Genética de poblaciones de dos peces marinos pelágicos: la sardina europea,
Sardina pilchardus (Walbaum, 1792) y el patudo, Thunnus obesus (Lowe, 1839)
Population genetics of two marine pelagic fishes: European sardine, Sardina pilchardus
(Walbaum, 1792)
and bigeye tuna, Thunnus obesus (Lowe, 1839)
Elena G. González
Ph. D. Thesis
Genética de poblaciones de dos peces marinos pelágicos: la sardina europea, Sardina pilchardus (Walbaum, 1792) y el patudo,
Thunnus obesus (Lowe, 1839)
Population genetics of two marine pelagic fishes: European sardine, Sardina pilchardus
(Walbaum, 1792) and bigeye tuna, Thunnus obesus (Lowe, 1839)
Elena G. González Madrid, 2007
Portada / Front:
Katsushika Hokusai; “La gran Ola de Kanagawa”/ “The Great Wave off Kanagawa”.
Memoria presentada por ELENA G. GONZÁLEZ JIMÉNEZ para optar al Grado de Doctor en Ciencias Biológicas
Elena G. González Jiménez
Vº Bº Director de Tesis Vº Bº Tutor de Tesis
Dr. Rafael Zardoya San Sebastián Prof. Ángel A. Luque del Villar
Madrid, Octubre 2007
DEPARTAMENTO DE BIOLOGÍA.
FACULTAD DE CIENCIAS DEPARTAMENTO DE DIVERSIDAD Y BIOLOGÍA EVOLUTIVA.
MUSEO NACIONAL DE CIENCIAS NATURALES
For Héctor and Adela
Acknowledgements
I would like to thank my advisor Rafael Zardoya for his invaluable help. Among many other things, he taught me how to appreciate science and the importance of quality in work and scientific integrity. I had the best scientific environment I could have asked for and I am grateful for his willingness to help me develop all the thoughts that materialize during a hard day of work (usually at a bar…).
Also thanks to the original work group and all the friends at the museum in Madrid, for sharing these “beer-moments” of relaxation and hard work. Lukas Rüber (Natural History Museum of London), Cristina Grande (University of California), Regina Cunha, Diego San Mauro, Soraya Villalba (Royal Botanic Gardens of London), Pilar Flores and Raquel Álvarez. I am in great debt to PF and LR for helping me to begin work at the lab (and later on as well). In Iñigo Martínez (University of Connecticut) I found a friend and an excellent collaborator. I had the pleasure of a great working environment (in and out of the museum) with A. Arrariol, A. Montilla, C. Pedraza, C. Toledo, D. Buckley, F. Alda, J. I. García, J. Rubines, L. Alcaraz, M.
Alcobendas, M. Cabria, P. Bloor, P. Cabezas, P. Ornelas, S. Perea and many others.
I would like to thank Rita Castilho (Universidade do Algarve, Faro, Portugal) for introducing to me the world of population genetics. Thanks to Touriya Atarhouch
(Institut Agronomique et Vétérinaire Hassan II of Rabat, Morocco) and Pilar Martínez for their help and advice in different projects. Thanks also, to Brent Emerson (University of East Anglia, UK), Robert Fleischer (National Museum of Natural History, Washington, DC) and Scott V. Edwards (Harvard University, Cambridge, MA) for hosting me at their labs and providing me with fantastic resources and the opportunity to learn and gain experience in my scientific career. Thanks to all the people that I met during these visits and who made me feel comfortable far from home, especially to Daniel Janes. Also, to Sarah Young, an English tutor, a traveling companion and a very good friend, no matter the distance.
From my life out of the museum, all my sincere gratitude goes first to my family, my parents, Luis and Maribel, my sisters (Isabel, Ana and Inés), Javier and Juan for their unfailing support of my academic interests and because they are the reason why this thesis finally saw the light. Héctor Amador and Adela Arcas are the two (for the moment) where all of our love and affection converges, and for that, I especially dedicate this work to them. Also, I would like to express tender gratitude to Ignacio García for his support and patience at the final stages (but no less difficult) of this thesis.
Nor can I forget all my friends that “always” had faith that I would finish this. A.
Saiz, A. Pérez, C. Ambite, D. Conejo, I. Tapias, I. Pereyra, J. Rodríguez, J. Romero, P. Parra and many more to mention.
The Ministerio de Educación y Ciencia (MEC) provided me with a predoctoral grant and travel support during the process of this work.
