significant (Table 1).
Null allele analyses were not significant for all loci (Table 1). Hence, there was no evidence
for scoring error due to stuttering and no evidence of large allele dropout. The harbour seal
population appears to be in Hardy Weinberg equilibrium with the global test (P=0.519).
With respect to the distribution of repeat numbers Appendix S1C, harbour seals in the
Moray Firth exhibited quite large distances between microsatellite alleles (up to 37 repeat
numbers) and a large range of missing values (between 10 and 24, and between 26 and 36)
that may be the signature of bottleneck events.
Genetic diﬀerentiation and clustering analysis
Analysis of sex-biased dispersal (Mossman & Waser, 1999) showed no significant differences
in the mean AIc with the polymorphic panel (Mann–Whitney U-test, p-value =0.69),
indicating no sex-biased dispersal. Futhermore, when males and females were considered
separately in AMOVA analyses, no genetic differentiation was found between the sexes for
the panel of polymorphic markers.
FIS values by permutations (sampling with replacement of individuals with all loci) were
negative −0.081 (with CI narrow and negative, −0.129 and −0.044) and by Jacknife result
on locus −0.079 (variance = 0.006). Negative FIS values indicate that there were more
heterozygotes than expected and individuals in the population may be less related than
expected under a model of random mating. Global frequency distribution of observed and
permuted FST values were not significant, as they lie well inside the distribution of FST
for the null hypothesis. FST were <0.05 between the three geographic areas with only the
value between Dornoch and Inverness (0.014) being significant according to permutation
Nikolic et al. (2020), PeerJ, DOI 10.7717/peerj.9167 10/29
tests Appendix S1D. Corrected average pairwise differences were low and not significant
Appendix S1D. These values indicate an absence of significant differentiation. Clustering
analyses (STRUCTURE and DAPC) supported the existence of one main cluster with no
spatial differentiation and an absence of structuring between sampling localities Appendix
S1E and sex. Moreover, the isolation by distance (Mantel test) was clearly not significant
Concerning migration, FCA on individuals identified 4 axes with eigenvalues of 0.15
(axis 1), 0.089 (axis 2), 0.078 (axis 3), and 0.072 (axis 4). The three dimensional FCA
Appendix S1F revealed two migrants as immigrants (one female from Cromarty and one
male from Inverness), i.e., a rate of 2.15%. The mean Nm value using private alleles, after
correction for size, and considering the individual geographic localisation was Nm = 4.52.
According to Wright (1969),Nm <1 indicates strong genetic differentiation, and Nm much
larger than 1 means that panmixia can be assumed for the localities of the population.
Based on Hastings (1993),Faubet, Waples & Gaggiotti (2007) suggest that m<0.1 is needed
to ensure demographic independence of populations. In this study, mwere higher than 0.1
between sampling localisations in the Moray Firth.
Thus, the genetic differentiation and clustering analysis indicate that the harbour seal
populations in the Moray Firth behave as a single genetic group. When considering
the Moray Firth as a single genetic group, mean frequency of private alleles was equal
to 0.03 which is consistent with the two immigrants detected by FCA. The Bayesian
method of Rannala & Mountain (1997) did not detect immigrants into the Moray Firth.
However, based on frequencies of Paetkau et al. (1995) and 10,000 resamplings, 5 of the
93 individuals were detected as immigrants (4 males and one female: 5%). Nei’s standard
distance (1972) with 10,000 resamplings with the algorithm of Paetkau et al. (2004) showed
4 immigrant individuals (2 females and 2 males), equivalent to around 4%. Hence, the
global immigration rate in the Moray Firth is in the range of 2 to 4%.
Most markers shared a similar range of mutation rates of around 0.0001–0.0002 with
MSVAR estimates. VarEff provided global estimates of ˆ
N u, roughly corresponding
to present (ˆ
θo=0.77), ancestral (ˆ
θa=38.90), and intermediate times (ˆ
help fix priors for effective size.
The global estimates in the present effective sizes of VarEff provided results in the same
order of magnitude as those from MSVAR for seals particularly in terms of median but
with lower standard deviations (Sd) (Table 2).
