Population genetic structure in gadoid fish with
focus on Atlantic cod Gadus morhua
Guðni Magnús Eiríksson
Faculty of Life and Environmental Sciences
University of Iceland
2015
Population genetic structure in gadoid fish with
focus on Atlantic cod Gadus morhua
Guðni Magnús Eiríksson
Dissertation submitted in partial fulfillment of a
Philosophiae Doctor degree in Biology
Advisor
Dr. Einar Árnason
PhD Committee
Dr. Einar Árnason
Dr. Jarle Mork
Dr. Kristinn Pétur Magnússon
Opponents
Dr. Dorte Bekkevold
Dr. William Stewart Grant
Faculty of Life and Environmental Sciences
School of Engineering and Natural Sciences
University of Iceland
Reykjavík, October 2015
Population genetic structure in gadoid fish with focus on Atlantic cod Gadus morhua
Dissertation submitted in partial fulfillment of a Philosophiae Doctor degree in Biology
Copyright © Guðni Magnús Eiríksson 2015
All rights reserved
Faculty of Life and Environmental Sciences
School of Engineering and Natural Sciences
University of Iceland
Sturlugata 7
101, Reykjavík
Iceland
Telephone: 525-4000
Bibliographic information:
Guðni Magnús Eiríksson, 2015, Population genetic structure in gadoid fish with focus on Atlantic cod Gadus morhua, PhD
dissertation, Faculty of Life and Environmental Sciences, University of Iceland, 128 pp.
ISBN 978-9935-9266-1-6
Printing: Háskólaprent ehf.
Reykjavík, Iceland, October 2015
Abstract
In the present study genetic variation and population genetic structure in spawning Atlantic cod, Gadus morhua, around Iceland was examined. Earlier research on population
genetic structure in cod has not been conclusive and the use of different molecular methods have shown different patterns. It is important to determine why different methods
show different patterns in order to describe the population genetic structure in cod. In
the present study both microsatellite DNA variation and mitochondrial DNA sequence
variation were estimated. Both methods have commonly been used in population genetic
studies in cod.
Findings of microsatellite DNA variation analysis showed a genetic difference
between cod from the waters south and north of Iceland. However, analysis of the
observed difference suggest that it can be explained by natural selection affecting
genetic variation at a single microsatellite loci (Gmo34). When disregarding this locus
from the analysis no genetic difference was observed. Small but significant genetic
difference was found among Atlantic cod sampled at different depths off the south
coast of Iceland, indicating that different populations of Atlantic cod may be found at
different depths. Analysis of mtDNA sequence variation showed no overall genetic
difference among different geographic areas around Iceland. Analysis of temporal
mtDNA sequence variation showed rapid changes in allele frequencies, particularly
in cod from NE-Iceland, suggesting that the mtDNA sequence variation can be useful
for detecting recent population divergence. Thus, the findings of the present study do
not suggest that cod around Iceland is geographically divided into distinct historical
populations as has been suggested in some earlier studies. On the contrary the findings
suggest high levels of gene flow in Atlantic cod around Iceland.
Genetic variation and population genetic structure across the North Atlantic ocean
was also examined in saithe Pollachius virens, haddock Melanogrammus aeglefinus
and whiting Merlangius merlangus using mtDNA sequence variation at the cytochrome
c oxidase subunit I locus. The results showed limited trans-Atlantic genetic structure
for all the species indicating high levels of gene flow or insufficient time for genetic
differentiation to have become established. The observed genetic sequence variation
for saithe and haddock suggest sex-biased migration pattern. The results indicate that
females may be more philopatric and males more migratory. Such behaviour has rarely
been described for marine fish and is worth further research.
The observed mitochondrial sequence variation for all the studied species indicate
sudden population expansion, reflected in high number of singletons and a shallow
genealogy. However, the estimated timing of expansion varies among the examined
species, suggesting that the biological, historical or analytical factors resulting in the
observed pattern may differ among the species.
Útdráttur
Meginviðfangsefni þessa verkefnis var rannsókn á erfðabreytileika og stofngerð þorsks,
Gadus morhua, umhverfis Ísland. Niðurstöður eldri rannsókna á stofngerð þorsks hafa
ekki verið samhljóma, en mismunandi greiningaraðferðir hafa sýnt ólíkar niðurstöður.
Mikilvægt er að leita skýringa á breytilegum og misvísandi niðurstöðum til þess að
skýra betur stofngerð þorsks. Stofngerð þorsks var rannsökuð með greiningu á örtunglabreytileika og einnig kirnabeytileika í hvatberaerfðaefni, en báðar þessar aðferðir hafa
mikið verið notaðar við greiningu á stofngerð þorsks.
Niðurstöður rannsóknar á örtunglabreytileika sýna erfðafræðilegan mun á milli
þorsks frá hafsvæðum norður og suður af Íslandi, líkt og fyrri rannsókn sýndi einnig.
Greining sýnir að sá munur er líklega til kominn vegna áhrifa náttúrulegs vals á eitt
örtungl (Gmo34). Þegar sá lókus er fjarlægður úr greiningu kemur enginn erfðamunur
fram fyrir þorsk frá mismunandi hafsvæðum. Lítill, en marktækur, erfðafræðilegur
munur greindist milli þorsks af mismunandi dýpi við suðurströnd Íslands, sem getur
bent til þess að þorskur aðgreinist í mismunandi stofna eftir dýpi. Niðurstaða greiningar
á kirnabreytileika í hvatberaerfðaefni sýnir engan heildarmun milli hafsvæða umhverfis
Ísland. Breytileiki í tíma var greindur með samanburði milli kynslóða fyrir þorsk
umhverfis Ísland. Örar breytingar á tíðni setgerða milli kynslóða, sérstaklega fyrir þorsk
frá hafsvæðum norðaustur af Íslandi, benda til þess að hvatberaerfðaefni henti vel til
að greina mun milli nýlega aðskildra hópa. Því styðja niðurstöður ekki að þorskur
umhverfis Ísland skiptist í æxlunarlega aðskilda stofna eftir hafsvæðum eins og sumar
eldri rannsóknir hafa bent til. Þvert á móti benda niðurstöður rannsóknarinnar til mikils
genaflæðis milli hafsvæða umhverfis Ísland.
Í verkefninu var erfðabreytileiki og stofngerð ufsa, Pollachius virens, ýsu Melanogrammus aeglefinus og lýsu Merlangius merlangus í Norður Atlantshafi einnig rannsökuð, með því að meta kirnabreytileika hvatberaerfðaefnis fyrir cytochrome c oxidase
subunit I lókusinn. Niðurstöður sýna afar lítinn erfðamun milli heimshluta fyrir þessar
tegundir sem bendir til mikils fars (genaflæðis) eða þess að of skammur tími sé liðinn
frá aðskilnaði mögulegra hópa til að greinanlegur erfðamunur hafi myndast. Rannsókn
á kirnabreytileikanum fyrir ufsa og ýsu bendir til þess að far kunni að vera ólíkt milli
kynja. Hængar kunni að fara víðar en að hrygnur haldi frekar kyrru fyrir. Slíku atferli
hefur sjaldan verið lýst fyrir sjávarfiska og gefur þessi vísbending tilefni til frekari
rannsókna.
Greining á kirnabreytileika fyrir allar tegundirnar sem rannsakaðar voru sýna merki
um skyndilega stofnstærðaraukningu, sem kemur fram í hárri tíðni sjaldgæfra setgerða
og grunnu ættartré. Mat á tímasetningu stofnstærðaraukningar er breytilegt milli tegunda sem bendir til þess að mismunandi líffræðilegar, sögulegar eða aðferðafræðilegar
ástæður séu fyrir séðu mynstri.
“Maðurinn finnur það sem hann leitar að,
og sá sem trúir á draug finnur draug”
Halldór Kiljan Laxness (Sjálfstætt fólk)
Table of Contents
Abstract
iii
Útdráttur
v
Table of Contents
ix
List of Original Papers
xi
Acknowledgments
xiii
1 General introduction
1
1.1
Genetic variation - Origin and dynamics . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1
1.1.2
1.1.3
1.1.4
1.1.5
1.2
Origin of genetic variation within a population
Population genetic structure . . . . . . . . . .
Natural selection . . . . . . . . . . . . . . . . .
Demography and molecular clock rate . . . .
Life history of marine organisms . . . . . . . .
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Biology and population genetics of the studied fish species . . . . . . . . . . . 7
1.2.1
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1.2.3
1.2.4
1.3
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Atlantic cod, Gadus morhua . . . . . .
Haddock, Melanogrammus aeglefinus
Saithe, Pollachius virens . . . . . . . .
Whiting, Merlangius merlangus . . . .
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The present study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.3.1
1.3.2
1.3.3
1.3.4
1.3.5
Microsatellite variation in Atlantic cod, Gadus morhua, around Iceland . . . . .
Mitochondrial DNA sequence variation in Atlantic cod, Gadus morhua . . . . .
Mitochondrial DNA sequence variation in saithe, Pollachius virens . . . . . . .
Mitochondrial DNA sequence variation in haddock, Melanogrammus aeglefinus
Mitochondrial DNA sequence variation in whiting, Merlangius merlangus . . .
15
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References
16
Paper I
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Paper II
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Paper III
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Paper IV
71
Paper V
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Appendix I. Supplementary tables
109
Appendix II. Supplementary figures
121
Appendix III
125
List of Original Papers
List of Original Papers
Paper I:
Paper II:
Paper III:
Paper IV:
Paper V:
Guðni Magnús Eiríksson and Einar Árnason. 2013. Spatial
and Temporal Microsatellite Variation in Spawning Atlantic cod,
Gadus morhua, around Iceland. Canadian Journal of Fisheries
and Aquatic Sciences. 70: 1151–1158.
Guðni Magnús Eiríksson and Einar Árnason. High levels of
gene flow and temporal genetic variation in Atlantic cod, Gadus
morhua in Icelandic waters — inferred by mitochondrial DNA
sequence variation. Unpublished manuscript
Guðni Magnús Eiríksson and Einar Árnason. 2015. Gene flow
across the N-Atlantic and sex-biased dispersal inferred from
mtDNA sequence variation in saithe, Pollachius virens. Environmental Biology of Fishes. 98: 67–79.
Guðni Magnús Eiríksson and Einar Árnason. Manuscript. Phylogeography of haddock Melanogrammus aeglefinus in the North
Atlantic — Postglacial expansion and male-biased dispersal. Unpublished manuscript
Guðni Magnús Eiríksson and Einar Árnason. 2014. Mitochondrial
DNA Sequence Variation in Whiting Merlangius merlangus in
the North East Atlantic. Environmental Biology of Fishes. 97:
103–110.
xi
Acknowledgments
Acknowledgments
I would like to thank my supervisor Professor Einar Árnason for his guidance, enthusiasm and stimulating discussions during this work. I thank Professor Jarle Mork for the
hospitality and great company during my visits to his lab in Throndheim. Also I would
like to thank Professor Kristinn Pétur Magnússon for his support.
I would like to thank Professor Snæbjörn Pálsson for useful discussions during the
work and the population genetics group at the University of Iceland, in particular Katrín
Halldórsdóttir, Ubaldo Benitez Hernandez and Svava Ingimarsdóttir, for great company
and various help during this work. I would like to thank Professor Zophonías Oddur
Jónsson for various help in the molecular lab.
I would also like to thank Professor Guðrún Marteinsdóttir and Jónas Páll Jónasson
for useful fishy discussions and Professor Skúli Skúlason for therapeutic lunch meetings.
I am thankful to all those that assisted in obtaining samples for the project. They are
listed in the respective chapters. In particular I am thankful to the assistance in sampling
by the staff of the Icelandic Marine Research Institute.
I would like to thank my family and friends for moral support during this work.
And finally I would like to thank my wife Gunnhildur Sveinsdóttir for her support and
encouragement throughout this project. I am happy to close this chapter together with
you and looking forward to the journey ahead.
This project was supported by the Icelandic research fund, the Icelandic research
fund for graduate students of The Icelandic Centre for Research and the Icelandic
Marine Research Institute.
xiii
1 General introduction
1.1 Genetic variation - Origin and dynamics
1.1.1 Origin of genetic variation within a population
The origin, maintenance and dynamics of genetic variation is a central topic in population genetics and evolutionary biology (Hedrick, 2005; Hartl and Clark, 2006; Futuyma,
2005). Genetic variation of populations is shaped by four basic factors: Mutations,
genetic drift, natural selection and gene flow. These factors are affected by effective
population size (Ne ), population subdivision, demography of populations and the life
history of organisms (Hartl and Clark, 2006). As mutation rate is generally low it can
be assumed that it will not alter the frequency of existing alleles in large populations
(assumptions of the infinite alleles model and the infinite sites model, Hartl and Clark,
2006). The allelic configuration at a locus within a population is thus more likely to be
the result of genetic drift, gene flow and/or natural selection that can potentially change
allele frequencies rapidly. A basic assumption in population genetics is that identical
alleles are identical by decent. Therefore information about the state carries information
about origin of mutation and thus information about the relationship among individuals
carrying the same allele (Wright, 1921). The allelic configuration, the relationship
among alleles and frequency of genotypes, can be used to make inference about the
relationship among groups, natural selection and migration (Hartl and Clark, 2006). In
order to discriminate between alternative factors shaping the genetic variation within a
population multiple analytical approaches must be used.
1.1.2 Population genetic structure
Population breeding structure is reflected in genetic difference at neutral loci. Divergence at a neutral loci between isolated populations occurs due to random genetic
drift. A study of historical divergence among populations is thus an exploration of the
effects of random genetic drift. The rate of genetic divergence between two isolated
populations, due to genetic drift alone, can be shown to be inversely related to the
effective population size (Ne ) (Hartl and Clark, 2006).
Inbreeding coefficients (F statistics) originally developed by Wright (1922) are
important in describing genetic variation and genetic structure. The F statistic reflects
the probability that two alleles at a locus in an inbred individual are identical by decent.
It corresponds to the correlation of uniting gametes.
The principle for F statistics is that the frequency of heterozygotes Ho is expected
to be reduced on average by a factor of 1 − 1/(2N) in each generation in a finite diploid
1
1
General introduction
population. The probability that two alleles drawn at random from a population are
identical is 1/2N. The fixation index F, the inbreeding coefficient, decreases with time
until F equals 1, and heterozygosity is 0. However, for mtDNA the probability of two
alleles drawn at random from a population is 1/N, the effective population size Ne is
smaller (one quarter) than that of nuclear traits in a diploid system.
Since mitochondrial genetic markers have lower effective population size Ne compared to nuclear markers (and thus higher rate of genetic drift) the rate at which isolated
populations can be detected due to local inbreeding (using FST ) is expected to be faster
for mitochondrial genetic markers on average.
The F statistic can be estimated at different levels of population comparison. Traditionally F is used as an indicator of the relative part of the overall variation resulting
from variation among correlation of gametes due to inbreeding within subpopulation
(FIS ), correlation of gametes within subpopulation (FST ) and correlation of gametes
within a sample (FIT ) (Hartl and Clark, 2006). The relationship among the different
inbreeding coefficients can be expressed as follows: 1 − FIT = (1 − FST )(1 − FIS ).
• due to inbreeding within subpopulation, FIS
• within subpopulation, FST
• in sample, FIT
For subdivided populations, genetic difference among subgroups (estimated by
FST ) will thus increase with time at a rate inversely related to population size. Thus,
it can take a long time for a genetic difference to become established for subdivided
populations if population size is large (Hartl and Clark, 2006).
Examination of population subdivision has been the subject of many studies, not
least for commercially important fish species as such information can be of great importance for responsible management (for an overview see Reiss et al., 2009). Variable
molecular markers are used and results are seen to vary from one study to another. It
is important to note that observed genetic differences between different environments
occupied by an organism may not reflect population breeding structure if the variation
is affected by natural selection (Guinand et al., 2004). Genetic difference can become
established between subdivided groups of individuals without reproductive isolation or
reduced gene flow (Williams et al., 1973; Guinand et al., 2004). For the identification
of a breeding structure of a population it is thus important to use genetic markers that
are not subject to natural selection, as the latter may reflect environmental heterogeneity
rather than the origin and isolation of the groups involved. The analysis of indications of natural selection should thus be an integral part of the analysis of population
sub-structuring (see e.g. Excoffier, 2007).
It is sometimes suggested that markers under natural selection can be useful for
detecting recent divergence, since not enough time has elapsed to allow divergence
at neutral markers (e.g. Reiss et al., 2009). It is true that recent divergence may not
be detectable with neutral loci, but it is the effects of random genetic drift that reflect
the relationship among groups and allow detection of population breeding structure.
Genetic markers that are affected by natural selection are useful for understanding
biological phenomena of adaptation in a similar manner as are phenotypic traits, such as
2
1.1
Genetic variation - Origin and dynamics
growth rate, meristic characters, otolith shape or behaviour. Indeed such characteristics
give important insight into the biology of the species, although they can not be used
independently to reflect a breeding structure of a population (Williams et al., 1973).
Many studies have demonstrated a lack of spatial genetic structure in marine species
indicating high levels of gene flow among groups across long distances (Reiss et al.,
2009). This has been attributed to lack of physical barriers in the marine environment
and high dispersal rate for eggs and larvae with ocean currents (Waples, 1987). Genetic
uniformity can also be maintained if population size is large (large Ne ) as it will
reduce the rate of genetic drift. A long time may thus be needed in order for genetic
differentiation to become established among populations of large size.
Genetic variation may reflect different migration patterns between sexes and such
patterns are common for vertebrate species (Purcell et al., 1996; Petit and Excoffier,
2009). In the case of females being more philopatric and males more migratorial, such
variation may be reflected in the mitochondrial DNA variation. Females would form
more geographically distinct populations but males would be homogeneous over larger
geographic areas, due to mixing of males of different geographical origin. Difference
among the mitochondrial genome and nuclear loci can also indicate sex difference in
migration pattern (Purcell et al., 1996).
What defines a population? A biological population is defined in different ways
in the literature. Hedrick (2005) defines a population as:
“...a population is a group of interbreeding individuals that exist together
in space and time” (Hedrick, 2005, p.62)
Hartl and Clark (2006) discuss the difficulty in defining a population and that it may
vary from one organism to another but that it is reflected in a non random distribution of
interbreeding individuals within a species. They define a local population as:
“...the fundamental units of population genetics. Local populations are
actual, evolving units of a species” (Hartl and Clark, 2006, p.46)
The definitions above reflect how populations are defined in population genetics and
evolutionary biology (Waples and Gaggiotti, 2006). However, there is a fundamental
difference in the way ecologists and evolutionary biologists define populations. Under an
ecological paradigm the focus is on the co-occurrence of individuals within a species in
space and time but under the evolutionary paradigm the emphasis is on the reproductive
interactions between individuals. Waples and Gaggiotti (2006) contrast the views about
populations in a useful way by summarizing the following:
“Ecological paradigm: A group of individuals of the same species that
co-occur in space and time and have an opportunity to interact with each
other.
Evolutionary paradigm: A group of individuals of the same species living
in close enough proximity that any member of the group can potentially
mate with any other member” (Waples and Gaggiotti, 2006, p.1421)
3
1
General introduction
These different views may cause confusion. Under the ecological paradigm a
population can be formed over a short period of time without any restrictions in gene
flow among populations, a scenario that under the evolutionary paradigm would be
considered panmixia (Waples and Gaggiotti, 2006).
Many different molecular methods are used for population genetic structure analysis.
Markers vary in nature, level of technology involved and cost. When selecting a
molecular marker for a study all these factors need to be considered. Mitochondrial
DNA (mtDNA) sequence variation has been extensively used in population genetics
and phylogenetic studies due to its characteristics. The effective population size Ne
is one quarter of that for nuclear genetic markers. Lower Ne of the mtDNA will
result in faster rate of genetic drift compared to nuclear markers. This makes mtDNA
well suited for detecting populations of recent divergence. On average it would be
expected that divergence would be detected earlier using mtDNA variation compared to
nuclear genome markers. Further, studies have shown that the most commonly found
polymorphism in mtDNA sequences is synonymous, suggesting weak or no selection
(e.g. Árnason, 2004). It has been shown to evolve at a faster rate than the coding nuclear
genome (Avise, 2000). Comparing various molecular markers currently available direct
sequencing of mtDNA has many favorable characteristics. The obvious weakness of
using only mtDNA for population genetic structure analysis is that it is a single locus
and different loci can have different histories within a population by chance alone
(genetic drift is a random process). Ideally a number of neutral loci should be used
(e.g. Hartl and Clark, 2006). However, if a high frequency polymorphism is observed
in a mtDNA marker, that can be shown to be synonymous, it can be a useful tool for
population genetic studies. In the present study mtDNA sequence variation is used for
the examination of population genetic structure in four different fish species.
1.1.3 Natural selection
Natural selection may favor different alleles in different environments. A difference
in allele frequency for a locus under selection may thus reflect the differences in
the environment in which it is found rather than the origin of the individuals or of
populations.
Direct observation of DNA sequence variation can be used to make inference about
observed polymorphism, for example by inspection of the frequency of synonymous
and nonsynonymous segregating sites. A number of statistical tests are available for
detecting natural selection for example by exploring the allelic distribution or level of
variability (Nielsen, 2001).
An outlier detection method (Beaumont and Nichols, 1996) is an important statistical
framework to detect if natural selection has affected some out of a set of loci. Outlier
detection method has been used extensively in population genetics. The distribution of
FST for a range of population structures, is strongly related to heterozygosity at a locus.
A simple genetic model is used to generate an FST null distribution for the detection of
outliers. The observed FST values for group comparison for different loci are compared
to the null distribution. Values that are found outside the 95% limits of the FST null
distribution are considered to be outliers and thus suspect of being under selection
(Beaumont and Nichols, 1996).
4
1.1
Genetic variation - Origin and dynamics
It is important to note that observed genetic difference in an organism between
different environments may not reflect a population breeding structure if the variation is
affected by natural selection. As described by Williams et al. (1973) a selection–caused
genetic difference can become established between subdivided group of individuals that
share the same origin.
Williams (1975) suggested that some of the observed genetic variation in cod
might have been shaped by contemporary natural selection and as such a reflection
of environmental heterogeneity rather than population breeding structure as had been
described for American eels, Anguilla rostrata (Williams et al., 1973). It has since
become evident that signs of natural selection are common in the cod genome (e.g. Moen
et al., 2008; Nielsen et al., 2009; Hemmer-Hansen et al., 2013, 2014). Moen et al. (2008)
found signals of natural selection in about 9% of SNP loci analysed in Atlantic cod.
Number of recent sudies have revealed adaptive evolution in Atlantic cod and genetically
different groups among geographical areas (e.g. Bradbury et al., 2013; Therkildsen
et al., 2013; Hemmer-Hansen et al., 2014; Berg et al., 2015). However, neutral loci have
not shown the same kind of population genetic structure (e.g. Hemmer-Hansen et al.,
2014).
In order to describe the historical population genetic structure for an organism it
is important to use neutral molecular markers. Selective neutrality can be statistically
tested based on allelic distributions or DNA sequence variation by comparison variation
of synonymous and nonsynonymous sites (Nielsen, 2001).
However, natural selection can have similar effects to that of demographic events,
such as a population bottleneck, on genetic variation. It may be difficult to discriminate
between alternative explanations (e.g. Hartl and Clark, 2006; Excoffier, 2007).
1.1.4 Demography and molecular clock rate
Genetic variation can be used to make inference about demographic history of a population. Nucleotide mismatch analysis can be used to examine haplotype tree structures
and make predictions of demographic history. Deep genealogies will produce bi- or
multimodal mismatch distributions but shallow genealogies will be unimodal (Rogers
and Harpending, 1992). More recent methods, for example the skyline method introduced by Pybus et al. (2000), have improved our ability to reconstruct past demographic
history embedded in the genealogical relationship among individuals of a population
(Emerson et al., 2001; Drummond et al., 2005). Whatever method used, the timing of
demographic events, such as a population bottleneck, critically relies on the molecular
clock rate. The Panamanian Isthmus provides an important system where a known geological event closed the connection between the Pacific Ocean and the Caribbean Ocean
roughly 3 million years ago. DNA sequence comparisons between between closely
related species of fish (Bermingham et al., 1997) and shallow water shrimp (Knowlton
and Weigt, 1998) has provided an estimate of mtDNA coding region substitution rate of
1.4–2%/MY. In this context it is important to note that according to population genetics
theory the substitution rate is not related to effective population size, Ne . This is due to
the fact that the probability of mutation to occur in a population (for a diploid gene in
an ideal population) is: µ, and the frequency of a new allele just after mutation is thus:
µ × 2Ne . The probability of fixation of a particular mutation, if neutral, is: 1/2Ne (at the
5
1
General introduction
time when the mutation emerges). Thus, the substitution rate is: µ̂ × 2Ne × 1/2Ne = µ̂
(Kimura, 1984). Thus, since substitution rate is not related to effective population size,
Ne , a known substitution rate in one system/population can be used to make inference
about timing of events in another system/population, although the Ne between the two
systems/populations may be different. However, the substitution rate varies among
genes and some variation has been found among taxa and thus a universal molecular
clock may be an illusive idea (Avise, 2004).
Recent studies have indicated a time dependency of molecular clock rates (Ho et al.,
2005; Burridge et al., 2008, e.g.) although this is debatable (see Emerson et al., 2001;
Ho et al., 2007). A differentiation is made between a ’pedigree rate’ and a ’phylogenetic
rate’ (e.g. Burridge et al., 2008; Rocha et al., 2005). The possible mechanism for time
dependency of molecular clock rates is not fully understood but purifying selection may
be responsible (Burridge et al., 2008). Positive selection has been suggested as common
for mtDNA (Bazin et al., 2006) and perhaps such selection results in similar patterns if
selection is weak and of long duration. Further, in a recent study it is pointed out how
the use molecular clock rate calibration with phylogenetic divergence can result in an
overestimation of timing of population events (Grant, 2015).
Genetic variation and genealogy of high latitude organisms have been strongly
influenced by climatic oscillations during the Pliocene and Pleistocene (Bernatchez and
Dodson, 1991; Hewitt, 1996; Avise and Walker, 1998; Wares and Cunningham, 2001;
Pálsson et al., 2009). The climatic oscillations have caused population bottlenecks
for many species followed by a period of expansion. Genetic variation is lost through
extinction of lineages during periods of bottlenecks generating shallow genealogy and
reducing the effective population sizes, Ne . During period of population expansion,
however, the probability of lineage extinction is reduced and genetic variation increases
in a population (Avise, 2000). The expected pattern of genetic variation as a result of
repeated demographic changes is a shallow genealogy with many rare types. This is
indeed the observed pattern in many marine fish species (e.g. Sigurgíslason and Árnason,
2003; Árnason, 2004; Pálsson et al., 2009; Liu et al., 2010; Eiríksson and Árnason,
2014, 2015).
1.1.5 Life history of marine organisms
Life history of organisms can be important in shaping genetic variation. High fecundity
and high mortality at early stages (type III survivorship curve) is characteristic of many
marine fish species. This can lead to high variation in offspring number (a sweepstake
recruitment) and reduction in the Ne /N ratio and to a shallow gene genealogy (Hedgecock, 1994; Árnason, 2004). Such patterns may also be generated by demographic
history, such as population bottlenecks or natural selection as mentioned earlier.
In order to discriminate among alternative factors that may have shaped genetic
variation a comparison can be made among related species. The species may have
variable life-histories, vary in geographic distribution and demographic history that can
be useful for evaluating the most likely factors shaping the genetic variation.
6
1.2
Biology and population genetics of the studied fish species
1.2 Biology and population genetics of the studied
fish species
In the present thesis I will report research on genetic variation in four species of the
family Gadidea: Atlantic cod (Gadus morhua; Linnaeus, 1758), haddock (Melanogrammus aeglefinus; Linnaeus, 1758), saithe (Pollachius virens; Linnaeus, 1758) and whiting
(Merlangius merlangus; Linnaeus, 1758).
1.2.1 Atlantic cod, Gadus morhua
Fisheries, reproduction and phenotypic variation. Atlantic cod is among the most
valuable commercial fish species in the North Atlantic and has been very important
for the fisheries around Iceland. It has a wide distribution and is found in a variety of
habitats, from shallow waters close to shore to deeper waters off the continental shelf
(Jónsson, 1992).
The main spawning grounds for Atlantic cod around Iceland are along the south
coast (Jónsson, 1992). Embryos and fish larvae are known to drift with water currents
from the main spawning grounds to the West, North and East Iceland. In some years
embryos and larvae may drift to the East coast of Greenland (Astthorsson et al., 1994;
Jónsson and Valdimarsson, 2005). The extent of embryo and larval drift depends on the
strength of the warm North Atlantic water current (Jónsson and Valdimarsson, 2005).
In addition to the main spawning grounds cod is known to spawn in different areas all
around the country with spatial and temporal variation in the contribution of the various
spawning grounds to each cohort (Marteinsdottir et al., 2000; Begg and Marteinsdottir,
2000; Jónsdóttir et al., 2007). Thus, in different locations around Iceland each cohort
may be of mixed origin, to a variable extent (e.g. Jónsdóttir et al., 2007; Thorisson et al.,
2011), depending on the strength of the North Atlantic water current and spatial and
temporal variation in spawning success.
Although the fish may be of mixed origin variable phenotypic differences have been
observed among cod from different locations. Differences in length at age, reflecting
differences in growth rate, between the north and south coast of Iceland are well known
(Jónsson, 1992). Growth related morphological variation, such as otolith shape, has been
described for cod around Iceland and has been used to argue for separate populations of
cod (e.g. Pétursdóttir et al., 2006; Jónsdóttir et al., 2006). A study on the Norwegian
coastal cod and the Northeast Arctic cod (that have been considered as two populations)
indicated that an observed difference in growth and age at maturation for coastal and
Northeast Arctic cod reflect differences in the environment rather than genetic variations
(Godø and Moksness, 1987).
In addition behavioral variation has been observed in Atlantic cod. Different behavioral types, identified as frontal and coastal behavioural types, have been described
(Pálsson and Thorsteinsson, 2003; Thorsteinsson et al., 2012). This difference in behavior has been linked to genetic variation at the Pan I locus (Pampoulie et al., 2008).
However, behavioral traits, like any other phenotypic trait, can be environmentally
induced. In fact, experiments have shown that the development of cod behavior can
be affected by environmental factors acting at an early age (Braithwaite and Salvanes,
7
1
General introduction
2005; Salvanes et al., 2007). The observed behavioral variation can thus be environmentally induced. A group of fish, that may share the same place of origin, may develop
variable behaviours if exposed to different environments (as has been reported for other
phenotypic traits, e.g. Gupta and Lewontin, 1982).
As a model phenotype (P) of an organism is the result of its genotype (G) and the
environment (E) in which the genotype develops in and an interaction between the two
(IGE ) and can be expressed as: P = G + E + IGE . Thus, phenotypic variation is not always an indicator of genetic variation, but may reflect variable environmental conditions.
This was recognized by Tåning (1944), that is that morphological variation would not
be a good indicator of population breeding structure in fish. The phenotypic variation
observed in cod may thus be environmentally introduced, that is cod of different origin
may develop similar phenotypic traits if exposed to the same or similar environmental
conditions. Environmental factors such as temperature or food availability or interaction
of environmental factors may be important in this respect. Phenotypically distinct
groups, as observed in Atlantic cod, can be defined as ecological populations although
they may not reflect population breeding structure (Waples and Gaggiotti, 2006). Thus,
such groups would not be considered different populations under population genetic or
evolutionary paradigm (Waples and Gaggiotti, 2006).
