solea solea and solea senegalensis and its relationships with life...
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Universidade de Lisboa
Faculdade de Ciências
Departamento de Biologia Animal
Genetic diversity and population structure of
Solea solea and Solea senegalensis and its
relationships with life history patterns
Tatiana Fonseca de Araújo Teixeira
Tese orientada por
Professora Doutora Maria Manuela Coelho
Professor Doutor Henrique Nogueira Cabral
Mestrado em Biologia e Gestão dos Recursos Marinhos
Especialidade em Gestão e Ordenamento do Meio Marinho
2007
"I recognize the right and duty of this generation to develop and use our
natural resources, but I do not recognize the right to waste them, or to rob by wasteful use, the generations that come after us."
Theodore Roosevelt
“Nature provides a free lunch, but only if we control our appetites.” William Ruckelshaus
- i -
ACKNOWLEDGMENTS
To all the people that somehow have contributed to this work I hereby
express my sincere gratitude, especially to
Professor Maria Manuela Coelho and Professor Henrique N. Cabral for their
support, supervision and advice, and for guiding me trough my first steps
into marine science,
Dr. Joana Marques for her enormous support, advice, help, and especially,
friendship in all the moments, at any time and any place, even from the
other side of the World,
Dr. Célia Teixeira, for her great help from the very beginning of this work,
especially for the late night sampling at MARL and all the North to South
trips, and for making all “peixes chatos” less boring,
All the team from the laboratory of Ecologia Evolutiva e Molecular –
Carina, Irene, Rita, Maria Ana, Cristiane and Vera – for their practical help,
shared knowledge and support, and a very especial thanks to Francisco, for
always having a nice word to say and a huge lot of patience,
All the team from the laboratory of Zoologia Marinha, at the Instituto de
Oceanografia, especially to Dr. Inês Cardoso and my “final rush hour”
colleagues, Marisa, Sofia and Miguel,
People from allover Europe that contributed to the sampling process,
Cris, my true and always present friend, for all the support even 300km
apart, and companion in “Lado lunar” song in the traffic, Ju, forever
dinosaurs friends, for all the patience and care trough my horst days and for
sharing the happiness of the better ones,
Acknowledgments
- ii -
Artur for the huge help to do such amazing maps, “colouring my work”,
My family, for all the love, care and outstanding support, especially to my
mother (for all the nights that she “worked” with me), father and sister that
were always at my side encouraging me and that had a lot of patience, and
to Tó, for everything, especially for always be at my side – thank you a lot,
there are not enough words to express my gratitude.
This thesis was supported by the Project SOLEA (FEDER 22-05-01-FDR-00024), “Biologia e
estado de exploração dos stocks de espécies de linguados e solhas com interesse comercial
da costa portuguesa: contributos para a gestão sustentável dos recursos”, co-financed by EC
(FEDER-MARE).
"Let us be grateful to people who make us happy; they are the charming gardeners who
make our souls blossom."
Marcel Proust
- iii -
RESUMO
A avaliação da variação genética e o conceito de estrutura geográfica das
populações de peixes são fundamentais para a compreensão da dinâmica
das mesmas e para a conservação e exploração sustentável dos recursos
pesqueiros.
Inúmeras espécies de peixes chatos (Pleuronectiformes) possuem um
elevado valor comercial sendo, por isso, fortemente exploradas em todo o
mundo, tanto a nível das pescas como da aquacultura. Nalgumas áreas
geográficas, e em particular nos oceanos Atlântico e Pacífico, os valores das
capturas dos peixes chatos correspondem entre 10% a 30% do total de
capturas. Na costa Portuguesa, e apesar do valor total de desembarques
não ser particularmente elevado, os peixes chatos são um importante
recurso dado o seu elevado valor comercial, especialmente o do linguado
legítimo, Solea solea (Linnaeus, 1758), uma das principais espécies alvo da
pesca de arrasto na Europa. No entanto, o conhecimento sobre a biologia e
estado de exploração dos stocks das espécies de Pleuronectiformes com
elevada importância económica, é ainda incipiente e limitado,
principalmente no Sul da Europa e no Norte de África, onde a importância
destes recursos é também elevada.
S. solea e Solea senegalensis, Kaup, 1858, são espécies de linguado
marinhas, com morfologia similar, cuja principal característica do ciclo de
vida consiste na existência de uma fase juvenil, predominantemente
estuarina, e uma fase adulta, marinha. Ambas utilizam os sistemas
estuarinos como áreas de viveiro, beneficiando, assim, da elevada
disponibilidade de alimento e da existência de poucos predadores nestas
áreas. Este padrão de história vital poderá ter impacto na estruturação das
Resumo
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populações adultas, que habitam áreas costeiras marinhas mais profundas,
particularmente no que se refere à sua diferenciação genética, uma vez que
poderá induzir reduzidos valores de fluxo genético, como resultado de
comportamentos “homing” a áreas de desova e de uma forte associação
entre os locais de desova e as zonas de viveiro.
Alguns estudos de diferenciação genética, considerando vastas áreas
geográficas, e recorrendo a diferentes tipos de marcadores moleculares,
têm sido desenvolvidos em peixes chatos com interesse comercial, sendo o
linguado legítimo uma das espécies mais estudadas. No entanto, nenhum
destes estudos usou como marcador molecular o citocromo b (gene do DNA
mitocondrial). Por outro lado, o único estudo populacional existente para S.
senegalensis foi desenvolvido apenas na costa Portuguesa. A análise de
nove loci polimórficos de isoenzimas, permitiu verificar uma grande
homogeneidade genética entre as diversas amostras.
O principal objectivo deste trabalho consistiu em determinar a diversidade
genética e a estrutura populacional de S. solea e S. senegalensis ao longo
das suas áreas de distribuição, desde o Mar Báltico até ao Norte de África e
Mar Mediterrâneo, relacionando-as com os seus padrões de história vital.
A diversidade genética e estrutura populacional de S. solea e de S.
senegalensis foram analisadas com base em sequências completas do
citocromo b do DNA mitocondrial (cerca de 1141 pares de bases) de
diversas amostras recolhidas ao longo das suas áreas de distribuição.
Valores de elevada variação genética (h) e reduzida a moderada diversidade
nucleotídica (π) foram observados em todas as populações analisadas de S.
solea e S. senegalensis (excepto na população do Norte de Portugal de S.
senegalensis), níveis esses característicos de espécies com amplas áreas de
distribuição geográfica e elevado fluxo de indivíduos. Os resultados da
análise demográfica, assim como as árvores haplotípicas em forma de
estrela sugerem expansão em termos do efectivo populacional das
populações de ambas as espécies.
Resumo
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Para S. solea, foram detectados níveis significativos de diferenciação
genética à escala inter-regional, verificando-se a existência de dois grupos
de populações: um correspondente às populações do Atlântico e outro
correspondente às populações do Mediterrâneo. Esta diferenciação genética
Atlântico – Mediterrâneo poderá ser explicada pelas características
biogeográficas, nomeadamente pela grande descontinuidade oceanográfica
do estreito de Gibraltar-Mar de Alboran. As populações de S. solea do
Atlântico demonstraram, ainda, constituir uma unidade panmíctica ou
“quasi-panmíctica”, presumivelmente devido aos elevados níveis de fluxo
genético que ocorrem em cada geração. No entanto, as populações do
Mediterrâneo exibiram níveis significativos de diferenciação populacional
entre as regiões Este e Oeste, o que poderá ser consequência de uma
possível recolonização do Mediterrâneo por populações do Atlântico. Uma
outra explicação para os elevados níveis de diferenciação encontrados entre
populações do Mediterrâneo, parece residir na complexa história do
Mediterrâneo, que sofreu a acção de várias forças estruturadoras durante os
últimos episódios glaciares. A diminuição do nível do mar nestas épocas
provocou alteração das linhas de costa, levando à criação de refúgios
distintos nesta área, separando as zonas Este e Oeste, que, desde então,
apresentam diferentes regimes hidrográficos, sendo o da zona Oeste mais
uniforme que o da zona Este devido à geografia de cada uma destas zonas.
A referida diferenciação poderá, ainda, ser atribuída a factores relacionados
com a biologia de S. solea, tal como o intervalo de temperaturas toleradas
pelas larvas desta espécie. Analisando todos os indivíduos amostrados, i.e.,
populações do Atlântico e Mediterrâneo, verifica-se a existência de
isolamento por distância entre as populações de S. solea.
S. senegalensis exibiu um padrão de heterogeneidade genética
significativa, mesmo entre populações separadas por curtas distâncias
geográficas, presumivelmente contidas na área potencial de migração dos
indivíduos desta espécie, e onde, a longo prazo, o balanço entre fluxo
genético e forças responsáveis pela diferenciação genética poderá resultar
em gradientes, aumentando a diferenciação genética em função do
aumento da distância geográfica. Por si só, as distâncias geográficas
parecem, de facto, ser muito importantes na estruturação das populações
Resumo
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de S. senegalensis, havendo uma forte associação entre estas e as
divergências genéticas verificadas no grupo de amostras analisadas,
estando esta associação fortemente relacionada com as diferenças
latitudinais entre amostras. Os resultados de diferenciação obtidos para S.
senegalensis, salientam, ainda, a importância da temperatura como factor
redutor do fluxo genético entre populações, uma vez que a variação da
temperatura condiciona a duração do período larvar, assim como a taxa de
sobrevivência das larvas, restringindo a dispersão larvar e,
consequentemente, o nível de fluxo genético, promovendo, assim, o
isolamento genético de populações geograficamente próximas.
A homogeneidade verificada entre as amostras de S. solea e a
diferenciação encontrada entre as amostras de S. senegalensis, observadas
no presente estudo, sugerem uma reduzida importância dos sistemas
estuarinos como forças estruturantes da diferenciação genética das
populações destas espécies, uma vez que, embora ambas possuam um
padrão similar de utilização dos sistemas estuarinos, exibem diferentes
níveis de diferenciação populacional.
Em conclusão, os resultados obtidos neste estudo demonstraram a
existência de estruturação populacional em ambas as espécies, embora a
diferentes escalas: enquanto em S. solea a diferenciação genética se
verifica entre as populações do Atlântico e as do Mediterrâneo, assim como
no interior do Mediterrâneo, entre as populações localizadas a Este e a
Oeste, em S. senegalensis foi detectada diferenciação genética entre
populações de acordo com a sua distância geográfica, sendo tanto maior
quanto mais distantes se encontram as populações. Os níveis de
diferenciação detectados para cada espécie reflectem, de uma forma
generalizada os seus padrões de história vital, estando particularmente
relacionados com a duração do período larvar pelágico e com a sua
tolerância a variações na temperatura.
O desenvolvimento de uma análise mais aprofundada, incluindo um
estudo combinado de vários marcadores moleculares, e uma amostragem
espacial mais alargada, incluindo amostras de locais representativos de toda
Resumo
- vii -
a área de distribuição das espécies e o maior número possível de amostras
de um mesmo local, poderá permitir uma análise mais precisa e completa,
conduzindo a uma melhor compreensão da estruturação das populações de
S. solea e S. senegalensis.
Os resultados deste estudo poderão ter particular interesse para sustentar
a definição de unidades populacionais (stocks) destas espécies,
contribuindo, assim, para uma gestão mais sustentável destes recursos
pesqueiros.
Palavras-chave: linguado comum, linguado do Senegal, DNA
mitocondrial, estrutura genética populacional, padrões de história vital.