“Theirs not to make reply, Theirs not reason why, Theirs but to do and die”
Alfred, Lord Tennyson The Charge of the Light Brigade, 1890.
This Ph.D. thesis produced the following publications, which will be referred to into the text by their Roman numerals:
Publication I.
Atarhouch, T., L. Rüber, E. G. Gonzalez, E. Albert, M.Rami, A. Dakkak and R.
Zardoya. 2006. Signature of an early genetic bottleneck in a population of Moroccan sardines (Sardina pilchardus). Mol. Phylogenet. Evol. 39: 373-383.
Publication II.
Gonzalez, E.G. and R. Zardoya. 2007. Isolation and characterization of polymorphic microsatellites for the sardine, Sardina pilchardus (Clupeiformes:
Clupeidae). Mol. Ecol. Notes. 7: 519-520.
Publication III
Gonzalez, E.G. and R. Zardoya. 2007. Relative role of life-history traits and historical factors in shaping genetic population structure of sardines (Sardina pilchardus). BMC Evol. Biol. Accepted (Published article on-line).
Publication IV
Martínez, P., E. G. Gonzalez, R. Castilho and R. Zardoya. 2006. Genetic diversity and historical demography of Atlantic bigeye tuna (Thunnus obesus). Mol.
Phylogenet. Evol. 39: 404-416.
Publication V
Gonzalez, E.G., P. Beerli and R. Zardoya. 2007. Migration patterns of Atlantic bigeye tuna (Thunnus obesus). BMC Evol. Biol. Submitted.
Table of contents
Resumen……….…...…..1
Summary………...…….…….5
Introduction………...……….………..…….….….9
Complex population structure and dynamics of marine pelagic species……..…9
Patterns of ecologic and genetic diversity in marine pelagic fishes………12
The studied species………..15
Genetic markers………...23
Population genetic analyses………...…30
Objetivos………...……...………...……...……..…….……41
Objectives………...………...………….……42
I: Signature of an early genetic bottleneck in a population of Moroccan sardines (Sardina pilchardus)……….…………...………...…….…….45
Introduction……….48
Materials and methods………50
Results……….55
Discussion………...61
Acknowledgements……….65
References………...66
II: Isolation and characterization of polymorphic microsatellites for the sardine, Sardina pilchardus (Clupeiformes: Clupeidae)………..…...…...………73
III: Relative role of life-history traits and historical factors in shaping genetic
population structure of sardines (Sardina pilchardus)………...………...81
Introduction……….84
Materials and Methods………87
Results……….91
Discussion……….100
Conclusions………...103
Acknowledgements………...104
Apendix……….104
References……….109
IV: Genetic diversity and historical demography of Atlantic bigeye tuna (Thunnus obesus)………...……….………....119
Introduction………...122
Materials and methods………..125
Results………...131
Discussion……….137
Acknowledgements………...144
References……….145
V: Genetic structuring and migration patterns of Atlantic bigeye tuna (Thunnus obesus)………..………..…153
Introduction………...157
Materials and methods………..160
Results………...165
Discussion……….174
Conclusions………...180
Acknowledgements………...181
References……….182
Discussion………...191
Conclusiones………...……….…...201
Conclusions………...……….….203
References………...……….…...205
1
Resumen
El principal objetivo de esta tesis es determinar la estructura genética poblacional de dos peces marinos pelágicos de interés pesquero, la sardina europea (Sardina pilchardus) y el patudo (Thunnus obesus), así como inferir patrones comunes sobre la diferenciación y dinámica de sus poblaciones. La definición de los patrones genéticos espacio-temporales y los procesos que los causan debería contribuir a entender y a estimar de forma efectiva el papel que ejercen las fuerzas naturales y humanas sobre las poblaciones de estos peces, ayudando en la conservación y el manejo efectivos de sus recursos pesqueros.
El estudio de la estructura genética poblacional de especies de peces marinos pelágicos presenta importantes obstáculos metodológicos. La mayoría de los peces marinos se caracterizan por una gran capacidad de dispersión y migración, así como grandes tamaños poblacionales que complican el muestreo. Dichas características, potenciadas por un ambiente marino relativamente homogéneo, aparentemente libre de barreras a la dispersión, pueden promover una diferenciación genética baja de las poblaciones, difícil de detectar mediante análisis de genética de poblaciones clásico.