Migration pattern around Iceland. Intensive tagging experiments were carried out
around Iceland by Jónsson (1996) in the period 1948–1985. The data show various
trends among different localities and the results have been interpreted in different ways
by scientists. Jónsson (1996) own interpretation was that cod would show clear signs of
migration from the North to the main spawning ground in the South of Iceland, where
most of the spawning took place. He supported this by data on movements of tagged fish
and also by data on age and length. He described an example when in 1949 there was a
dramatic reduction in length of 7 year old fish from approximately 85cm to 75cm in
between early and late March. His interpretation was that this was a clear support for the
hypothesis that the fish from the North had entered the spawning grounds, since the fish
from the North have a lower growth rate and are thus smaller at age than are cod from
the South. The same data have been reanalysed and said to reflect local populations,
with few migratory fish (e.g. Jónsdóttir et al., 2006).
There is a problem associated with the use of the results from a tagging experiments.
First, there is always an autocorrelation in tagging experiment data. That is, at the time
that a tagged fish is released the probability of catching it far from the location of release
is negligible since the fish requires time to travel. Data have shown that as time elapses
from the release of the tagged fish the more likely it is for it to be found further away
from the location of tagging (Jónsson, 1996). Many recent tagging experiment cover
a limited time span and can thus be affected by this, (Þorsteinsson and Marteinsdóttir,
1993; Sæmundsson, 2005; Pampoulie et al., 2006). Second, fish at different life-history
stages may vary in the migratory pattern, e.g. young immature fish may not migrate but
larger mature fish is more likely to migrate (Sæmundsson, 2005; Joensen et al., 2005;
Schopka et al., 2006). If a group of tagged fish belong to a given size/age class then the
experiment may be limited to reflecting the behaviour of this particular size/age class,
but other size classes may show different pattern. Information about the size/age of the
tagged fish is sometimes missing, making it hard to evaluate the results (e.g. Pampoulie
8
1.2
Biology and population genetics of the studied fish species
et al., 2006).
The tagging experiments show to some extent local groups of cod, although when
correcting for autocorrelation the fidelity to location is likely to be reduced. Thus, at
present it is not clear if (or to what extent) the Atlantic cod around Iceland is subdivided
into local groups.
Population genetic studies. Considerable research effort has been directed to the
analysis of cod population genetic structure for decades, in order to resolve if the
observed phenotypic variation may correlate with genetic variation. No commercial
fish species has been the subject to as many population genetic studies (for review see
Reiss et al., 2009). However, research results have to some extent led to conflicting
views. Different molecular genetic markers have shown different patterns, some markers
showing great population genetic structure but others little or none (Reiss et al., 2009).
It is important to understand what may cause differences among the different markers
in order to better understand the population genetic structure of Atlantic cod.
Of particular concern is the importance of natural selection in shaping of genetic
variation in Atlantic cod. Recent studies have shown that footprints of natural selection
are common in the cod genome (Moen et al., 2008) and fine scale population breeding
structure may in some cases be inflated due to selection (Nielsen et al., 2006; Eiríksson
and Árnason, 2013). This may cause environmental heterogeneity to be reflected in the
genetic variation, rather than restrictions in gene flow (Williams et al., 1973).
Early studies used hemoglobin polymorphism in order to identify cod populations
(e.g. Sick, 1961, 1965). Jamieson and Jónsson (1971) used hemoglobin polymorphism
among cod at Iceland and Greenland and in their paper they describe their findings as a
"moving mosaic of genetic isolates". Earlier Jamieson and Jones (1967) claimed, based
on hemoglobin polymorphism, that there are two races or subspecies of cod around
the Faroe Islands. For cod in Norway a gradient in hemoglobin variants as well as
serum transferrin variation was observed along the Norwegian coast (Frydenberg et al.,
1965; Moller, 1968). This polymorphism lead scientists to suggest that population
subdivision of Atlantic cod might explain this pattern (e.g. Sick, 1961, 1965; Frydenberg et al., 1965; Moller, 1968; Jamieson and Jónsson, 1971). However, Williams
(1975) pointed out that this pattern could be the result of natural selection, and thus that
the observed hemoglobin spatial variation might reflect environmental heterogeneity
rather that population substructuring. Karpov and Nivkov (1980) demonstrated the the
different hemoglobin forms differe in their capacity to combine oxygen at different
temperatures. Further, Petersen and Steffensen (2003) showed differences in temperature preference of different hemoglobin genotypes. The orientation and function of
the different hemoglobin genotypes has been addressed in recent studies (Andersen
et al., 2008; Halldórsdóttir and Árnason, 2009). When new methods became available
allozyme variation studies were to a large extent abandoned as it only revealed but part
of the overall genetic variation and concerns had been raised about the effects of natural
selection.
The use of direct mtDNA sequencing has been important in revealing the population
genetic structure in Atlantic cod. The observed genetic variation at mtDNA is mostly at
synonymous sites, suggesting weak or no selection (e.g. Árnason, 2004). Thus, recently
established population structure should become detectable earlier for mtDNA markers
9
1
General introduction
than for nuclear markers, on average. However, the mtDNA genome is a single locus
and different loci may reflect different history within a population (Hartl and Clark,
2006).
Although an obvious weakness of using mtDNA sequence variation is that it is
a single locus, studies have reported important findings. Population genetic studies
in Atlantic cod, revealed using mtDNA sequence variation, have indicated limited
fine scale population genetic structure and high levels of gene flow among geographic
regions (Carr and Marshall, 1991; Árnason et al., 1992; Carr et al., 1995; Árnason and
Pálsson, 1996; Árnason et al., 1998, 2000). Summarizing results from many studies of
cytochrome b sequence variation across the North Atlantic Árnason (2004) showed no
differences among localities within countries, but a trans Atlantic cline in the common
haplotype frequencies. The regularity of the cline indicated considerable gene flow
among different regions.
However, spatial genetic structure has been repeatedly reported for Atlantic cod
around Iceland. Jónsdóttir et al. (1999) using Pan I and hemoglobin loci among cod
at the south coast of Iceland, found a pattern which they interpreted as a small scale
population genetic structure. More recent studies have shown that variation at the Pan
I locus and hemoglobin is affected by natural selection (Karlsson and Mork, 2003;
Pogson and Mesa, 2004; Árnason et al., 2009). Pampoulie et al. (2006) described
population genetic structure in cod separating cod from NE- from SW-Iceland using
both the Pan I locus and microsatellite markers. The population differentiation was
small when based on the presumed neutral microsatellite markers (FST = 0.003). Such
subtle genetic difference may, however, not be biologically meaningful (Waples, 1998).
Furthermore, a thorough analysis of temporal stability and natural selection is needed in
order to asses the nature of the observed variation.
A number of studies have been carried out using single nucleotide polymorphism
(SNP) in Atlantic cod (Moen et al., 2008, 2009; Nielsen et al., 2009; Hubert et al.,
2010; Bradbury et al., 2011). Nielsen et al. (2009) used 98 SNP for the analysis of
natural selection and found clear indications of natural selection acting on number of
loci. In their study Moen et al. (2008) found signals of natural selection in ca. 9%
of SNP loci analysed. In recent study Bradbury et al. (2011) show the application of
SNP markers in population genetic studies in Atlantic cod. They suggested the use of
large scale SNP panels in assignment studies for high gene flow marine species as cod.
However, their analysis showed that when using a small number of loci ascertainment
bias can become a problem (Bradbury et al., 2011). SNP analysis has the potential to
improve our understanding of the Atlantic cod population genetic structure. However,
research findings need to be interpreted carefully because of the large number of loci
whose variation has been suggested to be shaped by natural selection (Moen et al., 2008;
Nielsen et al., 2009) and the concern due to ascertainment bias (Bradbury et al., 2011).
Future studies. Recently the sequencing of the whole Atlantic cod genome was
completed (Star et al., 2011). With rapid development of sequencing technology and
the reduced sequencing cost future studies will be able to solve many of the challenges
faced by earlier studies. One of the exciting possibility that becomes available with
genome wide information for number of individuals is to examine, in more details
than previously possible, the footprints of natural selection in the cod genome. Many
10
1.2
Biology and population genetics of the studied fish species
studies have suggested that natural selection is important in shaping the observed genetic variation in cod (Williams, 1975; Pogson, 2001; Nielsen et al., 2006; Moen et al.,
2008, e.g). Genome wide sequence information will make it possible to reevaluate the
data available on population genetic structure of Atlantic cod collected by hundreds
of scientists for decades. The major challenge will remain to describe the functional
importance of the observed genetic variation and possibly to identify the environmental
factors that are likely to affect the variation, that is the agents of natural selection. This
is a complex task but if successful it will revolutionize out understanding of the biology
of Atlantic cod.
1.2.2 Haddock, Melanogrammus aeglefinus
Haddock, Melanogrammus aeglefinus, is a demersal fish species most commonly found
at 80–200m depth at variable bottom substrates. Haddock is an omnivore, feeding
mainly on small benthic organisms. The age at first maturation varies among geographic
locations and is 3–5 years in the North-West Atlantic, but 2–3 years in the warmer
waters of the North Sea (Cohen et al., 1990; Jónsson, 1992). Fecundity varies greatly
with females size, larger females having higher fecundity (50,000–2,000,000 eggs per
female Jónsson, 1992). The eggs are small (1.2–1.7 mm in diameter). They are buoyant
and eggs and larvae are pelagic for about three months (Hislop, 1984). During this
period eggs and larvae can drift with water currents long distances as has been estimated
for Atlantic cod (e.g. Brickman et al., 2006). Timing of spawning varies greatly among
geographic regions (January to July). Haddock is a commercially important fish species,
the major fishing grounds being in the North-East Atlantic (Cohen et al., 1990; Jónsson,
1992)
In recent years some changes in fish communities have been observed in the North
Atlantic ocean, both in migratory patterns of some species and in changes in distributions
of others. These changes are most likely linked to environmental changes associated
with increased water temperature (Rose, 2005). For the last two decades the distribution
of haddock around Iceland has changed and haddock is now commonly found at the
North coast (Björnsson et al., 2007). In recent years (2004–2007) the haddock captures
increased in the Icelandic waters reflecting the increase in population size, followed by
reduction in the population size (Anonymous, 2011). Such demographic changes may
be reflected in genetic variation of populations.
The environmental conditions throughout the distribution range of haddock is
variable and different populations of haddock may occupy different habitats. An
environmental heterogeneity is found at the Faroe Islands where the Faroe Plateau and
the Faroe Bank, separated by 850m deep channel, differ in temperature and in abundance
of food for haddock. It has been shown that fish growth rate differs due to differences in
temperature and food abundance between the two ecosystems (Magnussen, 2007).
A number of studies have been carried out, using different molecular markers,
describing genetic variation and population genetic structure of haddock. Their findings
are to some extent conflicting. A study using transferrin polymorphism for the analysis
of the genetic variation in haddock indicated fine scale population sub structuring within
the North-East Atlantic (Jamieson and Birley, 1989). In another study of allozyme
variation in haddock (using 8 loci) limited differences were revealed among waters of
11
1
General introduction
different countries (Norway, Iceland, Faroe Islands, North Sea) (Giæver and Forthun,
1999). Lage et al. (2001) found temporal variation in allele frequencies in a microsatellite
study for haddock and an indication of fine scale population genetic structure in the
North-West Atlantic. However, a limited population sub-structuring was indicated by
mtDNA RFLP analysis for the same geographic region in another study (Zwanenburg
et al., 1992). In Lage et al. (2001) study the variation in FST among the different loci is
not reported and the effects of natural selection on shaping the observed pattern was
not considered. It is possible that natural selection and environmental heterogeneity
may explain the observed pattern (Williams et al., 1973; Guinand et al., 2004; Árnason
et al., 2009; Bradbury et al., 2011). In fact one of the microsatellites used in their
study (Gmo-132) has been shown to have an outlier FST value in spatial comparison for
Atlantic cod, and is suspected to be under natural selection (e.g. Nielsen et al., 2006).
In the present study mtDNA genetic sequence variation in haddock will be examined
exploring its population genetic structure and the potential effects of natural selection
will be tested. A mtDNA study indicates that gene flow among geographic region may
be responsible for maintenance of high genetic variation in haddock at the time of
collapse in the fisheries at Georges Bank (Purcell et al., 1996).
1.2.3 Saithe, Pollachius virens
Saithe, Pollachius virens, is a demersal fish species distributed on both sides of the
North Atlantic. It is distributed from Barents sea in the North to the Bay of Biscay in
the South, along the coast of Canada in the West and southwards to the coast of North
Carolina. It is common around Iceland but rare around Greenland. (Jónsson, 1992).
Spawning time varies across the Atlantic ocean. NE-Atlantic saithe spawn in
February-March but NW-Atlantic saithe are reported to have a wider spawning season,
from October to March (Jónsson, 1992). Eggs and larvae are pelagic and can thus be
carried with ocean currents (Brickman et al., 2006). Saithe is known to migrate long
distances in search of food but spawning takes place in shallow waters (Jónsson, 1992).
Saithe is an important commercial species in many European countries. For fisheries
management purposes saithe is divided into several units, e.g. Canada, Northeast
Arctic, North Sea and West of Scotland, Iceland and Faroe Islands. However, tagging
experiments have shown considerable migration among the different units (Jakobsen
and Olsen, 1987; Neilson et al., 2006), making the division questionable biologically.
In a long term tagging experiment Jakobsen and Olsen (1987) reported substantial
saithe migration from northern Norway to Iceland, frequently to the extent to be characterized as mass movements. This was evident both through the recapture of tagged fish,
but also through a change in size at age among saithe in Icelandic waters during a given
period indicating immigration of fish that had developed in a different environment, at
a different growth rate. Large scale migration from Norway to Faroe Islands was also
reported in the same study.
In a tagging study carried out on saithe around Iceland, only a limited number of
tagged saithe was found to have emigrated from the Icelandic waters, indicating no large
scale emigration (Jones and Jonsson, 1971). In a more recent study Armannsson et al.
(2007) found very clear inshore/offshore movements during the summer and variation
among saithe in migratory routes among tagging areas. This is seen as a possible sign
12
1.2
Biology and population genetics of the studied fish species
of more complex population structure around Iceland than previously thought. Further
more they found no evidence of large scale emigration of saithe from Icelandic waters.
In a tagging–recapture study Neilson et al. (2006) suggested that saithe of the coast
of Canada forms three separate populations. They further suggested that this needs to
be taken into account for management of saithe fisheries. This is based on phenotypic
observations, such as variation in growth rate and behaviour.
The different findings among the different studies suggest that migratory behaviour
may be associated with some life history stage, that is adult fish may be more likely to
migrate as has been reported for Atlantic cod Gadus morhua (Schopka et al., 2006) or
that migratory behaviour may vary among different saithe groups.
Limited work has been carried out to examine population genetic structure of saithe
(see Forthun and Mork, 1997; Reiss et al., 2009; Coucheron et al., 2011). As described
earlier saithe has high potential for egg and larval dispersal and is known for long
distance migrations of adult fish. This is likely to contribute to gene flow in saithe
among different geographic localities. A recent microsatellite study on closely related
species, the pollack (Pollachius pollachius), along the European coast revealed very
limited genetic variation among geographic localities (Charrier et al., 2006). Given
this potential for gene flow, the probability of genetic divergence among saithe groups
from different waters must be low. However, there are no reported cases of migrations
between NW-Atlantic and the NE-Atlantic, and this might be reflected in the population
genetic structure of the species.
1.2.4 Whiting, Merlangius merlangus
Whiting Merlangius merlangus is a commercially important fish species in the North
Atlantic especially in the North Sea. It is distributed from northern Norway and Iceland
in the North to the Bay of Biscay in the South. It is also found in the Mediterranean
and the Black sea. Whiting is a shallow water species mostly found at 10–200m depth
(Jónsson, 1992). It has a longer pelagic phase early in life compared to related gadoids
such as Atlantic cod Gadus morhua, haddock Melanogrammus aeglefinus and saithe
Pollachius virens. Potentially this results in a higher dispersal rate (Hislop, 1984). Age
at maturation is variable among geographic localities, however, in general maturation
is reached at 2–4 years overall, earlier in the southern part of its distribution (Hislop,
1984; Jónsson, 1992; Cohen et al., 1990)
Due to its commercial importance many studies have focused on whiting biology in
the North East Atlantic (Cohen et al., 1990). Tag and recapture studies and levels of
parasite infestation in different localities have been interpreted as reflecting limited migrations among localities (Hislop and MacKenzie, 1976). Thus two distinct populations
have been assumed to exist north and south of Dogger Bank in the North Sea (Hislop
and MacKenzie, 1976). Pilcher et al. (1989) described spatial and seasonal variation
in parasite prevalence and infestation intensity within the North Sea. A latitudinal
cline was observed for infestation of the monogenenan parasite Diclidophora merlangi.
Infestation prevalence and intensities increased steadily from South to North.
Charrier et al. (2007) using seven microsatellite marker for whiting in the North East
Atlantic showed limited genetic differentiation among sampling localities indicating
high level of gene flow over long geographic distances. However a significant genetic
13
1
General introduction
difference was observed among sampling stations within the North Sea, albeit with low
FST values. Similar results were obtained in an earlier microsatellite study based on
three loci where two loci show significant difference between sampling locations within
the North Sea (Rico et al., 1997).
1.3 The present study
The main objective of the present study is to examine the population genetic structure
of Atlantic cod around Iceland and to test if distinct historically stable populations can
be identified, as has been suggested by some earlier studies (e.g. Jónsdóttir et al., 1999).
In particular population genetic structure described by Pampoulie et al. (2006), showing
small but significant difference between Atlantic cod found in waters NW- and SEIceland, will be examined and tested if their findings can be confirmed. Pampoulie et al.
(2006) study is refereed to as providing an evidence of distinct geographic components
in Atlantic cod around Iceland (e.g. Pampoulie et al., 2014; Grabowski et al., 2015;
Sólmundsson et al., 2015). Thus, one of the objectives of the preset study is to examine
the observed pattern, with regards to temporal genetic variation and natural selection.
Although the present project is focusing on Atlantic cod around Iceland the findings
are relevant to the study of population genetic structure in cod in general and may thus
have wider implications.
Another objective of the present project is to compared genetic variation and population genetic structure observed in other gadoid fish species to that of Atlantic cod.
Comparison among related species may if different biological factors are reflected in the
genetic variation. Thus, in the present project genetic variation in Atlantic cod, Gadus
morhua, saithe, Pollachius virens, haddock, Melanogrammus aeglefinus and whiting
Merlangius merlangus will be examined. In the first paper the microsatellite variation
was examined in Atlantic cod. In the other four papers mitochondrial sequence variation
was used to examine population genetic structure in the different gadoid species. For
an overview of the mithochondrial DNA fragment size and sample size used for the
different species see table 1.1.
Table 1.1. Mithochondrial DNA fragment size and sample size (N) in the different
papers of the present study. cyt b:cytochrome b; COI: cytochrome oxidase c subunit I
Common name
Atlantic cod
Saithe
Haddock
Whiting
14
Species
Gadus morhua
Pollachius virens
Melanogrammus aeglefinus
Merlangius merlangus
N
2656
1163
884
138
Locus
cyt b
COI
COI
COI
bp
328
460
599
621
paper
II
III
IV
V
1.3
The present study
1.3.1 Spatial and temporal microsatellite variation in spawning Atlantic
cod, Gadus morhua, around Iceland
In this study 10 microsatellite loci were used for spatial and temporal genetic analysis
in spawning Atlantic cod around Iceland and compared to earlier findings (Árnason
et al., 2000; Pampoulie et al., 2006). The markers used included both markers that are
likely to be selectively neutral and some that have shown signs of natural selection from
outlier behaviour (Nielsen et al., 2006). The loci used here have all been extensively
used for cod (Reiss et al., 2009). It was tested if natural selection was likely to have
shaped the genetic variation at analyzed loci and the sensitivity of the methods were
evaluated.
1.3.2 High levels of gene flow and temporal genetic variation in Atlantic
cod, Gadus morhua in Icelandic waters — inferred by mitochondrial
DNA sequence variation
In this study mtDNA sequence variation was used for the analysis of population genetic
structure of spawning Atlantic cod around Iceland. Comparison was made among
different regions around the country and a comparison made between NE- and SWIceland, were population differentiation has been suggested (Pampoulie et al., 2006;
Jónsdóttir et al., 2006). Temporal genetic variation was evaluated by comparison
between sampling years and in particular by comparison among different cohorts.
1.3.3 Gene flow across the N-Atlantic and sex-biased dispersal inferred
from mtDNA sequence variation in saithe, Pollachius virens
In this study population mtDNA sequence variation was used to examine genetic
structure in saithe in the North-Atlantic ocean. Limited work has been done on the
population genetics of saithe (Reiss et al., 2009). A comparison is be made between
sexes and for saithe among and within geographic localities (Canada, Iceland, Faroe
Islands and Norway).
1.3.4 Phylogeography of haddock Melanogrammus aeglefinus in the North
Atlantic — Postglacial expansion and male-biased dispersal
This study was based on the analysis of sequence variation in a 599 base pair fragment
of the cytochrome oxidase c subunit I (COI) mitochondrial gene for haddock sampled in
the North Atlantic. The sequence variation was used to assess relationship in haddock
within and among different geographic regions. Comparison was made to earlier studies
on haddock and comparison made to what had been described for Atlantic cod, Gadus
morhua, and related gadoid fish species (Árnason, 2004; Pálsson et al., 2009, and
Eiriksson, unpubl data).
15
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General introduction
1.3.5 Mitochondrial DNA sequence variation in whiting Merlangius merlangus in the North East Atlantic
In this study genetic variation at a 621 base pair fragment of the cytochrome c oxidase
subunit I (COI) mitochondrial gene in whiting from South Iceland, Northern Norway
and the North Sea was examined. Demographic factors and spatial genetic variation
were examined. The findings were compared to observed genetic variation in related
species (Árnason, 2004; Pálsson et al., 2009; Liu et al., 2010)
16
References
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25
Paper I
Spatial and Temporal Microsatellite Variation in Spawning Atlantic cod,
Gadus morhua, around Iceland
Guðni Magnús Eiríksson and Einar Árnason, 2013
Canadian Journal of Fisheries and Aquatic Sciences, 70:1151–1158.
doi:10.1139/cjfas-2012-0494
© Canadian Journal of Fisheries and Aquatic Sciences
Guðni Magnús Eiríksson designed the study, selected samples of spawning fish and
did all the laboratory work: DNA isolation, microsatellite amplification and prepated
the samples for electrophoresis on an ABI-3100 automatic sequencer. He also did the
data analysis and interpretation. Guðni was in charge of writing the manuscript and
corresponded to the comments of reviewers and finalized the article. Professor Einar
Árnason actively participated in all steps of the work. Einar took part in the experimental
design, supervised the molecular work, took part in data analysis, interpretation of the
data and participated in writing and finalizing the manuscript.
27
Paper I
28
1151
ARTICLE
Spatial and temporal microsatellite variation in spawning Atlantic cod,
Gadus morhua, around Iceland
Guðni Magnús Eiríksson and Einar Árnason
Abstract: The present study suggests that the observed genetic difference between Atlantic cod, Gadus morhua, off the south and
the north coast of Iceland may be caused by natural selection affecting genetic variation at a microsatellite loci (Gmo34). When
disregarding this locus from the analysis, no genetic difference was observed between northern and southern Iceland. The
methods applied here were very sensitive, and differences as small as FST = 0.0005 are unlikely to go unnoticed. The difference
between cod off the south and the north coast of Iceland is thus likely to be smaller than that. Such a small difference is
negligible and is not likely to have any biological meaning. Genetic drift was detected by allele frequency comparison among
different cohorts (FST = 0.0007, P = 0.0209). A small but significant difference was observed among allele frequency for cod
grouped by depth at the south coast of Iceland (FST = 0.0017, P = 0.0002). This difference is very subtle and needs to be interpreted
with caution.
Résumé : La présente étude indique que les différences génétiques observées entre les morues, Gadus morhua, de la côte nord et
de la côte sud de l’Islande pourraient être le fait d’une sélection naturelle associée à la variation génétique à un site microsatellite
(Gmo34). Si ce site est exclu de l’analyse, aucune différence génétique n’est observée entre le nord et le sud de l’Islande. Étant
donné la grande sensibilité des méthodes utilisées, la probabilité que des différences de l’ordre de FST = 0,0005 ne soient pas
détectées est faible. La différence entre les morues des côtes sud et nord de l’Islande est donc probablement plus faible que ce
seuil. Une différence de cet ordre est négligeable et non susceptible d’avoir une signification biologique. La comparaison de la
fréquence allélique entre différentes cohortes a permis de déceler une dérive génétique (FST = 0,0007, P = 0,0209). Une différence
significative, bien que faible, a été observée sur le plan de la fréquence allélique pour différents groupes de morues répartis selon
la profondeur sur la côte sud de l’Islande (FST = 0,0017, P = 0,0002). Cette différence est très subtile et doit être interprétée avec
prudence. [Traduit par la Rédaction]
Introduction
Genetic variation
For the study of population breeding structure, it is important
to use neutral molecular markers whose variation is shaped
by genetic drift but not natural selection (Williams et al. 1973;
Guinand et al. 2004). For Atlantic cod, Gadus morhua, this is particularly important, as it has become evident that signs of natural
selection are common in the cod genome (e.g., Moen et al. 2008;
Nielsen et al. 2009; Bradbury et al. 2010). Moen et al. (2008) found
signals of natural selection in ⬃9% of single nucleotide polymorphism (SNP) loci analyzed in Atlantic cod, based on polymorphism
found in expressed sequence tags (EST). Bradbury et al. (2010) also
found high number of SNPs showing deviation from neutral expectations, although at lower relative frequency (4.2%). Thus, the
analysis of natural selection should be an integrated part of the
analysis of Atlantic cod breeding structure.
For the past two decades microsatellite markers have been
applied in many studies for the analysis of population genetic
structure in Atlantic cod (Reiss et al. 2009). Although many microsatellites may be selectively neutral, it has been shown that
microsatellites can be found within genes where they are likely to
be in linkage disequilibrium with coding regions that are potentially under natural selection (Li et al. 2004), and in some cases
microsatellites may have functional importance and may thus be
under direct natural selection (Li et al. 2002). For Atlantic cod it
has been shown that natural selection can affect microsatellite
variation (e.g., Nielsen et al. 2006).
Molecular markers, whose variation is affected by natural selection, may reflect the population breeding structure, but may also
be the result of contemporary selection in a heterogeneous environment (Williams et al. 1973; Guinand et al. 2004). The prerequisite for such variation to be useful for population breeding
structure analysis is evidence for its historical establishment,
rather than a reflection of contemporary variation in selective
mortality in a heterogeneous environment. For Atlantic cod, the
possibility of contemporary natural selection to shape frequencies at a given marker in different ways in different environmental conditions is a real concern, because of cod's high fecundity,
high dispersal capability, and high mortality at early life stages
(type III survivor ship curve). Although this is important for identification of the breeding structure of populations, markers under
selection may be useful for the identification of ecological groups
(see e.g., André et al. 2011) that can be defined as populations
under the ecological paradigm (Waples and Gaggiotti 2006). Such
variation may be important for fisheries management (e.g.,
Russello et al. 2012).
Neutral variation may fail to reflect population structure, in
particular for recently formed populations, as genetic differentiation at a neutral marker may evolve slowly. This particularly
accounts for large populations, as effective population size (Ne) is
inversely related to the rate of genetic drift (Hartl and Clark 2006).
When no genetic difference is realized in a study, it is important
to assess the statistical power of methods applied to determine
Received 15 November 2012. Accepted 26 May 2013.
Paper handled by Associate Editor Paloma Morán.
G.M. Eiríksson and E. Árnason. Institute of Life and Environmental Sciences, University of Iceland Sturlugata 7, 101 Reykjavík.
Corresponding author: Guðni Magnús Eiríksson (e-mail: [email protected]).
Can. J. Fish. Aquat. Sci. 70: 1151–1158 (2013) dx.doi.org/10.1139/cjfas-2012-0494
Published at www.nrcresearchpress.com/cjfas on 29 May 2013.
29
Paper I
1152
the magnitude of difference possible to detect (Ryman and Palm
2006).
Population structure of Atlantic cod
Phenotypic variation in Atlantic cod among geographic localities
has been documented in many studies. Variation in life history characteristics and otolith shape has been used to discriminate cod
into distinct spawning groups residing north and south of Iceland
(Jónsdóttir et al. 2006). Otolith shape has been used to identify
different groups of cod at a small scale at adjacent spawning
grounds at the south coast of Iceland (Pétursdóttir et al. 2006).
Distinct behavioural types, defined as coastal and frontal behavioural types (Thorsteinsson et al. 2012), have been suggested to
reflect different populations (Pálsson and Thorsteinsson 2003;
Thorsteinsson et al. 2012), and correlation of the different behavioural types to body morphology has been described (McAdam
et al. 2012). From these studies emerges the view that the Atlantic
cod around Iceland are divided into different populations, although the form and scale may vary. However, the observed phenotypic variation rarely correlates to the findings of population
genetic studies (but see Pampoulie et al. 2008).
Although the population genetics of Atlantic cod across its distribution range has been studied for decades (for overview see
Reiss et al. 2009), a consensus on the population structure is not in
place. Various molecular tools have been used, and the results
have been to some extent conflicting and the interpretation of
research has varied from one study to another. In some cases
results have suggested local populations at a fine scale, in particular studies that utilize microsatellite and pantophysin I locus
(Pan I) variation for resolving population genetic structure (e.g.,
Jónsdóttir et al. 2001; Pampoulie et al. 2006; Westgaard and
Fevolden 2007; Kovach et al. 2010). However, other studies suggest
high levels of gene flow and limited population genetic structure,
for example a study based on allozyme variation (e.g., Mork et al.
1985) and in particular studies that are based on mtDNA sequence
variation (e.g., Carr et al. 1995; Árnason et al. 2000; Árnason 2004).
Molecular methods vary in nature, and the evolution of different
molecular markers may vary within the same population (Hartl
and Clark 2006), and this may explain variable results. It has been
demonstrated that cod population genetic structure may be inflated as a result of natural selection in heterogeneous environments (Nielsen et al. 2006), and this is a possible explanation for
the apparent discrepancies among cod population genetic studies
in general. The observed apparent population structure for Atlantic cod in Icelandic waters maybe an example of such inflated
population genetic structure resulting from natural selection.
A number of studies have been carried out to reveal the population genetic structure of Atlantic cod around Iceland. Pampoulie
et al. (2006) reported small but highly significant genetic differentiation between Atlantic cod sampled north of Iceland compared
with cod sampled south of Iceland (FST = 0.003, P = 0.00013). Such
subtle genetic difference may not be biologically meaningful
(Waples 1998), although it has been suggested that indeed such a
small difference may be important in marine fish (Knutsen et al.