- ix -
SUMMARY
Soles (Solea spp.) are heavily exploited all round the world (fisheries and
aquaculture) due to their high commercial value. Nonetheless, the
knowledge on their biology and the exploitation status of their stocks is
limited to certain geographical areas. A key issue to management is the
population structure of fisheries resources.
The aim of the present study is to determine the genetic diversity and
population structure of Solea solea (Linnaeus, 1758), and Solea
senegalensis, Kaup, 1858, throughout their distribution range and to
evaluate its relationships with life history patterns.
The genetic diversity and population structure of both species were
analysed based on sequences of cytochrome b of mitochondrial DNA (about
1141 bp in length).
A low nucleotide diversity (π<0.005) and high haplotype diversity
(h>0.600) was observed for both species (except for Portugal-North
population of S. senegalensis, h=0.378). The pairwise Φ-statistics and
AMOVA for S. solea evidenced a high genetic divergence between Atlantic
and Mediterranean populations and between the Eastern and Western areas
of the Mediterranean. Significant differences were also observed between
samples of S. senegalensis, with geographical distance per se assuming a
very important role in structuring populations and presenting a strong
association with genetic divergence amongst the set of samples analyzed.
Minimum spanning network analyses, revealed star-shaped patterns for
populations of both species, suggesting that populations had undergone
Summary
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expansion following bottlenecks. Atlantic populations of S. solea, ranging
from the Baltic Sea to South Portugal could be considered as representative
of the same panmictic unit, presenting high levels of gene flow.
The higher levels of diversity observed in S. senegalensis compared to S.
solea may be due to differences in the duration of the pelagic larval phase,
spawning period and habitat use patterns, with water temperature
assuming a major role in restricting gene flow and consequently in the
population genetic structure of both species.
Key-words: common sole, Senegal sole, mitochondrial DNA, genetic
population structure, life history patterns.
- xi -
TABLE OF CONTENTS
AKNOWLEDGMENTS ............................................................................ i
RESUMO......................................................................................... iii
SUMMARY ....................................................................................... ix
Chapter 1
General Introduction......................................................... 3
Chapter 2
Genetic diversity and population structure of Solea solea and
Solea senegalensis and its relationships with life history
patterns ......................................................................... 19
2.1 Introduction .............................................................................. 19
2.2 Material and methods ................................................................. 23
2.2.1 Sampling and DNA extraction ............................................ 23
2.2.2 PCR amplification and sequencing ...................................... 24
2.2.3 Sequence alignment ......................................................... 25
2.2.4 Population genetic analysis ............................................... 25
2.2.5 Neutrality and demographic history .................................... 27
2.3 Results ..................................................................................... 28
2.3.1 Solea solea ..................................................................... 28
2.3.2 Solea senegalensis ........................................................... 37
2.4 Discussion ................................................................................ 42
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Chapter 3
Concluding remarks and challenges .................................... 59
ANNEX. Supplementary information
Chapter 1
Chapter 1
- 3 -
General Introduction
The use of molecular genetic techniques in fisheries research began in the
1950s with studies of blood group variants, primarily in tunas, salmonids
and cod, successfully demonstrating the existence of genetically controlled
variation which could be the basis of analyses of population structure of
those species (Ward & Grewe, 1994). In the 1960s decade, the study of
haemoglobin variants of whiting (Gadus melangus) and cod (Gadus
morhua), by Sick (1961), increased the use of protein electrophoresis
among fish geneticists (Avise & Smith, 1974; Allendorf et al., 1976), for
being a quicker, reasonably inexpensive, reproducible method, especially
because the majority of fish species with commercial interest showed
enough variation, allowing to develop a fast analysis of population structure.
Although the initial knowledge about fish genetics came from the use of
protein electrophoresis, this technique presents certain limitations, like the
requirement of fresh or frozen tissue, the need for a larger amount of tissue
samples than most DNA methods, and the inappropriate resolution to detect
interpopulational and interindividual differences (Ward & Grewe, 1994).
The use of mitochondrial DNA (mtDNA) in genetic studies is generally
assumed to be more powerful than the referred protein analysis, for both
phylogenetic and population studies, mostly due to the characteristics
presented by that marker: haploidy; maternal transmission - it presents ¼
of effective population size of nuclear DNA (nDNA), being, therefore, more
sensitive to detect reductions in genetic variation, particularly genetic drift
effects; putative lack of recombination; and relatively high mutation rates –
it has a large capacity to accumulate mutations that gives it a faster rate of
evolution than nDNA in higher animals (Ward & Grewe, 1994). Another
General Introduction
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advantage relies in the fact that the study of this molecular marker can be
done using fresh, frozen or ethanol-stored tissues and only requires a small
amount of sample (few nanograms). Moreover, the whole molecule may be
regarded as a single locus with multiple alleles (Park & Moran, 1994).
Mitochondrial genome has been widely used to study phylogenetic
relations between several taxa, from family level to population level, not
only because it evolves 10 times faster than nuclear DNA, but also due to
the characteristics that make it easy to amplify and sequence (Ward &
Grewe, 1994). However, the maternal inheritance of mtDNA can be a
limitation to its use since it does not provide information about the genome
of males, whose dispersal behaviour might be different from that of
females, from the same population.
Although the reference to tandemly repeated sequences of DNA in
eukaryotic genomes appeared during the 1960s, it was the development of
recombinant DNA technology, namely the development of “DNA
fingerprinting” techniques (Jeffreys et al., 1985), that enhanced the
importance of using this highly diverse sequences in studies regarding
population biology. With the finding of highly polymorphic microsatellite loci
in the middle 1990s, the potential to detect very low levels of differentiation
in species presenting a high gene flow increased substantially, allowing the
discrimination of individuals and populations (Carvalho & Hauser, 1994).
Such genetic markers have been routinely used in recent studies
considering fish species with high dispersal capabilities such as cod (Nielsen
et al., 2003), European seabass (Bahri-Sfar et al., 2000) and eel (Wirth &
Bernatchez, 2001), constantly detecting population substructure.
Due to their origin, microsatellites are multi-allelic in a population and di-
alellic in an individual. They are codominant markers that exhibit Mendelian
heredity, allowing the distinction of heterozygote or homozygote individuals
in a given loci, therefore enabling analyses of relationships, structure and
classification at the individual (genotype) and population (allelic
frequencies) levels (Wan et al., 2004).
Chapter 1
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Nonetheless, and independently of the molecular marker used, data from
DNA is easy comparable and allows to search for the relations among
organisms at any level, from sibling individuals to species of different
kingdoms (Avise, 1994).
Population structure of a species is the result of a complex interaction
between demographic parameters (e.g. growth, recruitment, reproduction,
mortality), life history patterns (e.g. homing behaviour, pelagic larval
stages) and genetic processes (e.g. selection, mutation, drift, gene flow).
Since the end of 19th century, population dynamics (life history and
demography) of several fish species with commercial interest have been
studied. However, several methods are quite difficult to be used on the
analyses of the structure of exploited stocks or populations of marine fish
species (ecological performance, parasites distribution, physiological and
behavioural characteristics, tagging, morphometric and meristic analysis,
calcified structures, cytogenetics, immunogenetics, blood pigments and
genetic molecular tools). Therefore, the resort to heterogeneity of molecular
markers, such as proteins and nucleic acids, has become of an universal
and undeniable utility (Carvalho & Hauser, 1994), but only more recently
have become available to complete studies of populational structure,
especially those aiming the definition of stocks, not only due to the
increasing availability of different molecular techniques in genetics, but also
due to the recognition of the importance of genetic data.
The analysis of genetic variation among fish species allows the
discrimination at the species, population and individual levels, the
identification of hybrids, the establishment of species and population
phylogeny and phylogeographic history, the discrimination of different
stocks and the analyses of their migration patterns and effective size, and
to assess individual stock contribution to mixed stock fisheries, evaluating
the response of stocks to fisheries exploitation (Wirgin & Waldman, 1994).
In the fisheries context, genetic tools have also been used in aquaculture,
to study pathogenic organisms in high commercial value species and the
expression of growth factors during maturation (Park & Moran, 1994). Other
General Introduction
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genetic studies have also considered introduced species, the effects of
fisheries and pollution on genetic diversity and the genetics of rare and
endangered species (Ward & Grewe, 1994).
Sustainable exploitation of fish stocks is a concept that dominates the
fisheries management for almost 50 years. The central idea is that each
stock has a harvestable surplus, and that fisheries that do not exceed this
will not compromise the stock’s natural perpetuation (Carvalho & Hauser,
1994). However, in 2002, almost 76% of the world’s marine stocks were
considered fully exploited or overexploited (FAO, www.fao.org).
Unfortunately, and even after the collapse of major fisheries, such as cod
and herring (Hutchings, 2000), remains the idea that marine fishes,
particularly pelagic fishes, are resilient to large population reductions. The
trend of stocks offering potential for expansion is clearly downwards,
decreasing from 40% in 1970, to 24%, in 2004 (FAO, 2005).
The definition of stocks and their boundaries has become an essential part
of fisheries management, especially since several studies developed in
Northern Europe have referred high pressure on marine resources, resulting
in a decrease of effective population size, being some stocks already out of
their biological safety limits. However, the geographic areas considered in
the establishment of management policies do not often coincide with the
biological stocks’ distribution, simply because of seasonal movements of
these stocks between the several management areas, or because different
fish stocks may occur simultaneously in a unique area, generally resulting in
a mismatch in the spatial scale of management and biological reality
(Pawson & Jennings, 1996).
To achieve the sustainable development of fisheries resources at a global
scale, it is necessary the accurate knowledge of the ecology and biology of
exploited species, particularly regarding their evolution and population
dynamics in space and time. Likewise, studies such as those already
developed for cod , by Knutsen et al. (2004), for herring by Ruzzante et al.
(2006), and for common sole, by Rolland et al. (2007), are needed in order
Chapter 1
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to predict the impact of fishing exploitation and to forecast the efficiency of
management measures.
The stock concept has numerous meanings in the fisheries context, such
as an abstract and undefined unit or a spatial location in which the fish are
found and can be exploited by a specific fishing technique, and in the
biological context – a genetic unit defined as a group of individuals that
mate randomly, with a variable degree of spatial and temporal integrity
(Carvalho & Hauser, 1994).
In marine species with high dispersal patterns, which includes most of
marine fishes, it is considered that high gene flow leads to population
homogeneity, at a large scale, as a result of their life cycle patterns; factors
like high fecundity, passive dispersal of larvae and active migration of
adults, lead to the lower levels of genetic differentiation exhibited by marine
species, relatively to fresh water or anadromous species (Carvalho &
Hauser, 1994; Ward et al., 1994; Ward, 2002).
The extent of gene flow will generally determine the genetic heterogeneity
of the species, in the absence of localised selection (Exadactylos et al.,
1998). Several marine fish exhibit low levels of haplotype diversity and
population divergence (Avise, 1994), that can be a result of low rates of
genomic evolution or recent bottleneck events. Mechanisms such as climatic
fluctuations, different time and place of spawning (Ruzzante et al., 2006),
and physical structuring forces, like geographical distance (Hoarau et al.,
2002), depth, ocean currents and fronts favouring larval retention (Nielsen
et al., 2005), transitions in temperature and salinity (Nielsen et al., 2003),
as well as strong phylopatry, can explain how population structure can
evolve in a environment without any obvious physical boundaries to gene
flow (Hemmer-Hansen et al., 2007), resulting in small, but significant and
usually temporally stable, levels of genetic divergence in many species of
marine fishes (Ruzzante et al., 1996; Wirth & Bernatchez, 2001; Nielsen et
al., 2003; Nielsen et al., 2004; Ruzzante et al., 2006).