According to Gelman and Rubin’s test (Gelman & Rubin, 1992), convergence in the
MSVAR estimations of the effective size occurred after 20,000 iterations; and run 4 was
the best supported Appendix S1G. The results from MSVAR suggested that the effective
size of the Moray Firth harbour seal population during our study period (1990–1995) was
821–1,669 (No) (sd 2,979), and that the ancestral effective size was much higher around
128,000–340,000 (Na) (sd 846,836) (Table 2). Values for mutation rates were around
0.0001–0.0002 (µ) (sd 0.00026), and the ancestral coalescent time was estimated to occur
37,000–96,000 years ago (Tf ) (sd 219,518) (Table 2).
Nikolic et al. (2020), PeerJ, DOI 10.7717/peerj.9167 11/29
Table 2 Mean (arithmetic), median and standard deviation of present (No) and ancestral (Na) effec-
tive population size, the mutation rate (µ), and ancestral time in years (Tf ) of Harbour seals in Moray
Firth (left part, MSVAR analysis). Mean (arithmetic and harmonic), median and standard deviation (Sd)
of present (No) and the estimated size 5.000 generations ago (N5000), and the global present ( ˆ
N o) and an-
cestral estimate ( ˆ
N a) (right part, VarEff analysis).
No Na µTf No N5000 ˆ
N o ˆ
Mean 1,669 342,874 0.00018 96,416 988 and 649 14,824
Median 821 128,186 0.00010 37,235 714 11,298 1,287 64,826
Sd 2,979 846,836 0.00026 219,518 2,089 230,55
VarEff harbour seal effective size priors were set between ˆ
No and ˆ
Ni from the global
θo) and intermediate (ˆ
θi) theta (4ˆ
N u) estimates: the prior mean was set to ˆ
=5,000 with a large variance (equal to 3 as suggested by Nikolic & Chevalet (2014a) for
the logarithms of N). The estimate of present effective size (mean harmonic (No) and
No) was around 700 and 1,300 and the ancestral effective size ( ˆ
N a) 65,000 (Table 2).
We also analysed females and males separately and obtained similar effective sizes which
is consistent with no sex-biased dispersal. The arithmetic mean, harmonic mean, mode,
and median of the posterior distributions for effective population sizes of harbour seals
revealed a trend pattern that looks similar with a recent decrease from a higher effective
size (Fig. 3A). A decrease in effective size of around 2.5% was suggested during the last
25 generations. The effective size at generation 0(No) was 649 for the harmonic mean,
988 for the arithmetic mean, 714 for the median and 729 for the mode. This represents a
388–2,181 95% confidence interval. The difference observed between estimators of past
Ne (mean, median and mode) (Fig. 3A) is likely to be due to the long tail of the posterior
probability distribution (Nikolic & Chevalet, 2014a).
According to global estimates (ˆ
θa) and effective size distribution (Fig. 3) from
VarEff, harbour seals in the Moray Firth came from a large ancestral population. The
estimated effective sizes in the past suggest that the extant population derived from an
ancestral population of around 10,000–15,000 individuals approximately 5,000-30,000
generations ago (Fig. 3). This seems to be preceded by a lower ancient size (around
2,000–3,000 individuals) some 30,000–100,000 generations ago (Fig. 3C); however this
should be considered with caution, given the flat posterior distribution in ancient times.
Concerning the recent and historical effective size, several decreases of the effective size
were observed. Firstly, a long period of decline over the last 2,000 generations (17,500
years) was suggested (Figs. 3A,3B), when population size decreased from tens of thousands
to less than 1,000 (Figs. 3A,4A) and followed by a further decline prior to 600 generations
ago (Fig. 4), estimated at around 800 generations ago (Fig. 5). Finally, a recent accelerated
decline during the last 100 generations was followed by a period when population size
remained approximately constant (Ne between ≈665 and 685) (Fig. 4).