2011). In any case, thorough analysis of temporal stability and
natural selection is needed to assess the nature of the observed
variation in Atlantic cod around Iceland. Much greater spatial
differentiation (FST = 0.261) was revealed for cod around Iceland
when using the Pan I locus (Pampoulie et al. 2006), a genetic variation that is most likely shaped by natural selection (e.g., Karlsson
and Mork 2003; Pogson and Fevolden 2003; Pogson and Mesa
2004). Long-term temporal stability of allele frequencies at a the
Pan I locus has been taken as an evidence for its stability over time,
making it useful for population structure analysis at a microgeographic scale (Nielsen et al. 2007). However, if environmental
characteristics are stable with respect to factors affecting the frequencies at a locus (e.g., depth), the frequencies can be the result
of selective mortality within each cohort, repeatedly arriving at
Can. J. Fish. Aquat. Sci. Vol. 70, 2013
similar allele frequencies reflecting the environmental heterogeneity rather than population breeding structure.
Some earlier studies on the population structure of Atlantic cod
around Iceland commonly used molecular markers that are suspected of being under the effects of natural selection (Jamieson
and Jónsson 1971; Jónsdóttir et al. 1999, 2001), and the reported
population genetic structure may thus be inflated (Nielsen et al.
2006). However, Árnason et al. (2000), using mitochondrial DNA
sequence variation, did not reveal any population genetic structure in Atlantic cod in the waters around Iceland (see also Árnason
2004). These conflicting findings call for further analysis of the
population genetic structure of Atlantic cod around Iceland.
For an observed spatial genetic structure in a population to be
historically meaningful, temporal stability is essential. If a spatial
structure is not temporally stable, it may have established because of chance or possibly because of nonrandom sampling
(Waples 1998). Nonrandom sampling may cause temporal genetic
variation to be mistaken for a spatial structure. This can, for example, be realized when there is a genetic difference among cohorts and when there is a difference in age combination among
fish among different geographic regions. It is well known that cod
in northern Iceland has a slower growth rate than cod in southern
Iceland, and a difference has been reported in age composition between cod sampled in northern and southern Iceland (Jónsdóttir
et al. 2006). For cod around Iceland, the above scenario is thus not
only a theoretical possibility, but a real concern. When a spatial
structure is observed, it is thus important to assess its temporal
stability to establish if it is truly reflecting the breeding structure
of the population.
The present study
In the present study 10 microsatellite loci were used that have
been extensively used for Atlantic cod (Reiss et al. 2009), including
both markers that are likely to be selectively neutral and some
that have shown signs of natural selection (Nielsen et al. 2006).
Spatial and temporal genetic variation for spawning cod around
Iceland were examined and compared with earlier findings (Árnason
et al. 2000; Pampoulie et al. 2006). We tested whether natural
selection is likely to have shaped the genetic variation at analyzed
loci and also evaluated the sensitivity of the methods.
This study analyzed temporal variation by comparing among
cohorts and age groups and assessed if that may affect the observed spatial genetic variation. To test if there are historically
stable populations in the north and the south of Iceland, and that
the reported difference (Pampoulie et al. 2006) is not a temporary
unstable phenomena, the present study targeted only mature
spawners. Although the focus of the study is on spawning cod
around Iceland, it may also have general implications for the
biology of Atlantic cod and the use of microsatellites for population genetic studies in general.
In all microsatellite studies, the quality of the analyzed loci is of
great importance. Genotyping errors such as null alleles and stuttering error are commonly encountered. The presence of null
alleles can inflate a genetic difference if any difference is found
among groups (increased FST), but in case of no difference, the FST
is unbiased (Chapuis and Estoup 2007).
Materials and methods
Fish sampling
Atlantic cod was sampled from Iceland in the annual spring
survey of the Icelandic Marine Research Institute in 3 consecutive
years, 2005–2007. The surveys mainly target spawning cod at shallow waters (<200 m) on breeding grounds at specific locations.
Samples were also taken at deeper waters in southern Iceland. Gill
tissue of fish was sampled and preserved in 96% ethanol for DNA
analysis. Age of sampled fish was determined by otolith inspection by experts at the Marine Research Institute.
Published by NRC Research Press
30
Eiríksson and Árnason
1153
Fig. 1. Atlantic cod sampling sites on a map of Iceland. Cod sampling stations in the north (triangles) and south (circles). Total number of cod
sampled: N = 845 (Nnorth = 340, Nsouth = 505).
Mature fish with ripe gonads were randomly selected for microsatellite analysis from the overall sample (Fig. 1). They represent
six cohorts (1996–2001) and four ages (6–9 years old) from northern and southern Iceland (Fig. 1). In total 845 individuals were
sampled and analyzed, 340 from the north and 505 from south.
Molecular methods
DNA isolation
DNA was isolated using a Chelex method (Walsh et al. 1991). A
stock solution was prepared consisting of 50 mg·mL–1 Chelex 100
(BioRad), 0.2% sodium dodecyl sulfate (SDS), 10 mmol·L–1 Tris
(pH 8), and 0.5 mmol·L–1 ethylenediaminetetraacetic acid (EDTA)
with proteinase K added to the mix, just prior to DNA isolation, at
final concentration of 200 ␮g·mL–1. A small tissue fragment (less
than 1 mm in diameter) was cut from the preserved gill tissue,
drained on a paper towel, and placed in a tube containing 250 ␮L
reaction mix. The mix was then placed in a Thermomixer (Eppendorf) at 700 r·min–1 for 3–5 h (or until no tissue fragments were
visible). After this the mix was placed in a 95 °C for 5 min for
denaturation of the proteinase K and spun at 3000 r·min–1 for
5 min to precipitate debris. The supernatant was carefully removed. A 1:19 dilution of the supernatant was prepared and used
for PCR (DNA concentration approximately 1 ng·␮L–1).
Microsatellite multiplex
Ten microsatellite loci were amplified in two multiplex PCR
reactions (Table 1) with modification of the method by Westgaard
and Fevolden (2007).
QIAGEN Multiplex PCR kit, composed of HotStartTaq DNA polymerase, multiplex PCR buffer ([MgCl2] = 6 mmol·L–1), and dNTP
mix was used for multiplex reactions. A pigtail (GTTTCTT) was
added to the 5=-end of the reverse primer to reduce stuttering in
the amplification process (Brownstein et al. 1996). The forward
primer had a 5=-end fluorescence label. Applied Biosystems dye set
DS-33 (filter set G5) was used for labeling. Primers were made by
Applied Biosystems.
The same PCR protocol was used for both multiplexes. The DNA
was denaturated at 95 °C for 1 min followed by 35 cycles of denaturation at 95 °C for 20 s, annealing at 56 °C for 30 s, and extension
at 68 °C for 1 min. Ten minutes extension at 68 °C completed the
PCR.
DNA precipitation
The amplified DNA was precipitated in plates of 96 samples.
50 ␮L of 0.3 mol·L–1 NaOAc was added to each well containing
10 ␮L reaction product followed by 125 ␮L cold (stored at −20 °C)
96% ethanol. The mix was spun at 4000 r·min–1 for 30 min at 2 °C,
the precipitation mix was poured off, and the sample and the tray
were spun inverted on top of a paper towel at 300 r·min–1 at 2 °C
for 2 min. For rinsing of the product, 250 ␮L of cold 70% ethanol
was carefully added and spun at 4000 r·min–1 for 5 min, and then
the tray was spun inverted as before, but this time for 5 min to
remove the rest of the ethanol. The samples were left in a dark and
dry place for at least 15 min to allow for evaporation of any remaining ethanol. The DNA was then dissolved in Hi-Di formamide
containing GeneScan-500 LIZ size standards (Applied Biosystems)
for length determination. An ABI 3100 automatic sequencer was
used for the electrophoresis of the reaction product.
Published by NRC Research Press
31
Paper I
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Can. J. Fish. Aquat. Sci. Vol. 70, 2013
Table 1. Microsatellite loci used in the present study.
Locus
MP
Primer
concentration
(␮mol·L–1)
Size
range
(bp)
k
Hexp
Hobs
PHWE
Reference
Gmo8
Gmo19
Gmo35
Gmo37
Tch11
Gmo2
Gmo3
Gmo34
Gmo132
Tch13
1
1
1
1
1
2
2
2
2
2
0.10
0.15
0.20
2.50
0.30
0.25
0.20
0.20
0.30
0.30
119–331
127–235
121–154
233–315
123–244
105–153
165–213
90–123
100–181
84–185
54
28
11
23
27
23
12
10
33
47
0.931
0.922
0.831
0.839
0.935
0.846
0.159
0.385
0.574
0.920
0.928
0.806
0.805
0.728
0.846
0.717
0.155
0.363
0.588
0.903
0.399
<0.001
0.619
<0.001
<0.001
<0.001
0.375
0.064
0.590
0.449
Miller et al. 2000
Miller et al. 2000
Miller et al. 2000
Miller et al. 2000
O'Reilly et al. 2000
Brooker et al. 1994
Miller et al. 2000
Miller et al. 2000
Brooker et al. 1994
O'Reilly et al. 2000
Note: The amplification was carried out in two multiplexes (MP). Listed are the concentration of the primers
used, the size range of alleles (base pairs, bp), the number of observed alleles (k), the expected and observed
heterozygosity (Hexp and Hobs, respectively), probability for loci to be in Hardy–Weinberg equilibrium (PHWE), and
the reference for primers used.
Data analysis
GeneMapper (version 3.7, Applied Biosystems 2004) was used
for data analysis. Bin sets were developed based on the observed
peak variation and alleles automatically assigned to bins. Allele
determination was manually confirmed by inspection of trace
files.
The software Micro-Checker (version 2.2.3, Oosterhout et al.
2004) was used to assess the quality of the analysis for each loci.
Micro-Checker detects the presence of null alleles and provides an
algorithm to adjust the frequency of genotypes to correct for null
alleles. Genotype frequency was adjusted for loci that showed
signs of null alleles. The effects of not correcting and correcting
on the results was assessed.
The R package “adegenet” (Jombart 2008) was used to calculate
summary statistics for observed and expected heterozygosity
(Hobs and Hexp, respectively) and number of alleles (k). Probability
test was used for assessing whether allele frequency at the loci
were in Hardy–Weinberg equilibrium, implemented in GENEPOP
version 4.0.7 (Raymond and Rousset 1995).
The method of Weir and Cockerham (1984) was used to calculate the fixation indexes (F) for spatial and temporal comparison
as implemented in GENEPOP. The significance of genic and genotypic differences was tested using Fisher' exact test as implemented in GENEPOP.
For assessing if natural selection is likely to have affected the
genetic variation at the studied loci, the simulation approach of
Beaumont and Nichols (1996) was used. An FST null distribution
was generated by simulation as implemented in FDIST2 software.
The simulated distribution was used for comparison with the observed data. First, all the data were included in the simulation,
and the target FST value was set at the overall FST for group differentiation. Upon the detection of an outlier, the outlier locus was
removed, the target FST value was recalculated using the remaining loci, and the simulation was repeated as recommended by
Beaumont and Nichols (1996). An FST null distribution was simulated for all cases where significant group differentiation was
found (contrasts of geographic variation, depth groups at the
south coast of Iceland, and cohorts in the overall sample). In the
simulation, we assume an island model with 100 islands, the number of populations set at the number of groups in the specific
contrasts, and the number of samples in contrast groups set to the
number of individuals in study groups. The stepwise mutation
model was selected and 100 000 loci were simulated in each
simulation.
The computer software POWSIM version 4.1 (Ryman and Palm
2006) was used to estimate the statistical power of sampling design used in the study. For the simulations, the empirical data
(allele number and frequency) was used to define the base population. However, POWSIM is limited to 50 alleles per locus. As
Gmo19 has 54 alleles, the frequencies of the three largest and the
three smallest alleles were combined in the base population before the simulation. This is justified because reducing the number
of alleles at Gmo19 will reduce the power of the locus to detect
genetic differentiation (e.g., Kalinowski 2002). The estimated
power of the design will thus be slightly underestimated. Ne was
set at 5000, and the number of generations simulated varied (0–9)
to generate populations with variable genetic differentiation (FST).
The generated populations were then compared using GENEPOP,
and statistical power was determined. It can be noted that the
conditions for simulation (Ne and generations) are not important
for the analysis, but only the resulting genetic differentiation.
Results
Nature of genetic variation
The amplification success rate varied among loci used in this
study. The loci were successfully amplified in 91%–96% of individuals for all loci, except for Gmo37, for which alleles could be
identified in 61% of individuals only. All loci were included in
further analysis of the data. The loci differed in the amount of
variation exhibited (Table 1).
Micro-Checker analysis of the genetic variation at the loci
showed homozygotic excess and possible presence of null alleles
at three loci (Gmo2, Gmo19, Gmo37). This is reflected in the difference in Hobs and Hexp in Table 1 as general excess of homozygotes for most allele size classes. Two loci had indications of
stuttering error (Gmo2 and Gmo19), but no indication of large
allele dropout was observed.
Spatial and temporal genetic variation and outlier
detection
Significant genetic difference was found between cod from
northern and southern Iceland (FST = 0.0016; Table 2). The overall
pattern was mostly due to Gmo34, an extreme outlier (far outside
the 99% limits of the FST null distribution; Fig. 2a). Another locus,
Gmo132, showed significant difference between the north and
south (Table 2), but the FST was not high enough for the locus to be
considered an outlier (Fig. 2a). When Gmo34 was removed from
the analysis, there was no overall significant population genetic
structure for the remaining loci between northern and southern
Iceland (overall FST = 0.0002). Adjusting genotype frequencies for
Gmo2, Gmo19, and Gmo37 (accounting for possible null alleles)
had very little effect on the estimated difference between cod
from the north and south (overall FST = 0.0017 when Gmo34 was
included, but FST = 0.0003 when Gmo34 was excluded).
Considering depth, a significant difference was found among
cod at 50 m depth classes in southern Iceland. Genic difference
was significant at five loci, and genotypic difference was signifiPublished by NRC Research Press
32
Eiríksson and Árnason
1155
Table 2. FST for each locus for three contrasts.
Contrast
Locus
FST between the
south and north
FST among 50 m
depth classes
in the south
FST among
cohorts
Gmo2
Gmo3
Gmo8
Gmo19
Gmo34
Gmo35
Gmo37
Gmo132
Tch11
Tch13
All
−0.0010
−0.0008
−0.0001
−0.0004
0.0250***, • • •
0.0004
−0.0000
0.0051**,•
−0.0003
−0.0001
0.0016***,• • •
0.0000
−0.0009
−0.0002
0.0015*
0.0171*, •
0.0024*,•
0.0019*
0.0030*, •
−0.0004
0.0012
0.0017**, • • •
0.0001
0.0012
0.0014
0.0008
0.0065
−0.0013
0.0005*, •
0.0035*,•
−0.0009
−0.0002
0.0007*
Fig. 2. Outlier detection for three contrasts. The observed FST for
the different loci plotted against Hobs (circles). The solid lines
represent the median of the FST null distribution, and the dashed
and dotted lines enclose the 95% and 99% limits of the distribution,
respectively. (a) Comparison between northern and southern
Iceland. (b) Comparison among depth groups at the south coast
of Iceland. (c) Comparison among cohorts.
Note: The significance of the exact G test for genotypic differentiation is
indicated with *, **, and *** at P values less than 0.05, 0.01, 0.001, respectively.
Similarly, the significance of the exact G test for genic differentiation is labelled
with • , • • , and • • • . In the first contrast, all the sampled fish are included: N = 845
(Nnorth = 340, Nsouth = 505). In the second contrast of depth classes, all the fish
sampled at the south coast of Iceland was included: Nsouth = 504. (Sample size for
different depth classes: N0–50 m = 34, N50–100 m = 320, N100–150 m = 75, N150–200 m =
21, N>200 m = 54; depth information was missing for one fish.) In the third
contrast, all sampled fish (where age information was not missing) were included: N = 835. (Sample size for different cohorts: N1996 = 67, N1996 = 135, N1996 =
206, N1996 = 216, N1996 = 150, N1996 = 61.)
cant at three loci (Table 2). None of the loci were found outside the
95% limits of the FST null distribution; however, Gmo34 also had a
considerably higher FST value compared with the other loci
(Fig. 2b). Removing Gmo34 from the analysis resulted in a smaller
but still significant difference among depth groups (FST = 0.0010*,••;
refer to Table 2 for a description of superscript asterisk(s) and bullet(s)).
In northern Iceland, cod was mostly sampled at shallow waters,
and no significant genetic difference was found at different depths.
Considering temporal variation, a significant difference was
also found among cohorts (Table 2). No locus was found outside
the 95% range of the FST null distribution for cohorts (Fig. 2c),
although again Gmo34 showed considerably higher FST value
compared with the other loci.
Statistical power analysis
POWSIM simulations generating groups of variable genetic difference showed that there was a fair chance (80%) of detecting a
significant difference at FST = 0.00034 and high probability (95%)
when FST = 0.00048 (Fig. 3).
Discussion
This study shows the same spatial genetic structure in Atlantic
cod around Iceland as reported in Pampoulie et al. (2006). However, the present study suggests that the genetic difference between Atlantic cod from the north and south coast of Iceland does
not reflect a population breeding structure. The observed genetic
difference was attributed to outlier behaviour of Gmo34 most
likely due to natural selection at Gmo34 locus or linked loci. When
removing this locus from the analysis, no difference was observed
between these geographic regions. In fact the Gmo34 locus has
shown similar deviation from FST null distribution in spatial comparisons in recent studies (Nielsen et al. 2006; Westgaard and Fevolden
2007; Pampoulie et al. 2011). Gmo34 was one of the loci used in
Pampoulie et al. (2006), but it was not tested whether its genetic
variation was likely to have been affected by natural selection. It
has been shown that Gmo34 is in linkage disequilibrium with the
Pan I locus (e.g., Westgaard and Fevolden 2007) and may thus
reflect the same pattern as has been described for that locus (e.g.,
Karlsson and Mork 2003; Pampoulie et al. 2006; Árnason et al.
Published by NRC Research Press
33
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Can. J. Fish. Aquat. Sci. Vol. 70, 2013
Proportion of significance (Fisher’s exact tests)
Fig. 3. Proportion of significance (Fisher's exact tests) against FST of simulated populations showing variable genetic differentiation.
2009). In the present study, we do not have data to resolve
whether the genetic variation at the Gmo34 locus is a contemporary phenomena or the observed frequencies at the locus is the
result of historical natural selection. Therefore, there is no basis
to use the locus for identification of a population breeding structure.
Further, the analysis of statistical power (after removing Gmo34)
showed that genetic differentiation as low as FST = 0.0003 can be
detected with a fair confidence (80% probability), and the probability of a difference higher than FST = 0.0005 is unlikely to go
unnoticed by the analytical tools applied here. Therefore, it can be
assumed that if there is a genetic difference between cod from
northern and southern Iceland due to breeding structure alone, it
is unlikely to be greater than FST = 0.0005. Even if such a difference
was observed, it is doubtful that such a negligible difference has
any biological meaning (Waples 1998), although it can not be
excluded (Knutsen et al. 2011). This result is in an agreement with
some earlier findings for cod around Iceland (e.g., Árnason et al.
1992, 2000; Árnason 2004), but may seem to be at variance with
studies reporting phenotypic variation in Atlantic cod around Iceland (Pálsson and Thorsteinsson 2003; Jónsdóttir et al. 2006;
McAdam et al. 2012). However, the observed phenotypic variation
can be environmentally induced, as noted in early fish research
(see e.g., Tåning 1944). Phenotypic plasticity may cause a group of
fish to develop variable phenotypic traits if exposed to different
environments (as in Gupta and Lewontin 1982). In fact a recent
study showed that Atlantic cod originating from the north and
south coast of Iceland, but reared in common experimental and
aquaculture facilities, showed no quantitative difference in
growth, proportion of maturation, or length (Kristjánsson 2013).
These findings suggest that the observed differences in these characteristics between northern and southern Iceland (Jónsdóttir
et al. 2006) can be environmentally induced. Also, experiments
have shown that the development of cod behaviour can be affected by environmental factors at early age (Braithwaite and
Salvanes 2005; Salvanes et al. 2007). Thus, it is obvious that phenotypic variation (including behaviour) can not be used, on its
own, as an indicator of population breeding structure.
Further, environmental heterogeneity in the waters around Iceland seems to favor variable phenotypes, most likely through
selective mortality at early stages. In cases where phenotypic variation is correlated with genotypic variation, this will affect allele
frequencies at loci, as may be the case with Gmo34. Water temperature may be an important environmental factor in this respect, as temperature varies between northern and southern
parts of Iceland (Jónsson and Valdimarsson 2005).
Although the present study does not support the view of separate cod populations around Iceland (as suggested in Pampoulie
et al. 2006), the variation at Gmo34 may suggest that different
groups of cod may occupy different geographical areas in the
waters around Iceland. Such groups or ecotypes can be defined as
different populations under the ecological paradigm (Waples and
Gaggiotti 2006). However, it is important to note that such patterns may persist although extensive gene flow may take place
between such groups, even to the extent that they can be considered to be in panmixia.
The present study shows the importance of considering the
effects of natural selection when analyzing population genetic
structure, as has been demonstrated by Nielsen et al. (2006). This
particularly applies to Atlantic cod, as recent studies have shown
that a high proportion of analyzed genetic loci show indications
that natural selection may have shaped their variation (e.g., Nielsen
et al. 2006; Moen et al. 2008; Pampoulie et al. 2011).
Genetic difference was observed when comparing samples
from different depths at the south coast of Iceland. Although the
overall genetic difference is very small (FST = 0.0017), 5 out of 10 loci
show significant difference with depth. The loci showing the highest
FST value was Gmo34 (FST = 0.0171). Gmo34 is not a significant
outlier when compared with the FST null distribution and can thus
not be regarded as being under natural selection with respect to
depth. However, knowing its relationship with Pan I (Westgaard
and Fevolden 2007), where a strong relationship with depth is also
found (Pampoulie et al. 2006; Árnason et al. 2009), it may be that
observed variation at Gmo34 is in fact due to this. Removing
Gmo34 from the analysis shows a smaller, but significant, difference between fish samples at different depths (FST = 0.0010*,••).
This variation may indicate that different populations of Atlantic
cod are at different depths and may be related to the behavioral
variation that has been described for Atlantic cod in annual migration patterns (Pálsson and Thorsteinsson 2003; Thorsteinsson
et al. 2012) and behaviour at spawning grounds (Grabowski et al.
2011). However, it must be pointed out that the grouping of samples into depth groups is not based on any known environmental
heterogeneity and may not properly reflect reproductively sepaPublished by NRC Research Press
34
Eiríksson and Árnason
rated groups, if present. It must also be pointed out that the
observed difference is very small, and it can be questioned if it is
biologically meaningful (Waples 1998).
Temporal changes in cod length at age on the spawning
grounds at the south coast of Iceland may suggest migration of
cod that have developed in a different environment (Jónsson
1996). Jónsson (1996) suggested that reduced length at age was an
indication of cod from the north coast was entering the spawning
ground at a particular time. However, this change may also be
caused by cod migrating from deeper waters to the shallow
spawning grounds, as studies using data storage tags have revealed (Pálsson and Thorsteinsson 2003).
The genetic variation among cohorts observed in the present
study showed the effects of genetic drift, as natural selection
seems not to have affected the allele frequencies at the loci with
respect to cohorts. However, the difference was very subtle, and
although a difference in age composition is known between the
north and south coast of Iceland (Jónsdóttir et al. 2006), such a
small genetic difference is not sufficient to generate an apparent
spatial structure, as discussed earlier. Further studies on genetic
drift can be used to estimate temporal effective population size,
Ne, in cod (Waples 1989).
Genotyping errors are of concern in all microsatellite studies,
particularly the presence of null alleles. Null alleles result from
PCR amplification failure of a particular allele that may be due to
primer site sequence mutation. Inspection of genotyping error
using Micro-Checker showed that three of the loci used in the
present study may contain null alleles, indicated by general homozygotic excess. This is a concern and suggests that the genetic
analysis may not represent the true allele and genotype frequencies. It has been shown that in populations where null alleles are
present and where significant population differentiation is found,
FST and genetic distance may be overestimated (Chapuis and
Estoup 2007). However, FST was shown to be unbiased in the absence of population structure (Chapuis and Estoup 2007).
Although the present study was focused on Atlantic cod in Icelandic waters, the results have implications for the study of cod in
general. It suggests that the reported genetic structure (Pampoulie
et al. 2006) may in fact be due to natural selection acting on
Gmo34. The same may be true in some other studies where population genetic structure has been proposed, as Gmo34 has been
commonly used in the study of population genetics in Atlantic cod
(for overview Reiss et al. 2009) and revision may be needed. This is
important because footprints of natural selection seem to be common in the cod genome (e.g., Nielsen et al. 2006; Moen et al. 2008).
Acknowledgements
We thank Snæbjörn Pálsson for useful discussions, particularly
on lab work and data analysis. We also thank Juha-Pekka Vähä
and Paula Lehtonen for advice on lab work. We thank Guðrún
Marteinsdóttir and Jónas P. Jónasson for stimulating discussions and
valuable inputs during the work. We thank Kristján Kristinsson at
the Icelandic Marine Reseach Institute for cooperation with sampling. We also thank staff members of the population genetics lab
at the University of Iceland for various lab assistance and moral
support. We thank two anonymous reviewers for their comments
on the paper. This project was supported by the Icelandic research
fund and the Icelandic research fund for graduate students of The
Icelandic Centre for Research.
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Published by NRC Research Press
36
Paper II
High levels of gene flow and temporal genetic variation in Atlantic cod,
Gadus morhua in Icelandic waters — inferred by mitochondrial DNA
sequence variation
Guðni Magnús Eiríksson and Einar Árnason
Unpublished manuscript
© Guðni Magnús Eiríksson and Einar Árnason
Guðni Magnús Eiríksson was in charge of genetic sampling and organization, on behalve
of the Population Genetics laboratory at the University of Iceland, in cooperation with
the Marine Research institute of Iceland. Guðni did all the laboratory work: DNA isolation, mtDNA amplification, purification and prepared the samples for electrophoresis
on an ABI-3100 automatic sequencer. He also did the data analysis and interpretation.
Guðni was in charge of writing the manuscript. Professor Einar Árnason actively participated in all steps of the work. Einar took part in the experimental design, supervised
the molecular work, took part in data analysis, interpretation of the data and participated
in writing the manuscript.
37
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38
1 Abstract
Genetic variation in spawning Atlantic cod, Gadus morhua, around Iceland was examined using mitochondrial DNA sequence information. A 328 base pair fragment of the cytochrome b gene in 2656 individuals
sampled all around Iceland was examined. Variation was found at 70 sites defining 128 haplotypes. The
observed polymorphism was mostly synonymous, suggesting weak or no natural selection acting on it.
Demographic analysis of the genetic variation suggest sudden expansion of the Atlantic cod population
estimated to have taken place 96 Kyr ago. Rapid changes in allele frequency observed in the present study,
particularly in cod from NE-Iceland, suggest that the mtDNA sequence variation is an ideal marker for
detecting recent population divergence. Temporal genetic analysis showed heterogenity in haplotype frequency change between NE- and SW-Iceland. This is in accordance with earlier findings that have shown
temporal and spatial variation in the contribution of different spawning grounds to each cohort. The variation is likely to be attributed to strength of the warm Atlantic ocean current, but temporal variation in
spawning success in different spawning grounds may also be important. Our analysis does not suggest the
Atlantic cod around Iceland is divided into distinct historical populations as has been suggested in earlier
studies. On the contrary it supports the view of high levels of gene flow in Atlantic cod around Iceland.
We suggest that the mixing of cod at times when ocean currents allow high levels of embryo/larvae drift,
prevents the evolution of distinct populations.
Keywords: Atlantic cod, mtDNA, cytb, natural selection, temporal genetic variation
39
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2 Introduction
2.1 General biology and phenotypic variation
Atlantic cod is among the most valuable commercial fish species in the North Atlantic and has been very
important for the fisheries around Iceland. It has a wide distribution and is found in a variety of habitats,
from shallow waters close to shore to deeper waters off the continental shelf (Jónsson, 1992).
The main spawning grounds for Atlantic cod around Iceland are along the South coast (Jónsson, 1992).
Embryos and fish larvae are known to drift with water currents from the main spawning grounds to the
West, North and East Iceland. Some years embryos and larvae may drift to the East coast of Greenland
(Astthorsson et al., 1994; Jónsson and Valdimarsson, 2005). The extent of embryo and larval drift depends
on the strength of the warm North Atlantic water current (Jónsson and Valdimarsson, 2005). In addition to
the main spawning grounds cod is known to spawn in different areas all around the country with spatial and
temporal variation in their contribution to each cohort (Marteinsdottir et al., 2000; Begg and Marteinsdottir,
2000; Jónsdóttir et al., 2007). Thus, in different locations around Iceland each cohort may be of mixed
origin, to a variable extent, depending on the strength of the North Atlantic water current and spatial and
temporal variation in spawning success. Indeed, studies have shown cohorts to be of mixed origin, and
temporal variation in the contribution of different spawning grounds to the cohorts (e.g. Jónsdóttir et al.,
2007; Thorisson et al., 2011).
Although the fish may be of mixed origin variable phenotypic differences have been observed in cod
from different locations. Difference in length at age, reflecting difference in growth rate, between cod from
the North coast of Iceland to that of the South is well known (Jónsson, 1992). Growth related morphological
variation, including otolith shape, has been described for cod around Iceland and has been used to argue for
separate populations of cod (e.g. Pétursdóttir et al., 2006; Jónsdóttir et al., 2006).
In addition behavioral variation has also been observed. Different behavioral types have been described,
identified as frontal and coastal behavioural types (Pálsson and Thorsteinsson, 2003; Thorsteinsson et al.,
2012). This migration pattern has been linked to genetic variation at the Pan I locus (Pampoulie et al.,
2008). However, behavioral traits, like any other phenotypic trait, can be affected by the environment that
the organism is exposed to (phenotypic plasticity). In fact, experiments have shown that the development
of cod behavior can be affected by environmental factors at early age (Braithwaite and Salvanes, 2005;
Salvanes et al., 2007). The observed behavioral variation can thus be environmentally induced. A group
of fish, that may share the same place of origin, may develop variable behaviours if exposed to different
environments during their development (c.f. Gupta and Lewontin, 1982).
The phenotypic variation observed in cod may thus be environmentally introduced, that is cod of different origin may develop similar phenotypic traits if exposed to the same/similar environmental conditions.
Environmental factors such as temperature or food availability or interaction of environmental factors may
be important in this respect. Such phenotypically distinct groups can be defined as ecological populations
although they may not reflect population breeding structure (Waples and Gaggiotti, 2006). Thus, such
groups would not be considered different populations under population genetic or evolutionary paradigm
(Waples and Gaggiotti, 2006).
2.2 Population genetic studies
Intense research effort, for decades, has been directed to the analysis of cod population genetic structure,
not least to resolve if the observed phenotypic variation correlates with genetic variation. No commercial
fish species has been the subject to as many population genetic studies (for review see Reiss et al., 2009).
40
However, research findings are to some extent conflicting. Different molecular genetic markers have shown
different patterns, some markers showing great population genetic structure but others showing little or
none (Reiss et al., 2009). It is important to understand what may cause the differences among the different
markers in order to better understand the population genetic structure of Atlantic cod.