General Introduction
- 8 -
The failure in detecting differentiation can be a consequence of the
incapability to detect variation, explained by insufficient resolution of the
markers used, or of the reduced knowledge of the biology of the studied
species.
Genetic structure of a species reflects individual’s organization. In a
simplified theoretic perspective, there are three basic models of structure in
natural populations (Richardson et al., 1986). The simplest model considers
the existence of a unique panmictic unit, where there is no population
subdivision, and random mating between individuals is verified, resulting in
the absence of significant genetic differentiation between groups of
individuals of certain regions. Another model considers discrete populations
in a major panmictic group, but genetic flow between them is limited,
suggesting an accentuated genetic differentiation between different units,
conducing to a discontinuity of allele frequencies in several loci within the
same geographical area. There are also several variants of these models
which take into account the genetic flow between several population units.
A third group of models, based on the isolation by distance (IBD) concept,
consider a continuous geographical distribution of populations, but gene
flow between them is limited by the dispersal capacity of each individual.
Although they consider the existence of genetic differentiation between
population units, genetic variation in several loci is gradual, and discrete
boundaries like those referred in the previous model do not exist. But more
important than the identification of a structural model, is the recognition of
the factors determining population genetic structure.
The knowledge on population structure is fundamental to manage fisheries
of high commercial value species that present a broad-scale distribution.
Several flatfish species (Pleuronectiformes) are heavily exploited all around
the world (fisheries and aquaculture). In some geographical areas,
particularly in the Atlantic and the Pacific, flatfish represent from 10% to
30% of total catches (Millner et al., 2005). In the Portuguese coast, and
although the value regarding their total catch is not particularly high,
flatfishes are amongst the species with the highest commercial value.
Chapter 1
- 9 -
In several geographical areas, especially in Northern Europe,
Pleuronectiformes have been studied for a long time. Aspects like
abundance of some of these resources (Rijnsdorp et al., 1992), feeding
ecology (Knutsen, 1992), growth (Andrade, 1992) and reproduction (Deniel,
1984; Deniel et al., 1989) are well documented in the literature.
Nonetheless, knowledge on their biology and exploitation status is limited,
especially in Southern Europe and Northern Africa, where the importance of
these resources in undeniably high. In the Portuguese coast, most studies
on the ecology of Pleuronectiformes have focused juvenile stages,
particularly in estuaries and coastal lagoons (e.g. Dinis, 1986; Andrade,
1990; Cabral, 1998, 2000, 2003; Cabral & Costa, 1999; Cabral et al.,
2002). However, the acquired knowledge in some geographic areas cannot
be transposed to other areas, because of their distinct oceanographic and
climatic characteristics.
Several differentiation studies have been conducted on commercially
important flatfish species considering a broad geographic scale. For plaice,
Pleuronectes platessa (Linnaeus, 1758), Purdom and Thompson (1976) and
Ward and Beardmore (1977) used allozyme loci to compare samples from
the Irish Sea, Bristol Channel and southern North Sea and were not able to
establish any differentiation. However, significant population differentiation
was detected by Hoarau et al. (2004) between shelf and off-shelf
populations (with microsatellites and mtDNA), and with mtDNA data they
were able to detect differentiation within the continental shelf.
For halibut, Hippoglossus hippoglossus (Linnaeus, 1758) (Foss et al.,
1998) and brill, Scophthalmus rhombus (Linnaeus, 1758) (Blanquer et al.,
1992), the conduced allozyme analysis were not able to detect
differentiation within North Atlantic populations. In turbot, Scophthalmus
maximus (Linnaeus, 1758), Nielsen et al. (2004) reported subtle
differentiation between the Baltic and the North Sea, and Florin and
Höglund (2007) found low, although significant, genetic differentiation
within the Baltic Sea, both studies with microsatellites analysis.
General Introduction
- 10 -
For flounders, Platichthys flesus (Linnaeus, 1758), and Platichthys
stellatus (Pallas, 1787), through the analysis of allozyme variation, Borsa et
al. (1997) detected a strong differentiation between Atlantic, Western
Mediterranean and Adriatic populations. High differentiation levels were also
found within the Mediterranean area. In the North Atlantic region, the levels
of differentiation were much lower although indicating some isolation by
distance. Considering the North Sea region, differentiation has been
reported, north and south of the Dogger Bank. Hemmer-Hansen et al.
(2007) analysed microsatellite loci and found a clearly large and highly
significant genetic structuring among the samples of P. flesus from several
localities of the Northeast Atlantic and Baltic Sea.
In sole, Solea solea (Linnaeus, 1758), Kotoulas et al. (1995) detected,
with allozyme loci analysis, significant differentiation between
Mediterranean and Atlantic populations, as well as a weak North-South
differentiation across the European Atlantic coast and East-West
differentiation across the Mediterranean, suggesting the existence of an
isolation by distance (IBD) model. The authors further conclude that the
Atlantic populations constitute however, one single panmictic or quasi-
panmictic unit. Considering the same geographical scale, Rolland et al.
(2007) obtained similar results of differentiation, with the analysis of
nuclear-DNA non-coding loci. Differentiation within the Mediterranean
region was also documented by Guarniero et al. (2002) and Garoia et al.
(2007), with the study of control region of mtDNA and microsatellites and
amplified fragment length polymorphisms (AFLPs), respectively.
The between-region differences found in flounder and sole are generally
attributed to narrow larval temperature tolerances of these two flatfish
species, compared with other flatfish of the same region (Hoarau et al.,
2002).
Within the Portuguese coast, Cabral et al. (2003) obtained low genetic
differentiation between samples of common sole and Senegal sole, Solea
senegalensis, Kaup, 1858, from several estuarine systems, and Pinheiro et
al. (2005) indicated a great variability among samples of sand sole, Solea
Chapter 1
- 11 -
lascaris, (Risso, 1810), suggesting low levels of differentiation of this
species, both using allozyme electrophoresis.
The main objective of this study was to determine the genetic diversity
and population structure of S. solea and S. senegalensis, throughout their
distribution range, from the Baltic Sea to North Africa and Mediterranean
Sea, evaluating the relationships found in the context of the life history
patterns and ecology of these flatfishes.
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Chapter 2
Chapter 2
- 19 -
Genetic diversity and population structure of Solea solea and
Solea senegalensis and its relationships with life history
patterns
T. F. Teixeira1, C. M.Teixeira1, M. M. Coelho2 and H. N. Cabral1
Universidade de Lisboa, Faculdade de Ciências, 1Instituto de Oceanografia & 2Centro de
Biologia Ambiental, Campo Grande, 1749-016 Lisboa, Portugal
Abstract: The genetic diversity and population structure of Solea solea and Solea senegalensis were analysed, based on the complete cytochrome b sequence of mitochondrial DNA (about 1141 bp in length), of samples obtained throughout the species distribution range. A low nucleotide diversity (π<0.005) and high haplotype diversity (h>0.600) was observed in both species (except for Portugal-North population of S. senegalensis, h=0.378). The pairwise Φ-statistics and AMOVA of S. solea samples evidenced the genetic divergence of Atlantic and Mediterranean populations and of Eastern and Western Mediterranean populations. Significant differences were also observed between samples of S. senegalensis. Atlantic populations of S. solea, ranging from Baltic Sea to South Portugal could be considered as representative of the same panmictic unit. Minimum spanning network analysis revealed star-shaped patterns for populations of both species, suggesting that populations have undergone expansion following bottlenecks. The higher levels of diversity observed in S. senegalensis, compared to S. solea, may be due to differences in the duration of the pelagic larval stage, spawning period and habitat use patterns, with water temperature assuming a major role in restricting gene flow and consequently in the population genetic structure of both species. Key-words: common sole, Senegal sole, mitochondrial DNA, genetic population structure, life history patterns.
2.1 Introduction
The assessment of genetic variation and the concept of geographical
structure in marine fish populations are fundamental to the understanding
of population dynamics and to the conservation and sustainable
management of fisheries resources (Carvalho & Hauser, 1994; Bailey,
1997). Soles (Solea spp.) are heavily exploited resources all around the
world (fisheries and aquaculture) presenting a high commercial value. In
Genetic diversity and population structure
- 20 -
fact, common sole, Solea solea (Linnaeus, 1758) is one of the main target
species for the European beam-trawl fishery, and heavy fishing pressure on
its stocks over the past years has reduced landings from 60.000 tones, in
1994, to 40.000 tones, in 2003 (FAO, www.fao.org). Concern for collapse of
this resource has emphasized the importance of conducting studies of its
stocks biology, distribution, origin and structure (Millner et al., 1996).
According to the Portuguese fisheries statistics of the past few years,
flatfishes represent approximately 1.1% of total fish landings, which
corresponds to 143.25 million euros, 7.3% of the total value of landings
(source: DGPA). This area has, in fact, a peculiar interest to flatfishes: it is
a border area between three biogeographic zones - the warm temperate
North Atlantic, the cold temperate North Atlantic and the Mediterranean –
constituting the northern or southern limit of distribution for many of the
species found here (Cabral et al., 2003a, 2003b), and allowing the
occurrence in simpatry of species characteristic from North Africa,
Mediterranean and North Europe, all presenting a high diversity of life
history patterns and habitat use (i.e. species typical of estuarine
environment, species that are exclusively marine, occurring in high depth,
with several feeding habitats, different reproductive strategies, different
distribution patterns, longevity, etc). The geomorphological particularities of
the Portuguese coast, namely a narrow continental shelf divided by deep
canyons, could induce a population substructuring pattern (Cabral et al.,
2003a).
Beyond common sole, other flatfishes such as the Senegalese sole, Solea
senegalensis, Kaup, 1858, sand sole, Solea lascaris, (Risso, 1810), flounder,
Platichthys flesus, (Linnaeus, 1758), brill, Scophthalmus rhombus,
(Linnaeus, 1758), turbot, Scophthalmus maximus, (Linnaeus, 1758),
Microchirus azevia, (Capello, 1867) and Atlantic spotted flounder, Citharus
linguatula, (Linnaeus, 1758), are also of high commercial value in
Southwest Europe fisheries.
S. solea and S. senegalensis are marine species, similar in their
morphology and distribution, from North Africa and the Western
Chapter 2
- 21 -
Mediterranean up to the Bay of Biscay, being found on sandy and muddy
bottoms at depths of 0-200 m (Quéro et al., 1986). The main feature of
their life cycle is a division into a juvenile phase, predominantly estuarine,
and an adult phase, which is marine. Both species use estuarine systems as
nursery areas, in order to benefit from the high food availability and the
existence of few predators (Cabral et al., 2003a). This life history pattern
may have an impact on the structuring of offshore adult populations,
particularly on their genetic differentiation, since it may be related to a low
gene flow, as the result of the possible homing behaviour to spawning
grounds and a strong association between spawning and nursery areas.
Moreover, the existence of physical barriers to larval dispersion, such as
sharp temperature and salinity gradients, hydrodynamic eddies favouring
larval retention, and the active vertical swimming of larvae, by which they
can avoid passive transport off nursery areas (Kotoulas et al., 1995a), can
also contribute to a high genetic differentiation. However, high genetic
exchanges can be induced by the long duration of spawning season and
larval period and high fecundity (Kotoulas et al., 1995a) as well as by the
planktonic stages, although these are limited in their dispersal by
temperature tolerance (Borsa et al., 1997).