The VarEff results describing the global evolution of the population are consistent
with the tests of heterozygosity excess (sign, standardized and Wilcoxon tests) assuming
SMM as proposed in BOTTLENECK Appendix S1H. The latter analysis suggested a
Nikolic et al. (2020), PeerJ, DOI 10.7717/peerj.9167 12/29
Figure 3 Effective size (Ne) of harbour seals in the north-east UK (from Moray Firth genetic group) as
a function of past generation time (G) using 17 microsatellites (VarEff analysis). (A) Arithmetic (red)
and harmonic (green) mean, mode (blue), and median (black) from sampling time (0) to 30,000 genera-
tions ago. (B) Posterior densities at the past generation time (G): 2,000 (black), 3,000 (blue), 4,000 (red),
5,000 (green), 6,000 (grey), 7,000 (purple), 8,000 (orange), 9,000 (pink), and 10,000 (red) generations ago.
(C) Posterior densities at the past generation time (G) 10,000 (black), 20,000 (blue), 30,000 (red), 40,000
(blue), 50,000 (grey), 60,000 (purple), 70,000 (orange), 80,000 (green), 90,000 (brown), and 100,000 (red)
Full-size DOI: 10.7717/peerj.9167/fig-3
Figure 4 Harbour seal’s effective size (harmonic mean of the posterior distribution, VarEff analysis)
within Moray Firth (using 17 microsatellites) in generation time (from 0 to 1,000 generations ago) (A)
and in calendar years (from 1995 (sampling date) to 500 AD. The arrows represent the main trends: re-
duction (red) and increase (blue). (B) shows the latest tendency enlarged, the last red arrow.
Full-size DOI: 10.7717/peerj.9167/fig-4
Nikolic et al. (2020), PeerJ, DOI 10.7717/peerj.9167 13/29
Figure 5 Posterior distribution of the Time to Most Recent Common Ancestor allele (TMRCA, VarEff
analysis). Suggested coalescent events are given as generation numbers and as years for harbour seals in
the Moray Firth (Scotland). Each peak, in the posterior distribution, represents a potential bottleneck.
Full-size DOI: 10.7717/peerj.9167/fig-5
recent bottleneck in the north-eastern UK population. Bayesian methods make use of
coalescence theory and MCMC sampling to characterize demographic history and can have
a higher probability of detecting bottlenecks than heterozygosity excess tests (contrasts
heterozygosity expected under Hardy–Weinberg equilibrium with heterozygosity expected
under mutation-drift equilibrium calculated from observed number of alleles) (Cornuet &
Luikart, 1997;Girod et al., 2011;Peery et al., 2012). Concerning the coalescent results from
VarEff, Fig. 4A represents the harmonic mean of the last 1,000 generations (A), which was
characterized by a decrease followed by a slight increase before a further decrease in the last
100 generations. Variation over the last 1,500 years is also shown in Fig. 4B, based upon
the assumed generation time of 8.75 years.
According to Fig. 5, which shows the distributions of TMRCA, the harbour seal
population underwent drastic bottlenecks. The more recent times of the bottlenecks
were estimated in generation time (G) and in years, assuming a generation time of 8.75
years.Figure 5 also reveals that huge coalescence events occurred between 7,000–10,000
years (around the first peak in Fig. 5). These coalescence events occurred after a period
of decline of the population size (the drastic bottleneck seen around 17,000 years ago,
the time when the density is at a minimum between the two peaks). Before that period,
coalescence events occurred at times given by the second peak of the distribution, which
corresponds to the large ancestral population size, as illustrated in Figs. 3 and 4. Harbour
seal showed declines over the last 1,000 years, with a steeper decline over the last few
centuries. Futhermore, the analysis run without the two detected immigrants provided
exactly the same results.
Nikolic et al. (2020), PeerJ, DOI 10.7717/peerj.9167 14/29
Results from the PI and SPOTG simulations suggest that the number of individuals and
polymorphic markers are sufficient to provide high assignment discrimination and to detect
evolutionary and ecological processes. The low genetic diversity in Moray Firth harbour
seals, with an overall average heterozygosity of 38% and 3.7 alleles per microsatellite
locus, was lower than in many pinnipeds (summarised in Curtis, Stewart & Karl, 2011).