Of particular concern is the importance of natural selection in the shaping of genetic variation in Atlantic
cod. Recent studies have shown that footprints of natural selection are common in the cod genome (Moen
et al., 2008) and fine scale population breeding structure may in some cases be inflated due to selection
(Nielsen et al., 2006; Eiríksson and Árnason, 2013). This may cause environmental heterogeneity to be
reflected in the genetic variation, rather than restrictions in gene flow (Williams et al., 1973).
A spatial genetic structure has been repeatedly reported for Atlantic cod around Iceland. Jónsdóttir et al.
(1999) showed an interesting variation of the Pan I and hemoglobin loci among cod at the South coast of
Iceland, a pattern interpreted as a small scale population genetic structure. However, variation at the Pan I
locus and the hemoglobin has been shown to be be affected by natural selection (Karlsson and Mork, 2003;
Pogson and Mesa, 2004; Andersen et al., 2008; Árnason et al., 2009). Pampoulie et al. (2006) described
population genetic structure in cod separating cod from NE- from SW-Iceland using both the Pan I locus
and microsatellite markers. The population differentiation was small when based on the presumed neutral
microsatellite markers (FST = 0.003). A recent study indicates that the observed difference between cod
from North and South Iceland may in fact be caused by natural selection connected to one particular locus
(Gmo34) (Eiríksson and Árnason, 2013). However, in the present study this reported population structure
will be reviewed and examined further.
The use of direct mtDNA sequencing has been important in revealing the population genetic structure
in Atlantic cod. The observed genetic variation at mtDNA is mostly at synonymous sites, suggesting weak
or no selection (e.g. Árnason, 2004). The effective population size (Ne ) of the mtDNA genome is a quarter
of that of nuclear markers making the rate of genetic drift faster (as genetic drift rate is inversely related
to Ne ) (Hartl and Clark, 2006). Thus, recently established population structure should become detectable
earlier for mtDNA markers than for nuclear markers, on average. However, the mtDNA genome is a single
locus and different loci may reflect different history within a population (Hartl and Clark, 2006).
Although an obvious weakness of using mtDNA sequence variation is that it is a single locus, studies
have reported important findings that differ from patterns reveald from loci that are likely to be under
selection. Population genetic studies in Atlantic cod, using mtDNA sequence variation, have indicated
limited fine scale population genetic structure and high levels of gene flow among geographic regions (Carr
and Marshall, 1991; Árnason et al., 1992; Carr et al., 1995; Árnason and Pálsson, 1996; Árnason et al.,
1998, 2000). Summarizing results from many studies of cytochrome b sequence variation across the North
Atlantic Árnason (2004) showed no difference among localities within countries, but a trans-Atlantic cline
in the common haplotype frequencies. The regularity of the cline indicated considerable gene flow among
different regions.
2.3 The present study
In the present study mtDNA sequence variation will be used for the analysis of population genetic structure
of spawning Atlantic cod around Iceland. Comparison will be made among different regions around the
country and a comparison made between NE- and SW-Iceland, were population differentiation has been
suggested (Pampoulie et al., 2006; Jónsdóttir et al., 2006). Temporal genetic variation will be evaluated by
comparison between sampling years and in particular by comparison among different cohorts.
41
Paper II
Table 1: Sample size in different areas around Iceland (Figure 1) and different depth groups. Depth information was missing (DIM) for 160 fish .
Area 0-50m 50-100m 100-150m 150-200m >200m DIM
1
39
189
22
0
0
50
2
31
279
34
0
0
10
3
53
31
0
0
0
0
4
62
120
31
19
0
0
5
173
0
0
0
0
35
7
12
40
26
0
0
0
8
46
319
63
0
26
23
9
86
534
145
53
63
42
Total:
502
1512
321
72
89
160
Total
300
354
84
232
208
78
477
923
2656
3 Materials and methods
3.1 Fish samples
Atlantic cod was sampled around Iceland in the annual spring survey of the Icelandic Marine Research
Institute (MRI) in two consecutive years, 2005 and 2006 (Figure 1). The surveys mainly target spawning
cod at shallow waters (< 200m), at known breeding grounds. Samples are also taken at deeper waters in
South of Iceland. Gill tissue samples were preserved in 96% ethanol. In total 2656 fish were sampled
and successfully sequenced. Sample size varied among the different sampling areas and the depth range at
which the fish were caught (Table 1).
The waters around Iceland were divided into nine different areas (Figure 1), the same divisions as used
in recent studies (e.g. Jónsdóttir et al., 2006; Pampoulie et al., 2006). All areas except number 6 were
sampled.
Age of fish were determined from otoliths at the Icelandic Marine Research Institute.
3.2 Molecular methods
Three different fragments of the cod mitochondrial genome were amplified and sequenced. The fragments
are parts of cox1, cyt b and the noncoding TP-spacer and upstream, corresponding to base pairs 5787-6017,
14310-14711 and 15556-15616 in the cod complete mitochondrial genome, respectively (Johansen and
Bakke, 1996, GenBank accession number: X99772.1). All three fragments of the mtDNA (992 base pairs)
was successfully sequenced in 875 individuals and used for demographic analysis. The cytochrome b was
sequenced for a larger sample, 2656 individuals in total. Due to poor quality for parts of the cytochrome
b sequences (base pairs 14662-14676) the base pairs 14352-14661 and 14677-14694 (328 base pairs) were
used for spatial and temporal population genetic analysis.
3.3 Data analysis
Base calling, data assembly and sequence alignment was carried out in Phred (Ewing et al., 1998) and
Phrap (Gordon et al., 1998). Sequence data was inspected using Consed (Gordon et al., 1998) and Seaview
(Galtier et al., 1996). Trace files for all observed haplotypes were inspected manually, the sequence integrity
confirmed and sequences of low quality rejected.
Different neutrality tests, Tajima’s D test, Chakraborty’s test and Fu’s FS test, as implemented in Arlequin 3.5.1.2 (Excoffier and Lischer, 2010) were applied. The neutrality tests were applied to the total
42
Figure 1: Atlantic cod sampling locations around Iceland. The numbers refer to divisions used in earlier
studies (e.g. Jónsdóttir et al., 2006; Pampoulie et al., 2006).
43
Paper II
sample of cytochrome b (N = 2656) and the combined fragment of cytochrome b, spacer and cytochrome
oxidase I (N = 875).
Genetic differentiation among various groups was analyzed using analysis of molecular variation (AMOVA)
(Excoffier et al., 1992), pairwise comparison of ΦST and FST between groups and pairwise comparison
using exact test, as implemented in Arlequin. Spatial genetic variation was examined both using genetic
distance based on Tamura and Nei (1993) (Φ) and conventional F -statistics (based on frequency of different
haplotypes only) but temporal genetic variation was based on conventional F -statistics only (as temporal
difference, over a short period of time, will be reflected as change in frequency rather than as a change in
genetic distance among haplotypes). The significance of fixation indices in pairwise group comparison was
tested using 10,000 permutations (Excoffier et al., 1992).
Spatial genetic comparison was made among fish sampled in the defined areas (Figure 1). For further
spatial comparison fish sampled in NE-Iceland (areas 3, 4, 5 and 7; N = 602) were compared to fish
sampled SW-Iceland (areas 1, 2, 8 and 9; N = 2054; Figure 1) as Pampoulie et al. (2006) suggest that there
is a barrier to gene flow among the two components. However, fish sampled in area 7 and the sampled fish
in area 8 North of 64◦ N are at the margin of the two suggested areas. Therefore, similar comparisons were
first made by grouping all fish from area 8 and 7 with SW-Iceland (moving the potential boundary between
the areas further North) and second by leaving out the marginal samples (samples from area 7 and area 8
North of 64◦ N).
Comparison was also made among fish sampled at different depth. Depth at which fish were sampled
were available for most of the sampled fish (N = 2496, Table 1). Fish were grouped into five 50m depth
groups (Table 1). Comparison was made including all sampling areas and also for sampling areas at the
South coast of Iceland separately (areas 8 and 9, Table 1). A comparison was also made grouping into
shallow (< 200m) and deep (> 200m) for cod sampled at the South coast of Iceland.
Bonferroni correction (Sokal and Rohlf, 1995) was used to account for repeated comparisons in order
to further examine the significance of observed differences.
Analysis of pairwise nucleotide mismatch (mismatch distribution Rogers and Harpending, 1992) was
carried out for the overall sample and 10,000 simulated mismatch distributions used to test if the observed distribution differed to the expectations of a population expansion model (Schneider and Excoffier,
1999). The mismatch analysis was implemented in Arlequin. If a mismatch distribution does not deviate
from a model predicting population sudden expansion τ can be used to estimate the time from expansion:
t = τ /2u, where t is time in years, τ is mode of the mismatch distribution and u is mutation rate in substitutions/year (Rogers and Harpending, 1992). A substitution rate µ̂ = 2 × 10−8 /site/year was used (Avise,
2004). As this is the mutation rate estimated for coding region of the animal mtDNA, the mutation rate for
the fragment used here may be slightly higher, as it includes part of the noncoding spacer and tRNA.
4 Results
4.1 Genetic variation
Variation was found at 70 sites in the cytochrome b fragment (328 base pairs) defining 128 haplotypes
(Appendix I: Tables 1.4–1.7). Number of sites had more than one substitution and in total 83 substitutions
were observed, 69 transitions and 14 transversions. Most of the observed polymorphism was synonymous.
However, 14 nonsynonymous mutations were found among 15 individuals (Appendix I: Table 1.8). Four
different mutations were detected in one codon (at sites 14587–14589, site 82 of the protein), three of
which lead to amino acid substitution. Thus, at this site in the cytochrome b four different amino acids were
44
Figure 2: Atlantic cod cyt b haplotype frequencies in North East (NE) and South West (SW) Iceland.
Different haplotypes are represented with different shades. The most common haplotype is indicated with
black color and the less common once with gradually decreasing intensity of gray. White represents a pool
of rare haplotypes.
observed (M,V, I and T). These amino acids all have hydrophobic side chains apart from threonine (T),
which is uncharged. One site had three different amino acids: E, G and D (E and D are negatively charged
but glycine (G), the smallest amino acid, has no side chain) at sites 14521–14523, site 60 of the protein. One
haplotype was found at high frequency (> 40%) and another four haplotypes were found at relatively high
frequency (three at > 5% and one at 4.7%). Other haplotypes were found at lower frequencies and more than
half (69 of the 128) were singletons in the sample. The overall nucleotide diversity was π̂ = 0.0042±0.0029
and haplotype diversity was ĥ = 0.7608 ± 0.0063.
Tests of selective neutrality showed deviation from neutral expectations (P value for all neutrality tests
applied were < 1 × 10−5). Both Chakraborty’s and Fu’s neutrality tests showed that the number of observed
haplotypes exceeded expected number more than ten times. The high number of observed haplotypes
resulted in a large deviation between FExp and FObs. in the Ewens-Watterson test and Tajima’s D = −2.17.
4.2 Spatial genetic variation
Spatial genetic variation analysis, using AMOVA, showed no overall differences among different areas
along the coast of Iceland. Pairwise comparison among different areas around Iceland showed a single significant difference, between area 4 and 8 (FST = 0.0035, P = 0.0450). However, this is not different from
what might be expected by chance alone and after Bonferroni correction, no comparison was statistically
significant.
The frequencies of different haplotypes were similar between NE-Iceland and SW-Iceland (Figure 2)
and no significant difference was detected when using conventional F -statistics or when using Tamura and
Nei’s genetic distance. Including fish from area 7 together with the SW-Iceland group did not change the
result. Neither did excluding the samples from area 7 and area 8 North of latitude 64◦ N (samples that were
at the margin of the two areas).
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Paper II
The sampled cod was also divided by depth at which it had been caught but no overall difference was
observed.
A pairwise difference was observed between cod at 50–100m and 100–150m at the South coast of
Iceland, when using conventional FST statistics (FST = 0.0046, P = 0.045). However, when correcting for
repeated sampling this is not different from what would be expected from chance alone.
No significant difference was observed between fish at deep waters (< 200m) compared to fish at shallow
waters (> 200m) at the South coast of Iceland (area 8 and 9). For both AMOVA using conventional F statistics and Tamura and Nei pairwise difference, the genetic differentiation (FST ) was negative, interpreted
as nill.
4.3 Temporal genetic variation
Comparison using AMOVA did not show significant differences between sampling years.
Using AMOVA for comparison among the most abundant cohorts (1994–2002) did not show overall
significant differences (FST [8,2520] = 0.0001N S , P = 0.409). However, a pairwise FST comparison showed
that the 1995 cohort differed significantly from four of the other eight cohorts and that overall it deviated
from most of the other cohorts (Figure 3). However, correcting for repeated testing showed that this pairwise
difference was not significant. The nucleotide diversity, π̂, was low for the 1995 cohort compared to other
cohorts and the haplotype diversity the lowest among the cohorts (Table 2). Other cohorts did not differ
significantly amongst each other (Figure 3).
Figure 3: Atlantic cod cyt b haplotype frequencies among cohorts. Different haplotypes are represented
with different shades. The most common haplotype is indicated with black color and the less common
once with gradually decreasing intensity of gray. White represents a pool of rare haplotypes.
Using AMOVA to compare cohorts separately for NE-Iceland showed overall significant difference
(FST [8,559] = 0.0102, P = 0.0164, Figure 4a). Pairwise FST comparison revealed that in particular the 1998
cohort deviated from other cohorts in NE-Iceland, being significantly different from four of the other eight
cohorts.
Using AMOVA to compare cohorts separately for SW-Iceland showed no overall difference (FST [8,1952] =
−0.0004, P = 0.613, Figure 4b). Only two significant pairwise differences were observed (1994:1995,
P = 0.027 and 1997:2001, P = 0.045). Taking repeated testing into account, this difference was not
significant.
Comparison of the deviations of the most common haplotype relative frequency from its overall mean
frequency in NE-Iceland and SW-Iceland show that there is a significantly greater deviations in the NE-
46
Table 2: Molecular diversity among different Atlantic cod cohorts. π̂ is nucleotide diversity; ĥ is haplotype
diversity; SD is standard deviation.
Cohort π̂ ± SD(×100)
1994
0.466± 0.315
1995
0.397± 0.277
1996
0.474± 0.315
1997
0.430± 0.292
1998
0.395± 0.274
1999
0.417± 0.285
2000
0.424± 0.289
2001
0.442± 0.301
2002
0.447± 0.306
ĥ ± SD
0.799 ± 0.0306
0.709 ± 0.0424
0.785 ± 0.0244
0.769 ± 0.0155
0.739 ± 0.0147
0.766 ± 0.0125
0.766 ± 0.0144
0.774 ± 0.0401
0.806 ± 0.0406
Iceland (mean: 8.9%) than in SW-Iceland (mean: 4.3%) (Pairwise t-test: t = 2.64, df = 16, P = 0.018;
figure 4c).
Heterogenity tests were used for further comparison of different cohorts between regions around Iceland. A comparison of haplotype frequencies between cohorts showed significant differences between the
NE- and SW-Iceland (Figure 4). Comparing the frequency of the two most common haplotypes and the
pooled frequency of other haplotypes showed that the 1998 cohort was significantly different between NEand SW-Iceland (G = 11.56, df = 2, P < 0.01) and that there was an overall difference in haplotype frequencies between the cohorts in NE- and SW-Iceland (G = 27.60, df = 16, P < 0.05). Using only the most
common haplotype and the others pooled showed that the 1998 and 1999 cohorts were significantly different between NE- and SW-Iceland (1998: G = 7.86, df = 1, P < 0.01; 1999: G = 3.56, df = 1, P < 0.05)
and that there was also an overall difference in haplotype frequencies between the cohorts in NE- and
SW-Iceland (G = 17.46, df = 8, P < 0.05).
4.4 Historical demograpic analysis
The mismatch distribution for the 328 bp cytochrome b fragment did not deviate from prediction of a population sudden expansion model (PSSD = 0.067) (Rogers and Harpending, 1992). The mismatch distribution
of the combined 992 bp mitochondrial DNA fragment did not deviate from the prediction of a population
sudden expansion model (PSSD = 0.885, Figure 5).
The distribution of pairwise mismatches is indicative of sudden population expansion event estimated
to have taken place 96 Kyr ago (CI: 37-142 Kyr).
5 Discussion
The observed polymorphism in the present study is primarily found at synonymous sites, therefore it is not
likely to be affected by natural selection. However, neutrality tests indicate that natural selection may have
shaped the observed polymorphism. In particular the high number of rare haplotypes generate deviation
from neutral expectations in these tests. Such deviation may also be caused by demographic effects of
population sudden expansion. A mismatch analysis suggests that this may in fact be the case, that a sudden
expansion of the Atlantic cod population may have occurred around 96 Kyr ago. If this estimated timing
of expansion is correct, it may suggest that expansion of the cod population may by correlated with the
earths cooling climate that initiated ca. 100 Kyr ago (Andersen et al., 2004). The estimate of the timing
47
Paper II
Figure 4: Atlantic cod cytochrome b haplotype frequencies among cohorts. Different haplotypes are
represented with different shades. The most common haplotype is indicated with black color and the less
common one’s with gradually decreasing intensity of gray. White represents a pool of rare haplotypes. (a)
NE-Iceland, (b) SW-Iceland. (c) The deviation of the most common haplotype relative frequency from its
overall mean frequency in NE-Iceland (in blue) and SW-Iceland (in red).
48
Figure 5: Pairwise nucleotide mismatch distribution for a 992 base pair mtDNA fragment in Atlantic cod
and prediction of a sudden expansion model expressed with line and dots.
of population sudden expansion is based on the mtDNA mutation rate (Avise, 2004). Recent studies have
suggested that mutation rate may be much higher than previously estimated (Burridge et al., 2008). If that
is the case, the timing of population expansion may be different. Also, Árnason and Halldórsdóttir (2014)
suggest that time–scales may be mush faster in high fecundity organisms like Atlatic cod. However, it
is likely that population sudden expansion may explain the deviation from neutral expectations, although
natural selection cannot be ruled out (e.g. see Bazin et al., 2006).
Our analysis suggests that Atlantic cod around Iceland is not divided into distinct historical populations
as has been suggested in earlier studies (e.g. Jamieson and Jónsson, 1971; Jónsdóttir et al., 1999; Pampoulie
et al., 2006; Jónsdóttir et al., 2006). The variation among cohorts, in NE-Iceland in particular, shows
how rapidly haplotype frequencies can change, implying small effective population size (Ne ). Such rapid
frequency changes make the observed mtDNA sequence variation useful for detecting recent population
divergence. However, no such population structure is observed which supports the view of high levels of
gene flow in Atlantic cod around Iceland (Árnason, 2004; Eiríksson and Árnason, 2013).
Although high levels of gene flow is evident from the present study it is interesting, and may at first
seem contradicting, to find genetic differences within cohorts between NE- and SW-Iceland. However, this
may reflect temporal variation in the strength of the North Atlantic water current (Jónsson and Valdimarsson, 2005) and/or temporal variation in spawning success at different spawning grounds. Given the large
allele frequency changes one would expect large genetic difference to accumulate among different localities around Iceland in the absence of gene flow. For example if changes like those observed in 1998 would
persist over prolonged period of time large differences would accumulate. However, the changes seem to
be random with the same allele increasing or decreasing in frequency and therefore any such structure is
expected to be temporally unstable and to break down easily. Overall difference between the areas would
most likely have established already, given the rapid changes in allele frequencies observed in the present
study (particularly in NE-Iceland). It is thus possible that the reported difference between NE-Iceland and
SW-Iceland (c.f. Pampoulie et al., 2006) does reflect a temporally unstable phenomena rather than histor-
49
Paper II
ically stable population genetic structure. The present study suggests that high levels of gene flow to be
more appropriate description of Atlantic cod breeding structure around Iceland.
Natal homing has been suggested for cod to variable extent in different regions (Robichaud and Rose,
2004). The results of the present study suggests that homing is not likely to apply to Atlantic cod around
Iceland. However, Atlantic cod is known to be philopatric, particularly at juvenile stage, larger/older fish being more likely to undertake long distance migrations (Jónsson, 1992, 1996). This may result in phenotypic
variation due to phenotypic plasticity in cod developing in heterogenous environment. Different groups
of Atlantic cod may develop distinct phenotypes due to exposure to different environmental conditions.
However, high levels of gene flow, even panmixia, may exist between phenotypically distinct groups. Such
phenotypic variation can be used to define ecological populations (Waples and Gaggiotti, 2006). However,
they would not be considered separate populations under a population genetic or evolutionary paradigm as
limited genetic differentiation will establish due to high levels of gene flow (Waples and Gaggiotti, 2006).
The present study supports earlier findings on temporal and spatial variation in the contribution of
different spawning grounds to each cohort (e.g. Marteinsdottir et al., 2000; Begg and Marteinsdottir, 2000,
2003; Jónsdóttir et al., 2006, 2007; Thorisson et al., 2011), reflected in the observed temporal genetic
variation. The variation is likely to be attributed to strength of the warm Atlantic ocean current (Jónsson and
Valdimarsson, 2005), but temporal variation in spawning success in different spawning grounds may also be
important. We suggest that mixing of cod at times when ocean currents allow high levels of embryo/larvae
drift, and thus high levels of gene flow, prevents the evolution of genetically distinct populations.
6 Acknowledgement
We would like to thank Dr Snæbjörn Pálsson for useful discussions particularly on lab work and data analysis. We thank Dr Guðrún Marteinsdóttir and Jónas P. Jónasson for stimulating discussions and valuable
inputs during the work. We thank Kristján Kristinsson at the Icelandic Marine Reseach Institute for cooperation in sampling. We also like to thank staff members of the population genetics lab in the University of
Iceland for various lab assistance and moral support. This project was supported by The Icelandic research
fund and the Icelandic research fund for graduate students of The Icelandic Centre for Research.
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53
Paper III
Gene flow across the N-Atlantic and sex-biased dispersal inferred from
mtDNA sequence variation in saithe, Pollachius virens
Guðni Magnús Eiríksson and Einar Árnason, 2015
Environmental Biology of Fishes, 98: 67–79.
doi:10.1007/s10641-014-0237-8
© Environmental Biology of Fishes
Guðni Magnús Eiríksson actively particiated in assembling samples from different parts
of the world, took part in sampling trips around Iceland in 2006 and around Faroe Islands
the same year. Guðni did all the molecular laboratory work: DNA isolation, mtDNA
amplification, purification and prepared the samples for electrophoresis on an ABI-3100
automatic sequencer. Guðni was also in charge of data analysis and interpretation.
Guðni was in charge of writing the manuscript, corresponded to the comments of
reviewers and finalized the article. Professor Einar Árnason actively participated in all
steps of the work. Einar took part in the experimental design, supervised the molecular
work, took part in data analysis, interpretation of the data and participated in writing the
manuscript and finalizing the article.
55
Paper III
56
Environ Biol Fish (2015) 98:67–79
DOI 10.1007/s10641-014-0237-8
Gene flow across the N-Atlantic and sex-biased dispersal
inferred from mtDNA sequence variation in saithe,
Pollachius virens
Guni Magnús Eirı́ksson · Einar Árnason
Received: 12 May 2013 / Accepted: 20 January 2014 / Published online: 19 March 2014
© Springer Science+Business Media Dordrecht 2014
Abstract Genetic variation in saithe Pollachius virens
was examined using a 460 base pair fragment of the
cytochrome c oxidase subunit I mitochondrial gene in
1163 individuals sampled in Canada, Iceland, Faroe
Islands and Norway. In all, 43 segregating sites were
observed, almost all synonymous, defining 51 haplotypes. High frequency polymorphism was present
at the analyzed fragment making it suitable for population genetic studies. The results showed limited
trans Atlantic genetic structure indicating high levels of gene flow. However, a spatial genetic structure
was observed when considering the sexes separately.
This may suggest sex-biased migration pattern. The
data indicate that females may be more philopatric
and males more migratory. Such behaviour has rarely
been described for marine fish and is worth further
research. The observed genetic variation also indicates
that saithe has undergone sudden population expansion, reflected in high number of singletons and a
shallow genealogy.
Keywords mtDNA · COI · Gene flow · Sex-biased
dispersal · Demography
G. M. Eirı́ksson () · E. Árnason
Institute of Life and Environmental Sciences,
University of Iceland, Sturlugata 7, 101 Reykjavı́k, Iceland
e-mail: [email protected]
Introduction
The life history of many marine organisms is characterized by high fecundity, planktonic eggs and larvae
and high dispersal potential with oceanic currents
(Hedgecock 1994; Hedgecock et al. 2007; Lee and
Boulding 2009). Genetic differentiation among populations, FST , has been shown to be negatively correlated to estimated dispersal capability (Waples 1987)
although recent study showed that this may not always
be the case (Weersing and Toonen 2009). Studies have
shown low population differentiation among marine
fish species compared to freshwater species. This pattern might be explained by lack of physical barriers
in the marine environment, migration of adult fish as
well as passive dispersal of eggs and larvae with ocean
currents (Ward et al. 1994).
It is also clear that it will take time for genetic
difference to become established between isolated
groups. Separate populations may thus be similar due
to insufficient time for divergence. This is particularly true for large populations (large Ne ) as rate of
genetic drift is inversely related to effective population
size, and thus more time will be needed for genetic
difference to become established in large populations
(e.g. Kimura 1984).
Many factors may contribute to the generation of
population genetic structure, even in the face of high
gene flow. For example natal homing has been suggested for Atlantic cod, Gadus morhua, to variable extent
in different regions (Robichaud and Rose 2004). Such
57
Paper III
68
behavioural patterns may contribute to geographic
population genetic structure although larvae may drift
long distances with water currents. Genetic variation may also reflect different migration patterns
between sexes. Such sexual variation is common for
vertebrate species (Prugnolle and de Meeus 2002;
Petit and Excoffier 2009), although few cases have
been reported for marine fish (but see Schrey and
Heist 2003; Shaw et al. 2004). If females are more
philopatric and males more migratorial, mtDNA variation is expected to show geographic population structure in the females, but less so for males. Different
migration patterns between sexes may also be revealed
by comparison between mitochondrial and nuclear
loci (Prugnolle and de Meeus 2002).
A study of population genetic structure of organisms is important for basic understanding of relationships among different groups, their origin and gene
flow. Stock structure is fundamental information for
fisheries management and knowledge of the dynamics
of the resource in space and time is essential for reliable assessment and successful management (Cadrin
et al. 2004).
Genetic variation and genealogy of high latitude organism have been strongly influenced by climatic oscillations during the Pliocene and Pleistocene
(Bernatchez and Dodson 1991; Hewitt 1996; Avise
and Walker 1998; Wares and Cunningham 2001;
Pálsson et al. 2009). Climatic oscillations have caused
population bottlenecks for many species followed
by a period of expansion. Genetic variation is lost
through extinction of lineages during bottleneck periods generating shallow gene genealogies and reducing
effective population sizes, Ne . During periods of population expansion, however, the probability of lineage
extinction is reduced and genetic variation increases
in a population (Avise 2000). The expected pattern
of genetic variation as a result of repeated demographic changes is a shallow genealogy with many
rare types. This is indeed the observed pattern in many
marine fish species (e.g. Sigurgı́slason and Árnason
2003; Árnason 2004; Pálsson et al. 2009; Liu et al.
2010; Eirı́ksson and Árnason 2014). Thus, genetic
variation can be used to make inference about demographic history of a population. Nucleotide mismatch
analysis has been used extensively to make predictions of demographic history (Rogers and Harpending
1992) but more recent methods, for example the skyline method introduced by Pybus et al. (2000), have
58
Environ Biol Fish (2015) 98:67–79
improved our ability to reconstruct past demographic
history embedded in the genealogical relationship
among individuals of a population (Emerson et al.
2001; Drummond et al. 2005).
Saithe, Pollachius virens, is a commercially important demersal fish species distributed on both sides
of the North Atlantic. It is distributed from Barents
sea (Spitsbergen) in the North to the Bay of Biscay
(France) in the South, along the coast of Canada in the
West and Southwards to the coast of North Carolina. It
is common around Iceland but rare around Greenland
(Jónsson 1992).
Spawning time varies across the Atlantic ocean.
NE–Atlantic saithe spawn in February–March but
NW–Atlantic saithe are reported to have a wider
spawning season, from October to March (Jónsson
1992). Egg and larvae are pelagic and can thus be carried with ocean currents (Brickman et al. 2006). Saithe
is known to migrate long distances in search of food
but spawning takes place in shallow waters (Jónsson
1992).
Results of a long term tagging experiment
(Jakobsen and Olsen 1987) reported substantial saithe
migration from northern Norway to Iceland, frequently to an extent characterized as mass movements.
In a more recent study clear inshore/offshore movements during summer was observed for saithe around
Iceland and variation in migratory routes among tagging areas (Armannsson et al. 2007). Neilson et al.
(2006) suggested that saithe off the coast of Canada
forms three separate populations based on findings
in a tagging recapture study. The different findings
among the different studies suggest that migratory
behaviour may be associated with some life history
stage (e.g. adult fish may be more likely to migrate
as has been reported for Atlantic cod Gadus morhua,
Schopka et al. 2006) or that migratory behaviour may
vary among different saithe groups.
As described earlier saithe has high potential for
egg and larval dispersal and is known for long distance
migrations of adult fish. This is likely to contribute to
gene flow in saithe among different geographic localities. A recent microsatellite study on closely related
species, the pollack (Pollachius pollachius), along the
European coast revealed very limited genetic variation
among geographic localities (Charrier et al. 2006).
Given this potential for gene flow, little genetic divergence among saithe groups from different waters is
predicted. However, there are no reported cases of
Environ Biol Fish (2015) 98:67–79
migrations between NW-Atlantic and the NE-Atlantic,
and this might be reflected in the population genetic
structure of the species.
Limited work has been done on the population
genetics of saithe (Reiss et al. 2009). In the present
study we examine population genetic structure in
saithe in the North-Atlantic ocean. We compare differences between sexes and compare differences among
and within geographic localities for the waters around
Canada, Iceland, Faroe Islands and Norway.
Materials and methods
Fish samples
Fish samples were obtained in co-operation with various marine research institutions (Table 1, Fig. 1). In
all cases gill tissue samples were taken and preserved
in 96 % ethanol. No individual biological information was available for Norway samples. Sex, length
and weight of individual fish was recorded for the
other countries and age was determined by otolith
inspection for fish sampled in Iceland and Canada.
Molecular methods
DNA was isolated using a chelex method with modifications (Walsh et al. 1991). A 460 base pair
fragment was amplified from the 5 region of the
cytochrome c oxidase subunit I (COI) mitochondrial
gene using pvL6082-5CCTGCTGGAGGAGGTGATCC3 and pvH6580-5CCTGCTGGAGGAGGTGATCC3 . The fragment corresponds to sites 6082–6542
in the saithe complete mtDNA genome sequence
(Coucheron et al. 2011). A 19μl PCR reaction mix
contained 0.17mM dNTP, 0.11mg/μl BSA, 0.36pM
of each primer (L6082, H6580), and 0.05U Taq Polymerase buffered with 10× ThermoPol buffer (New
England BioLabs - NEB) together with DNA template
(median concentration 0.26ng/μl in reaction mix).