Teniendo en cuenta las dificultades metodológicas antes descritas, para poder cumplir los objetivos de esta tesis se ha puesto particular atención en obtener tanto datos de polimorfismo de ADN mitocondrial (secuencias de la región de control)
2
como nuclear (tamaños de alelos de microsatélites), en maximizar el esfuerzo de muestreo (tanto a escala temporal como espacial), y en aplicar tanto herramientas estadísticas potentes (tales como métodos de coalescencia y agrupación bayesiana), como métodos clásicos de genética de poblaciones. Todo ello con el fin de determinar los factores ecológicos y procesos históricos que han modelado la estructura genética actual de la sardina europea y del patudo, así como para reconocer patrones comunes.
Tanto la sardina europea como el patudo son especies representativas de pez pelágico pequeño y grande, respectivamente, que difieren principalmente en su distribución, así como en diversas características biológicas y ecológicas. La sardina presenta una distribución restringida desde el mar del Norte hasta Senegal, mar Mediterráneo, mar de Mármara y mar Negro. En cambio, el patudo está preferentemente distribuido en aguas templadas y tropicales de los océanos Atlántico, Índico y Pacífico (excepto en el mar Mediterráneo), con una única zona de transición entre ambos océanos a través del cabo de Buena Esperanza. Además, mientras la sardina ocupa las zonas epipelágicas costeras más superficiales, el patudo, con una capacidad de migración mucho mayor que la sardina, se desplaza por las zonas epipelágicas y mesopelágicas (adultos) en mar abierto. De acuerdo con los resultados obtenidos en esta tesis doctoral, estas diferencias se encuentran reflejadas en distintos patrones de estructura genética de sus poblaciones.
Las oscilaciones climáticas ocurridas durante el Pleistoceno indujeron cambios sucesivos de regresión y expansión de las poblaciones de sardina, que han quedado reflejados a nivel del ADN mitocondrial. La estructura genética actual de la sardina indica la existencia de dos grupos mitocondriales diferenciados, que se corresponden geográficamente con dos subespecies descritas mediante estudios de morfología y merísticos, Sardina pilchardus pilchardus (Walbaum, 1792) y S. pilchardus sardina (Risso, 1826). Desde el Pleistoceno, las diferentes poblaciones de sardina se encuentran en proceso de expansión, con la excepción de una población de la costa de Marruecos (Safi), donde se ha detectado la huella de un cuello de botella más reciente.
En el caso del patudo, los cambios de temperatura y del nivel de los océanos durante
3 los máximos glaciales del Pleistoceno han tenido una influencia importante en su estructura genética, debido al aislamiento de sus poblaciones al desaparecer la conexión entre el océano Atlántico y los océanos Índico y Pacífico a través del cabo de Buena Esperanza. Nuestros resultados confirman la existencia de dos clados mitocondriales muy divergentes, uno con una distribución global (clado I) y el otro restringido al océano Atlántico (clado II). También se confirma que las poblaciones de patudo en el Atlántico actúan como una unidad panmíctica.
En la actualidad, el modelado de la estructura genética de las poblaciones de sardina y patudo está influido principalmente por factores biológicos y ecológicos, como indican los resultados de los datos de microsatélites. La sardina presenta una sutil, pero significativa, diferenciación genética, que sigue un modelo de aislamiento por distancia, probablemente debida a factores a nivel local (tales como corrientes oceánicas) que modelan la dispersión de sus larvas pelágicas. Los resultados obtenidos del patudo indican la existencia de un único grupo panmíctico mundial y altas tasas de migración desde el océano Atlántico. Para explicar el flujo genético existente hacia los océanos Índico y Pacífico, así como las proporciones siempre estables en el océano Atlántico de los clados mitocondriales anteriormente mencionados, se han propuesto dos hipótesis alternativas no excluyentes. Los machos son los que se desplazan entre océanos y regresan a las zonas de desove (comportamiento filopátrico), mientras que las hembras son sedentarias respecto al océano. Alternativamente, el mayor tamaño efectivo de la población de patudo del océano Atlántico (debido a la existencia de un clado II propio), podría minimizar las estimas relativas de migración desde los océanos Índico y Pacífico hacia el Atlántico.