However, this value falls within the range recorded for other North Atlantic harbour seal
populations, which were estimated between 24% (Coltman, Bowen & Wright, 1996), 38%
(Olsen et al., 2017) and 50% (Goodman, 1998). In contrast, higher levels of heterozygosity
(>65%) have been recorded in the North Pacific (Burg et al., 1999). There is concern that
small populations with low genetic diversity may become susceptible to environmental,
demographic, and genetic stochasticity, increasing their risks for extinction (Franklin,
1980;Newman & Pilson, 1997;Brook et al., 2002). However, low genetic diversity appears
to be common amongst many marine mammals (Hoelzel, Goldsworthy & Fleischer, 2002),
and the conservation implications of contemporary patterns are difficult to assess without
some understanding of the relative role of natural and anthropogenic influences on
historic population sizes. For example, low levels of genetic diversity may simply exist in
some populations because of the nature of their social structure (Whitehead, 1998), or
due to founder effects that occurred following natural post-glacial habitat change (Palsbøll,
Heide-Jørgensen & Dietz, 1997). It is likely to be of greater concern to managers where low
diversity has resulted from more recent over-exploitation (Hoelzel et al., 1993) or bycatch
(Pichler & Baker, 2000).
Our data suggest that the Moray Firth acts as one genetic group, in accordance with the
results of Olsen et al., 2017. Migration per generation above 5–10% can strongly influence
the estimate of effective size (Waples & England, 2011) and it was therefore important to
exclude this potential bias by evaluating gene flow. Current gene flow of harbour seals
into the Moray Firth was low (immigration 2–4%), likely because the adults are strongly
philopatric (Thompson & Hall, 1993;Härkönen & Harding, 2001). Goodman’s (1998) study
of European harbour seal population differentiation suggests a critical range over which
animals are philopatric of around 485 km. In that study, samples collected in the Moray
Firth (Scottish east coast) were further than 500 km from neighbouring genetic populations
(Scottish west coast, Olsen et al., 2017). In a Neighbor-joining phenogram presented in the
Goodman study (1998), harbour seals from the Scottish east coast were distinguable from
the Scottish west coast with only 7 microsatellite loci, even though the number of seals
from the Scottish west coast was very low (18 individuals). In a recent genetic clustering
analysis with 12 microsatellite loci (Olsen et al., 2017), north-west and north-east UK were
considered two geographically distinct genetic clusters. The genetic homogeneity between
the Moray Firth and other north-eastern localities (Orkney and Shetland; Olsen et al., 2017),
and associated low immigration, suggest that we can develop a robust reconstruction of
evolutionary history for the north-eastern Scottish population.
Nikolic et al. (2020), PeerJ, DOI 10.7717/peerj.9167 15/29
Eﬀective population size (Ne)
Inferring Ne from an estimate of θcan be obtained assuming a mutation rate. We estimated
the mutation rate at around 0.0001–0.0002, similar to that found in ringed seals (Phoca
hispida) (Palo et al., 2003), allowing us to estimate effective population size. Using the
complete panel of polymorphic markers with and without immigrants identified by
the assignment method, the effective size estimated by the coalescent tool (VarEff) at
generation 0 (No) was around 500–1,000 (CI [388–2181]). The advantage of VarEff is that
it can estimate the recent historic abundance from theta (ˆ
θo) (average of the effective size on
the last evolutionary stage) and the current abundance from No (effective size at generation
time 0). Ne is a theoretical measure of an idealized population size that would be expected
to experience the same rate of genetic diversity loss due to genetic drift (Wright, 1931). Ne
is hence defined as the size of an ‘ideal’ population with the observed rate of genetic drift
(Wright, 1931;Wright, 1969) and thus does not represent census population size (Palsbøll
et al., 2013). In ecology, Ne is usually thought of as the number of breeding individuals that
successfully transmit their genes to the next generation (Frankham, 1995) and thus should
equal the ‘genetic’ effective population size (Palsbøll et al., 2013). In practice, the exact
relationship is rarely known and most studies apply a generic ratio representing a range of
estimates (Palsbøll et al., 2013). Small Ne with no or limited gene flow among populations
tend to accelerate stochastic loss of genetic diversity and can increase population risk as it
leads to inbreeding depression and reduced fertility, and increases the potential fixation of
deleterious alleles (Fagan & Holmes, 2006;Gilpin & Soulé, 1986;Palstra & Ruzzante, 2008;
Lonsinger, Adams & Waits, 2018). Franklin (1980) suggested a minimum Ne ≥50 may be
required to avoid short-term inbreeding depression, and an Ne ≥500 may be necessary to
maintain long-term adaptive potential. However this rule (50/500) is open to criticism as
the estimates do not take into account the force of selection. According to Lande (1995),
wild populations could not bear the same consanguinity as farm populations (purged
by humans); wild populations would fall more quickly into inbreeding depression and
therefore the 50/500 rule would be underestimated. Lande (1995) recommended an Ne of
5,000 for long-term viability. Other analyses (Allendorf & Ryman, 2002) suggested an Ne
of 1,000 to prevent accumulation of harmfull mutations. All these recommended values
must be taken with caution, as they ignore uncertainty arising from environmental and
demographic factors, but they encourage conservation of small populations (Grove, 2003).