Few studies of broad-scale geographic differentiation have considered
commercially important flatfish, but common sole inhabiting the northern
areas of the Eastern Atlantic have been particularly well studied using
different kinds of genetic markers: allozymes (Quignard et al., 1986;
Kotoulas et al., 1995a; Exadactylos et al., 1998; Cabral et al., 2003a),
control region of mitochondrial DNA (mtDNA) (Guarniero et al., 2002),
RAPDs (random amplification of polymorphic DNA) (Exadactylos et al.,
2003), nuclear-DNA non-coding loci (Rolland et al., 2007), microsatellites
and amplified fragment length polymorphisms (AFLPs) (Garoia et al., 2007).
Kotoulas et al. (1995a) detected significant differentiation between
Mediterranean and Atlantic S. solea populations, as well as a North-South
differentiation across the European Atlantic coast and East-West
differentiation across the Mediterranean, suggesting the existence of an
isolation by distance (IBD) model in population structure. At a smaller
geographical scale (Central Mediterranean region), Guarniero et al. (2002)
Genetic diversity and population structure
- 22 -
revealed the existence of genetic structure among adjacent basins, related
to the hydrogeographical features of the region. Local and regional studies
with North European and Mediterranean samples (e.g. Exadactylos et al.,
1998; Cabral et al., 2003a) suggested the absence of population structure,
although the results of Cabral et al. (2003a) supported an IBD model.
However, most studies conducted with different nuclear markers pointed
out the differentiation between Atlantic and Mediterranean populations of S.
solea, being the differences within the Mediterranean explained by historical
processes favouring vicariance, and to combined effects of contemporary
gene flow modulated by ecological traits and hydrologic features (Garoia et
al., 2007; Rolland et al., 2007).
Less is known about S. senegalensis populations. Ecological information
concerning this species is scarce and refers almost exclusively to the
Portuguese coast (e.g. Andrade, 1992; Cabral & Costa, 1999, Vinagre et al.,
2006). The generality of genetic studies developed with this flatfish focuses
its systematics and phylogeny, particularly in the Mediterranean (Goucha &
Pasteur, 1983; Goucha et al., 1987; Borsa & Quignard, 2001; Infante et al.,
2004), only recently including a broader-scale area (Pardo et al., 2005).
The only population genetic study was developed by Cabral et al. (2003a)
that analysed nine polymorphic allozyme loci, pointing out a low genetic
differentiation and the absence of population structure for S. senegalensis
inhabiting several estuarine systems along the Portuguese coast.
The aim of the present study is to determine the genetic diversity and
population structure of S. solea and S. senegalensis throughout their
distribution range, using mtDNA cytochrome b data, and to evaluate its
relationships with species life history patterns, that could be especially
useful for the sustainable management of these resources.
Chapter 2
- 23 -
2.2 Material and methods
2.2.1 Sampling and DNA extraction
A total of 172 soles, belonging to both species, were collected from 13
different locations, covering their distribution range, from the Baltic Sea to
the Mauritanian coast, and throughout the Mediterranean Sea (Figure 1).
The number of individuals analysed for each species and the location of
sampling areas are presented in Table 1.
Fig. 1- Sampling areas for each species analysed in this study and their distribution
range (blue – S. solea; orange – S. senegalensis). Codes of sampling areas are
defined in Table 1.
Genetic diversity and population structure
- 24 -
Table 1- Geographic location of the sampling areas, their codes, species sampled and number of individuals collected in each area (SS - Solea solea; SN - Solea senegalensis).
Geographical location Area
Code Coordinates
Sampled
Species
Sample
size
Denmark (Baltic Sea–North Sea transition-Kattegat) MBA N 54.13º E 011.26º SS 10 Denmark (North Sea) MN N 54.80º E 008.13º SS 10 Belgium (North Sea) B N 51.57º E 003.24º SS 10 United Kingdom (Irish Sea) UK N 53.90º W 003.30º SS 12 France (Biscay Bay) BY N 45.84º W 001.48º SS/ SN 12/10 Portugal-North (Atlantic Ocean–off Figueira da Foz) PN N 40.14º W 008.89º SS/SN 10/10 Portugal-Centre (Atlantic Ocean–off Setúbal) PC N 38.45º W 008.99º SS/SN 10/9 Portugal-South (Atlantic Ocean-off Olhão) PS N 37.00º W 007.79º SS/SN 10/10 Mauritânia (Atlantic Ocean) MAUR N 18.28º W 016.40º SN 10 France(Gulf of Lyon) GL N 43.45º E 004.01º SS 10 Italy (Adriatic Sea- off Venice) IT N 45.36º E 012.44º SS 10 Greece (Aegean Sea-off Thessaloniki) ME N 40.36º E 022.77º SS 9 Turkey (Aegean Sea-off Izmir) TQ N 38.79º E 026.60º SS 10
Total genomic DNA was extracted from tissue samples (fin or muscle),
following a phenol-chloroform protocol, as described by Wasko et al.
(2003).
2.2.2 PCR amplification and sequencing
The entire cytochrome b (cyt b) mitochondrial DNA gene (about 1141 bp
in length) was amplified by PCR, using two specific primers (Infante et al.,
2004): GLU1 (5’-GGGGATTTTAACCTCAGGCGTTCAGTTTAC-3’) and Thr2 (5’-
GGACTAATCGCTTGAAAAAACCACCGTTG-3’).
PCR reactions of 25 µl total volume, containing approximately 50 ng of
template DNA, 2 mM of MgCl2, 0.2 µM of dNTP´s, 0.5 µM of each primer, 2
U of Taq DNA Polymerase (Fermentas) and 10x Taq buffer (10mM Tris-HCl,
ph 9.0; 50 mM KCl) (Fermentas), were conducted as follows: an initial
preheat step at 92ºC for 120s, followed by 5 cycles of denaturing at 92ºC
for 15s, annealing at 51ºC for 45s and extension at 72ºC for 90s, and 30
cycles of denaturing at 92ºC for 15s, annealing at 52ºC for 45s and
extension at 72ºC for 90s, finishing with an extension step at 72ºC for 7
min.
Chapter 2
- 25 -
PCR products were subjected to electrophoresis in 1% agarose gels,
containing ethidium bromide staining, and visualized under UV light.
Products were then purified using 10 U of Exonuclease I (Fermentas), 1 U of
Shrimp Alkaline Phosphatase (SAP) (Fermentas), and 6.25x SAP buffer
(Fermentas). The protocol for purifying PCR products consisted of 30 min at
37ºC, 15 min at 80ºC and 5 min at 12ºC. All products were sequenced in
both directions, using the PCR primers and the BigDye Terminator Cycle
Ready reaction Kit (Applied Biosystems), and visualized in an AbiPrism 377
Automated Sequencer (Applied Biosystems) (Stabvida®).
2.2.3 Sequence alignment
Contiguous sequences were assembled in Sequencher ver. 4.0
(GeneCodes Corp.) and compared to similar sequences deposited in
GenBank, using the Basic Local Alignment Search Tool (BLAST) available on
the NCBI website (NCBI, http://www.ncbi.nlm.nih.gov/Genbank). All
sequences were aligned using Sequencher ver. 4.0 (GeneCodes Corp.) and
checked by eye.
2.2.4 Population genetic analysis
Intrapopulation diversity was analysed by estimating gene diversity (h),
and nucleotide diversity (π) (Nei, 1987), using DNASP ver. 4.10.9 (Rozas et
al., 2003). Population structure and genetic variation were characterised by
Φ–statistics (analogous to the F- Statistics of Wright (1969)), which
incorporate genetic distance between haplotypes and haplotipic frequencies,
using Arlequin ver. 3.11 (Excoffier et al., 2005). The software Modeltest 3.7
(Posada & Crandall, 1998) was used to find the best model of evolution that
fitted the data, according with the AKAIKE criterion. Although resultant
models were different for both species (GTR+I+G - general time reversible
plus Proportion invariant plus Gamma, for S. solea, and GTR- general time
reversible for S. senegalensis) the pairwise distance method, with γ = 0,
Genetic diversity and population structure
- 26 -
was considered for both species analyses, since the resultant models were
not included in Arlequin v. 3.11.
Analysis of molecular variance (AMOVA) was used to assess the population
configuration and the geographical pattern of population subdivision
(Excoffier et al., 1992). For hierarchical analyses, populations were grouped
according to their geographic location. Several other rearrangements were
tested and the one that maximised among group variation (θCT) was
assumed to be the most probable subdivision. Simulations with 1000
permutations were made to test the statistic significance of results. The
isolation by distance (IBD) model was analysed by testing the association
between geographic and genetic distances (Smouse et al., 1986) through a
Mantel test (Mantel, 1967) with 10 000 permutations, as implemented in
Arlequin ver. 3.11. Geographical distances between populations were
measured as a straight line along the coast between each two areas. A
standard Bonferroni a posteriori correction was applied to determine the
level of significance in multiple tests.
Minimum spanning networks (using the median joining agglomeration
method) were constructed with Network ver. 4.201 (Bandelt et al., 1999)
based on haplotype data of the sampled populations, and generated with
MacClade 4.08 (Maddison & Maddison, 1989). Network ver. 4.201 uses the
maximum parsimony method for reconstructing trees, choosing the smallest
and simplest as the best. Median-joining algorithm was used with default
parameters, as recommended for this kind of data (Bandelt et al., 1999).
The population structure was also investigated using the program BAPS
4.1 (Corander et al., 2007), which allows the analysis of sequence data.
Given a maximum value of partitions, the algorithm uses a stochastic
optimization procedure to find the clustering solution with the highest
‘marginal likelihood’ of K (i.e., an approximation of the most probable
number of differentiated genetic populations conditional on observed data).
The maximum number of partitions, K, was set as ranging from 5 to 20 (S.
senegalensis) and from 12 to 20 (S. solea) and, in each case, we the
Chapter 2
- 27 -
analyses were ran several times, recording the best partition found and the
corresponding ‘marginal likelihood’.
2.2.5 Neutrality and demographic history
Arlequin ver. 3.11 was used to test for departures from mutation-drift
equilibrium with Tajima’s D-test (Tajima, 1989) and Fu’s Fs (Fu, 1997). In a
constant-size neutral equilibrium population, Tajima's D is expected to be
nearly zero, and when some kind of balancing selection is acting Tajima's D
tends to be positive. However, changes in population size can also affect
Tajima's D and, therefore, in a population with decreasing size, Tajima's D
is expected to be positive, while a negative Tajima's D is predicted for a
population with increasing size. Fu's Fs statistics also provide a test for
population growth. Negative Fs values may indicate the existence of an
excess of rare alleles, which can be explained by an excess of recent
mutation, and processes like growth of effective populational size or
hitchhiking, occurred in the past, can also be evidenced by the values of
this statistic (Fu, 1997). The relationship between nucleotide (π) and
haplotype (h) diversities can also provide some information on history of
populations (Grant & Bowen, 1998).
Demographic history of the two species was also studied by analysing
mismatch distributions of pairwise differences between all individuals of
each population using Arlequin ver. 3.11. These analyses allowed the
discrimination of a rapid population expansion (possibly after a bottleneck)
or a state of stability over time. The mismatch distribution appears like a
Poisson curve (unimodal), if accumulation of new mutations is greater than
the loss of variation through genetic drift (resulting in population
expansion), or multimodal if the generation of new mutations is offset by
random genetic drift, maintaining population size (Rogers & Harpending,
1992).