Los patrones encontrados son similares a los descritos para otros peces marinos pelágicos grandes y pequeños. Debido al carácter más ubicuo de los peces pelágicos grandes, la estructura genética está más influenciada por variaciones históricas (climáticas) que provocan cambios en su distribución a nivel global. Sin embargo, en los peces pelágicos pequeños, la estructura genética parece más dependiente de inestabilidades demográficas, características biológicas (tales como reclutamientos
4
locales), así como de peculiaridades de la hidrografía local (tales como giros y remolinos), que en conjunto dan lugar a procesos de aislamiento por distancia.
Finalmente, los resultados obtenidos ponen de manifiesto que, en el caso de los peces pelágicos, la integración de datos moleculares con información ecológica y el uso de diferentes marcadores moleculares son claves para la obtención de una visión completa de la historia poblacional y del flujo genético de sus poblaciones, y deben redundar en una mejor gestión pesquera.
5
Summary
The primary goal of this Ph.D. work includes determining population genetic structure of two pelagic marine fishes of commercial interest, the European sardine (Sardina pilchardus) and the bigeye tuna (Thunnus obesus), as well as inferring common patterns of population differentiation and dynamics. The definition of the spatio- temporal genetic patterns of these marine pelagic fishes and of the corresponding causing processes might effectively contribute to understand and estimate the role of natural and anthropogenic forces on its populations, which could aid in effectively preserving and managing their fishery resources.
The study of population genetic structure of marine pelagic fish species is hampered by important methodological challenges. Most marine pelagic fishes have great dispersal and migratory capabilities, as well as large population sizes that complicate sampling. These characteristics, enhanced by a relatively homogeneous habitat that lacks apparent barriers to gene flow, may limit genetic differentiation, which becomes intrinsically difficult to detect using classic population genetic analyses.
Considering the above methodological complexities, and in order to achieve the goals of this thesis, we put particular attention to gather both mitochondrial (mt control region sequences) and nuclear (microsatellite allele size) polymorphism data,
6
in maximizing sampling effort (at both temporal and spatial scales) and in using powerful statistical tools (such as coalescent and Bayesian clustering based methods), as well as classical population genetic methods, in order to determine the ecological factors and historical processes that shaped present-day structure of sardine and bigeye tuna populations, as well as, to recognize common patterns.
Both the sardine and the bigeye tuna are representative species of small and large marine pelagic fish, respectively, that differ mainly in their distribution, as well as several biological and ecological characteristics. The sardine is found from the North Sea to Senegal, as well as in the Mediterranean, Marmara and Black Seas. On the other hand, the bigeye tuna is preferentially distributed in temperate and tropical waters of the Atlantic, Indian and Pacific oceans (except in the Mediterranean) with a unique transition path between oceans through the Cape of Good Hope. Furthermore, whereas the sardine is observed in the epipelagic regions in shallow inshore waters, the bigeye, which has a higher migratory capacity than the sardine, roams in the epipelagic and mesopelagic (in the case of adults) regions of the ocean open waters.
According with the results obtained in this Ph.D. thesis, these differences are reflected in their different patterns of genetic structure.
Climatic oscillations that occurred during the Pleistocene induced succession of population regressions and expansions that are reflected in the contemporary sardine genetic structure at the mitochondrial DNA level. The present-day structure indicates the existence of two mitochondrial groups, which geographically correspond to two subspecies previously identified based on morphological and meristic studies, Sardina pilchardus pilchardus (Walbaum, 1792) and S. pilchardus sardina (Risso, 1826).
Since the Pleistocene, the sardine populations are under expansion, with the exception of a population (Safi) off the Moroccan Atlantic Ocean coast, in which a signature of an early genetic bottleneck has been detected. In the case of the bigeye tuna, changes in temperature and sea levels during the Pleistocene glacial maxima had strongly influenced its genetic structure, isolating the populations in the Atlantic, Indian and Pacific oceans when the path through the Cape of Good Hope disappeared. Results
7 confirm the existence of two highly divergent mitochondrial groups, one with a global distribution (Clade I) and the other restricted to the Atlantic Ocean (Clade II).
At present, the biological and ecological factors are the ones that mostly influence in shaping the present-day genetic structure of sardine and bigeye tuna, as it have been shown by the results based on the analyses of microsatellite data. Sardine populations show a subtle significant genetic differentiation, which follows an isolation-by- distance model, probably due to local factors (such as currents) that model the dispersion of the sardine pelagic larvae. Results for the bigeye tuna support a single worldwide panmictic unit and high migration rates from the Atlantic Ocean. To explain the gene flow rates towards the Indian and Pacific oceans and the stable proportions of mitochondrial clades in the Atlantic Ocean, it has been proposed two alternative but not exclusive hypotheses. Males migrate across oceans and return to the spawning areas (homing behaviour), whereas females are sedentary with regards to the ocean basin. Alternatively, the larger effective bigeye population size at the Atlantic Ocean (due to the existence of an exclusive clade), may minimize the relative importance of immigration from the Indian and Pacific oceans towards the Atlantic Ocean.