The DNA was denatured at 94 ◦ C for 5 minutes
followed by 35 cycles of denaturing for 1 minute,
annealing at 56 ◦ C for 30 seconds extension at 72 ◦ C
for 1 minute, followed by 7 minutes extension at
72 ◦ C. A 5 μl of the product was run for 10 min at
90 Volts on an 1.5 % agarose gel containing ethidium bromide. The gel was then inspected under UV
light for amplified DNA. A sample of 5μl succesfully
69
amplified DNA was purified using 2U Exonuclease I (NEB) and 1U Antarctic phosphatase (NEB).
Sequencing was carried out using ABI BigDye Terminator v3.1 using 0.1 pM of the pvH6580 primer and
5μl of the purified DNA in a total reaction volume of
15μl using standard protocol with modifications. The
reaction product was analyzed on ABI-3100 automatic
sequencer.
Data analysis
Base calling, data assembly and sequence alignment
were done using Phred and Phrap (Ewing et al. 1998)
and sequence data were viewed using Consed (Gordon
et al. 1998) and Seaview (Galtier et al. 1996). Trace
files for all observed haplotypes were inspected manually, sequence integrity confirmed and bad sequences
rejected based on Phred quality scores.
The software DNAsp (Librado and Rozas 2009)
and Network 4.5.1.6 (Bandelt et al. 1999) was used
to produce median joining network among haplotypes. The tree is based on the most parsimonious
relationship among the observed DNA sequences and
haplotype frequency used as additional criteria for
resolving homoplasy. MEGA 4 (Tamura et al. 2007)
was used for translation of nucleotide sequence to
amino acid sequence for the observed haplotypes and
for the counting of synonymous and nonsynonymous
sites.
Analysis of Molecular variation (AMOVA) was
carried out using Arlequin 3.5.1.2 (Excoffier and
Lischer 2010). Pairwise comparison was made among
countries for ST , analog to FST based on Weir
and Cockerham (1984) and gene flow M estimated
among countries. Selective neutrality was tested using
Chakraborty’s test, Fu’s FS tests, Ewens-Watterson
neutrality test and Tajimas’s D carried out in Arlequin.
Method of false discovery rate was used to adjust for
multiple testing (Benjamini and Hochberg 1995).
Geographic distance between sampling locations
was calculated as minimum distance between the sampling localities based on mean values for coordinates
for each group using the fields package (Furrer et al.
2011) in R (R development core team 2008). In reality
the distance separating saithe in the different sampling
locations is longer, since the effects of suitable environments and ocean currents is not taken into account.
Mantel tests (with 10,000 permutations) of relationship between genetic and geographical distance was
59
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Environ Biol Fish (2015) 98:67–79
Table 1 Sampling year and sampling sites for saithe in different countries
Country
Area
Month
Year
Lat.
Lon.
N
Canada
Canada
Iceland
Iceland
Iceland
Iceland
Iceland
Faroe Islands
Faroe Islands
Norway
Norway
Norway
Norway
Norway
Norway
Norway
Norway
Canada-E
Canada-W
Iceland-N
Iceland-SE
Iceland-SE
Iceland-SW
Iceland-SW
Faroe Bank
Faroe Plateau
Malangsgrunnen
Solbergfjorden
Vågsfjorden
Outer Lofoten
Vistenfjorden
Sklinnabanken
Frøyabanken
Romsdalsfjorden
July
July
April
October
March
October
March
March
March
September
August
August
August
September
September
October
October
2006
2006
2005
2004
2005
2004
2005
2006
2006
1992
1994
1993
1993
1993
1994
1994
1994
43.41
42.94
65.24
63.80
63.80
63.62
63.62
60.94
62.22
69.53
69.08
68.54
67.49
65.43
64.57
63.47
62.35
−61.46
−66.29
−22.59
−14.36
−14.36
−22.50
−22.50
−8.44
−6.59
17.42
17.37
16.56
14.57
12.34
8.28
7.06
7.25
42
91
32
78
91
21
154
57
53
44
46
81
79
82
83
60
69
In total 1163 fish were sampled and successfully analyzed. Position is given as an average of the latitude (Lat.) and the longitude
(Lon.) of the sampling stations within areas, in degrees and decimal minutes. N sample size
carried out separately for each sex, using the ape
package (Paradis et al. 2004) in R.
We used Bayesian skyline plot method (BSP) in
BEAST v1.6.1 (Drummond et al. 2005) to estimate
effective population size with time using MCMC to
Fig. 1 Sampling sites are
marked with black dots
60
average over tree space, each tree weighted against its
posterior probability. Strict molecular clock was set
at 2 % substitutions/site/Myr, as commonly used for
mtDNA sequence analysis (Avise 2004). Two independent chains were run for 300 million iterations
61
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COI-11
COI-12
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COI-42
COI-43
COI-44
COI-45
COI-46
COI-47
COI-48
COI-49
COI-50
COI-51
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N
612
325
70
61
21
12
7
5
3
3
2
2
2
1
1
1
1
1
1
1
1
1
1
1
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1
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1
The site numbers refer to the equivalent site in the complete mtDNA sequence (Coucheron et al. 2011). The combined frequency among sampling localities is presented, N. The dots
represent identity of a sequence to the most common haplotype COI-01
6
0
8
3
Table 2 Segregating sites of 51 haplotypes (Hapl.) of the 460 base pairs fragments of cytochrome c oxidase subunit I found among 1163 individual saithe, Pollachius virens, sampled
in the North Atlantic
Environ Biol Fish (2015) 98:67–79
71
Paper III
72
Environ Biol Fish (2015) 98:67–79
Fig. 2 A median joining network for the 51 observed COI
haplotypes. The sizes of circles are proportional to the frequencies of the given haplotype (smallest, N = 1). Different
haplotypes are represented with different shades. Black: COI01, dark gray represents C0I-02, intermediate gray: C0I-03 and
light gray: C0I-04. Rare haplotypes are represented with open
circles. The red circle represents an unobserved but inferred
haplotype. Lines between haplotypes reflect single mutational
steps between alleles
each and the results combined using LogCombiner
(Drummond et al. 2005). The first 10 million iterations were discarded in each run to allow for burn-in.
jModelTest (Posada 2008) was used to determine that
a suitable substitution model for the analysis was
Hasegawa-Kishino-Yano with empirical base frequencies and site heterogeneity set to Gamma (4 categories) and invariant sites (HKY+G+I). Tree prior was
set at: Coalescent, Bayesian skyline, and piecewiseconstant skyline model selected, with group size m =
10 and auto optimize. The BEAST input file was
produced using BEAUTi v.1.6.1 and the results were
inspected using TRACER v.1.6.1 (Drummond et al.
2005). A generation time of 4.5 years was used to
calculate the Ne after the run (Jónsson 1992). The
results were compared to changes in δO 18 from the
North Greenland ice core Project as it is indicative of
the earth’s climate (δO 18 data available at: www.gfy.
ku.dk/∼www-glac/ngrip, (Andersen et al. 2004)).
Results
Observed genetic variation at COI
In total a 460 base pair fragment of the cytochrome
c oxidase subunit I (COI) was successfully sequenced
in 1163 individual saithe from Canada, Iceland, Faroe
Table 3 AMOVA between sexes of saithe in Canada using Tamura and Nei (1993) genetic distance (upper) and based on haplotype
frequency only (lower)
Source of
variation
d.f.
Variance
component
Percentage
of variation
Between sexes
Within sexes
1
112
0.010
0.403
Between sexes
Within sexes
1
112
0.019
0.299
/F are the various intraclass correlation coefficients
62
/F statistic
P
2.44
97.56
ST = 0.024
0.075
5.96
94.04
FST = 0.060
0.009
Environ Biol Fish (2015) 98:67–79
Islands and Norway (Table 1 and Fig. 1). Altogether
43 segregating sites were observed defining 51 haplotypes, at variable frequencies (Table 2). The overall
nucleotide diversity was π̂ = 0.0019 ± 0.0015 and
the overall haplotype diversity was ĥ = 0.64 ± 0.01.
The relationship among haplotypes showed a shallow genealogy (Fig. 2). Two haplotypes (COI-01 and
COI-02) were found at high frequencies (>25 %) and
a
73
another two (C0I-03 and C0I-04) were found at relatively high frequencies (>5 %). Additionally many
rare types different by one mutation from the more
common haplotypes were observed (Fig. 2).
All but two segregating sites were synonymous. An
Alanine to Glycine change (haplotype COI-50, site
6522) and a Valine to Leucine change (haplotype COI14, site 6536). Both haplotypes having the amino acid
substitutions were found as singletons in the sample
(Table 2).
The observed transition/transversion ratio was 36/8
with 23 C-T and 13 A-G transitions vs. 6 C-G, one
A-C and one G-T.
Genetic differences between sexes
1.0
b
AMOVA between sexes in the overall sample showed
no difference. However, comparing sexes within countries revealed a significant difference in Canada when
using haplotype frequency only (conventional F statistics) but not significant when using (Tamura and
Nei 1993) genetic distance (Table 3). Comparison
between sexes in Iceland and Faroe Islands did not
show a significant difference.
Molecular diversity was higher in females compared to males in Canada. The nucleotide diversity
was π̂ = 0.0020 ± 0.0016 and π̂ = 0.0015 ± 0.0013,
and the haplotype diversity was ĥ = 0.68±0.04 and
ĥ = 0.53±0.07 for females and males, respectively.
Relative frequency of the most common haplotype
0.6
0.4
0.0
0.2
Relative frequency
0.8
c
Canada
Fig. 3 Comparison of COI haplotype frequency between sexes
in a Canada, b Iceland and c Faroe Islands. Different haplotypes are represented with different shades. Black: COI-01,
dark gray: COI-02, intermediate gray: COI-03 and light gray:
COI-04. White represents a pool of rare haplotypes
Iceland
Faroe Isl.
Norway
Fig. 4 Comparison of COI haplotype frequency among countries. Different haplotypes are represented with different shades.
Black: COI-01, dark gray: COI-02, intermediate gray: COI03 and light gray: COI-04. White represents a pool of rare
haplotypes
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Environ Biol Fish (2015) 98:67–79
Table 4 AMOVA among countries using Tamura and Nei (1993) genetic distance (upper) and based on haplotype frequency only
(lower)
Source of
variation
d.f.
Variance
component
Percentage
of variation
/F statistic
P
Among countries
Among areas within countries
Within areas
3
11
1148
−0.0006
−0.0004
0.4405
−0.14
−0.08
100.22
CT = −0.0014
SC = −0.0008
ST = −0.0022
0.80
0.53
0.71
Among countries
Among areas within countries
Within areas
3
11
1148
−0.0012
0.0015
0.3187
−0.37
0.48
99.89
FCT = −0.0037
FSC = 0.0048
FST = 0.0011
0.93
0.10
0.22
/F are the various intraclass fixation indices
(COI-01) was lower in females compared to males
(Fig. 4).
The results of Mantel test did not indicate isolation
by distance for either males or females (Z = 495.29,
P = 0.480 for females and Z = 403.14, P = 0.082
for males).
In a pairwise FST comparison between countries,
separately for each sex (where sample sex information
was available), a significant difference was observed
between females sampled in Canada and Faroe Island
(FST = 0.051; P = 0.017), and also for males
(FST = 0.033; P = 0.037) (Fig. 3). The difference
between females, but not males, remained significant
after accounting for repeated testing.
The frequencies of different haplotypes for females
sampled in Iceland-SW and Iceland-SE were different
(pairwise comparison: FST = 0.022 and P = 0.038).
However, taking repeated testing into account this was
not significant.
Genetic variation among ocean regions
The frequency of the most common haplotypes (COI01, COI-02, COI-03 and COI-04) was similar among
waters around countries (Fig. 4). One rare haplotype (COI-08) is shared among waters around Iceland,
Norway and Canada.
AMOVA results showed no difference among
waters around countries (Table 4). Only a small part,
non significant, of the observed variation was due to
variation among areas within countries (conventional
F comparison, Table 4, lower). Pairwise comparison
between countries showed very low ST values and
the estimated gene flow was thus high, in most cases
unlimited (Table 5).
Genetic variation among areas within countries
An AMOVA analysis revealed a genetic difference
between sampling stations within Canada (FST =
0.040, P = 0.028). However, the sample from
Canada East was dominated by males (61 %) whereas
in Canada West the sex ratio was equal (males,
51 %). Comparison between sampling stations in
Canada independently for sexes did not reflect a spatial genetic structure. No significant differences were
observed among sampling stations within the other
countries.
Table 5 Gene flow, M = Ne m (upper part) and ST (lower part) for saithe, pairwise comparison between countries
64
Country
Canada
Iceland
Faroe Isl.
Norway
Canada
Iceland
Faroe Islands
Norway
*
−0.00294
−0.00373
−0.00245
∞
*
−0.00349
−0.00110
∞
∞
*
0.00011
∞
∞
4370.014
*
Environ Biol Fish (2015) 98:67–79
75
Temporal genetic variation
Temporal genetic variation was examined around Iceland (between saithe samples from October and April)
and haplotype frequency was not significantly different between sampling events (AMOVA, haplotype
frequencies only: FST = −0.005, P = 0.93). No
difference was observed among the most abundant
cohorts (1993–2002), for the pooled data for Canada
and Iceland (AMOVA, haplotype frequencies only:
FST = −0.001, P = 0.51).
Demographic analysis
The Bayesian Skyline analysis indicated a recent population expansion in saithe (Fig. 5a). This event is
estimated to have started 7–8 Kyr ago. Overall, the
Fig. 5 a Bayesian skyline
plot for saithe reflecting
changes in female effective
population size (Ne ) against
time in thousands of years
(Kyr) before present.
b Changes in the δO 18
concentration in the North
Greenland ice core
effective population size of saithe is estimated to have
increased 330 fold, during the last 10Kyr. The warming of the earth’s climate after the last Ice age is
estimated to have occurred earlier, about 10–15Kyr
ago (climate change indicated by increased δ018 in
the Greenland Ice core (NGRIP data: www.gfy.ku.
dk/∼www-glac/ngrip) (Fig. 5b).
Tests of neutrality
The observed number of haplotypes by far exceeds
the expected number in the overall samples according to Chakraborty’s test (Chakraborty 1990) in
the overall sample (kExp. = 6.9, kObs. = 51, P <
1 × 10−5 ). The same is true for the Fu’s FS
(FS = −28.27, P < 1 × 10−5 ) (Fu 1997). The
frequency distribution of the observed haplotypes
a
b
65
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76
differ from expectation of Ewen’s sampling formula for a population at mutation drift equilibrium
(FExp. = 0.084, FObs. = 0.362, P = 8 × 10−5 )
(Ewens 1972). Tajima’s neutrality test also reflected
the high number of rare haplotypes (Tajima’s
D = −2.18, P < 1 × 10−5 ) (Tajima 1989). In general
the same applies to the analysis when carried out for
samples within the different countries.
Discussion
Spatial and temporal genetic variation
The results of the present study show limited overall mtDNA sequence differences among geographic
locations throughout the North Atlantic ocean for
saithe. However, when considering genetic variation
between the sexes a certain pattern is revealed. Differences between sexes in Canadian waters indicate
that the sexes originate from groups differing in haplotype frequencies, and that there might be a sex-biased
migration pattern between these groups (Prugnolle
and de Meeus 2002). Alternatively, a nuclear and
mtDNA incompatibility might be reflected in haplotype specific mortality in either sex, changing the
haplotype frequency in one sex through sex selective mortality. However, since the observed genetic
variation is mostly synonymous this is an unlikely
explanation for the observed sex differences. A more
likely explanation are differences in migratory patterns of adult individuals between the sexes. Spatial
genetic structure is only revealed when comparing
the sexes separately, females showing slightly more
differences than the males in a pairwise comparison
between Canada and the Faroe Islands (Fig. 3). This
may suggest that females are more philopatric than
males. Similar indications have been observed for haddock (Eirı́ksson, unpubl. data). Sex-biased dispersal
is well known in a wide range of taxa (for overview
see Petit and Excoffier 2009) and a number of cases
have been reported for freshwater fish (e.g. Fraser et
al. 2004; Cano et al. 2008; Brunelli et al. 2010). However, there are not many reported cases of sex-biased
dispersal in marine fish (but see Shaw et al. 2004),
therefore, these indications in saithe are worth further
research.
Although there is an indication of spatial genetic
structure across the N-Atlantic, when considering the
66
Environ Biol Fish (2015) 98:67–79
sexes separately, the results suggest high levels of gene
flow among geographic regions. This is not surprising for saithe geographic localities in the NE-Atlantic,
where tag recapture studies have shown migration
among countries (Jakobsen and Olsen 1987). However, saithe migration between the NE-Atlantic and
NW-Atlantic, as suggested by the present study, has
not been reported to our knowledge. Not only is the
relative frequency of the most common haplotypes
similar among different waters, but a rare haplotype
(COI-08) is found in saithe from the waters around
Canada, Iceland and Norway (Table 2). These findings, and the finding of sex differences, suggest regular saithe migrations and/or passive egg or larval drift
with water currents between the West and the East of
the North Atlantic as well as among areas within the
NE-Atlantic.
The present study does not support the idea of population structure around Iceland as Armannsson et al.
(2007) suggested based on a tagging and migration
study. The same is true for saithe sampled at different localities around Faroe Islands and Norway. No
significant differences were observed.
The observed variation among sampling areas in
Canada (Canada West and Canada East) might be
considered spatial variation as the groups have been
suggested to belong to different populations based
on phenotypic difference (Neilson et al. 2006). However, the observed difference is more likely to reflect
the observed variation between sexes and skewed
sex ratio rather than spatial variation. The observed
variation in saithe in Canada is thus worth further
investigation.
Temporal variation is small as indicated by
AMOVA among cohorts. This may be due to large
effective population size, Ne , which reduces the rate
of genetic drift. Large population size will thus mean
that a long time is necessary for genetic drift to alter
the allelic configuration of neutral genetic variation
among groups. The marine ecosystems of the North
Atlantic underwent a dramatic change at the end of
last Ice age 10–15 Kyr ago. This may be too short a
time for population genetic structure to be established
by drift alone.
Comparing the observed variation in saithe to transAtlantic cline seen in Atlantic cod, Gadus morhua,
(Árnason 2004), suggests different migration pattern
with a more limited trans Atlantic gene flow in cod
compared to saithe.
Environ Biol Fish (2015) 98:67–79
Demography and natural selection
The observed DNA sequence variation is almost
exclusively synonymous and is thus likely to be
either neutral or under weak purifying natural selection. However, the neutrality test results showed that
the observed genetic variation deviates from expectations of selective neutrality. Factors other than natural selection may produce similar pattern. The Fu’s
FS test is sensitive to recent population expansion,
resulting in high negative FS values as observed in
the present study. Tajima’s test result might indicate purifying selection, but as for Fu’s test the same
result is expected for a population that has undergone
recent expansion. It is possible that natural selection is responsible for the observed pattern (see e.g.
Bazin et al. 2006). However, given that the observed
polymorphism is almost exclusively synonymous we
assume that the apparent deviation from neutrality,
suggested by the other neutrality tests, are indicative
of a recent population expansion rather than natural
selection. Indeed the demographic analysis of genetic
variation in saithe suggests a recent population expansion (Bayesian skyline analysis). The expansion event
does not correlate with changes in Earth’s climate.
However, the timing of the expansion event is dependent on the mutation rate used for the analysis (2 %
sub./site/Myr in the present study). Other estimates
suggest that the mutation rate may be slightly lower
(Bermingham et al. 1997; Knowlton and Weigt 1998)
and a recent study suggests that mutation rates may
be much higer (Burridge et al. 2008) and also time
dependent. Using a different mutation rate would alter
the assessment of the timing of population expansion. Also, as different loci may have different history
within a population, analysing more loci will improve
the prediction and might change the estimated timing
of expansion.
Indication of a sudden population expansion has
been observed in related species, but the estimated
timing varies (e.g. Pálsson et al. 2009; Liu et al. 2010;
Eirı́ksson and Árnason 2014). The varible estimated
timing may suggest multible historical and/or biological explanatations for the observed genetic vartiation
in these fish species.
Acknowledgment This project was supported by the Icelandic research fund and the Icelandic research fund for graduate students of The Icelandic Centre for Research. We thank Dr.
77
Snæbjörn Pálsson for stimulating discussions during the work
and Prof. Jarle Mork for providing us with access to his samples and his hospitality and stimulating discussions during lab
work at NTNU. We thank people at the Faroese Fisheries laboratories for cooperation during their annual survey in 2006. We
also thank Hlynur Ármannsson for assisting in providing samples from Canadian waters collected by Canadian Department
of Fisheries and Oceans, Maritimes Region. We thank Kristján
Kristinsson and workers at Icelandic Marine Research Institute
for providing saithe samples from Iceland. Finally we thank the
people at the population genetics lab at the University of Iceland
for discussions and help during the work.
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Paper IV
Phylogeography of haddock Melanogrammus aeglefinus in the North
Atlantic — Postglacial expansion and male-biased dispersal
Guðni Magnús Eiríksson and Einar Árnason
Unpublished manuscript
© Guðni Magnús Eiríksson and Einar Árnason
Guðni Magnús Eiríksson actively particiated in assembling samples from different parts
of the world, took part in sampling trips around Iceland in 2006 and around Faroe
Islands the same year. Guðni did all the molecular laboratory work: DNA isolation,
mtDNA amplification, purification and prepared the samples for electrophoresis on
an ABI-3100 automatic sequencer. Guðni was also in charge of data analysis and
interpretation. Guðni was in charge of writing the manuscript. Professor Einar Árnason
actively participated in all steps of the work. Einar took part in the experimental design,
supervised the molecular work, took part in data analysis, interpretation of the data and
participated in writing the manuscript.
71
Paper IV
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1 Abstract
Genetic variation in haddock, Melanogrammus aeglefinus, was examined using the mitochondrial DNA
sequence information. A 599 base pair fragment of the cytochrome oxidase c subunit I gene in 884 individuals sampled across the North Atlantic was used. The overall nucleotide diversity was π̂ = 0.0026 and
haplotype diversity was ĥ = 0.81. The observed genetic variation suggests weak or no natural selection
acting on the observed polymorphism. Analysis of genetic variation revealed difference between sexes in
the Faroe Islands and Norway, suggesting male-biased dispersal. Such examples are very rare for marine
fish. However, genetic variation was almost uniform across the North Atlantic suggesting high levels of
gene flow. Indications of population structure within Iceland and Faroe Islands are worth further examination. Bayesian skyline analysis reconstructing the past demographic history indicate post-glacial population
expansion that correlate with the change in climate at the end of the last glacial maximum.
Keywords: mtDNA, COI, male-biased dispersal, postglacial expansion, demography
73
Paper IV
2 Introduction
2.1 Genetic variation and population subdivision
Genetic difference among groups, for traits that is not affeced by natural selection, will establish over
time due to genetic drift. Thus using a molecular markers, that are not affected by natural selection, can
be used to identify reproductively isolated groups. The rate of drift is inversely related to the effective
population size (Ne ). Examination of population subdivision has been the subject of many studies, not least
for commercially important fish species as such information can be of great importance for responsible
management (for overview see Reiss et al, 2009) Variable molecular markers are used and results are seen
to vary from one study to another. It is important to note that observed genetic differences between different
environments occupied by an organism may not reflect population breeding structure if the variation is
affected by natural selection (Guinand et al, 2004). Genetic difference can become established between
subdivided group of individuals that share the same origin (Williams et al, 1973; Guinand et al, 2004). For
the identification of a breeding structure of a population it is thus important to use genetic markers that are
not subject to natural selection, as the latter may reflect environmental heterogeneity rather than the origin
of groups involved. The analysis of indications of natural selection should thus be an integrated part of the
analysis of population sub-structuring (see e.g. Excoffier, 2007).
In the analysis of population sub-structuring the a priori definition of groups or populations, for example using analysis of molecular variation (AMOVA) (Excoffier et al, 1992) will affect the results. If
sub-structuring is not properly recognized it can greatly affect our interpretation of the genetic diversity
(Excoffier, 2007). The SAMOVA method (Dupanloup et al, 2002) maximizes genetic difference between
a specified number of groups without a priori assignment of group membership. It can be used to identify
populations without prior grouping of samples. However, the results for different grouping can only be
indicative of the true number of populations (Dupanloup et al, 2002). Population sub-structuring may follow environmental variation not considered by the observer. For example different populations may occupy
different depth as has been suggested for Atlantic cod, Gadus morhua (Pampoulie et al, 2008). Such variation would go unnoticed in a SAMOVA analysis were genetic difference between groups from different
geographic locations is maximized.
Genetic variation may reflect different migration patterns between sexes and such patterns are common
for vertebrate species (Purcell et al, 1996; Petit and Excoffier, 2009). In the case of females being more
philopatric and males more migratorial, such variation may be reflected in the mitochondrial DNA variation. Females would form more geographically distinct populations but males would be homogeneous over
larger geographic areas, due to mixing of males of different geographical origin. Difference among the
mitochondrial genome and nuclear loci can also indicate sex difference in migration pattern (Purcell et al,
1996).
Many studies have demonstrated a lack of spatial genetic structure in marine species indicating high
levels of gene flow attributed to lack of physical barriers in the marine environment and high dispersal
rate for eggs and larvae with ocean currents (Waples, 1987). Large effective population size (Ne ) may also
account for the genetic uniformity as it may take a long time for a genetic difference to become established
as rate of genetic drift will be low. This may be the case for many marine species (Waples, 1987).
Direct observation of DNA sequence variation can be used to make inference about observed polymorphism, for example by inspection of the frequency of synonymous and nonsynonymous segregating sites.
A number of statistical tests are available for detecting natural selection for example by exploring the allelic
distribution or level of variability (Nielsen, 2001). However, natural selection can be similar to the effects
that demographic events, such as populations bottleneck, can have on genetic variation and it may be dif-
74
ficult to discriminate between the alternative explanations. Nucleotide mismatch analysis can be used to
examine the haplotype tree structure and make predictions of demographic history (Rogers and Harpending, 1992) but more recent methods, for example the skyline method introduced by Pybus et al (2000), have
improved our ability to reconstruct past demographic history embedded in the genealogical relationship
among individuals of a population (Emerson et al, 2001; Drummond et al, 2005).
2.2 Haddock — Biology and environment
Haddock, Melanogrammus aeglefinus, is a demersal fish species most commonly found at 80–200m depth at
variable bottom substrates. Haddock is an omnivore, feeding mainly on small benthic organisms. The age at
first maturation varies among geographic locations and in the North-West Atlantic the age of first maturation
is 3–5 years, but maturation occurs at 2–3 years in the warmer waters of the North Sea (Cohen et al, 1990;
Jónsson, 1992). Fecundity varies greatly with females size, larger females having higher fecundity (50,000–
2,000,000 eggs per female Jónsson, 1992). The eggs are small (1.2-1.7 mm in diameter). They are buoyant
and eggs and larvae are pelagic for about three months (Hislop, 1984). During this period eggs and larvae
can drift with water currents long distances as has been shown for Atlantic cod (e.g. Brickman et al, 2006).
Timing of spawning varies greatly among geographic regions (January to July). Haddock is a commercially
important fish species, the major fishing grounds being in the North-East Atlantic (Cohen et al, 1990;
Jónsson, 1992)
In recent years some changes in fish communities have been observed in North Atlantic ocean, both in
migratory patterns of some species and in changes in the distribution of others. These changes are most
likely linked to environmental changes associated with increased water temperature (Rose, 2005). For
the last two decades the distribution of haddock around Iceland has changed and haddock is now commonly found at the North coast (Björnsson et al, 2007). In recent years (2004–2007) the haddock captures
increased in the Icelandic waters reflecting the increase in population size, followed by reduction in the
population size (Anonymous, 2011). Such demographic changes may be reflected in genetic variation of
populations.
The environmental conditions throughout the distribution range of haddock are variable and different
populations of haddock may occupy different habitats. An environmental heterogeneity is found at the
Faroe Islands where the Faroe Plateau and the Faroe Bank are separated by 850m deep channel. It has been
shown that fish growth rate is different due to difference in temperature and food abundance between the
two ecosystems (Magnussen, 2007).
A number of studies have been carried out, using different molecular markers, in order to describe
the genetic variation and the population genetic structure of haddock and the findings are to some extend
conflicting. A study using transferrin polymorphism for the analysis of the genetic variation in haddock
indicate fine scale population sub structuring within the North-East Atlantic (Jamieson and Birley, 1989).
In another study of allozyme variation in haddock (using 8 loci) limited difference was revealed among
countries (Norway, Iceland, Faroe Islands, North Sea) (Giæver and Forthun, 1999). Lage et al (2001) found
a temporal variation in allele frequencies in a microsatellite study for haddock and an indication of fine scale
population genetic structure in the North-West Atlantic. However, a limited population sub-structuring was
indicated by mtDNA RFLP analysis for the same geographic region in another study (Zwanenburg et al,
1992). In Lage et al (2001) study the variation in FST among the different loci is not reported and the effects
of natural selection on shaping the observed pattern was not considered. It is possible that natural selection
and environmental heterogeneity may explain the observed pattern (Williams et al, 1973; Guinand et al,
2004). In fact one of the microsatellites used in the study (Gmo-132.1) has been shown to have an outlier
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FST value in spatial comparison for Atlantic cod, and is suspected to be under natural selection (e.g. Nielsen
et al, 2006). A mtDNA study indicates that gene flow among geographic region may be responsible for
maintenance of high genetic variation in haddock at the time of collapse in the fisheries at Georges Bank
(Purcell et al, 1996).
2.3 The present study
Here we analyse mtDNA sequence variation using a 599 base pair fragment of the cytochrome oxidase c
subunit I (COI) gene for haddock sampled in the North Atlantic. The sequence variation will be used to
assess relationship in haddock within and among countries. An important characteristic of mtDNA is that
its effective population size (Ne ) is quarter of that for a nuclear marker, due to maternal inheritance and a
single allele carried by each individual, making the rate of genetic drift potentially faster (Hartl and Clark,
2006). Comparison will be made to earlier studies on haddock and comparison made to what has been
described for Atlantic cod, Gadus morhua, and related gadiod fish species (Árnason, 2004; Pálsson et al,
2009; Eiríksson and Árnason, 2014, 2015)
3 Materials and methods
3.1 Fish samples
In total 884 haddock samples were obtained from five geographic regions from the North-East Atlantic.
Gill tissue samples were preserved in 96% ethanol. Samples were grouped in 14 different areas within
countries: Greenland NW, SW and E, Iceland S, W, N and E, Faroe Bank and Faroe Plateau, four sampling
stations along the coast of Norway (79◦ N, 69◦ N, 66◦ N, 63◦ N) and the North Sea (Table 1 and Figure 1). The
weight and length of sampled fish was recorded and sex determined for most fish, except for fish sampled
in Greenland. The age of the haddock sampled at the Faroe Bank and in Iceland was determined by otolith
inspection at the Faroese Fisheries Laboratory and the Icelandic Marine Research Institute, respectively.
Three individuals where sampled at the east coast of Greenland (Table 1). These samples are not included when comparing different groups within countries, but pooled together with other samples from
Greenland in the analysis of variation among countries.