Ne is typically smaller than census population size (N) and this ratio may help for
assessing the genetic health of a population and for predicting short-term and long-term
risk (Palstra & Ruzzante, 2008). This ratio varies from 10−5in many marine invertebrate
species to nearly 1.0 in some terrestrial vertebrates (Frankham, 1995;Hedrick, 2005), but
comparative data for other pinnipeds are difficult to interpret due to the high level of
uncertainty in census population size (Curtis, Stewart & Karl, 2011). Surveys during the
sampling period resulted in a direct estimate (N, census population size) of approximately
1,650 individuals (1993) for Moray Firth locality (Thompson et al., 1997a), 773 individuals
in 1992 and 575 in 1994 for Firth of Tay and Eden Estuary (Hanson et al., 2015), 9,000
and 6,000 individuals in 1997 for north coast and Orkney, then Shetland respectively (Fig.
4 from Duck & Morris, 2014). These different values suggest that the ratio No/N can be
Nikolic et al. (2020), PeerJ, DOI 10.7717/peerj.9167 16/29
very different depending on sampling location considered. The No estimate in this study
represents one genetic cluster, the north-east UK, and including all Nwould be unwise
until we verify that No from other localities are in the same general estimate. Although
Nis a relatively straightforward entity, it can be difficult to obtain (Palsbøll et al., 2013).
Hence, in order to infer Nfrom Ne, a rigorous estimate of the ratio is warranted (Palsbøll
et al., 2013).
Over 80% of UK harbour seals are found in Scotland and estimating the historic effective
size of harbour seal in the Moray Firth showed that seals have recently experienced a
decline, as also revealed by direct counts in parts of both the north-east Atlantic (Thompson
et al., 2019;Lonergan et al., 2007;Hanson et al., 2015) and the north-west Atlantic (Bowen
et al., 2003). Concerning the ancestral harbour seal population size (MRCA) in the Moray
Firth, both models suggested that effective population size had once been extremely large;
in the region of 65,000–130,000 derived from ancestral theta for VarEff and the median
for MSVAR. One advantage of VarEff is the ability to infer demographic history from a
single temporal sample, rather than depending on two or more samples that span multiple
generations. Detecting past bottlenecks using the method relies on observing pairs of alleles
that have coalesced during the bottleneck event (Nikolic & Chevalet, 2014a). The VarEff
method enables various mutation models to be considered, and some trials determined
that the risk of false bottleneck detection due to an inappropriate mutation model would
be unlikely. Results also indicated that the Moray Firth harbour seal population has
undergone a drastic bottleneck, which is concordant with the analysis of detection by
the heterozygote deficiency tests. Figures 3A and 3B show that a recent strong bottleneck
occurred approximately 2,000 generations ago (17,500 years), when population size
decreased from more than 10,000 to less than 1,000 in about 1,000 generations. The model
suggested an ancestral population size of about 15,000 in ancient times, 30,000 generations
or 262,000 years ago (the ultimate peak in the TMRCA distribution, Fig. 5). However, other
evolutionary schemes could lead to a similar distribution of TMRCA, such as recurrent
immigration from a large population into the Moray Firth. Thus, there are several possible
interpretations for this pattern. The population may derive from ancestral fragmentation
followed by permanent introductions from a metapopulation made of similar colonies, or
it may be the result of a sharp decline some time in the past. Several arguments support
the bottleneck hypothesis: the gaps in the distribution of repeat numbers Appendix S1C,
the rather low immigration rate, and the high probability that coalescent events occurred
recently. A more detailed answer will require archaelogical samples (ancient DNA) or
samples from a number of distant populations.