Genetic diversity and population structure
- 28 -
2.3 Results
2.3.1 Solea solea
Genetic diversity was very high, with 75 haplotypes recovered from 123
individuals. Of these, 77% were unique. The most common haplotype, H7
(15% of the samples) was shared by 18 individuals from Northeast (NE)
Atlantic populations (B, BY, MBA, MN, PC, PN and UK), and did not included
any of the Mediterranean populations. Moreover, no haplotypes were shared
between individuals from NE Atlantic and Mediterranean populations. The
second major common haplotype, H38, was shared by 10 individuals, all
from Mediterranean populations (ME and TQ) (Table 2).
Table 2- Haplotype frequencies and composition of Solea solea populations. Haplotype MBA MN B UK BY PN PC PS GL IT ME TQ TOTAL
1 - - 1 - - - - - - - - - 1
2 - - 1 1 - - - - - - - - 2
3 - - 1 - - - - - - - - - 1
4 - - 1 - 1 - - - - - - - 2
5 - - 1 - - - - - - - - - 1
6 - - 1 - - - - - - - - - 1
7 1 6 2 4 2 2 1 - - - - - 18
8 - - 1 - - - - - - - - - 1
9 3 - 1 - - - - - - - - - 4
10 - - - - 1 - - - - - - - 1
11 - - - - 1 - - - - - - - 1
12 - - - - 1 - - - - - - - 1
13 - - - - 1 - 1 - - - - - 2
14 - - - - 1 - - - - - - - 1
15 - - - - 2 - - - - - - - 2
16 - - - 1 1 - - - - - - - 2
17 - - - 1 1 1 - - - - - - 3
18 - - - - - - - - 1 - - - 1
19 - - - - - - - - 1 - - - 1
20 - - - - - - - - 1 - - - 1
21 - - - - - - - - 1 - - - 1
22 - - - - - - - - 1 - - - 1
23 - - - - - - - - 1 - - - 1
24 - - - - - - - - 1 - - - 1
25 - - - - - - - - 1 - - - 1
26 - - - - - - - - 1 - - - 1
27 - - - - - - - - 1 2 - - 3
28 - - - - - - - - - 1 - - 1
29 - - - - - - - - - 1 - - 1
30 - - - - - - - - - 2 - - 2
31 - - - - - - - - - 1 - - 1
32 - - - - - - - - - 1 - - 1
33 - - - - - - - - - 1 - - 1
34 - - - - - - - - - 1 - - 1
35 3 1 - - - - - - - - - - 4
Chapter 2
- 29 -
Table 2- continued
The overall level of haplotype diversity (h) was high, ranging from 0.667
in the North Sea (MN) and Turkey (TQ) populations, to 1.000 in Portugal-
South (PS) and Gulf of Lyon (GL) populations. On the other hand,
nucleotide diversity (π) exhibited by all populations was low, ranging from
0.001 in Turkey (TQ) to 0.007 in Portugal-South (PS). The number of
haplotypes presented by each population varied, ranging from 5 in the
Baltic Sea (MBA), North Sea (MN) and Turkey (TQ) populations to 10 in Bay
of Biscay (BY), Portugal-South (PS) and Gulf of Lyon (GL) (Table 3).
Haplotype MBA MN B UK BY PN PC PS GL IT ME TQ TOTAL
36 2 - - - - - - 1 - - - - 3
37 1 - - - - - - 1 - - - - 2
38 - - - - - - - - - - 4 6 10
39 - - - - - - - - - - 1 - 1
40 - - - - - - - - - - 1 - 1
41 - - - - - - - - - - 1 - 1
42 - - - - - - - - - - 1 1 2
43 - - - - - - - - - - 1 - 1
44 - 1 - - - - - - - - - - 1
45 - 1 - - - - - - - - - - 1
46 - 1 - - - - - - - - - - 1
47 - - - - - - 2 - - - - - 2
48 - - - - - - 1 - - - - - 1
49 - - - - - - 1 - - - - - 1
50 - - - - - - 1 - - - - - 1
51 - - - - - - 1 - - - - - 1
52 - - - - - - 1 - - - - - 1
53 - - - - - - 1 - - - - - 1
54 - - - - - 1 - - - - - - 1
55 - - - - - 2 - - - - - - 2
56 - - - - - 1 - - - - - - 1
57 - - - - - 1 - - - - - - 1
58 - - - - - 1 - - - - - - 1
59 - - - - - 1 - - - - - - 1
60 - - - - - - - 1 - - - - 1
61 - - - - - - - 1 - - - - 1
62 - - - - - - - 1 - - - - 1
63 - - - - - - - 1 - - - - 1
64 - - - - - - - 1 - - - - 1
65 - - - - - - - 1 - - - - 1
66 - - - - - - - 1 - - - - 1
67 - - - - - - - 1 - - - - 1
68 - - - - - - - - - - - 1 1
69 - - - - - - - - - - - 1 1
70 - - - - - - - - - - - 1 1
71 - - - 1 - - - - - - - - 1
72 - - - 1 - - - - - - - - 1
73 - - - 1 - - - - - - - - 1
74 - - - 1 - - - - - - - - 1
75 - - - 1 - - - - - - - - 1
Genetic diversity and population structure
- 30 -
Table 3- Genetic diversity of cyt b sequences for Solea solea populations (standard deviation is presented between brackets).
Population Nucleotide diversity
(π) Haplotype diversity
(h) Number of haplotypes
MBA 0.005 (0.003) 0.844 (0.080) 5
MN 0.003 (0.002) 0.667 (0.163) 5
B 0.005 (0.003) 0.978 (0.054) 9
UK 0.005 (0.003) 0.909 (0.080) 9
BY 0.004 (0.003) 0.970 (0.044) 10
PN 0.004 (0.003) 0.956 (0.059) 8
PC 0.004 (0.003) 0.978 (0.054) 9
PS 0.007 (0.004) 1.000 (0.045) 10
GL 0.003 (0.002) 1.000 (0.045) 10
IT 0.003 (0.002) 0.956 (0.059) 8
ME 0.002 (0.001) 0.833 (0.127) 6
TQ 0.001 (0.001) 0.667 (0.163) 5
From all the tested rearrangements (see Tables I and II of results in
Annex, page i), the one considering the existence of two groups, one
including NE Atlantic populations and another including Mediterranean
populations, was the combination that maximised the among group
variation (θCT=0.485; P<0.001) (Table 4), therefore being assumed as the
most probable genetic subdivision.
Table 4- Results of the AMOVA performed for Solea solea, considering two groups (Atlantic and Mediterranean).
Source of variation Total variance (%) Fixation indices P value
Among groups 48.5 θCT=0.485 <0.001 Among populations within groups 4.31 θSC=0.084 <0.001
Within populations 47.19 θST=0.528 <0.001
Low ΦST values were found in all pairwise analyses within the NE Atlantic
populations, while those obtained between the group of NE Atlantic
populations and the group of Mediterranean populations were high (0.385-
0.725) and significant (P<0.001), after the Bonferroni correction being
applied. High and significant ΦST values (0.191-0.368; P<0.001) were also
obtained by pairwise analysis between Western (GL and IT) and Eastern
(ME and TQ) Mediterranean populations (Table 5).
Chapter 2
- 31 -
Table 5- ΦST values for Solea solea samples (* indicates significant values, P<0.001)
A Mantel test including all sampled populations (from Baltic Sea to Aegean
Sea) revealed a clear correlation between genetic and geographical distance
(Z= 0.63; P<0.05). On the other hand, Mantel tests including only Atlantic
populations or Mediterranean populations, revealed that genetic distances
are not correlated with geographical distances (Z= 0.30, P>0.05 and Z=
0.44, P>0.05, respectively) (see Figure I in Annex, page ii).
The haplotype network derived from cyt b sequences, and using the
maximum parsimony method, is presented in Figure 2. Size of circles is
proportional to the number of individuals within each haplotype (Table 2).
Two major common haplotypes were found, and represent individuals
from NE Atlantic populations (H7, shared between 18 individuals) and
Mediterranean populations (H38, shared between 10 individuals of Aegean
Sea (ME) and Turkey (TQ) populations).
These two haplotypes differ from each other by 8 mutations, and 3
haplotypes are missing between them. It is also possible to visualize a third
haplotype (H27), shared by the individuals from Gulf of Lyon (GL) and Italy
(IT) populations, from which several haplotypes derive by only one
mutation. This network suggests the existence of two main sub-populations,
Atlantic and Mediterranean, and population structuring within the
Mediterranean, as no shared haplotypes were found between individuals
from Western (GL and IT) and Eastern (ME and TQ) Mediterranean
MBA MN B UK BY PN PC PS GL IT TQ ME MBA - MN 0.080 - B -0.028 0.013 - UK 0.023 0.174 0.007 - BY 0.021 0.021 -0.09 0.083 - PN 0.044 0.158 0.027 0.047 0.015 - PC 0.030 0.046 -0.003 0.075 0.000 0.007 - PS 0.085 0.207 0.085 0.083 0.095 0.001 0.020 - GL 0.462* 0.599* 0.465* 0.414* 0.502* 0.482* 0.488* 0.385* - IT 0.528* 0.657* 0.530* 0.480* 0.563* 0.537* 0.531* 0.412* 0.056 - TQ 0.627* 0.771* 0.630* 0.566* 0.652* 0.664* 0.664* 0.553* 0.232* 0.368* - ME 0.587* 0.725* 0.587* 0.533* 0.617* 0.618* 0.618* 0.506* 0.191* 0.285* 0.017 -
Genetic diversity and population structure
- 32 -
populations. These results are in agreement with the ones obtained by the
pairwise ΦST analysis. The star-shaped network also suggests that
populations have undergone expansion following bottlenecks.
Fig. 2- Minimum spanning network analysis of haplotypes identified in samples of Solea solea. Distances between haplotypes are proportional to the number of mutational steps. Colours correspond to populations as follows: Baltic Sea-North Sea transition (MBA), Denmark coast (MN), United Kingdom (UK), Belgium coast (B), Bay of Biscay (BY), Portugal-North (PN), Portugal-Centre (PC), Portugal-South (PS), Gulf of Lyon (GL), Italy (IT), Aegean Sea (ME) and Turkey (TQ).
The population analysis performed in BAPS (Figure 3) suggests the
existence of three different clusters, represented by the blue, red and green
vertical bars. Two of the clusters are present in all Atlantic populations (blue
and green), but not in Mediterranean populations, and the red cluster is
only found in Mediterranean populations.
Chapter 2
- 33 -
Fig. 3- Population structure obtained with BAPS in Solea solea populations. Each colour corresponds to a different cluster. Legend: B- Belgium coast; BY- Bay of Biscay; GL- Gulf of Lyon; IT- Italy; MBA Baltic Sea-North Sea transition; ME- Aegean Sea; MN- Denmark coast; PC- Portugal-Centre; PN- Portugal-North; PS- Portugal-South; TQ- Turkey; UK-United Kingdom.
The population structure presented in both analyses, haplotype network
and BAPS, is concordant, indicating the absence of genetic structure within
the Atlantic. However, the sub-structure in the Mediterranean presented by
the haplotype network (absence of shared haplotypes between Western and
Eastern Mediterranean populations) was not detected trough BAPS analysis.
All together, these results suggest a significant genetic differentiation
between NE Atlantic and Mediterranean populations, low levels of genetic
differentiation within the Mediterranean area and absence of differentiation
within NE Atlantic region.