3.2 Molecular methods
DNA was isolated using a chelex method (Walsh et al, 1991) with some modifications. A pair of primers
FishF1-5′ TCAACCAACCACAAAGACATTGGCAC3′ and FishR2-5′ ACTTCAGGGTGACCGAAGAATCAGAA3′ (Ward et al, 2005) was used to amplify a 599 base pair fragment of the haddock COI mitochondrial gene, corresponding to base pairs 5529–6127 in the haddock complete mitochondrial genome (Ursvik
et al, 2007, GenBank accession number: AM489717.1). A 19µ l PCR reaction mix contained 0.17mM
dNTP, 11µ g/µ l Bovine serum albumin (BSA), 0.36pM of each primer (FishF1, FishR2), and 0.05U Taq
Polymerase buffered with 10× Taq buffer (New England BioLabs (NEB)), DNA template (median concentration 0.26ng/µ l in reaction mix). The reaction mix was prepared on ice and placed in a preheated
thermocycler when ready. The DNA was denatured at 94◦ C for 5 minutes in the first cycle followed by
35 cycles of denaturing for 1 min, annealing at 54◦ C for 30 seconds and extension at 72◦ C for 1 minute
followed by 7 minutes extension period at 72◦ C. A 5µ l of the product was run for 10 min at 90 Volts on an
1.5% agarose gel containing ethidium bromide and then inspected under UV light to determine amplification quality. PCR were then enzymatically purified using Exonuclease I (NEB) and Antarctic Phosphatase
76
(NEB). Products were sequenced using Applied Biosystems-BigDye Terminator v3.1 kit (using the FishF1
primer) applying standard protocol with some modifications and ran on an ABI-3100 automatic sequencer.
3.3 Data analysis
Base calling, data assembly and sequence alignment was carried out in Phred (Ewing et al, 1998) and
Phrap (Gordon et al, 1998). Sequence data was viewed using Consed (Gordon et al, 1998) and Seaview
(Galtier et al, 1996). Trace files for all observed haplotypes were inspected manually, the sequence integrity
confirmed and sequences of low quality rejected. Haplotype tree was produced using DNAsp (Librado
and Rozas, 2009) and Network 4.5.1.6 (Bandelt et al, 1999). The tree is based on the most parsimonious
relationship among the observed DNA sequences and haplotype frequency used as an additional criterion
for resolving homoplasy. MEGA 4 (Tamura et al, 2007) was used for translation of nucleotide sequence
to amino acid sequence for the observed haplotypes (vertebrate mtDNA) for identification of amino acid
substitutions. The genetic variation was examined by application of different neutrality tests: Tajima’s D
test, Chakraborty’s test and Fu’s FS test. The neutrality tests were applied to the total sample and separately
for the different geographic regions sampled (by countries) and carried out in Arlequin 3.5.1.2 (Excoffier
and Lischer, 2010). Analysis of pairwise nucleotide mismatch (Rogers and Harpending, 1992) was carried
out for the overall sample and 1000 simulated mismatch distributions used to test if the observed distribution
differed to the expectations of a population expansion model (Schneider and Excoffier, 1999).
We also used the Bayesian skyline plot method (BSP) in BEAST v1.6.1 (Drummond et al, 2005) to
estimate effective population size with time and to reconstruct demographic history. Estimations were based
on Markov chain Monte Carlo technique to average over tree space, each tree weighted against its posterior
probability. Strict molecular clock was set at 2%substitutions/site/Myr (Avise, 2004). The chain was run
for 500 million iterations. After the run the first 10 million iterations were discarded to allow for burn-in
(Drummond et al, 2005). Substitution model used was Hasegwa-Kishino-Yano setting base frequencies at
empirical and site heterogeneity set to Gamma (4 categories) and invariant sites (HKY+G+I). jModelTest
(Posada, 2008) was used to determine the suitable substitution model for the analysis. Tree prior was
set at: Coalescent, Bayesian skyline, and piecewise-constant skyline model selected, setting group size
(m) at 10. Auto optimize was selected for the analysis operators. The BEAST input file was produced
using BEAUTi v.1.6.1 and the results were inspected using TRACER v.1.6.1 (Drummond et al, 2005). A
generation time of 4 years was used to calculate the Ne after the run. There is considerable variation in age
at maturation for haddock, increasing with higher latitude, but estimated age at maturation usually ranges
between 3-5 years (e.g. Hislop, 1984; Jónsson, 1992). The results were compared to changes in δ O18 from
the North Greenland ice core Project as it is indicative of the earths climate (Andersen et al, 2004). Genetic
differentiation among groups was analyzed using Analysis of Molecular variation (AMOVA) based on the
method developed by Excoffier et al (1992). Genetic variation among groups was examined, both using
genetic distance based on Tamura and Nei (1993) (Φ) and conventional F-statistics (frequency of different
haplotypes only) to compare genetic variation among groups. Pairwise comparisons using ΦST and FST
were carried to further analyze any possible differences among groups, as implemented in Arlequin. The
significance of fixation indices were tested using 10,000 permutations (Excoffier et al, 1992).
In the present study temporally spaced samples were not obtained for any sampling location. However,
genetic variation among cohorts, age composition and body length (length used as an indicator of age were
age information was not available) was examined among groups where significant genetic difference was
found. This was done in order to assess if temporal genetic variation could be responsible for the observed
genetic variation.
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Pairwise genetic difference (ΦST ) was compared among countries and between areas within countries.
The significance of the pairwise ΦST values was tested by a generation of ΦST null distribution based on the
hypothesis of no difference between groups by 10,000 permutations of haplotypes between groups. The Pvalue was obtained as the proportion of permutations leading to an ΦST value larger or equal to the observed
one (Excoffier et al, 1992). Assessment of false discovery rate (Benjamini and Hochberg, 1995) was used
to account for repeated comparisons in order to further examine the significance of observed difference.
Spatial AMOVA (SAMOVA) was carried out for the samples obtained at 13 geographic localities (all but
Greenland E, Table 1) as implemented in the SAMOVA software (Dupanloup et al, 2002). The analysis was
carried out for 2–12 groups and the variance components and fixation indices inspected. The significance
of the fixation indices was assessed using 1000 permutations.
The geographic distance between sampling locations was calculated as minimum distance between the
sampling localities based on mean values for coordinates for each group using the fields package (Furrer
et al, 2011) in R (R development core team, 2008). In reality the distance separating haddock in the different
sampling locations is longer, since the effects of suitable environments and ocean currents is not taken into
account. Mantel tests (with 10,000 permutations) of relationship of genetic and geographical distance was
carried out using the ape package (Paradis et al, 2004) in R.
Gene flow M (absolute number of migrants) was estimated among the countries based on its relationship
with FST , as M = 1 − FST /2FST as calculated in Arlequin.
4 Results
4.1 Genetic variation
Variation was found at 75 sites defining 102 haplotypes (GenBank accession numbers: JQ340919–JQ341020)
(Appendix I: Tables 1.9-1.12). All but five mutations were synonymous. Four of the nonsynonymous mutations were observed as singletons in the sample and one was found in two individuals (Table 2). Four
haplotypes (COI-001 – COI-004) were found at relatively high frequencies (>5%) and formed a shallow
base of the geneaology (Figure 2). The rare types mostly deviated by one mutation from the main haplotypes, but deeper branches were also present, particularly branches from COI-003 (Figure 2).
The overall nucleotide diversity was π̂ ± σ̂π (×100) = 0.26 ± 0.17 and haplotype diversity was ĥ ±
σ̂h = 0.805 ± 0.009. The diversity measures differed slightly among area and countries, π̂ ranging from
0.20 ± 0.14 in Iceland N to 0.37 ± 0.24 in Norway 69◦ N and ĥ ranging from 0.74 ± 0.04 in Iceland W and
0.89 ± 0.04 in Greenland NW (Table 3).
4.2 Population genetic structure — Variation among countries
The frequency of different COI haplotypes was similar in the different countries, although haplotype combination of haddock from Greenland was slightly different from the other countries sampled (Figure 3). An
AMOVA showed no overall difference among countries. However, part of the observed genetic variation
was explained by variation among areas within countries (Table 4). Pairwise comparison among countries
suggest panmixia (unrestricted gene flow) for haddock among Iceland, Faroe Islands and Norway and no
genetic differentiation (negative ΦST values) (Table 5). Significant differences were observed in a pairwise
comparison between Greenland on one hand and Iceland, Norway and North Sea at an α -level of 0.05
(Table 5). However, after adjusting for repeated sampling using false discovery method (Benjamini and
Hochberg, 1995) this difference was not significant.
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4.3 Population genetic structure — Variation among areas within countries
Analysis of genetic variance among the 13 defined areas within countries showed nine significant pairwise
differences at an α -level of 0.05. Greenland-NW showed genetic differences to four other areas (Faroe
Plateau, Iceland-N, North Sea and Norway 70◦ N). The haddock sampled in Iceland-N differed to both
sampling areas in Faroe Islands and to Iceland-W. Differences were observed between Faroe Plateau and
Faroe Bank as well as between Iceland-W and the North Sea. Using false discovery method (Benjamini and
Hochberg, 1995) to correct for repeated sampling none of the observed differences remained significant.
A trend towards increased genetic differentiation (ΦST ) was observed with increased geographical distance
between groups (Figure 4). However, the relationship was not significant (Mantel test, Z = 591, P = 0.149).
AMOVA results showed no overall difference between sampling stations around Iceland (using Tamura
and Nei (1993) genetic distance: ΦST (3,171) = 0.009, P = 0.16, and conventional haplotype frequencies:
FST (3,171) = 0.013, P = 0.08). However, a pairwise difference was observed between haddock sampled at
the West coast and North coast of Iceland (ΦST = 0.063, P = 0.018, Figure 5a).
AMOVA results comparing Faroe Bank and Faroe Plateau showed a significant difference when considering Tamura and Nei (1993) genetic distance but not when considering haplotype frequency only (Table
6). None of the observed variance was explained by a comparison among stations within areas. Pairwise
ΦST comparison between the two areas showed significant genetic differentiation (ΦST = 0.011, P = 0.009,
Figure 5b).
The SAMOVA results showed that for grouping into two groups the maximum genetic difference
was obtained by comparing Greenland NW against all the other samples pooled, although not significant
(FCT (1,868) = 0.0314, P = 0.070). As the number of groups was increased the fixation indices FSC (among
populations within groups) and FST (within population) decreased, as would be expected when groups become less similar, but the FCT (among groups) had an initial drop but was not reduced when more than four
groups were formed (Figure 8). For grouping of sampling locations into 3 groups a maximum genetic difference was observed when groups consisted of: Greenland NW; Iceland N and North sea; all other pooled
(FCT (3,868) = 0.0200, P = 0.001). Highly significant difference among groups was observed when grouping
in 4–11 groups with variable combinations but no clear geographical structure could be identified.
4.4 Population genetic structure — Variation between sexes
There was no difference between sexes in the overall sample. However, AMOVA results revealed a significant overall difference between males (N = 103) and females (N = 78) within Norway (FST (1,179) =
0.022, P = 0.015, Figure 9). Genetic difference was not observed for sexes in the other countries sampled.
AMOVA was carried out comparing genetic variation among countries for the two sexes separately for
Iceland, Faroe Islands and Norway (excluding haddock sampled in Greenland as sex information was not
available and North Sea due to small sample size). No difference was observed among countries for either
sex.
However, comparing the sexes separately between Faroe Bank and Faroe Plateau using AMOVA revealed significant difference between females when considering Tamura and Nei (1993) genetic distance
(ΦST (1,183) = 0.020, P = 0.029 ,Figure 10a), but the same was not true for males (ΦST (1,205) = 0.004, P =
0.233, Figure 10b). The sample sex ratio was different between sampled haddock in areas within Faroe
Islands (female ratio 0.36 and 0.66 in the Faroe Bank and the Faroe Plateau respectively, χ 2 = 31.03, d f =
1, P = 2.5 × 10−8 ).
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4.5 Population genetic structure — Temporal variation
Haplotype frequencies were not different among the most abundant cohorts (fish of age: 4-7 years, cohorts:19992002) within the Faroe Bank (AMOVA: FST (3,234) = −0.0004, P = 0.45). The age was not determined for
the haddock sampled at the Faroe Plateau but length of the haddock sampled at the Faroe Plateau and the
Faroe Bank did not differ significantly (ȲFP = 48.1cm and ȲFB = 49.0cm; t = 1.34, d f = 242, P = 0.18). The
length of female and male haddock from the Faroe Islands did not differ (Y♀ = 48.9cm and Y♂ = 48.81cm;
t = −0.13, d f = 360, P = 0.90) and the same was true for female and male comparison within the Faroe
Plateau and the Faroe Bank separately. Also, the age composition of male and female haddock sampled
at the Faroe Bank did not differ (χ 2 = 3.78, d f = 5, P = 0.58). The frequency of haplotypes did not differ among cohorts around Iceland for the most abundant cohorts (fish of age: 1–5 years, cohorts:1999–
2003)(AMOVA: FST (4,155) = 0.004, P = 0.31) and age composition of haddock was not significantly different between Iceland-W and Iceland-N (χ 2 = 8.24, d f = 4, P = 0.083). The age was not determined
for haddock sampled in Norway, but total length of individuals did not differ significantly between sex
(Ȳ♀ = 38.5cm and Ȳ♂ = 39.6cm; t = 0.71, d f = 170, P = 0.48).
4.6 Genetic variation and demographic history
Neutrality tests showed that allele frequency distributions deviated from predictions of selective neutrality
for the pooled data. Charkraborty’s test showed that the number of haplotypes in the observed sample by
far exceeded the expected number (kobs = 102, kexp = 10.3, P < 1 × 10−5 ). The same was true for Fu’s FS
test (FS = −27.0, P < 1 × 10−5 ). Similarly Tajima’s D reflected excess of low frequency polymorphisms
(Tajima’s D = −2.34, P < 1 × 10−5 ). As population sub-structuring may affect this result, the tests were
also carried out for haddock sampled in the various countries. In general the same results were obtained as
for the pooled samples. The haplotype numbers exceeded expectations and Tajima’s D was negative.
The observed mismatch distribution did not fit the prediction of the sudden expansion model (Figure
6, τ = 1.55, P < 1 × 10−5 ). The most obvious difference was that the observed distribution had more
sequences that differed by two nucleotides than expected for a population having undergone a sudden expansion (Figure 6).
The Bayesian skyline plot analysis indicated population expansion event in the last 25 kyr. Expansion
was estimated to have started approximately 22 kyr ago (Figure 7a). The haddock population was predicted
to have increased more than ten fold 18-19 kyr ago. After this the population expansion continues, but at a
faster rate, extending almost to the present day. The overall effective population size was estimated to have
increased about 360 fold since approximately 22 kyr ago (Figure 7a). The haddock population expansion is
estimated to have started towards the end of the last glacial maximum (Figure 7b).
5 Discussion
The present study reveals sex related genetic variation that may suggest male-biased dispersal pattern in
haddock. The difference between female haddock from Faroe Bank and Faroe Plateau suggests historical separation between the two ecosystems, evidenced by genetic distances among of female haplotypes
between the two regions. The same does not apply to males. This may suggest that females are more
philophatric but males more migratory. Difference in migration patterns between the sexes is also suggested by a skewed sex ratio in from these localities. The well known ecological difference between the
systems are likely to play a role in generating this pattern (e.g. Magnussen, 2007). However, there is no
overall difference between sexes in Faroe Islands. Also, the observed difference between males and females
80
in the Faroe Islands is not likely to be of a temporal origin, as no difference was observed in body length
or age between the two sexes. Similarly, no difference was observed between sexes overall for haddock
sampled around Iceland and neither was sex difference found among sampling stations within Iceland. Difference in the physical environments between Iceland and Faroe Islands (e.g. the channel between the Faroe
Bank and Faroe Plateau) and/or sampling at different time of the year may explain the difference between
the countries. The genetic difference between sexes in Norway is difficult to account for. A possible mechanism for such patter to arise is a male-biased migration among genetically different groups (Prugnolle
and de Meeus, 2002). However, no such groups were identified in the present study. We have observed
similar pattern for saithe, Pollachius virens (Eiríksson and Árnason, 2015). Sex-biased migration pattern
is well known in freshwater fish species (e.g. Fraser et al, 2004; Cano et al, 2008; Brunelli et al, 2010)
and in many other different vertebrate taxa (for overview see Petit and Excoffier, 2009). However, only
few cases of such pattern have been described for marine fish (but see Schrey and Heist, 2003; Shaw et al,
2004). Based on a game-theoretic analysis (Perrin and Mazalov, 2000) this pattern may suggest intense
local mate competition and polygyny in haddock. The present study was not designed to address the issue
of sex-biased migration in haddock in particular. A strategy for further research on sex-biased migration
in haddock would be to compare nuclear and mitochondrial molecular markers (Prugnolle and de Meeus,
2002). Also, future studies on haddock migration and behaviour (e.g. tag recapture studies) should take sex
variation into account. Alternative explanation for the observed difference between sexes is that the difference was due to nuclear and mitochondrial genetic incompatibility. However, as the observed variation is
almost exclusively synonymous this is not a likely explanation.
The observation of apparently fine scale structure for females in Faroe Islands would suggest that more
difference might be expected among geographic localities further apart. Such pattern is not found. The
present study shows weak population genetic structure in haddock across the North Atlantic, although
Greenland was seen as an outlier (Figure 3, Table 5). A weak geographic population structure is common
for marine organisms, attributed to lack of physical barriers in the marine environment and high dispersal
rate for eggs and larvae with ocean currents (Waples, 1987). Extensive gene flow is predicted in the present
study that may partly be due to such processes. The weak spatial genetic structure differs from what has
been shown for Atlantic cod, that shows more trans Atlantic genetic differentiation (Árnason, 2004). The
similarity of genetic variation observed in different geographic regions for haddock may have different explanations that can be difficult to disentangle. It may suggest enough gene flow among geographic regions
to act against differentiation due to genetic drift. Theoretically it has been shown that a low level of gene
flow may be sufficient to maintain genetic homogeneity among neutral loci (Wright, 1931). Egg and larvae drifting with water currents can account for such gene flow (Brickman et al, 2006). Although juvenile
and adult haddock is mostly found at shallow waters, extensive migrations are known to take place (Cohen
et al, 1990), which also may contribute as homogenizing effect. In fact sex differences indicate that such
migrations are likely, at least for adult males. Gene flow for haddock among Iceland, Faroe Islands and Norwegian waters was estimated to be high enough that these localities may be considered panmictic (Table 5).
However, it must be acknowledged that ΦST may not be a valid indicator of migration rate (Whitlock and
McCauley, 1999). Alternative explanation for the observed pattern is that the haddock effective population
size (Ne ) is large, making genetic drift slow, and that the observed genetic similarity among geographic regions is maintained due to short time since the current population structure has become established. Indeed
the marine ecosystem in the North Atlantic, in its present state, can be considered young as the end of the
last ice age occurred only about fifteen thousand years ago (Andersen et al, 2004).
Overall a small part of the observed genetic variation was explained by variation within countries
(AMOVA, Table 4). Looking more closely at the within countries comparisons showed that genetic dif-
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ference between Western and Northern Iceland (ΦST = 0.063) is more than twice that found between
Greenland and the North Sea (ΦST = 0.025). The difference between Greenland and the North Sea was
the largest genetic pairwise difference observed between countries. This genetic variation within Iceland
might be associated with variation in population density and dynamics around Iceland (Anonymous, 2011;
Björnsson et al, 2007). However, the observed variation between sampling locations in Iceland could be a
temporally unstable phenomena although the present study shows that there is no difference in genetic variation among different haddock cohorts. Also the age composition between the samples for N-Iceland and
W-Iceland is not significantly different. Further studies are needed to determine the temporal stability and
the significance of the observed variation. No genetic difference was observed among sampling locations
in Norway. This is in accordance with the findings of Giæver and Forthun (1999). No genetic difference
was observed within Greenland and the difference found within Faroe Islands was related to sex difference.
Significant differences among groups within countries contrasts with limited differences among countries. Perhaps grouping by countries does not reflect population genetic structure for haddock. However,
an attempt to identify spatial genetic structure using SAMOVA failed to reflect any obvious geographical
pattern for the observed genetic variation. The data may suggest a fine scale population genetic structure
in haddock as pooling of groups over larger geographic areas (as done for countries) may level out differences among groups, as the mean haplotype frequencies can be the same when different groups are pooled
(Excoffier, 2007). Although there are indications of possible fine scale/hidden spatial structure the present
study is not conclusive on this. The limited trans Atlantic population genetic structure may be due to high
levels of gene flow. This is in contrast to some earlier findings for haddock, where spatial genetic structure
was described (Jamieson and Birley, 1989; Lage et al, 2001). However, as addressed earlier the effects of
natural selection can explain the difference (Williams et al, 1973; Guinand et al, 2004). High levels of gene
flow, as other studies have also indicated (Purcell et al, 1996; Giæver and Forthun, 1999), is more likely to
explain the present data.
The fact that nonsynonymous substitutions are rare suggests weak or no natural selection acting on the
observed polymorphism in haddock. This makes the COI fragment used in the present study suitable for
population genetic studies in haddock. However, neutrality tests used in the present study showed deviations from neutral expectations. The tests are sensitive to demographic events and the observed pattern
could be the result of recent population expansion (Tajima, 1993; Excoffier, 2007). Similar pattern has
been found in many related taxa and (Pálsson et al, 2009; Eiríksson and Árnason, 2014, 2015). In fact the
observed genetic variation indicates two phases of variable population expansion rate for haddock (Figure
7). The earlier phase estimated to have taken place towards the end of the last glacial maximum (as defined
by Clark et al, 2009) but before the sudden warming of the earths climate ca. 14 kyr ago (Figure 7). The
timing of these events are based on the molecular clock rate used. Although 2% sub./site/Myr is commonly
used for animal mtDNA (Avise, 2004) variable mutation rates have been estimated (Bermingham et al,
1997; Knowlton and Weigt, 1998; Avise, 2004; Burridge et al, 2008). Using strict molecular clock rate
and mutation of 2% sub./site/Myr may be too low, in particular when considering the ’pedigree rate’ (ca.
6% sub./site/Myr) in the study by Burridge et al (2008). Underestimating the mutation rate will lead to
overestimation in time to the event. Thus, the observed expansion events in the present study may have
taken place more recently, if mutation rate is higher. If mutation rate is as high as suggested for ’pedigree
rate’ in Burridge et al (2008), the timing of the expansion event would be much more recent, or ca. 5 kyr
ago. Using the ’phylogenetic rate’ the observed pattern of population size change fits quite well to the ecological change occurring in the marine environment at the end of last ice age. However, if ’pedigree rate’
is used other explanations will be needed for the observed genetic variation in haddock, such as effects of
natural selection (Bazin et al, 2006). This finding shows the importance of better understanding of molec-
82
ular clock rate for timing of demographic events, for proper interpretation of observed genetic variation in
general. However, the overall distribution of nucleotide mismatch differed slightly form expectations of
population sudden expansion model. The deviation may be the result of extended and variable expansion
event and/or weak population genetic structure (Figure 6). The present study shows the shortcoming of the
mismatch analysis in reconstructing demographic history. Although useful for identifying genetic pattern
resulting from a single demographic expansion (Rogers and Harpending, 1992), more complicated expansion event may explain the poor fit to the sudden expansion model. The Bayesian skyline analysis makes
better use of the genealogical information, allowing for better reconstructing of the demographic history
(see e.g. Emerson et al, 2001; Ho and Shapiro, 2011). Here it can be noted that Bazin et al (2006) suggested
that repeated adaptive selection events was the dominant factor shaping the variation in animal mitochondrial genome. However, later studies have not supported the generality of this finding (e.g. McCusker and
Bentzen, 2010, and references therein). The observed genetic variation in haddock may thus be shaped by
adaptive selection dating back to the predicted expansion event. However, population expansion correlating
with environmental change must be regarded as more likely explanation for the observed genetic variation
in haddock.
6 Acknowledgements
This project would not have been possible without assistance in obtaining samples from different geographic regions. We are thankful for assistance in obtaining samples to Bo Bergstrøm at the Greenland
Nature Institute, Greenland, Kristján Kristinsson at the Icelandic Marine Research Institute (MRI), Iceland,
Petur Steingrund at Faroe Marine Research Institute (FAMRI), Faroe Islands, Jarle Mork at the Norwegian
University of Science and Technology (NTNU), Norway, and Remment ter Hofstede at Institute for Marine
Resources and Ecosystem Studies (IMARES) in the Netherlands.
We also would like to thank Snæbjörn Pálsson for useful discussions during the work and the population
genetics group at the University of Iceland for various help during laboratory work.
This project was supported by the Icelandic research fund and the Icelandic research fund for graduate
students of The Icelandic Centre for Research and the Icelandic Marine Research Institute.
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7 Tables
Table 1: Sampling year and sampling sites for haddock in different countries. In total 884 fish were sampled
and successfully analyzed. Position is given as an average of the latitude (Lat.) and the longitude (Lon.) of
the sampling region. Position is given in degrees and decimal minutes. N sample size.
Locality
Month
Year
Lat.
Lon.
N
Greenland NW
Greenland SW
Greenland E
Iceland N
Iceland E
Iceland S
Iceland W
Faroe Plateau
Faroe Bank
Norway 70◦ N
Norway 69◦ N
Norway 66◦ N
Norway 63◦ N
North Sea
July
July
July
October
October
October
October
March
March
March
September
October
August
February
2006
2006
2006
2004
2004
2004
2004
2006
2006
1994
1993
1992
1992
2006
63◦ 81 52◦ 81
60◦ 27 46◦ 87
65◦ 06 33◦ 08
65◦ 94 19◦ 64
65◦ 74 13◦ 89
63◦ 61 15◦ 16
64◦ 83 24◦ 04
61◦ 05
8◦ 66
61◦ 93
7◦ 89
70◦ 15 −28◦ 81
69◦ 34 −18◦ 65
66◦ 27 −13◦ 58
63◦ 11 −6◦ 78
56◦ 13 −2◦ 24
22
38
3
41
46
45
43
144
279
75
17
38
51
42
Table 2: Amino acid substitutions of various haplotypes and their frequencies (N).
Haplotype Substitution
N
C0I-026
C0I-043
C0I-064
C0I-079
C0I-100
Valine (V)
Isoleucine (I)
Isoleucine (I)
Aspartic acid (D)
Aspartic acid (D)
↔
↔
↔
↔
↔
Methionine (M)
Valine (V)
Serine (S)
Glycine (G)
Asparagine (N)
2
1
1
1
1
Table 3: Molecular diversity in haddock from the North Atlantic.S number of segregating sites; k number
of haplotypes; ĥ haplotype diversity; π̂ nucleotide diversity; σ̂ standard error.
Locality
S k
ĥ ± σ̂ĥ
π̂ ± σ̂π̂ (×100)
Greenland NW
Greenland SW
Iceland N
Iceland E
Iceland S
Iceland W
Faroe Plateau
Faroe Bank
Norway 70◦ N
Norway 69◦ N
Norway 66◦ N
Norway 63◦ N
North Sea
88
11
8
9
16
13
8
26
38
19
10
11
9
14
10
9
10
17
12
8
25
51
18
9
10
9
13
0.89 ± 0.04
0.80 ± 0.03
0.74 ± 0.05
0.84 ± 0.04
0.79 ± 0.05
0.72 ± 0.04
0.78 ± 0.03
0.83 ± 0.01
0.81 ± 0.03
0.83 ± 0.09
0.81 ± 0.04
0.76 ± 0.04
0.81 ± 0.04
0.33 ± 0.21
0.22 ± 0.16
0.20 ± 0.15
0.27 ± 0.18
0.25 ± 0.17
0.23 ± 0.16
0.24 ± 0.16
0.27 ± 0.18
0.27 ± 0.18
0.37 ± 0.24
0.26 ± 0.18
0.25 ± 0.17
0.24 ± 0.17
Table 4: AMOVA among countries using Tamura and Nei (1993) genetic distance (upper) and based on
haplotype frequency only (lower). Φ/F are the various intraclass fixation indices.
Source of
Variance
Percentage
variation
d.f. component of variation Φ/F statistic
P
Among countries
4
Among areas within countries 8
Within areas
868
−0.001
0.004
0.772
−0.12
0.53
99.60
ΦCT = −0.001 0.57
ΦSC = 0.005
0.11
ΦST = 0.004
0.10
Among countries
4
Among areas within countries 8
Within areas
868
-0.0003
0.001
0.401
−0.07
0.35
99.73
FCT = −0.001
FSC = 0.004
FST = 0.003
0.44
0.16
0.16
Table 5: Gene flow M = Ne m (upper part) and ΦST (lower part in bold) for haddock, pairwise comparison
between countries. Where significant genetic difference (P<0.05) was observed, ΦST value is labelled with
*.
Country Greenl. Iceland Faroe Isl. Norway N. Sea
Greenl.
Iceland
Faroe Isl.
Norway
N. Sea
0.016*
0.009
0.016*
0.025*
32
-0.002
-0.002
0.002
56
∞
-0.002
0.006
30
∞
∞
0.006
20
271
91
79
-
Table 6: AMOVA between areas within Faroe Islands using Tamura and Nei (1993) genetic distance (upper)
and based on haplotype frequency only (lower). Φ/F are the various intraclass fixation indices.
Source of
Variance
Percentage
variation
d.f. component of variation Φ/F statistic
P
Among areas
Among stations within areas
Within stations
1
18
378
0.011
-0.002
0.782
1.34
-0.19
98.85
ΦCT = 0.013
0.02
ΦSC = −0.002 0.47
ΦST = 0.012
0.28
Among areas
Among stations within areas
Within stations
1
18
378
0.001
-0.002
0.408
0.28
-0.60
100.32
FCT = 0.003
FSC = −0.006
FST = −0.003
0.13
0.83
0.73
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Paper IV
8 Figures
Figure 1: Sampling stations for haddock are indicated with red dots.
90
Figure 2: The overall relationship among the 102 observed COI haplotypes. The size of circles are proportional to the frequency of the given haplotype (smallest, N = 1). Different haplotypes are represented
with different shades. Black: COI-001, dark gray: C0I-002, intermediate gray: COI-003 and light gray:
COI-004. Rare haplotypes are represented with open circles. The red circles represent an unobserved but
inferred haplotype. Lines between haplotypes reflect mutational steps between alleles.
Figure 3: Comparison of haddock COI haplotype frequency among countries. Different haplotypes are
represented with different shades. Black: COI-001, dark gray: COI-002, intermediate gray: COI-003 and
light gray:COI-004. White represents rare haplotypes pooled.
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Paper IV
Figure 4: The relationship among pairwise genetic differentiation (ΦST ) and geographic distance (km) for
the defined groups. The filled circles represent ΦST values that are significant at α -level 0.05, open circles
show non significant values. The trend line shows small, non-significant, increase in ΦST with geographic
distance.
Figure 5: Comparison of haddock COI haplotype frequency among different localities around Iceland (a)
(S: South; W: West; N: North; E: East) and areas within the Faroe Islands (b) (FB: Faroe Bank; FP: Faroe
Plateau). Different haplotypes are represented with different shades. Black: COI-001, dark gray: COI-002,
intermediate gray: COI-003 and light gray:COI-004. White represents rare haplotypes, pooled in (b)
92
Figure 6: The pairwise nucleotide mismatch distribution for the 599 base pair COI fragment in haddock
and the prediction of a sudden expansion model is expressed with line and dots.