During the period corresponding to the drastic bottleneck detected, the current
geographical range of this population would have been uninhabitable as it was covered
by the last British Ice Sheet (Bradwell et al., 2008), and this ancestral population would
likely have re-distributed to the margins of the ice sheet. Whilst the size of large terrestrial
mammal populations in Europe is likely to have been reduced during this period (Marshall
et al., 1982), harbour seals breed on ice in certain areas, and these colder conditions could
Nikolic et al. (2020), PeerJ, DOI 10.7717/peerj.9167 17/29
have led to population expansion. For example, analysis of mtDNA variation in Antarctic
ice-breeding seals indicated that effective population size expanded suddenly during times
of intensified glaciations (Curtis, Stewart & Karl, 2009), while southern elephant seals,
which require access to open beaches, show the opposite trend (De Bruyn et al., 2009).
The strong bottleneck detected at around 17,500 years ago appears to have occurred in
the period following the retreat of ice from the current study area, as glacial moraines in
the Inverness Firth indicate that the ice margin occurred here around 15,000 years ago
(Merritt, Auton & Firth, 1995). Subsequently, the harbour seals may have recolonized this
area once the ice retreated, as was the case for ringed seals which colonized the Baltic Sea
basin soon after deglaciation (11,500 years) (Palo et al., 2001).
The analysis of TMRCA revealed a peak of coalescence events around 800 generations
(7,000 years) ago in a period when mean effective size was approximately 5,000, and may
correspond to a transient sharp decrease of effective size. This coincides with the warmest
stages in the post-glacial period in the mid-Holocene 8,000-7,000 years ago (Andersson,
1902;Andersson, 1909;Seppä, Bjune AE & Veski, 2009). Current global distribution patterns
indicate that harbour seals could have been restricted to more northerly waters at this time.
Archaeological records also suggest that populations of harbour seals in the eastern Baltic
were founded around 8,000 years ago (Härkönen & Johannesson, 2005), which may also
reflect a northward shift in distribution during this period. Here, we see that this period
may correspond to a transient decrease of the harbour seal population, long after it was
derived from a larger ancestral population. Several dramatic events therefore appear to have
influenced the structure and effective size of this population. This complexity inevitably
brings uncertainty, particularly as the model cannot fully account for migration events.
Assuming immigration from a large external population, the distribution of coalescence
time TMRCA depends on the proportion mof immigrants per generation and on the
ratio εof the considered population size (Ne) to the size of the larger external population
(Nikolic & Chevalet, 2014a). Using VarEff provides the distribution of TMRCA which is
converted to past effective sizes in an isolated population. In the case of small 4*Ne*m,
such as found from the harbour seal analysis, the estimated population size remains in the
order of magnitude of the true Ne while increasing with m(Nikolic & Chevalet, 2014a).
However, for larger immigration rates from a larger external population, a false current
bottleneck could be predicted in a small population. Applying the equation (8) of Nikolic
& Chevalet (2014a), with hypothetical immigration (2–4%) from larger populations of
harbour seals around the UK (Lonergan et al., 2007, which brings εabout 0.05 to 0.10)
and specifically around our North-eastern Scottish population, we estimated the time of a
potentially false bottleneck at 100–200 generations ago. This period matches with the last
event detected in Fig. 4. Thus, immigration from an external population could potentially
lead to a false current bottleneck (100–200 generations ago), and we have to be cautious in
the final decline in effective size observed (Fig. 4). We encourage studies on seal migration
in this area by the monitoring of individuals between the geographically distinct genetic
clusters in UK and neighbouring waters.
Following the same analysis, the older bottlenecks detected at 800 and 2,000 generations
ago (Figs. 3 and 5) should not be an effect of immigration. Either way, it is clear that
Nikolic et al. (2020), PeerJ, DOI 10.7717/peerj.9167 18/29
these past events led to a decrease in population size resulting from natural environmental
changes. This study suggests that, more recently, the harbour seal population has declined
over the last century at a rate of 2–6% in terms of effective size. This downward trend could
also result from natural changes in environmental conditions, or from human exploitation
that is known to have occurred at many European and Arctic sites over the last millenia
(Härkönen & Johannesson, 2005;Murray, 2008).