Genetic diversity and population structure
- 34 -
In the demographic analysis, all populations exhibited moderate to highly
negative D-values in Tajima’s D-test, except for Italy (IT) and Baltic Sea
(MBA), although significant D-values were only obtained for the Turkey (TQ)
population (P<0.05). Fs-values were negative and significant in all
Mediterranean populations (Gulf of Lyon (GL), Italy (IT), Aegean Sea (ME)
and Turkey (TQ)), and in some Atlantic populations (Belgium (B), Bay of
Biscay (BY), Portugal-Centre (PC) and Portugal-South (PS)) (Table 6).
Table 6- Results of Tajima’s D-test (Tajima, 1989) and Fu’s Fs-test (Fu, 1997) with associated probability for Solea solea populations (significant values in bold).
Tajima's D P Fu’s Fs P
MBA 0.929 >0.05 1.838 >0.05
MN -1.023 >0.05 0.322 >0.05
B -0.856 >0.05 -3.284 <0.05
UK -0.853 >0.05 -1.593 >0.05
BY -0.769 >0.05 -0.371 <0.05
PN -0.773 >0.05 -2.153 >0.05
PC -1.016 >0.05 -3.604 <0.05
PS -0.872 >0.05 -4.380 <0.05
GL -1.189 >0.05 -6.974 <0.05
IT 0.087 >0.05 -3.291 <0.05
ME -1.286 >0.05 -2.410 <0.05
TQ -1.667 <0.05 -2.847 <0.05
The analysis of the parameters of the sudden expansion model and the
goodness-of-fit test applied to it, (Table 7), suggested the occurrence of
expansion in all S. solea populations, except for Baltic Sea (MBA) and
Portugal-North (PN), where significant SSD-values ( Sum of squared
deviations) results in the rejection of null hypothesis of sudden expansion in
population size.
Chapter 2
- 35 -
Table 7- Parameters of the sudden expansion model and its goodness-of-fit test significance, applied to Solea solea populations. S, number of polymorphic sites; θ0, pre-expansion population size; θ1, post-expansion population size; τ, time in number of generations; SSD, sum of squared deviations and P value (* indicates significant values, P<0.05) .
S θ0 θ1 Τ SSD P value
MBA 13 0 99999 8.0 0.113* 0.003
MN 11 0 3.725 5.9 0.094 0.220
B 19 0 36.367 7.4 0.022 0.358
UK 23 0 99999 7.9 0.181 0.204
BY 18 0 13.567 6.9 0.014 0.712
PN 16 0 99999 5.6 0.101* 0.003
PC 18 2.438 50.625 3.4 0.011 0.700
PS 26 0 99999 8.0 0.008 0.691
GL 15 0 99999 4.1 0.006 0.783
IT 9 0.017 99999 3.3 0.008 0.712
ME 7 0 99999 1.6 0.018 0.568
TQ 4 0 99999 1.0 0.031 0.292
Results from the analysis of mismatch distributions of pairwise
differences between all individuals of each population, as revealed from the
mutimodal curves, suggested that Atlantic populations are stable over time,
with the exception of Portugal-Centre (PC) and Portugal-South (PS).
Histograms from Mediterranean populations showed a pattern proximal to
unimodal curves, suggesting a sudden expansion in population size
(P>0.05) (Figure 4). However, only Turkey (TQ) population exhibited
concordant results of SSD-values (non-significant), mismatch distributions
(unimodal curve), Tajima’s D-value (negative and significant) and Fu’s Fs-
value (negative and significant) supporting a sudden expansion on its size.
According to all the previous analyses, Atlantic populations seem to
constitute a panmictic unit (i.e. very low ΦST values between Atlantic
populations), demographic analyses were also performed considering all the
Atlantic populations as one unit, and the results suggested a sudden
expansion in population size (see Figure II, in Annex, page iii), concordant
with the star-shaped haplotype network.
Genetic diversity and population structure
- 36 -
MBA
0
5
10
15
20
25
30
0 1 2 3 4 5 6 7 8 9
MN
0
5
10
15
20
25
30
0 1 2 3 4 5 6 7 8 9 10
B
0
5
10
15
20
25
30
0 1 2 3 4 5 6 7 8 9 10
UK
0
5
10
15
20
25
30
0 1 2 3 4 5 6 7 8 9 10
BY
0
5
10
15
20
25
30
0 1 2 3 4 5 6 7 8 9 10 11
PN
0
5
10
15
20
25
30
0 1 2 3 4 5 6 7 8
PC
0
5
10
15
20
25
30
0 1 2 3 4 5 6 7 8 9 10 11
PS
0
5
10
15
20
25
30
0 1 2 3 4 5 6 7 8 9 10 11 12
GL
0
5
10
15
20
25
30
0 1 2 3 4 5 6 7 8 9
IT
0
5
10
15
20
25
30
0 1 2 3 4 5 6 7 8
ME
0
5
10
15
20
25
30
0 1 2 3 4 5 6
TQ
0
5
10
15
20
25
30
0 1 2 3
Fig. 4- Pairwise mismatch distributions (Rogers & Harpending, 1992) and simulated model of sudden expansion (Rogers, 1994) for each population of Solea solea.
Observed Simulated
Number of pairwise differences
Pairw
ise m
ism
atc
h
Chapter 2
- 37 -
2.3.2 Solea senegalensis
The cyt b diversity was relatively high, with 15 haplotypes recovered from
the 49 individuals analysed, being 60% of these haplotypes unique. A major
haplotype, corresponding to 45% of the samples, was shared between 22
individuals belonging to the Bay of Biscay (BY) and Portugal-North, Centre
and South populations (PN, PC and PS, respectively) (Table 8).
Table 8- Haplotype frequencies and composition of Solea senegalensis populations
Haplotype BY PN PC PS MAUR TOTAL
1 2 - - - - 2
2 3 8 6 5 - 22
3 4 - - - - 4
4 1 - - - - 1
5 - - - - 6 6
6 - - - - 1 1
7 - - - - 1 1
8 - - - - 1 1
9 - - - - 1 1
10 - - 1 3 - 4
11 - 1 1 - - 2
12 - - 1 - - 1
13 - 1 - - - 1
14 - - - 1 - 1
15 - - - 1 - 1
Haplotype diversity (h) presented a wide range of values, from 0.378 in
the Portugal-North population to 0.778 in Bay of Biscay population.
However, the nucleotide diversity (π) exhibited by all populations was low
(0.001 to 0.002) and the number of haplotypes per population was also low,
varying between 3 in Portugal-North (PN) to 5 in Mauritania (MAUR) (Table
9).
Table 9- Genetic diversity of cyt b sequences for Solea senegalensis populations (standard deviation is presented between brackets).
Population Nucleotide diversity
(π) Haplotype diversity
(h) Number of haplotypes
BY 0.002 (0.001) 0.778 (0.091) 4
PN 0.001 (0.001) 0.378 (0.181) 3
PC 0.001 (0.001) 0.583 (0.183) 4
PS 0.001 (0.001) 0.711 (0.118) 4
MAUR 0.001 (0.001) 0.667 (0.163) 5
Genetic diversity and population structure
- 38 -
The AMOVA performed to the three specified groups (BY; PN, PC and PS;
MAUR) indicated that a high and significant proportion of the total variance
was attributed to differences between the predefined groups of populations
(P<0.001) (Table 10). Nevertheless, the highest, and significant,
percentage of variation (52.66%, P<0.001) was obtained within populations
(the other tested rearrangements, Tables III and IV, are presented in the
Annex, page i).
Table 10- Results of the AMOVA performed for Solea senegalensis, considering three groups (Bay of Biscay (BY); Portugal-North, Centre and South (PN, PC and PS); Mauritania (MAUR))
Source of variation Total variance (%) Fixation indices P value
Among groups 48.55 θCT=0.485 <0.001
Among populations within groups (-) 1.21 θSC= (-) 0.024 <0.001
Within populations 52.66 θST=0.473 <0.001
The highest levels of genetic differentiation were obtained between the
Bay of Biscay (BY) population and all the others under study (Table 11). No
genetic differentiation was found between Portuguese coast samples, which
were the geographically closest ones, being the highest and significant
values of ΦST presented by a pair of the most geographically distant
populations, BY and MAUR (ΦST =0.502). It is also worth noticing that
Portugal-Centre (PC) population did not present significant genetic
differentiation from any of the sampled populations.
Table 11- ΦST values for Solea senegalensis samples (*indicates significant values, P<0.01)
BY PN PC PS MAUR BY - PN 0.453* - PC 0.405 -0.032 - PS 0.429* 0.095 0.000 -
MAUR 0.502* 0.444* 0.301 0.405* -
Chapter 2
- 39 -
The Mantel test including all the sampled populations (from Bay of Biscay
to Mauritania) failed to show a significant correlation between genetic and
geographical distance (P=0.06), despite the high value of the correlation
coefficient obtained (Z=0.75) (see Figure III in Annex, page iii).
The haplotype network based on the cyt b sequences presented a star-
shape (Figure 5), suggesting population expansion. Size of circles is
proportional to the number of individuals within each haplotype.
Fig. 5- Minimum spanning network analysis of haplotypes identified in samples of Solea senegalensis. Distances between haplotypes are proportional to the number of mutational steps. Colours correspond to samples as follows: Bay of Biscay (BY), Portugal-North (PN), Portugal-Centre (PC), Portugal-South (PS), Mauritania (MAUR).
The most common haplotype, H2, was shared by individuals from four of
the five sampled populations – Bay of Biscay (BY), Portugal-North (PN),
Portugal-Centre (PC) and Portugal-South (PS). All other haplotypes derived
from this one, by one to five mutations, with the highest differentiation
being found between the Bay of Biscay (BY) and Mauritania (MAUR)
populations, which was also evidenced by the absence of shared haplotypes
between these populations. Since Mauritania (MAUR) shared no haplotypes
with the other populations, and it is the most southern area sampled, a
latitudinal differentiation was also suggested.
The structure obtained trough BAPS analysis (Figure 6) revealed three
different clusters, represented by blue, red and green vertical bars.
Whereas the green cluster is present in all populations, the blue one occurs
Genetic diversity and population structure
- 40 -
only in Bay of Biscay (BY) population, corroborating the pattern obtained in
the network. The presence of the red cluster only in Portugal-Centre (PC)
and Mauritania (MAUR) populations is concordant with the non-significance
of the ΦST value exhibited in the pairwise analysis of genetic differences
between these two populations.
Fig. 6- Population structure obtained with BAPS in Solea
senegalensis populations. Each colour corresponds to a different
cluster. Legend: BY: Bay of Biscay; MAUR- Mauritania; PC-
Portugal-Centre; PN- Portugal-North; PS- Portugal-South.
In the demographic analyses using Tajima’s D-test and Fu’s Fs-test,
Portugal-Centre (PC) population was the only one that presented negative
and significant D-values and Fs-values (D=-1.610; Fs=-1.283; P<0.05).
Mauritania (MAUR) and Portugal-North (PN) populations also exhibited
negative and significant Fs-values (Table 12).
Table 12- Results of Tajima’s D-test (Tajima, 1989) and Fu’s Fs-test (Fu, 1997) with associated probability for Solea senegalensis populations (significant values in bold).