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Paper IV
Figure 7: (a) Bayesian skyline plot for haddock of changes in female effective population size (Ne ) against
time in kilo years (kyr) before present. (b) Changes in the δ O18 concentration in the North Greenland
Ice core. The shady interval covers the last glacial maximum (LGM)(Clark et al, 2009) (δ O18 data from:
www.gfy.ku.dk\~www-glac\ngrip)
94
Figure 8: Fixation indexes for the SAMOVA results as samples are assigned into variable number of groups
(2-12).
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Paper IV
Figure 9: Comparions of haddock COI haplotype frequency between males (a) and females (b) sampled
in the Nortern (N) part (>67◦ N)and Southern (S) part (>67◦ N) of along the Norwegian Norwegina waters.
(Females N, N = 45; Females S, N = 33; Males N, N = 47; Males S, N = 56). Different haplotypes are
represented with different shades. Black: COI-001, dark gray: COI-002, intermediate gray: COI-003 and
light gray:COI-004. White represents rare haplotypes pooled.
96
Figure 10: Comparison of haddock COI haplotype frequency between males (a) and females (b) sampled
in waters within different areas around Faroe Islands (FB: Faroe Bank, FP: Faroe Plateau). (Females
FB, N = 90; Females FP, N = 94; Males FB, N = 158; Males FP, N = 49). Different haplotypes are
represented with different shades. Black: COI-001, dark gray: COI-002, intermediate gray: COI-003 and
light gray:COI-004. White represents rare haplotypes pooled.
97
Paper V
Mitochondrial DNA Sequence Variation in Whiting Merlangius merlangus
in the North East Atlantic
Guðni Magnús Eiríksson and Einar Árnason, 2014
Environmental Biology of Fishes, 97: 103–110.
doi:10.1007/s10641-013-0143-5
© Environmental Biology of Fishes
Guðni Magnús Eiríksson actively particiated in assembling samples from different parts
of the world. Guðni did all the molecular laboratory work: DNA isolation, mtDNA
amplification, purification and prepared the samples for electrophoresis on an ABI-3100
automatic sequencer. Guðni was also in charge of data analysis and interpretation.
Guðni was in charge of writing the manuscript, corresponded to the comments of
reviewers and finalized the article. Professor Einar Árnason actively participated in all
steps of the work. Einar took part in the experimental design, supervised the molecular
work, took part in data analysis, interpretation of the data and participated in writing the
manuscript and finalizing the article.
99
Paper V
100
Environ Biol Fish (2014) 97:103–110
DOI 10.1007/s10641-013-0143-5
Mitochondrial DNA sequence variation in whiting
Merlangius merlangus in the North East Atlantic
Guðni Magnús Eiríksson & Einar Árnason
Received: 14 August 2012 / Accepted: 17 April 2013 / Published online: 12 May 2013
# Springer Science+Business Media Dordrecht 2013
Abstract Genetic variation in whiting Merlangius
merlangus was examined using a 621 base pair
fragment of the cytochrome c oxidase subunit I
mitochondrial gene in 138 individuals sampled from
Iceland, Norway and the North Sea. In total 10
segregating sites were observed defining 12 haplotypes. Three of the haplotypes were found at high
frequencies (>5 %). All but one mutations were
synonymous and the nonsynonymous mutation was
found as a singleton. This suggests weak or no
natural selection acting on the observed polymorphism making it useful for examination of population breeding structure. The genetic variation
suggests that the whiting population has undergone
sudden expansion in the past, estimated to have
started 70 Kyr ago, during the last glacial period.
Spatial genetic analysis reveals genetic uniformity
across long geographic distances suggesting high
level of gene flow. The long pelagic phase at early
age, allowing for high dispersal rate, may partly
explain the observed pattern.
Keywords mtDNA . COI . Demography . Gene flow
G. M. Eiríksson (*) : E. Árnason
Institute of Life and Environmental Sciences,
University of Iceland, Sturlugata 7,
101 Reykjavík, Iceland
e-mail: [email protected]
Introduction
Genetic variation and genealogy of high latitude organism have been strongly influenced by climatic
oscillations during the Pliocene and Pleistocene
(Bernatchez and Dodson 1991; Hewitt 1996; Avise
and Walker 1998; Wares and Cunningham 2001;
Pálsson et al. 2009). The climatic oscillations have
caused population bottlenecks for many species
followed by a period of expansion. Genetic variation
is lost through extinction of lineages during bottleneck
period generating shallow genealogy and reducing the
effective population size, Ne. During period of population expansion however the probability of lineage
extinction is reduced and genetic variation increases in
a population (Avise 2000). The expected pattern of
genetic variation as a result of repeated demographic
changes is a shallow genealogy with many rare types.
This is indeed the observed pattern in many marine
fish species (e.g. Sigurgíslason and Árnason 2003;
Árnason 2004; Pálsson et al. 2009; Liu et al. 2010,
Eiríksson unpubl. data). Many studies have demonstrated a lack of spatial genetic structure in marine
species indicating high level of gene flow among
groups across long distances (for overview see Reiss
et al. 2009). This has been attributed to lack of physical barriers in the marine environment and high dispersal rate for eggs and larvae with ocean currents
(Waples 1987). Genetic uniformity can also be
maintained if population size is large (large Ne) as it
will reduce the rate of genetic drift. A long time may
101
Paper V
104
thus be needed in order for genetic differentiation to
become established among populations of large size.
Knowledge of the population genetic structure of
harvested fish species is important for responsible management (Reiss et al. 2009). Fish distribution in the
North Atlantic is affected by environmental conditions
and changes have been observed, in particular in the last
few decades, most likely due to climate change (Rose
2005; ter Hofstede et al. 2010). Understanding of gene
flow among geographic regions may be useful for
predicting the effects of shifts in distributions.
Whiting Merlangius merlangus is a commercially
important fish species in the North Atlantic especially
in the North Sea. It is distributed from northern Norway
and Iceland in the north to the Bay of Biscay in the
south. Whiting is a shallow water species mostly found
at 10–200 m depth (Jónsson 1992). It has a long pelagic
phase early in life compared to related gadoids such as
Atlantic cod Gadus morhua, haddock Melanogrammus
aeglefinus and saithe Pollachius virens. Potentially this
results in a higher dispersal rate (Hislop 1984). Age at
maturation is variable among geographic localities,
however, in general maturation is reached at 2–4 years
overall, earlier in the southern part of its distribution
(Hislop 1984; Jónsson 1992; Cohen et al. 1990). Due to
its commercial importance many studies have focused
on whiting biology in the North East Atlantic (Cohen et
al. 1990). Tag and recapture studies and levels of parasite infestation in different localities have been
interpreted as reflecting limited migrations among localities (Hislop and MacKenzie 1976). Thus two distinct
populations have been assumed to exist north and south
of Dogger Bank in the North Sea (Hislop and
MacKenzie 1976). Pilcher et al. (1989) described spatial
and seasonal variation in parasite prevalence and infestation intensity within the North Sea. A latitudinal cline
was observed for infestation of Diclidophora merlangi’s
infections with infestation prevalence and intensity increasing steadily from south to north. A population
genetic study on whiting, using three microsatellite loci,
showed significant difference between sampling locations within the North Sea for two loci (Rico et al. 1997).
Charrier et al. (2007) used seven microsatellite markers
for whiting in the North East Atlantic but limited genetic
differentiation among sampling localities was observed,
indicating high level of gene flow over long geographic
distances. However, significant genetic difference was
also observed among sampling stations within the North
Sea, albeit with low FST values.
102
Environ Biol Fish (2014) 97:103–110
In the present study genetic variation at a 621 base
pair fragment of the cytochrome c oxidase subunit I
(COI) mitochondrial gene in whiting from south
Iceland, north Norway and the North Sea was examined. Demographic factors and spatial genetic variation were examined. The findings were compared to
observed genetic variation in related species (Árnason
2004; Pálsson et al. 2009; Liu et al. 2010, Eiríksson
unpubl. data).
Materials and methods
Fish samples
Whiting was sampled from Icelandic and Norwegian
waters and from the North sea (Table 1). In all cases gill
tissue samples were preserved in 96 % ethanol. It can be
noted that samples from Norway were collected in 1992,
14 years earlier than those collected in North sea.
However, this should not be a concern when analyzing
historical population genetic breeding structure.
Molecular methods
DNA was isolated using chelex method (Walsh et al.
1991) with some modifications. A pair of primers,
FishF1-5′TCAACCAACCACAAAGACATTGGCA
C3′ and FishR1-5′TAGACTTCTGGGTGGCCA
AAGAATCA3′, was used to amplify a 621 base pair
fragment corresponding to positions 5530–6150 in
whiting complete mitochondrial genome (Roques et al.
2006; GenBank accession number: NC_007395.1). The
primers used were developed by Ward et al. (2005) and
have been used for many different fish species. A 19 μl
PCR reaction mix contained 0.17 mM dNTP, 11 μg/μl
Table 1 Whiting samples from different areas. The whiting
from the Icelandic and Norwegian waters were sampled in a
single sampling location. Whiting from North Sea were sampled
in seven different locations (all south of Dogger Bank), the
position is given as an average of the latitude and the longitude
of the sampling locations. Location is given in degrees and
decimal minutes
Area
N
Month
Year
Location
Iceland
31
October
2004
64°39N,23°04W
Norway
78
September
1992
70°6N,21°43E
North Sea
30
January
2006
53°88N,6°39E
Environ Biol Fish (2014) 97:103–110
105
and sequence data was viewed using Consed (Gordon et
al. 1998) and Seaview (Galtier et al. 1996). Trace files
for all observed haplotypes were inspected manually,
the sequence integrity confirmed and bad sequences
were rejected. The software Network 4.5.1.6 was used
to produce median joining network among haplotypes
(Bandelt et al. 1999). MEGA 4 (Tamura et al. 2007) was
used for translation of nucleotide sequence to amino
acid sequence for the observed haplotypes and for the
counting of synonymous and nonsynonymous sites.
Analysis of Molecular variation (AMOVA), based on
the method developed by Excoffier et al. (1992), was
carried out using Arlequin 3.5.1.2 (Excoffier and
Lischer 2010) both using genetic distance (Tamura and
Nei 1993) and the haplotype frequency only. In both
cases 10000 permutations were used to test the genetic
structure of the observed sample. Pairwise FST comparison was made among countries. In order to test the
significance of the FST between countries a null distribution of FST was generated by 10000 permutations.
Gene flow, M=Nem, was estimated among the countries
(where m is migration rate and M is the absolute number
of migrants per subpopulation per generation). The
migration M is derived directly from its relationship
with the FST as M=1− FST /2FST , as calculated in
Arlequin. Selective neutrality was tested using Fu’s
FS test (number of simulations=1000), Chakraborty’s
Bovine serum albumin (BSA), 0.36 pM of each primer
(FishF1, FishR1), and 0.05 U Taq Polymerase buffered
with 10×ThermoPol buffer (New England BioLabs
(NEB), #M0267) together with DNA template (concentration ca. 0.26 ng/μl in reaction mix) and distilled and
deionized water. The reaction mix was prepared on ice
and immediately placed in a thermocycler when ready.
The DNA was denatured at 94 °C for 5 min in the first
cycle followed by 35 cycles of denaturation, annealing
at 54 °C for 30 s and extension at 72 °C for 1 min. The
cycles were followed by 7 min extension period at
72 °C. A 5 μl of the product was run for 10 min at
90 Volts on an 1.5 % agarose gel containing ethidium
bromide. The gel was then inspected under UV light for
amplified DNA. PCR products were enzymatically purified using Exonuclease I (NEB, #M0293L) and
Antarctic Phosphatase (NEB, #M0289L). The purification was followed by direct sequencing using Applied
Biosystems-BigDye Terminator v3.1 kit (and FishF1
primer) using standard protocol with modifications.
The reaction product was analyzed on ABI-3100 automatic sequencer.
Data analysis
Base calling, data assembling and sequence alignment
was carried out in Phred and Phrap (Ewing et al. 1998)
Table 2 Segregating sites observed in a 621 base pair fragment
of the COI for whiting. The number above each segregating site
is equivalent to the site number as recorded for whiting
complete mitochondrial genome (Roques et al. 2006). N, frequency of each haplotype in the overall sample
Haplotype
5
5
6
7
5
7
0
3
5
7
4
8
5
7
6
0
5
8
6
2
5
8
8
9
5
9
9
1
6
0
0
6
6
1
3
8
6
1
4
2
N
H01
G
C
C
C
G
C
C
C
G
C
83
H02
.
.
.
.
.
.
.
G
.
.
31
H03
.
T
.
.
.
.
.
G
.
.
16
H04
.
T
.
.
.
.
.
.
.
.
1
H05
.
.
T
.
.
.
.
G
.
.
1
H06
.
.
.
.
.
T
.
.
.
.
1
H07
.
.
.
.
.
.
T
.
.
.
1
H08
.
.
.
.
.
.
.
G
.
T
1
H09
.
.
.
.
.
.
.
.
A
.
1
H10
.
.
.
.
C
.
.
G
.
.
1
H11
.
.
.
A
.
.
.
.
.
.
1
H12
A
T
.
.
.
.
.
G
.
.
1
103
Paper V
106
Environ Biol Fish (2014) 97:103–110
Fig. 1 The overall relationship among the 12 observed
COI haplotypes. The size of
circles are proportional to
the frequency of the given
haplotype (smallest, N=1).
Different haplotypes are
represented with different
shades. Black: H01, dark
gray: H02, light gray: H03.
Rare haplotypes are represented with white colour.
Lines between haplotypes
reflect mutational steps between alleles
test and Tajima’s D test (number of simulations=
1000), implemented in Arlequin.
Demographic population expansion was studied using
the distribution of pairwise nucleotide mismatch (Rogers
and Harpending 1992). The observed data was tested
against simulated distributions predicting sudden population expansion as described in Schneider and Excoffier
(1999). The mismatch calculations were carried out in
Arlequin. If a mismatch distribution does not deviate from
a model predicting population sudden expansion τ can be
used to estimate the time from expansion: t=τ/2μ, where t
is time in years, τ is mode of the mismatch distribution and
μ is mutation rate in substitutions year−1(Rogers and
Harpending 1992). In the analysis synonymous substitution rate μ=3.86×10−8 site−1year−1 was used, estimated
for Atlantic cod (Árnason 2004), as it is likely to reflect
neutral genetic variation. The population parameter θ was
estimated using two different methods: θH based on observed homozygosity (or the probability of two randomly
chosen sequences to be identical) and θπ is based on the
pairwise nucleotide mismatches. Estimations of θ were
carried out in Arlequin. θ is the effective population size
Ne scaled with mutation rate. For mtDNA θ=2Neμ, where
Ne is the effective population size and the μ is the overall
mutation rate. Effective population size was calculated
using the above relationship between θ and Ne.
JN168869–JN168880). Three of the haplotypes were
found at high frequencies (>5 %). The haplotypes form
a shallow geneaology (Fig. 1). All but one mutations
were synonymous. The nonsynonymous mutation was
found as a singleton (H12) at site 5567: Serine ↔
Asparagine. Similar molecular diversity was observed
in the samples from the different sampling sites
(Table 3). Estimation of θ did not vary greatly among
countries (Table 4) and reflect small effective population
size (mean Ne =4.23×104 and 3.20×104 when based on
θH and θπ respectively).
Fu’s FS test for the pooled sample showed departure
from mutation-drift equilibrium (FS =−7.15, P=0.005)
were observed number of alleles is higher that expected.
The same is true for Chakraborty’s test (P=0.006).
Tajima’s D was negative but not significantly different
from neutral expectation (D=−1.353, P=0.067).
Population genetic structure —spatial variation
Comparison of genetic variation among countries
showed no genetic differentiation, tested both using genetic distance and haplotype frequency only (Table 5,
Fig. 2). Pairwise FST between countries were all negative
Table 3 Whiting molecular diversity at 621bp fragment of the
COI gene. N, Sample size; h, haplotype diversity; π, nucleotide
diversity
Results
Genetic variation
In total 10 segregating sites were observed defining 12
haplotypes (Table 2; GenBank accession numbers:
104
π±S.E.(×100)
Sampling area
N
h±S.E.
Iceland
30
0.58±0.08
0.12±0.10
Norway
78
0.60±0.05
0.13±0.11
North Sea
31
0.59±0.09
0.15±0.12
Environ Biol Fish (2014) 97:103–110
107
Table 4 Estimates of θ for whiting from Iceland, Norway and
the North Sea
Statistic
Iceland
Norway
North Sea
θH
1.02
1.10
1.08
σθH
0.33
0.24
0.39
θπ
0.72
0.81
0.92
σθπ
0.62
0.66
0.73
(interpreted as close to zero) reflecting no genetic differentiation among countries. Therefore gene flow, M,
among countries was estimated at infinity.
The observed mismatch distribution for whiting is
unimodal and does not deviate significantly from the
sudden expansion model (PSSD =0.24, Fig. 3). The
mode of the distribution τ is 0.891 (95 % CI=0.609–
1.312). Based on this the time since sudden expansion
was estimated to be 70 Kyr (95 % CI=48–103 Kyr).
Discussion
Observed genetic variation in whiting indicate population sudden expansion estimated to originate within the
middle of the most recent glaciation period, extending
from approximately 110–10 Kyr before present (Petit et
al. 1999; Andersen et al. 2004). This is similar to what
has been observed in Atlantic cod (Bigg et al.
2008; Carr and Marshall 2008), Greenland cod
Gadus ogac (Pálsson et al. 2009) and Pacific cod
Gadus macrocephalus (Liu et al. 2010) and other related
species (Eiríksson, unpubl. data). If the estimated timing
of sudden expansion is correct it seems that factors other
than the climatic oscillations alone may be responsible
for the demographic history in whiting as well as for
other related species. This may be due to complicated
effects of water temperature and water level variation as
well as interaction among different fish species.
The observed polymorphism in the present project
is mostly at synonymous sites and as such natural
selection is not likely to explain the observed pattern.
Although Fu’s FS and Chakraborty’s test indicate departure from selective neutrality other factors, such as
demographic history, may be responsible for this outcome. Both tests use θ estimators to predict the total
number of haplotypes in a sample. High number of
alleles (haplotypes) are expected in expanding
populations as the probability of lineage extinction is
reduced (Avise 2000). The tests are known to be
sensitive to sudden population expansion and as the
data show good fit to the sudden expansion model this
is a more likely explanation for the high number of
observed haplotypes. The same has been observed in
many other related species (Árnason 2004; Pálsson et
al. 2009, Eiríksson unpubl. data).
The present study indicates high level of gene flow
for whiting among geographic localities in the North
East Atlantic. No indication of population genetic
substructure was revealed. The observed effective
population size, Ne, is similar among the countries
and much smaller than the actual population size as
has been observed for other fish species (e.g., Árnason
2004). The high level of gene flow might be attributed
to the long pelagic phase of whiting at young age
(Hislop 1984). Genetic uniformity among groups can
be maintained by regular migrations among geographic
regions and the number of migrants does not need to be
large. In fact, Wright (1931) theoretically showed that an
exchange of a single individual per subpopulation every
second generation could be sufficient to prevent genetic
differentiation of neutral variation between groups.
The evidence of genetic uniformity among samples
across long geographical distances throughout the distribution range of whiting is at variance with a potential small scale spatial structure as has been suggested
in the North Sea (Hislop and MacKenzie 1976; Pilcher
Table 5 Whiting AMOVA among countries. Based on Tamura and Nei’s genetic distance (Tamura and Nei 1993) (upper) and based on
haploype frequency only (lower)
Source of variation
d.f.
Sum of squares
Percentage of variation
Ф/F statistic
P
ФST =−0.0067
0.901
Among countries
2
0.28
−1.65
Within countries
136
55.75
101.65
ФIT =0.4100
FST =−0.0048
Among countries
2
0.19
−1.67
Within countries
136
40.11
101.67
FIT =0.2950
105
0.921
Paper V
108
Environ Biol Fish (2014) 97:103–110
Fig. 2 Comparison of whiting COI haplotype frequency among countries.
Different haplotypes are
represented with different
shades. Black: H01, dark
gray: H02, light gray: H03.
White represents rare
haplotypes
et al. 1989). The present study is, however, not conclusive on this since sampling was not carried out in
different parts of the North Sea. Parasite composition
of whiting in the North Sea may suggest separate
populations but seasonal variation shows that the parasite composition can change over relatively short
periods of time (Pilcher et al. 1989). Therefore a
considerable migration among different sites within
the North Sea may take place, followed by change in
parasite infestation pattern due to different environments, without changing the observed pattern.
Results of microsatellite studies for whiting indicate
high level of gene flow as the present project also does
(Rico et al. 1997; Charrier et al. 2007). However, in
contrast to the present study a small scale spatial structure was indicated in earlier studies (Rico et al. 1997;
Charrier et al. 2007). In both studies natural selection is
considered unlikely for explaining the observed genetic
variation, but selective neutrality was not tested (Rico et
al. 1997; Charrier et al. 2007). Charrier et al. (2007)
reported that only one of the seven microsatellite loci
that they used (Pop18a) showed significant global genetic differentiation among samples (FST =0.0052, P<
0.05). Although this is a very low FST value it is nevertheless considerably higher than found for the other loci,
making it a possible outlier and thus potentially under
natural selection (Beaumont and Nichols 1996). Rico et
al. (1997) provided the allele frequency of the three
microsatellite loci used in their study. The frequency
distribution of alleles showed clear deviation from
Ewen’s prediction for allele frequency distribution for
selective neutrality for two of the loci (Mmer-UEAW02
and Gmo2). Highly significant deviation from HWE was
observed for all the loci in the study (Rico et al. 1997).
Both of these phenomena may be due to natural selection
106
affecting the observed genetic variation. Although selective neutrality is very common for a polymorphism at a
molecular level (as predicted by Kimura 1968) natural
selection can shape genetic variation (Hartl and Clark
2006). It has been shown that in many cases
microsatellites may have functional importance and
may thus be under direct natural selection (Li et al.
2002). It is also common to find microsatellites within
genes where they are likely to be in linkage disequilibrium with coding regions that are potentially under natural
selection (Li et al. 2004). For Atlantic cod it has been
shown that hitch-hiking selection can affect microsatellite
variation (Nielsen et al. 2006). It is thus possible that a
spatial genetic structure observed in the microsatellites
studies (Rico et al. 1997; Charrier et al. 2007) was shaped
Fig. 3 Pairwise nucleotide mismatch distribution for a 621 base
pair COI fragment in whiting and prediction of a sudden expansion model expressed with line and dots
Environ Biol Fish (2014) 97:103–110
by heterogeneous environment and reflect different habitats rather than the breeding structure of whiting.
The genetic variation examined in the present study
is almost exclusively synonymous and it is thus unlikely
that the observed polymorphismis affected by natural
selection. The microsatellite results for whiting need to
be interpreted with caution as recognized by Rico et al.
(1997). The present study and the main findings of the
microsatellite studies (Rico et al. 1997; Charrier et al.
2007) indicate that high levels of gene flow, over long
geographic distances, is a likely explanation for the
observed low levels of genetic differentiation in whiting.
Acknowledgments We are grateful to Kristján Kristinsson at
Icelandic Marine Research Institute (MRI), Remment ter Hofstede
at Institute forMarine Resources and Ecosystem Studies
(IMARES) in the Netherlands and Jarle Mork at the Norwegian
University of Science and Technology (NTNU) in Trondheim,
Norway, for assisting in providing samples for the present project.
We are thankful to Snæbjörn Pálsson for useful discussions during
this work and members of the population genetics laboratory at the
University of Iceland for help with molecular analysis during the
work. We also thank two anonymous reviewers for their comments
on the paper.
This project was supported by the Icelandic research fund,
the Icelandic research fund for graduate students of The Icelandic Centre for Research and the Icelandic Marine Research
Institute.
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DNA barcoding Australia’s fish species. Phil Trans R Soc
B 360:1847–1857
Wares JP, Cunningham CW (2001) Phylogeography and historical ecology of the North Atlantic intertidal. Evolution
55:2455–2469
Wright S (1931) Evolution in mendelian populations. Genetics
16:97–159
Appendix I. Supplementary tables
Appendix I. Supplementary tables
109
Appendix I. Supplementary tables
Table 1.2. Paper I. Microsatellite loci used in the present study. The amplification was
carried out in two multiplexes (MP). Listed are the primer sequences and fluorescence
label, concentration of the primers used [Primer], the size range of alleles, the number of observed alleles (k), the expected and observed heterozygosity (Hexp and Hobs ,
respectively), and reference for primers.
Locus
Gmo8
MP
1
Gmo19
1
Gmo35
1
Gmo37
1
Tch11
1
Gmo2
2
Gmo3
2
Gmo34
2
Gmo132
2
Tch13
2
Primer seq.(50 –30 )
R: GGGGGAGGCATCTGTCATTCA
F: GCAAAACGAGATGCACAGACACC
R: GTCTTGCCTGTAAGTCAGCTTG
F: CACAGTGAAGTGAACCCACTG
R: CCTTATCATGTACGTTGTTAAC
F: GGAGGTGCTTTGAAGATG
R: CGTGGGATACATGGGTACT
F: GGCCAATGTTTCATAACTCT
R: TCGAGTTCAGGTGGACAA
F: ATCCATTGGTGTTTCAAC
R: GTGTGAGATGACTGTGTCG
F: CCCTCAGATTCAAATGAAGGA
R: GCAGCACGAGAGAGCTATTCCTC
F: AGGCACGCAGGTGGACAGGAAC
R: GGTTGGACCTCATGGTGAA
F: TCCACAGAAGGTCTCCTAA
R: CGAAAGGACGAGCCAATAAC
F: GGAACCCATTGGATTCAGGC
R: AATCCACTGGTGCAGACC
F: TTTCCGATGAGGTCATGG
50 -Label
NED
VIC
6-FAM
6-FAM
PET
NED
6-FAM
6-FAM
VIC
PET
[Primer]
0.10µM
Size range(bp)
119-331
k
54
Hexp
0.931
Hobs
0.928
Reference
Miller et al. (2000)
0.15µM
127-235
28
0.922
0.806
Miller et al. (2000)
0.20µM
121-154
11
0.831
0.805
Miller et al. (2000)
2.50µM
233-315
23
0.839
0.728
Miller et al. (2000)
0.30µM
123-244
27
0.935
0.846
O’Reilly et al. (2000)
0.25µM
105-153
23
0.846
0.717
Brooker et al. (1994)
0.20µM
165-213
12
0.159
0.155
Miller et al. (2000)
0.20µM
90-123
10
0.385
0.363
Miller et al. (2000)
0.30µM
100-181
33
0.574
0.588
Brooker et al. (1994)
0.30µM
84-185
47
0.920
0.903
O’Reilly et al. (2000)
Table 1.3. Paper I. Statistical power for detecting genetic differentiation FST using 9
microsatellite loci. The power is expressed as the proportion of 1000 simulations that
result in a statistically significant difference between two populations at an α-level of
0.05. The Gmo34 locus was excluded from base population in the analysis.
True FST
0.0000
0.0003
0.0004
0.0005
0.0006
0.0007
0.0008
0.0009
X2
0.046
0.743
0.883
0.966
0.992
0.998
0.998
1.000
Fisher’s exact test
0.061
0.753
0.882
0.962
0.990
0.996
0.999
1.000
111
Haplotype
cytb24.hap.001
cytb24.hap.002
cytb24.hap.003
cytb24.hap.004
cytb24.hap.005
cytb24.hap.006
cytb24.hap.007
cytb24.hap.008
cytb24.hap.009
cytb24.hap.010
cytb24.hap.011
cytb24.hap.012
cytb24.hap.013
cytb24.hap.014
cytb24.hap.015
cytb24.hap.016
cytb24.hap.017
cytb24.hap.018
cytb24.hap.019
cytb24.hap.020
cytb24.hap.021
cytb24.hap.022
cytb24.hap.023
cytb24.hap.024
cytb24.hap.025
cytb24.hap.026
cytb24.hap.027
cytb24.hap.028
cytb24.hap.029
cytb24.hap.030
cytb24.hap.031
cytb24.hap.032
cytb24.hap.033
cytb24.hap.034
cytb24.hap.035
cytb24.hap.036
cytb24.hap.037
cytb24.hap.051
cytb24.hap.038
cytb24.hap.039
cytb24.hap.040
cytb24.hap.041
cytb24.hap.042
cytb24.hap.043
cytb24.hap.044
cytb24.hap.045
cytb24.hap.046
cytb24.hap.047
cytb24.hap.048
cytb24.hap.049
cytb24.hap.050
cytb24.hap.052
cytb24.hap.053
cytb24.hap.054
cytb24.hap.055
cytb24.hap.056
cytb24.hap.057
cytb24.hap.058
cytb24.hap.059
cytb24.hap.060
cytb24.hap.061
cytb24.hap.062
cytb24.hap.063
cytb24.hap.064
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N
1122
431
376
296
124
22
18
16
11
10
9
8
8
7
7
6
5
5
4
4
4
4
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1
Appendix I. Supplementary tables
112
Table 1.4. 1A. Paper II. Atlantic cod segregating sites table, part 1 of 4 (part 1: Table 1.4; part 2: Table 1.5; part 3: Table 1.6; part 4: Table
1.7).Segregating sites of the cytochrome b fragment (328 base pairs) defining 128 haplotypes found among 2656 individual Atlantic cod,
Gadus morhua, sampled around Iceland. The site numbers (above the horizontal line) refer to the equivalent site, plus 14000, in the complete
mtDNA sequence (GenBank accession number: X99772.1). The combined frequency among sampling localities is presented, N. The dots
represent identity of a sequence to the most common haplotype cytb24_hap_001.
Table 1.5. 1B. Paper II. Atlantic cod segregating sites table, part 2 of 4 (part 1: Table 1.4; part 2: Table 1.5; part 3: Table 1.6; part 4: Table
1.7). Segregating sites of the cytochrome b fragment (328 base pairs) defining 128 haplotypes found among 2656 individual Atlantic cod,
Gadus morhua, sampled around Iceland. The site numbers (above the horizontal line) refer to the equivalent site, plus 14000, in the complete
mtDNA sequence (GenBank accession number: X99772.1). The combined frequency among sampling localities is presented, N. The dots
represent identity of a sequence to the most common haplotype cytb24_hap_001.
5
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6
8
8
C
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
T
.
.
.
.
.
.
.
.
6
9
1
G
.
.
T
.
T
T
.
.
.
.
A
.
A
.
.
.
.
.
.
.
T
.
.
.
.
.
.
.
.
.
.
.
.
.
T
T
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
T
T
T
C
.
.
.
.