Previous population genetic studies of UK harbour seals have focused on using molecular
tools to support conservation management by identifying appropriate spatial management
units (Olsen et al., 2017). Our study illustrates how genetic tools based on coalescent theory
can also provide insights into evolutionary history and temporal changes in population
size within those management units. Critically, they contribute knowledge about the state
of a population by estimating changes in effective size. Estimates of the current effective
population size of this harbour seal population are small compared to theoretical estimates
of the minimum viable population size (Reed et al., 2003). Our analyses indicate that the
harbour seal population in the north-east UK has remained at a broadly similar level
following the bottleneck that occurred after post-glacial recolonization of the area but
have slowly declined more recently. Subsequent demographic studies indicate that the
Moray Firth population has declined further, by approximately 40%, since the early 1990’s
when our DNA samples were collected (Thompson et al., 2019). Thus, whilst contemporary
estimates of Ne are expected to remain well above the critical threshold of Ne =50
suggested to avoid short-term effects of inbreeding (Franklin, 1980) they will now be closer
to the Ne =500 potential threshold for maintainence of long-term adaptive potential
(Franklin, 1980). Molecular evidence of connectivity within the much larger European
metapopulation exists (Olsen et al., 2017), but further work is required to better understand
the extent to which contemporary movements between management units may offset these
risks. Contrasting population trends in different UK management units (Thompson et al.,
2019) also highlight the need for conservation managers to identify regional demographic
drivers. Crucially, potential drivers include both anthropogenic stressors (which might
require conservation inerventions) and others such as competition with recovering grey
seal populations (Halichoerus grypus) (Wilson & Hammond, 2019); where intervention is
less likely to be appropriate. Previous studies in the Baltic Sea indicate that populations of
harbour and grey seal have co-varied over historical time-scales (Härkönen & Johannesson,
2005). Our work illustrates how parallel coalescent studies on material from sympatric grey
and harbour seal populations in different regions now provides an opportunity to inform
conservation practices by exploring potential interactions between these two species at
much larger temporal and spatial scales.
The authors wish to thank Shaneve Tripp (NYU School of Law) and Wendy West (DAFF)
for their english corrections. Ludovic Hoarau (IFREMER) for his help on ArcGis. Katia
Feve (INRAE) for her help with the DNA extraction protocol. DNA samples were extracted
Nikolic et al. (2020), PeerJ, DOI 10.7717/peerj.9167 19/29
at INRAE and genotyped at the Toulouse Genopole Platform (http://www.genotoul.fr/).
Anonymous reviewers provided many helpful comments on an earlier version of the
ADDITIONAL INFORMATION AND DECLARATIONS
This work was supported by INRAE (FRANCE), Genotoul platform (FRANCE), and
University of Aberdeen. The funders had no role in study design, data collection and
analysis, decision to publish, or preparation of the manuscript.
The following grant information was disclosed by the authors:
Genotoul platform (FRANCE),.
University of Aberdeen.
The authors declare there are no competing interests.
•Natacha Nikolic conceived and designed the experiments, performed the experiments,
analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the
paper, and approved the final draft.
•Paul Thompson performed the experiments, authored or reviewed drafts of the paper,
and approved the final draft.
•Mark de Bruyn authored or reviewed drafts of the paper, and approved the final draft.
•Matthias Macé and Claude Chevalet performed the experiments, analyzed the data,
authored or reviewed drafts of the paper, and approved the final draft.
The following information was supplied relating to ethical approvals (i.e., approving body
and any reference numbers):
Blood samples were collected under Home Office licence issued to the University of
Aberdeen under the Animal (Scientific Procedures) Act 1986 (PPL number 60/01351).
The following information was supplied regarding data availability:
Data is available at INRAE:
Nikolic, Natacha; Thompson, Paul; De Bruyn, Mark; Macé, Matthias; Chevalet, Claude,
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