Tajima's D P Fu’s Fs P
BY 1.284 >0.05 0.942 >0.05
PN 1.401 >0.05 -1.164 <0.05
PC -1.610 <0.05 -1.283 <0.05
PS -0.658 >0.05 -1.176 >0.05
MAUR -1.388 >0.05 -1.896 <0.05
Chapter 2
- 41 -
Pairwise mismatch distributions in each population and the parameters of
the model of sudden expansion and the values of the goodness-of-fit test
applied to it are given in Figure 7 and Table 13, respectively.
BY
0
10
20
30
40
0 1 2 3 4 5 6
PN
0
10
20
30
40
0 1 2 3
PC
0
10
20
30
40
0 1 2 3 4
PS
0
10
20
30
40
0 1 2 3
MAUR
0
10
20
30
40
0 1 2 3 4
Fig.7- Pairwise mismatch distributions (Rogers & Harpending, 1992) and simulated model of sudden expansion (Rogers, 1994) for each population of Solea senegalensis.
Observed Simulated
Pairw
ise m
ism
atc
h
Number of pairwise differences
Genetic diversity and population structure
- 42 -
Histograms resulting from the analysis of mismatch distributions of
pairwise differences between all individuals showed a pattern proximal to a
mutimodal curve for the Bay of Biscay (BY) population, indicating its
stability over time. The other populations presented distributions similar to
unimodal curves in the mismatch analysis, and non-significant SSD values
(P>0.05).
Table 13- Parameters of the sudden expansion model and its goodness-of-fit test significance, applied to Solea senegalensis populations. S, number of polymorphic sites; θ0, pre-expansion population size; θ1, post-expansion population size; τ, time in number of generations; SSD, sum of squared deviations and P values.
S θ0 θ 1 τ SSD P value
BY 5 0.002 4.817 4.5 0.066 0.221
PN 2 0 99999 0.5 0.006 0.693
PC 4 0.197 3.450 0.9 0.001 0.920
PS 3 0 99999 1.1 0.040 0.163
MAUR 5 0.007 3.492 1.7 0.012 0.621
Portugal-Centre (PC) was the only population that exhibited concordant
results of SSD-values (non-significant), pairwise mismatch distributions
(unimodal curve), Tajima’s D-value (negative and significant) and Fu’s Fs-
value (negative and significant) supporting a sudden expansion in
population size.
2.4 Discussion
Nucleotide and haplotype diversities can provide some information on
history of populations. High genetic variation (h) and low to moderate
nucleotide diversity (π) were found in all populations of S. solea and S.
senegalensis analysed, except for Portugal-North population of S.
senegalensis. The values obtained for both diversity indices were similar to
those obtained for another flatfish species, plaice, in the Atlantic area
(Hoarau et al., 2004) and are characteristic of species with wide geographic
Chapter 2
- 43 -
distribution areas, which can be related to a higher number of available
habitats and the possible diversification associated with their colonization.
The detected pattern of genetic diversity showed by both species can be
attributed to a recent population expansion after a low effective population
size caused by bottlenecks or founder events (Grant & Bowen, 1998).
Although the results of all methods used in the present study to
investigate the demographic history of populations of S. solea and S.
senegalensis, were not always concordant, which can be due to the low
number of individuals examined (approximately 10 individuals per
population), a general pattern of sudden expansion in population size,
consistent with the star-shaped haplotype networks, was obtained for both
species (Figure 2 and 5, respectively).
A general pattern of low genetic divergence among populations of marine
fish species has been pointed out (Smith & Fujio, 1982) but, for some
marine species with high dispersal capabilities, several studies have
detected the existence of population differentiation (e.g. Diplodus sargus
(Linnaeus, 1758) (Planes & Lenfant, 2002) and Dicentrarchus labrax
(Linnaeus 1758) (Castilho et al., 1998)). In the present study, results
obtained from Φ-statistics, AMOVA, haplotype networks and BAPS, all
indicated significant genetic structure in S. solea and S. senegalensis
populations, although at different scales for each species.
S. solea spawns large number of pelagic eggs (about 1 000 000), resulting
in high levels of gene flow and genetic uniformity (Katoulas et al., 1995a).
In the present study, significant genetic differentiation was detected at an
interregional scale, mainly between two major groups of populations, the
Atlantic and the Mediterranean, whereas little or no differentiation could be
detected beneath that scale. This Atlantic-Mediterranean differentiation was
demonstrated not only by AMOVA results and significant ΦST values (Tables
4 and 5 respectively), but also by the haplotype network (Figure 2) that
showed two main groups of haplotypes: one comprising Atlantic
population’s and another comprising Mediterranean individuals, with no
shared haplotypes between them. BAPS diagram (Figure 3) also
Genetic diversity and population structure
- 44 -
corroborated these results. The geographical distance between these two
major areas seems to be the main cause underlying the genetic
differentiation found, as revealed by the significant results of the Mantel
test performed to these two groups, evidencing a clear relationship between
geographical distances and genetic differentiation (see Figure Ia in Annex,
page ii). These results are, therefore, in agreement with the existence of an
isolation by distance model (IBD), as suggested in Kotoulas et al. (1995a).
Separation between Atlantic and Mediterranean S. solea populations can
be explained by the colonization of the Mediterranean, from the Atlantic,
during the early Pliocene and their settlement there since then, which is
consistent with conclusions of Mediterranean biogeographers (Kotoulas et
al., 1995a). Thus, an interruption of gene flow between these populations,
probably due to the major oceanographic discontinuity between these
areas– the Gibraltar Strait-Alboran Sea region – might be the reason for the
exhibited pattern, that has already been reported for other fish species, like
sea bass, D. labrax (Bahri-Sfar et al., 2000), mackerel, Scomber scombrus
(Linnaeus 1758) (Zardoya et al., 2004) and black anglerfish, Lophius
budegassa (Spinola, 1807) (Charrier et al., 2006).
Results of the present study also support the eastward-westward
differentiation among Mediterranean populations of common sole,
suggested by previous studies using allozymes (Kotoulas et al., 1995a),
control region of mtDNA (Guarniero et al., 2002) nuclear-DNA intronic loci
markers (Rolland et al., 2007) and amplified fragment length
polymorphisms (AFLPs) (Garoia et al., 2007). This Mediterranean
differentiation is supported by significant ΦST values between the
populations of Gulf of Lyon and Italy (western) and the populations from
Aegean Sea and Turkey (eastern) and by the haplotype network, where no
haplotypes were shared between this two Mediterranean areas, being also
possible to detect two major haplotypes – one corresponding to eastern
Mediterranean populations and the other western Mediterranean
populations.
Chapter 2
- 45 -
Differentiation among such populations could be due to the complex
history of the Mediterranean that was strongly impacted during last glacial
episodes. During these periods the lower sea level modified coast lines,
creating distinct refuges in the Mediterranean and allowing the splitting of
the eastern and western basins; since then, they present different
hydrographic regimes, the western one being much more uniform than the
eastern one because of their respective geographies (Bahri-Sfar et al.,
2000). The possible partial recolonization by populations from the Atlantic
can, therefore, also be an explanation for the detected differentiation within
the Mediterranean (Rolland et al., 2007). These differences have also been
attributed to larval temperature tolerances, for S. solea (Kotoulas et al.,
1995a) and P. flesus (Borsa et al., 1997) and to local adaptations to
different salinities for S. maximus (Nielsen et al., 2004). The present study
excludes the geographical distances as a structuring force responsible for
the genetic differentiation within the Mediterranean, since the Mantel test
performed, considering only Mediterranean populations revealed the
absence of a significant correlation between genetic and geographical
distances (see Figure Ic, in Annex, page ii). This “Mediterranean division”
has also been reported in other flatfish (S. maximus, Suzuki et al., 2004)
and roundfish (D. labrax, Bahri-Sfar et al., 2000).
According to Kotoulas et al. (1995a), S. solea populations from the
Atlantic region are expected to constitute panmictic or quasi-panmictic units
(structural units that occur in neighbouring localities within a radius of 100
km), presenting high levels of gene flow that, presumably, occur each
generation through the gathering of individuals from different areas of
spawning, and the passive diffusion of eggs and larvae back to coastal and
estuarine nursery areas. Results of the present study are also in agreement
with this model, as there was no indication of within-population genetic
structure at the regional scale. Low levels of differentiation from the Baltic
Sea to South Portugal exhibited by S. solea populations, supported by low
ΦST values and high number of shared haplotypes by Atlantic populations,
with no particular geographical organization, were also obtained for other
flatfishes, such as flounders P. flesus, and Platichthys stellatus (Pallas,
1787) (Borsa et al., 1997), plaice, Pleuronectes platessa (Linnaeus, 1758)
Genetic diversity and population structure
- 46 -
(Hoarau et al., 2002; 2004) and turbot, S. maximus (Nielsen et al., 2004),
and also for roundfish bonito, Sarda sarda (Bloch, 1793) (Viñas et al.,
2004). In general, marine species seem to be more genetically variable
than anadromous and freshwater species (Dewoody & Avise, 2000) and at
the same time, less differentiated into populations (Ward, 2002). In the
light of these considerations, the absence of genetic differentiation among
S. solea samples throughout the NE Atlantic is not unexpected, as well as
the absence of correlation between geographical distances and genetic
differentiation of samples (see Figure Ib, in Annex, page ii).
Considering the NE Atlantic area, S. solea and S. senegalensis occur in
simpatry from Bay of Biscay to North Africa. Since both species present
similar life history pattern (a division into a juvenile phase, predominantly
estuarine and an adult phase, marine, that may have an impact on the
structuring of offshore adult populations, particularly on their genetic
differentiation, since a strong association between a particular spawning
and nursery area can be expected), similar patterns of genetic
differentiation throughout their simpatry area were, somehow, expected.
However the level of genetic differentiation obtained for each species was
different, with S. solea samples presenting genetic homogeneity, conversely
to significant genetic differentiation among S. senegalensis samples,
contradicting the assumption that marine organisms capable of extensive
dispersal (those that undergo lengthy planktonic larval development) will
necessarily demonstrate widespread genetic homogeneity (Exadactylos et
al., 1998).
S. senegalensis exhibited a pattern of significant genetic heterogeneity
among populations separated by geographic distances presumably short
enough to lie within the fishes´ potential migration range, and where the
balance between gene flow and the forces responsible for genetic
differentiation may, in a long term, result in clines, with genetic
differentiation increasing with geographic distances (Borsa et al., 1997).
This pattern has also been reported for other marine fishes, like swordfish,
Xiphias gladius (Linnaeus, 1758) (Kotoulas et al., 1995b).
Chapter 2
- 47 -
For S. senegalensis, geographical distance per se seems to be very
important for the structuring of populations, as it was strongly associated
with genetic divergence amongst the set of samples analyzed (see Figure
III, in Annex, page iii). This association appeared to be highly related with
latitudinal differences between samples, since more geographically distant
S. senegalensis populations, such as Bay of Biscay and Mauritania, showed
the highest pairwise ΦST value (0.502), absence of shared haplotypes in the
network and unique clusters in BAPS analysis. Results obtained for the
geographically closest S. senegalensis populations, such as those from the
Portuguese coast - North, Centre and South of Portugal - presented no
genetic differentiation at all (ΦST ranging from 0.000 to 0.095), and shared
the most common haplotype present in the minimum spanning network of
this species. These results are in agreement with those obtained by Cabral
et al. (2003a) using allozymes, which detected the absence of genetic
differentiation throughout the Portuguese coast. Considering the strong
association obtained between geographic distances and genetic distances, a
significant IBD model, confirmed by the Mantel test would be expected.