N
1122
431
376
296
124
22
18
16
11
10
9
8
8
7
7
6
5
5
4
4
4
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
1
1
1
1
Appendix I. Supplementary tables
113
Haplotype
cytb24.hap.001
cytb24.hap.002
cytb24.hap.003
cytb24.hap.004
cytb24.hap.005
cytb24.hap.006
cytb24.hap.007
cytb24.hap.008
cytb24.hap.009
cytb24.hap.010
cytb24.hap.011
cytb24.hap.012
cytb24.hap.013
cytb24.hap.014
cytb24.hap.015
cytb24.hap.016
cytb24.hap.017
cytb24.hap.018
cytb24.hap.019
cytb24.hap.020
cytb24.hap.021
cytb24.hap.022
cytb24.hap.023
cytb24.hap.024
cytb24.hap.025
cytb24.hap.026
cytb24.hap.027
cytb24.hap.028
cytb24.hap.029
cytb24.hap.030
cytb24.hap.031
cytb24.hap.032
cytb24.hap.033
cytb24.hap.034
cytb24.hap.035
cytb24.hap.036
cytb24.hap.037
cytb24.hap.051
cytb24.hap.038
cytb24.hap.039
cytb24.hap.040
cytb24.hap.041
cytb24.hap.042
cytb24.hap.043
cytb24.hap.044
cytb24.hap.045
cytb24.hap.046
cytb24.hap.047
cytb24.hap.048
cytb24.hap.049
cytb24.hap.050
cytb24.hap.052
cytb24.hap.053
cytb24.hap.054
cytb24.hap.055
cytb24.hap.056
cytb24.hap.057
cytb24.hap.058
cytb24.hap.059
cytb24.hap.060
cytb24.hap.061
cytb24.hap.062
cytb24.hap.063
cytb24.hap.064
Haplotype
cytb24.hap.001
cytb24.hap.065
cytb24.hap.066
cytb24.hap.067
cytb24.hap.068
cytb24.hap.069
cytb24.hap.070
cytb24.hap.071
cytb24.hap.072
cytb24.hap.073
cytb24.hap.074
cytb24.hap.075
cytb24.hap.076
cytb24.hap.077
cytb24.hap.079
cytb24.hap.080
cytb24.hap.081
cytb24.hap.082
cytb24.hap.083
cytb24.hap.084
cytb24.hap.085
cytb24.hap.086
cytb24.hap.087
cytb24.hap.088
cytb24.hap.089
cytb24.hap.090
cytb24.hap.091
cytb24.hap.092
cytb24.hap.093
cytb24.hap.094
cytb24.hap.095
cytb24.hap.096
cytb24.hap.097
cytb24.hap.098
cytb24.hap.099
cytb24.hap.100
cytb24.hap.101
cytb24.hap.102
cytb24.hap.103
cytb24.hap.104
cytb24.hap.105
cytb24.hap.106
cytb24.hap.107
cytb24.hap.108
cytb24.hap.109
cytb24.hap.110
cytb24.hap.111
cytb24.hap.113
cytb24.hap.114
cytb24.hap.115
cytb24.hap.116
cytb24.hap.117
cytb24.hap.118
cytb24.hap.119
cytb24.hap.120
cytb24.hap.121
cytb24.hap.122
cytb24.hap.124
cytb24.hap.125
cytb24.hap.126
cytb24.hap.127
cytb24.hap.128
cytb24.hap.129
cytb24.hap.130
cytb24.hap.131
3
6
7
T
.
.
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.
C
.
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.
.
3
7
3
C
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T
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3
7
9
A
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3
8
5
T
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C
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3
8
8
T
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.
C
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.
.
3
9
0
G
.
.
.
.
.
.
.
.
.
.
.
A
.
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.
.
.
.
.
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.
A
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.
3
9
1
C
.
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.
T
.
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3
9
2
G
.
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.
A
.
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.
.
.
.
.
.
.
.
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.
.
.
.
.
.
.
.
.
3
9
8
G
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
A
.
.
.
.
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.
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.
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.
.
.
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.
.
.
.
.
.
.
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.
.
A
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4
0
9
C
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
T
.
.
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.
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.
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.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4
1
0
G
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
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.
.
A
.
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.
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.
.
.
.
.
.
A
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4
1
8
C
.
.
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.
T
.
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.
.
.
.
.
G
.
.
.
4
2
7
A
.
.
.
.
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.
.
.
.
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.
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.
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.
.
.
G
.
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.
.
.
.
4
3
0
A
.
.
.
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.
.
.
.
.
.
.
.
.
G
.
.
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.
.
4
3
6
A
.
.
.
.
.
.
.
.
.
.
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.
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.
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.
G
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.
.
4
4
5
C
.
.
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.
.
.
.
.
.
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.
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.
G
.
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.
.
4
5
1
T
.
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.
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.
.
C
.
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4
5
4
A
.
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.
G
T
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4
5
7
C
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4
6
0
T
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C
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4
6
3
C
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T
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4
6
6
A
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4
7
5
A
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4
8
1
A
.
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G
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G
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4
8
7
A
.
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G
.
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C
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G
.
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G
.
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.
4
8
8
C
.
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.
T
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.
T
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4
9
0
A
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4
9
6
A
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5
0
2
A
.
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.
G
.
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.
.
G
G
.
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.
5
0
8
T
C
.
C
.
.
C
.
C
.
.
C
C
C
C
C
C
C
.
.
.
.
.
.
.
.
.
.
.
C
C
.
C
.
C
.
C
C
C
.
.
.
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.
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.
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.
C
.
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5
1
4
A
.
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G
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1
Appendix I. Supplementary tables
114
Table 1.6. 2A. Paper II. Atlantic cod segregating sites table, part 3 of 4 (part 1: Table 1.4; part 2: Table 1.5; part 3: Table 1.6; part 4: Table
1.7). Segregating sites of the cytochrome b fragment (328 base pairs) defining 128 haplotypes found among 2656 individual Atlantic cod,
Gadus morhua, sampled around Iceland. The site numbers (above the horizontal line) refer to the equivalent site, plus 14000, in the complete
mtDNA sequence (GenBank accession number: X99772.1). The combined frequency among sampling localities is presented, N. The dots
represent identity of a sequence to the most common haplotype cytb24_hap_001.
Table 1.7. Paper II. Atlantic cod segregating sites table, part 4 of 4 (part 1: Table 1.4; part 2: Table 1.5; part 3: Table 1.6; part 4: Table 1.7).
Segregating sites of the cytochrome b fragment (328 base pairs) defining 128 haplotypes found among 2656 individual Atlantic cod, Gadus
morhua, sampled around Iceland. The site numbers (above the horizontalline) refer to the equivalent site, plus 14000, in the complete mtDNA
sequence (GenBank accession number: X99772.1). The combined frequency among sampling localities is presented, N. The dots represent
identity of a sequence to the most common haplotype cytb24_hap_001.
5
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T
T
T
T
T
T
N
1122
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Appendix I. Supplementary tables
115
Haplotype
cytb24.hap.001
cytb24.hap.065
cytb24.hap.066
cytb24.hap.067
cytb24.hap.068
cytb24.hap.069
cytb24.hap.070
cytb24.hap.071
cytb24.hap.072
cytb24.hap.073
cytb24.hap.074
cytb24.hap.075
cytb24.hap.076
cytb24.hap.077
cytb24.hap.079
cytb24.hap.080
cytb24.hap.081
cytb24.hap.082
cytb24.hap.083
cytb24.hap.084
cytb24.hap.085
cytb24.hap.086
cytb24.hap.087
cytb24.hap.088
cytb24.hap.089
cytb24.hap.090
cytb24.hap.091
cytb24.hap.092
cytb24.hap.093
cytb24.hap.094
cytb24.hap.095
cytb24.hap.096
cytb24.hap.097
cytb24.hap.098
cytb24.hap.099
cytb24.hap.100
cytb24.hap.101
cytb24.hap.102
cytb24.hap.103
cytb24.hap.104
cytb24.hap.105
cytb24.hap.106
cytb24.hap.107
cytb24.hap.108
cytb24.hap.109
cytb24.hap.110
cytb24.hap.111
cytb24.hap.113
cytb24.hap.114
cytb24.hap.115
cytb24.hap.116
cytb24.hap.117
cytb24.hap.118
cytb24.hap.119
cytb24.hap.120
cytb24.hap.121
cytb24.hap.122
cytb24.hap.124
cytb24.hap.125
cytb24.hap.126
cytb24.hap.127
cytb24.hap.128
cytb24.hap.129
cytb24.hap.130
cytb24.hap.131
Appendix I. Supplementary tables
Table 1.8. Paper II. Amino acid substitutions observed in the analysis of Atlantic cod
mtDNA data of various haplotypes and their frequencies (N).
Haplotype
cytb24.hap.041
cytb24.hap.067
cytb24.hap.070
cytb24.hap.076
cytb24.hap.083
cytb24.hap.094
cytb24.hap.099
cytb24.hap.100
cytb24.hap.108
cytb24.hap.109
cytb24.hap.115
cytb24.hap.116
cytb24.hap.117
cytb24.hap.122
116
Site
14542
14587
14588
14390
14398
14522
14410
14589
14398
14392
14523
14410
14390
14686
DNA sub.
G
A
T
G
G
A
G
A
G
G
G
G
G
A
↔
↔
↔
↔
↔
↔
↔
↔
↔
↔
↔
↔
↔
↔
AA Sub.
A
G
C
A
A
G
A
T
A
A
C
A
A
G
V
M
M
S
V
E
A
M
V
A
E
A
S
I
↔
↔
↔
↔
↔
↔
↔
↔
↔
↔
↔
↔
↔
↔
N
I
V
T
N
I
G
T
I
I
T
D
T
N
V
2
1
1
1
1
1
1
1
1
1
1
1
1
1
Table 1.9. Paper IV. Haddock segregating sites table, part 1 of 4 (part 1: Table 1.9; part 2: Table 1.10; part 3: Table 1.11; part 4: Table
1.12). Segregating sites of the cytochrome oxidase subunit I fragment (599 base pairs) defining 102 haplotypes found among 884 individual
haddock, Melanogrammus aeglefinus, sampled in the NE-Atlantic ocean. The site numbers (above the horizontal line) refer to the equivalent
site in the complete mtDNA sequence (Genebank accession number: AM489717.1). The combined frequency among sampling localities is
presented, N. The dots represent identity of a sequence to the most common haplotype mel.aeg.CO1-001.
5
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C
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5
8
1
4
C
T
T
T
.
.
T
T
.
T
.
T
.
T
T
T
T
T
T
T
T
T
T
T
T
T
T
.
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.
T
T
T
T
T
T
.
.
T
T
.
.
T
.
.
T
T
T
T
5
8
1
7
A
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
G
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
N
306
182
150
53
29
10
8
7
6
5
5
4
4
4
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Appendix I. Supplementary tables
117
Haplotype
mel.aeg.CO1-001
mel.aeg.CO1-002
mel.aeg.CO1-003
mel.aeg.CO1-004
mel.aeg.CO1-005
mel.aeg.CO1-006
mel.aeg.CO1-007
mel.aeg.CO1-008
mel.aeg.CO1-009
mel.aeg.CO1-010
mel.aeg.CO1-011
mel.aeg.CO1-012
mel.aeg.CO1-013
mel.aeg.CO1-014
mel.aeg.CO1-015
mel.aeg.CO1-016
mel.aeg.CO1-017
mel.aeg.CO1-018
mel.aeg.CO1-019
mel.aeg.CO1-020
mel.aeg.CO1-021
mel.aeg.CO1-022
mel.aeg.CO1-023
mel.aeg.CO1-024
mel.aeg.CO1-025
mel.aeg.CO1-026
mel.aeg.CO1-027
mel.aeg.CO1-028
mel.aeg.CO1-029
mel.aeg.CO1-030
mel.aeg.CO1-031
mel.aeg.CO1-032
mel.aeg.CO1-033
mel.aeg.CO1-034
mel.aeg.CO1-035
mel.aeg.CO1-036
mel.aeg.CO1-037
mel.aeg.CO1-038
mel.aeg.CO1-039
mel.aeg.CO1-040
mel.aeg.CO1-041
mel.aeg.CO1-042
mel.aeg.CO1-043
mel.aeg.CO1-044
mel.aeg.CO1-045
mel.aeg.CO1-046
mel.aeg.CO1-047
mel.aeg.CO1-048
mel.aeg.CO1-049
mel.aeg.CO1-050
Haplotype
mel.aeg.CO1-001
mel.aeg.CO1-002
mel.aeg.CO1-003
mel.aeg.CO1-004
mel.aeg.CO1-005
mel.aeg.CO1-006
mel.aeg.CO1-007
mel.aeg.CO1-008
mel.aeg.CO1-009
mel.aeg.CO1-010
mel.aeg.CO1-011
mel.aeg.CO1-012
mel.aeg.CO1-013
mel.aeg.CO1-014
mel.aeg.CO1-015
mel.aeg.CO1-016
mel.aeg.CO1-017
mel.aeg.CO1-018
mel.aeg.CO1-019
mel.aeg.CO1-020
mel.aeg.CO1-021
mel.aeg.CO1-022
mel.aeg.CO1-023
mel.aeg.CO1-024
mel.aeg.CO1-025
mel.aeg.CO1-026
mel.aeg.CO1-027
mel.aeg.CO1-028
mel.aeg.CO1-029
mel.aeg.CO1-030
mel.aeg.CO1-031
mel.aeg.CO1-032
mel.aeg.CO1-033
mel.aeg.CO1-034
mel.aeg.CO1-035
mel.aeg.CO1-036
mel.aeg.CO1-037
mel.aeg.CO1-038
mel.aeg.CO1-039
mel.aeg.CO1-040
mel.aeg.CO1-041
mel.aeg.CO1-042
mel.aeg.CO1-043
mel.aeg.CO1-044
mel.aeg.CO1-045
mel.aeg.CO1-046
mel.aeg.CO1-047
mel.aeg.CO1-048
mel.aeg.CO1-049
mel.aeg.CO1-050
5
8
2
0
T
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5
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2
6
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5
8
3
2
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C
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5
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3
8
T
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5
8
4
7
A
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G
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5
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5
3
C
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5
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7
7
C
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T
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T
.
T
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5
8
9
9
C
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5
9
1
0
T
.
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.
C
.
.
C
.
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5
9
2
5
A
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G
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5
9
3
1
T
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5
9
4
0
T
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5
9
7
3
T
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5
9
7
9
A
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5
9
8
5
A
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G
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6
0
1
5
T
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6
0
2
1
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6
0
2
4
G
.
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A
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.
.
A
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A
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6
0
3
0
G
.
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A
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.
6
0
3
6
A
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G
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.
6
0
3
9
T
.
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A
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6
0
4
2
T
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C
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6
0
6
0
T
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C
.
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.
C
.
.
C
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C
.
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.
.
.
.
G
6
0
6
2
T
.
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6
0
6
3
C
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6
0
6
6
A
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G
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6
0
7
5
A
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6
0
8
0
A
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.
6
0
8
1
T
.
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C
.
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C
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6
0
8
4
T
.
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A
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.
.
A
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.
A
.
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.
6
0
8
7
C
.
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6
0
9
0
C
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T
.
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.
T
.
6
1
0
8
T
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C
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6
1
1
1
T
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6
1
2
0
A
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6
1
2
3
T
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G
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.
N
306
182
150
53
29
10
8
7
6
5
5
4
4
4
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Appendix I. Supplementary tables
118
Table 1.10. Paper IV. Haddock segregating sites table, part 2 of 4 (part 1: Table 1.9; part 2: Table 1.10; part 3: Table 1.11; part 4: Table
1.12). Segregating sites table for the cytochrome oxidase subunit I fragment (599 base pairs) defining 102 haplotypes found among 884
individual haddock, Melanogrammus aeglefinus, sampled in the NE-Atlantic ocean. The site numbers (above the horizontal line) refer to the
equivalent site in the complete mtDNA sequence (Genebank accession number: AM489717.1). The combined frequency among sampling
localities is presented, N. The dots represent identity of a sequence to the most common haplotype mel.aeg.CO1-001.
Table 1.11. Paper IV. Haddock segregating sites table, part 3 of 4 (part 1: Table 1.9; part 2: Table 1.10; part 3: Table 1.11; part 4: Table
1.12). Segregating sites of the cytochrome oxidase subunit I fragment (599 base pairs) defining 102 haplotypes found among 884 individual
haddock, Melanogrammus aeglefinus, sampled in the NE-Atlantic ocean. The site numbers (above the horizontal line) refer to the equivalent
site in the complete mtDNA sequence (Genebank accession number: AM489717.1). The combined frequency among sampling localities is
presented, N. The dots represent identity of a sequence to the most common haplotype mel.aeg.CO1-001.
5
5
4
8
C
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5
5
5
3
C
.
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5
5
5
6
T
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C
5
5
6
5
G
.
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A
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5
5
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8
A
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5
5
7
4
A
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5
5
9
3
G
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A
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5
5
9
5
T
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C
C
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5
6
0
7
T
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5
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A
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5
6
1
9
C
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5
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2
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A
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5
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2
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C
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5
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3
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C
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5
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4
3
T
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C
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C
5
6
5
8
A
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G
.
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5
6
7
3
A
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5
6
8
8
A
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5
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2
A
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5
6
9
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T
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5
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0
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T
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.
C
.
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.
5
7
2
4
T
.
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5
7
4
5
A
.
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5
7
5
1
C
.
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.
.
5
7
5
4
A
.
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.
.
5
7
6
0
T
.
.
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.
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.
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.
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.
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.
.
.
C
.
.
.
5
7
6
3
T
.
.
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.
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.
.
.
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.
.
.
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.
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.
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.
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.
.
.
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.
.
.
.
.
.
C
C
.
.
.
.
.
.
.
.
.
.
5
7
7
5
G
.
.
.
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.
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.
.
.
.
.
C
.
.
.
.
.
.
.
.
.
.
.
.
5
7
9
0
A
C
C
C
C
C
C
C
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
C
.
.
.
C
C
C
.
.
.
.
C
5
7
9
3
T
.
.
.
.
.
.
.
.
.
.
.
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.
.
.
.
.
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.
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.
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.
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.
.
.
.
.
C
.
.
.
.
.
.
.
.
.
.
.
.
.
5
7
9
6
A
.
.
.
.
.
.
.
G
G
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
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.
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.
.
.
5
7
9
7
G
.
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.
.
.
.
.
.
.
5
8
0
2
A
.
.
.
.
.
.
.
.
.
G
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
.
.
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.
.
.
.
.
.
.
.
.
5
8
0
5
T
.
.
.
.
.
.
G
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
C
C
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5
8
0
8
A
.
.
.
.
.
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.
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.
.
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.
.
.
.
.
.
5
8
1
1
T
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
C
.
.
.
.
C
.
.
.
.
.
.
.
.
.
.
.
5
8
1
4
C
T
T
T
T
T
.
T
.
.
.
T
T
T
T
T
T
T
T
T
T
T
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
T
.
T
.
T
.
.
T
T
T
T
T
T
T
.
T
5
8
1
7
A
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
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.
.
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.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
N
306
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Appendix I. Supplementary tables
119
Haplotype
mel.aeg.CO1-001
mel.aeg.CO1-051
mel.aeg.CO1-052
mel.aeg.CO1-053
mel.aeg.CO1-054
mel.aeg.CO1-055
mel.aeg.CO1-056
mel.aeg.CO1-057
mel.aeg.CO1-058
mel.aeg.CO1-059
mel.aeg.CO1-060
mel.aeg.CO1-061
mel.aeg.CO1-062
mel.aeg.CO1-063
mel.aeg.CO1-064
mel.aeg.CO1-065
mel.aeg.CO1-066
mel.aeg.CO1-067
mel.aeg.CO1-068
mel.aeg.CO1-069
mel.aeg.CO1-070
mel.aeg.CO1-071
mel.aeg.CO1-072
mel.aeg.CO1-073
mel.aeg.CO1-074
mel.aeg.CO1-075
mel.aeg.CO1-076
mel.aeg.CO1-077
mel.aeg.CO1-078
mel.aeg.CO1-079
mel.aeg.CO1-080
mel.aeg.CO1-081
mel.aeg.CO1-082
mel.aeg.CO1-083
mel.aeg.CO1-084
mel.aeg.CO1-085
mel.aeg.CO1-086
mel.aeg.CO1-087
mel.aeg.CO1-088
mel.aeg.CO1-089
mel.aeg.CO1-090
mel.aeg.CO1-091
mel.aeg.CO1-092
mel.aeg.CO1-093
mel.aeg.CO1-094
mel.aeg.CO1-095
mel.aeg.CO1-096
mel.aeg.CO1-097
mel.aeg.CO1-098
mel.aeg.CO1-099
mel.aeg.CO1-100
mel.aeg.CO1-101
mel.aeg.CO1-102
Haplotype
mel.aeg.CO1-001
mel.aeg.CO1-051
mel.aeg.CO1-052
mel.aeg.CO1-053
mel.aeg.CO1-054
mel.aeg.CO1-055
mel.aeg.CO1-056
mel.aeg.CO1-057
mel.aeg.CO1-058
mel.aeg.CO1-059
mel.aeg.CO1-060
mel.aeg.CO1-061
mel.aeg.CO1-062
mel.aeg.CO1-063
mel.aeg.CO1-064
mel.aeg.CO1-065
mel.aeg.CO1-066
mel.aeg.CO1-067
mel.aeg.CO1-068
mel.aeg.CO1-069
mel.aeg.CO1-070
mel.aeg.CO1-071
mel.aeg.CO1-072
mel.aeg.CO1-073
mel.aeg.CO1-074
mel.aeg.CO1-075
mel.aeg.CO1-076
mel.aeg.CO1-077
mel.aeg.CO1-078
mel.aeg.CO1-079
mel.aeg.CO1-080
mel.aeg.CO1-081
mel.aeg.CO1-082
mel.aeg.CO1-083
mel.aeg.CO1-084
mel.aeg.CO1-085
mel.aeg.CO1-086
mel.aeg.CO1-087
mel.aeg.CO1-088
mel.aeg.CO1-089
mel.aeg.CO1-090
mel.aeg.CO1-091
mel.aeg.CO1-092
mel.aeg.CO1-093
mel.aeg.CO1-094
mel.aeg.CO1-095
mel.aeg.CO1-096
mel.aeg.CO1-097
mel.aeg.CO1-098
mel.aeg.CO1-099
mel.aeg.CO1-100
mel.aeg.CO1-101
mel.aeg.CO1-102
5
8
2
0
T
.
.
.
.
C
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
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.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5
8
2
6
T
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
C
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5
8
3
2
T
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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5
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N
306
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
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1
1
1
1
1
1
1
1
1
1
1
1
Appendix I. Supplementary tables
120
Table 1.12. Paper IV. Haddock segregating sites table, part 4 of 4 (part 1: Table 1.9; part 2: Table 1.10; part 3: Table 1.11; part 4: Table
1.12). Segregating sites of the cytochrome oxidase subunit I fragment (599 base pairs) defining 102 haplotypes found among 884 individual
haddock, Melanogrammus aeglefinus, sampled in the NE-Atlantic ocean. The site numbers (above the horizontal line) refer to the equivalent
site in the complete mtDNA sequence (Genebank accession number: AM489717.1). The combined frequency among sampling localities is
presented, N. The dots represent identity of a sequence to the most common haplotype mel.aeg.CO1-001.
Appendix II. Supplementary figures
Appendix II. Supplementary figures
121
150
250
300
(h) Gmo132
Allele size (bp)
150
1200
800
90
100
110
120
Allele size (bp)
(j) Tch13
Frequency
150
Frequency
800
Frequency
260
Allele size (bp)
300
100
120
140
160
Allele size (bp)
180
0
50
220
0
0
0 50
140
50
(i) Tch11
400
250
150
Frequency
300
200
Frequency
100
0
130
30
Allele size (bp)
250
(g) Gmo37
10
120
160
200
Allele size (bp)
240
80 100
140
180
Allele size (bp)
Figure 1.1. Paper I. Bargraph of allele sizes of the microsatellite loci used. (a) Gmo2, (b) Gmo3, (c) Gmo8, (d) Gmo19, (e) Gmo34, (f)
Gmo35, (g) Gmo37, (h) Gmo132, (i) Tch11, (j) Tch13.
123
Appendix II. Supplementary figures
120
Frequency
0
Allele size (bp)
400
(f) Gmo35
200
0
0
210
Allele size (bp)
150
190
100
Frequency
150
200
Frequency
0
170
100
150
Allele size (bp)
(e) Gmo34
50
130
50 100
1000
Frequency
0
500
300
Frequency
100
0
110
(d) Gmo19
400
(c) Gmo8
50
(b) Gmo3
1500
(a) Gmo2
●
●
●
●
●
●
●
●
1.0
(e) Gmo34
●
●
●
●
●
●
●
●
0.8
●
0.8
●
0.8
0.8
●
●
●
●
●
0.8
●
●
(d) Gmo19
1.0
1.0
(c) Gmo8
1.0
1.0
(b) Gmo3
●
●
●
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.2
0.4
0.4
●
0.2
Heterozygosity
0.6
●
●
●
●
●
3
4
5
1
2
3
4
5
0.0
1
(h) Gmo132
1.0
(g) Gmo37
1.0
(f) Gmo35
2
0.0
0.0
1
2
3
4
●
●
●
●
●
●
●
●
●
●
3
4
5
●
●
●
4
5
●
●
●
●
●
1
2
●
●
●
0.8
●
●
●
0.8
0.8
0.8
●
●
●
●
●
2
(j) Tch13
●
●
●
1
(i) Tch11
●
●
5
1.0
5
●
0.8
4
0.2
●
●
1.0
3
●
●
●
●
1.0
2
●
●
●
0.0
1
0.2
●
●
●
●
0.0
0.2
●
●
●
●
●
●
●
●
●
●
●
●
●
0.4
0.4
●
0.4
0.4
0.6
0.6
0.6
0.6
0.6
●
●
0.4
Heterozygosity
●
1
2
3
4
Depth group
5
1
2
3
4
Depth group
5
1
2
3
4
Depth group
5
0.2
0.0
0.2
0.0
0.2
0.0
0.2
0.0
0.0
0.2
●
1
2
3
4
Depth group
5
3
Depth group
Figure 1.2. Paper I. The observed heterozygosity HObs (open circles and dashed trend line) and the expected heterozygosity HExp (filled
circles dotted trend line) for the the different microsatellite loci for Atlantic cod sampled at the south coast of Iceland grouped at different
depth intervals (1:0-50m, 2:50-100m, 3:100-150m, 4:150-200m, 5:>200m). (a)Gmo2, (b)Gmo3, (c)Gmo8, (d)Gmo19, (e)Gmo34, (f)Gmo35,
(g)Gmo37, (h)Gmo132, (i)Tch11, (j)Tch13.
Appendix II. Supplementary figures
124
(a) Gmo2
Appendix III
Appendix III
125
Appendix III
Molecular methods
In the present project I used mainly analysis of mtDNA sequence variation. The
molecular methods used are basically the same in all studies. Following is a detailed
description of the molecular methods used for the study on saithe. This is for my own
reference and might be of help to others.
DNA isolation. DNA was isolated using a chelex method (Walsh et al., 1991). A
stock solution was prepared consisting of 50mg/mL Chelex 100, 0.2% SDS, 10mM
Tris (pH 8) and 0.5mM EDTA. Just before DNA isolation Proteinase K was added
to the mix at final concentration of 200 µg/mL. A small tissue fragment (less than 1
mm) was cut of the preserved gill tissue, drained on a paper towel and placed in a tube
with the 250 µl reaction mix. The mix was then placed at Thermomixer (Eppendorf,
Thermomixer Comfort) at 700 rpm for 3–5 hours (or until no tissue fragments were
visible). After this the mix was placed in a 95◦ C for 5 minutes for denaturation of the
Proteinase K followed by spinning at 3000 rpm for 5 minutes. The supernatant was
carefully removed, leaving the chelex on the bottom of the tube. A 1:19 dilution of the
isolated DNA was prepared and used for PCR (DNA concentration ca. 1 ng/µl).
PCR. A 460 bp fragment was amplified from the 50 region of the cytochrome c
oxidase subunit I (COI) mitochondrial gene using pvL6082-50 CCTGCTGGAGGAGGTGATCC30 and pvH6580-50 CCTGCTGGAGGAGGTGATCC30 . The fragment
is corresponding to sites number 6082-6542 in the saithe complete mtDNA genome
sequence (GenBank accession number: FR751399, Coucheron et al., 2011). Primer
numbers refer to the first fragment sites upstream and downstream of the L and H
primers respectively. A 19µl PCR reaction mix contained 0.17mM dNTP), 0.11mg/µl
BSA, 0.36pM of each primer (L6082, H6580), and 0.05U Taq Polymerase buffered with
10× ThermoPol buffer (New England BioLabs - NEB) together with DNA template
(median concentration 0.26ng/µl in reaction mix). The reaction mix was prepared on
ice and when ready it was immediately placed in a thermocycler (DNA Engine Tetrad 2,
Bio-Rad Laboratories). The DNA was denatured at 94◦ C for 5 minutes followed by 35
cycles of denaturing for 1 minute, annealing at 56◦ C for 30 seconds and extension at
72◦ C for 1 minute. This was followed by 7 minutes extension at 72◦ C.
After the PCR 5µl of the product was run for 10 minutes at 90 Volts on an 1.5%
agarose gel containing ethidium bromide. The gel was inspected under UV light for
amplified DNA. Successfully amplified DNA was then taken for direct DNA sequencing.
Purification of amplified DNA. A sample of 5µl amplified DNA was purified using
2U Exonuclease I (NEB) for the removal of single stranded DNA and 1U Antarctic
phosphatase (NEB) for the removal of 50 phosphoryl groups, dNTPs and pyrophosphate
in the PCR reaction mix (preventing fragments from self-ligating). The reaction was
buffered with Antarctic phosphatase buffer (NEB). The reaction mix was incubated at
38◦ C for 35 minutes followed by 80◦ C denaturation step for 20 minutes. The sample
was then cooled down to room temperature.
Sequencing reaction. Sequencing was carried out using 0.5 µl of the ABI BigDye
Terminator v3.1, 2.75 µl of the ABI BigDye Terminator v3.1 kit 5×buffer, using 0.1
pM of the pvH6580 primer and 5µl of the purified DNA in a total reaction volume of
15µl. The reaction mix was prepared on ice and when ready it was immediately placed
in a thermocycler at an initial 96◦ C for 10 seconds followed by 25 cycles of denaturing
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Appendix III
at 96◦ C for 10 seconds, annealing at 50◦ C for 5 seconds and extension at 60◦ C for 2
minutes.
DNA precipitation. The sequencing product was precipitated with ethanol using
glycogen as carrier in 96 well plates. A stock solution of 0.3 M NaOAc and 0.1µg/ml
glycogen was prepared. 50µl stock solution was added to the 10µl sequencing product
followed by 125µl cold (stored at −20◦ C) 96% ethanol. The mix was spun at 4000
rpm for 30 minutes at 2◦ C, the precipitation mix poured off and the sample tray spun
inverted on top of a paper towel at 300 rpm at 2◦ C. For rinsing the product 250µl of cold
70% ethanol was carefully added to each sample and spun at 4000 rpm for 5 minutes for
rinsing of the product. After this the ethanol was poured off and the tray spun inverted
to remove the rest of the ethanol. The 70% ethanol rinse was done twice. The samples
were left in a dark and dry place for at least 15 minutes to allow for evaporation of
any remaining ethanol. Each sample was then dissolved in 10µl HiDi formamide and
mixed by vortexing for 1 minutes. A quick spin down was made before the samples
were placed in an ABI-3100 automatic sequencer for analysis.
Reference: Walsh, P., Metzfer, D., and Higuchi, R. (1991). Chelex 100 as a
medium for simple extraction of DNA for PCR-based typing from forensic material.BioTechniques, 10(4):506–513.
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