However, the resulting correlation coefficient was high (Z=0.75), but not
significant (P=0.06), probably due to the reduced number of populations
analysed and/or to a low sample size.
The importance of geographical distances per se, acting as a structuring
force in Northeastern Atlantic populations, has been found in other marine
fishes such as cod, Gadus morhua (Linnaeus, 1758) (Hutchinson et al.,
2001), plaice, P. platessa (Hoarau et al., 2002) and Atlantic herring, Clupea
harengus (Linnaeus, 1758) (Mariani et al., 2005). A weak pattern of
isolation by distance along a latitudinal axis was also found in European
flounder, P. flesus, from the Western Baltic Sea to Portugal (Borsa et al.,
1997).
The results obtained in the present study, by revealing homogeneity
among S. solea samples and differentiation among S. senegalensis samples
throughout the Northeastern Atlantic, suggest a small role for estuarine
systems as structuring forces of genetic differentiation, according to that
pointed out by Cabral et al. (2003a). In fact, and although both species
Genetic diversity and population structure
- 48 -
have a similar pattern of use of estuarine systems, their genetic
differentiation among sympatric populations is different. The low genetic
differentiation exhibited by Atlantic S. solea populations and Portuguese
coast S. senegalensis populations, can be due to a high genetic flow
between populations at different stages of their life cycles, namely adult
migration between spawning grounds and juvenile dispersion after the
estuarine phase. Other authors suggest that the larval period is the most
important in this context, with genetic flow increasing with the duration of
the pelagic larval period. This latter fact should lead to lower values of
genetic differentiation in S. senegalensis relatively to S. solea, when
analysing samples from the same geographical area, since this species
presents a widest spawning period. Whereas the main spawning period of S.
senegalensis occurs from February to May, with a secondary spawning in
Autumn (Anguis & Cañavate, 2004), being the pelagic larval duration (PDL)
of this species of about 38 days (Dinis et al., 1999), S. solea presents a
similar value of PDL (35 days) (Fuchs, 1982), but a reduced spawning
period (approximately 3 months) (Koutsikopoulos et al., 1991).
Kotoulas et al. (1995a) demonstrated the important role of temperature
as a factor contributing to restrict larval dispersion and promote geographic
isolation. The temperature interval for the development of common solea
eggs ranges from 7ºC to approximately 20ºC (Koutoulas et al., 1995a)
while for Senegal sole the temperature should be above 16ºC (Imsland et
al., 2003). Temperatures outside these limits are lethal, and can
circumscribe the distribution range of both species. Within these limits, the
planktonic stage duration is known to decrease with the variation of
temperature, since the length of the larval phase in flatfish is highly
dependent on this factor (Amara, 2003; Hutchinson & Hawkins, 2004). For
this reason it is possible that the PDL in the Bay of Biscay is smaller,
resulting in a decrease of genetic flow, evidenced by the larger and
significant ФST values obtained between this population and the Portuguese
coast samples of S. senegalensis, not observed in S. solea.
The comparison of these two sole species suggests that water
temperature during the larval period, which is critical for the survival of
Chapter 2
- 49 -
offspring during their pelagic stage, is a major factor affecting gene flow
and consequently the population genetic structure of a species. Similar
results were observed in other flatfishes, such as flounder, P. flesus (Borsa
et al., 1997).
In conclusion, results showed a significant population structure in S. solea,
between Atlantic and Mediterranean populations, and also throughout the
Mediterranean, and an increasing genetic differentiation with increasing
geographic distances for S. senegalensis. The differences found between
these species reflect some aspects of their life history patterns, namely
duration of pelagic larval stage, larval temperature tolerances and fecundity
(Avise, 1994; Kotoulas et al. 1995a; Hilbish, 1996).
However, the detection of global genetic differentiation in high gene flow
species is especially troublesome and subjected to several potential errors
(Waples, 1998). It is also important to note that a lack of genetically
detectable differentiation between populations may arise from: (i) sufficient
gene flow to maintain panmixia; (ii) occasional “sweepstake” events such as
sporadic recruitment from distant, non-neighbouring areas which could
produce the appearance of panmixia; (iii) stabilizing selection arising from
exposure to similar environments; (iv) recent divergence of the compared
populations or (v) failure to detect genetic variants due either to the
technique employed or to insufficient sample sizes (Carvalho & Hauser,
1994). Considering the high dispersal capacity of soles, further studies
using higher sampling sizes, and the analyses of both mitochondrial and
nuclear molecular markers, should be conducted for S. solea and S.
senegalensis in order to define more accurately the genetic stock structure
and gene flow within each species.
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Wright S. (1969) Evolution and the genetics of populations. v.2. The theory
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Zardoya R., Castilho R., Grande C., Favre-Krey L., Caetano S., Marcato S.,
Krey G. and Patarnello T (2004) Differential population structuring of
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Chapter 3
Chapter 3
- 59 -
Concluding remarks and challenges
This work has brought new insights to the knowledge on Solea solea
(Linnaeus, 1758) and Solea senegalensis, Kaup, 1858, population structure.
The main conclusions of the present work were:
• S. solea and S. senegalensis presented diversity indices
characteristic of species with broad distribution areas;
• There is significant genetic differentiation at an inter-regional scale
for S. solea populations, mainly between two major groups, the
Atlantic and the Mediterranean, that can be explained by
biogeographic and oceanographic features;
• S. solea populations from the Atlantic region constitute panmictic or
quasi-panmictic units, but it can be noticed a eastward-westward
differentiation among Mediterranean populations, probably as a
consequence of glacial episodes in the Mediterranean or of a partial
recolonization by populations from the Atlantic;
• Geographical distance per se seems to be very important for the
structuring of S. senegalensis populations, as it was significantly
associated with genetic divergence amongst the set of samples
analyzed;
• Since both species have a similar pattern of use of estuarine
systems, but the genetic differentiation exhibited between samples
of both species is different, results reduced the importance of
estuarine systems as structuring forces of genetic differentiation;
• Water temperature during the larval period, is probably one of the
major factors restricting planktonic stage duration and survival of
Concluding remarks and challenge
- 60 -
larvae, restricting gene flow and consequently contributing to the
population genetic structure of S. senegalensis;
• The comparison of two closely related species with very similar life-
history characteristics brought to light differences in their genetic
population structure and demographic histories;
• This study proved that mitochondrial DNA (mtDNA) is a molecular
marker with high value to detect genetic stock structure and gene
flow in species with large population size and high dispersal
potential such as S. solea and S. senegalensis.
The combined analysis of the results from this study and those from
previous ones, mainly conducted in S. solea populations, allowed the
conclusion that the use of molecular markers with high polymorphism, such
as microsatellites, might not be of such extreme importance since the
results obtained with mtDNA and microsatellites were similar. For this
reason, microsatellites analyses for S. senegalensis, within the same
sampled area, should give similar results to the ones obtained in the
present study using mtDNA. However, it has already been proved that the
combination of different molecular markers is more informative and allows a
more precise analysis and complete conclusions, and, as so, it would be
interesting to develop a study combining the use of several molecular
markers in a broader scale, including samples from the entire distribution
range of both species, and a higher sample sizes (which was the major
limitation of the present work, mainly for the study of S. senegalensis).
Samples from North and South Spain should also be included in the analysis
since they could be very informative about the Atlantic-Mediterranean
genetic differentiation for S. solea, in this transition area. For S.
senegalensis analysis, samples from the Mediterranean should also be
included to verify if the Atlantic-Mediterranean genetic differentiation
obtained for S. solea, and detected in other fish species, is also present in
S. senegalensis. Samples from North Africa are also worth to include as
they would allow the comparison between several Solea species. Associated
biological and environmental data should be collected, especially in areas
less studied, to help interpreting the genetic variation.
Chapter 3
- 61 -
All the conclusions obtained in this study emphasize the importance of
investigating marine fishes with wide geographical and ecological
distribution, such as S. solea and S. senegalensis, to increase our
understanding of evolution in the marine environment. Of particular interest
is the applicability of such studies to the sustainable management and
conservation of these species, especially of those intensively exploited by
fisheries.
Annex
Annex
- i -
Supplementary Information
Table I- Results of the AMOVA performed for Solea solea, considering three groups (Atlantic,
Western Mediterranean and Eastern Mediterranean).
Source of variation Total variance (%) Fixation indices P value
Among groups 45.06 θCT=0,45060 <0.001
Among populations within groups 5.03 θSC=0,09160 <0.001
Within populations 49.91 θST=0,50093 <0.01
Table II- Results of the AMOVA performed for Solea solea, considering three groups
(Atlantic North, Atlantic South and Mediterranean).
Source of variation Total variance (%) Fixation indices P value
Among groups 38.97 θCT=0,38968 <0.001
Among populations within groups 4.37 θSC=0,07152 <0.001
Within populations 56.67 θST=0,43333 <0.001
Table III- Results of the AMOVA performed for Solea senegalensis, considering three groups (Bay of Biscay (BY) Portugal-North (PN); Portugal-Centre and South (PC and PS); Mauritania
(MAUR))
Source of variation Total variance (%) Fixation indices P value
Among groups 8.87 θCT=0,08866 > 0,05
Among populations within groups 30.86 θSC=0,33862 < 0,001
Within populations 60.27 θST=0,39725 < 0,001
Table IV- Results of the AMOVA performed for Solea senegalensis, considering two groups (Bay of Biscay (BY) Portugal-North, Centre and South (PN, PC and PS); Mauritania (MAUR))
Source of variation Total variance (%) Fixation indices P value
Among groups 14.54 θCT=0,14542 > 0,05
Among populations within groups 29.4 θSC=0,34408 < 0,001
Within populations 56.05 θST=0,43946 < 0,001
Supplementary Information
- ii -
Solea solea TOTAL
0
0.2
0.4
0.6
0.8
0 5000 10000 15000
Kilometers
FST
Fig.Ia
Solea solea ATLANTIC
0
0.2
0.4
0.6
0.8
0 5000 10000 15000
Kilometers
FST
Fig.I.b
Solea solea MEDITERRANEAN
0
0.2
0.4
0.6
0.8
0 5000 10000 15000
Kilometers
Fst
Fig. Ic
Fig. I- Relation between genetic differentiation (Pairwise ΦSTs) and geographical distances
(Km) for Solea solea populations. Samples are included following a geographical transect
going from the Baltic Sea (MBA) to Aegean Sea (ME) (Fig. Ia), from Baltic Sea (MBA) to
Portugal-South (PS) (Fig. Ib) and from Gulf of Lyon (GL) to Aegean Sea (ME) (Fig. Ic).
ΦSTs
ΦSTs
ΦSTs
Annex
- iii -
Solea solea ATLANTIC
0
100
200
300
400
500
600
0 1 2 3 4 5 6 7 8 9 10 11 12 13
Nº of paiwise differences
Pa
iwis
e m
ism
atc
h
Observed
Simulated
Fig. II- Pairwise mismatch distributions (Rogers & Harpending, 1992) and simulated model
of sudden expansion (Rogers, 1994) for Atlantic populations of Solea solea.
Solea senegalensis
0
0.2
0.4
0.6
0.8
0 5000 10000 15000
Kilometers
FST
Fig. III- Relation between genetic differentiation (Pairwise ΦSTs) and geographical distances
(Km) for Solea senegalensis populations. Samples are included following a geographical
transect going from the Bay of Biscay (BY) to Mauritania (MAUR)
ΦSTs
Number of pairwise differences
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