The genetic patterns found are similar to other genetic patterns described for large and small marine pelagic fishes. Due to the marked ubiquitous character of large pelagic fishes, genetic structuring has been largely influenced by historic (climatic) variations that affected their distribution at global levels. However, the genetic population structure of small pelagic fishes is more dependent on high demographic instabilities, biological characteristics (such as local recruitments), as well as local hydrographic peculiarities (such as gyres and eddies), that together promote isolation by distance. Finally, the results obtained here highlight that for the studying of marine pelagic fishes, an integration of molecular data with ecological information and the use of different molecular markers are fundamental in achieving a comprehensive view of the population history and the gene flow of these species, and should lead to a better fishery management.
8
9
Introduction
1. Complex population structure and dynamics of marine pelagic species
1.1 Background
Marine pelagic organisms are often less accessible for empirical observations of their behaviour and natural history than terrestrial or coastal organisms due to logistical limitations imposed by the nature of the open marine environment (Avise 1998). For example, tagging studies that directly evaluate migration of adults and dispersal of larvae provide valuable information but are technically difficult to carry out (reviewed by Pawson and Jennings 1996). While these types of studies have contributed largely to discerning distribution ranges of marine pelagic species, genetic studies can provide new and complementary insights on the ecology, natural history, population history and dynamics of those species (Palumbi 1994; Purcell et al. 2006).
The scales of population distribution and dispersal of marine pelagic species are vastly greater than those exhibited by terrestrial and freshwater species, which together with the fewer physical barriers in the marine realm, contribute to a greater
10
potential for gene flow among marine populations (Avise 1998; Graves and McDowell 2003; Waples 1998). Despite the fact that genetic data surveys constitute a valuable source of information to discern recent demographic events such as, e. g.
larval dispersal, the mentioned physical peculiarities of the marine environment can also constrain analyses of genetic variation, and must be taken into consideration.
Not all marine pelagic organisms show the same genetic structure patterns. While some species show relative genetic homogeneity over large distances (Dannewitz et al.
2005; Graves 1998; Graves and McDowell 2003), others exhibit weak but significant population structure (Bekkevold et al. 2005; Nesbø et al. 2000; Zardoya et al. 2004).
This variety of observed genetic structure patterns results from a combination of ecological factors, life-history traits and historical processes (Alvarado Bremer et al.
2005a; Magoulas et al. 2006; Zardoya et al. 2004). Thus, marine pelagic species with high adult mobility or long larval periods tend to have significant levels of gene flow between populations whereas those species showing local larval retention may show higher levels of isolation and genetic differentiation (Bekkevold et al. 2005; Carreras- Carbonell et al. 2006; Nesbø et al. 2000). In addition, present and past physical barriers to dispersal such as ocean fronts and currents, as well as biological features such as philopatric behaviour may promote genetic differentiation (Alvarado Bremer et al. 2005a; Magoulas et al. 2006). The combination of these characteristics (some described in detail in the next section) generates and maintains high levels of marine diversity encompassing a wide variety of ecological adaptations and life-history traits (Gagnon and Angers 2006; Imron et al. 2007). In this context, discordant patterns of genetic structuring among closely related species may be interpreted as differences in spawning time, dispersal capacity, environmental tolerance or as the results of distinct past demographic events (such as e. g. bottlenecks) (Bargelloni et al. 2003; Zardoya et al. 2004).
In addition to biological considerations, analytical problems inherent to the study of population genetics of species with high levels of gene flow also arise (Waples 1998). Classical methods that provide indirect estimates of gene flow from genetic
11 data (e. g. F - statistics of Wright 1951) are based on assumptions that are rarely met
in marine pelagic populations (Bohonak 1999; Whitlock and McCauley 1999).
Wright’s island model of migration is based on the following formula (Wright 1951):
(1) FST ≈ 1/(1 + 4 mNe); m being the migration rate and Ne the effective population size.
This equation assumes that equilibrium is attained between migration and genetic drift. Thus, if the assumption of the island model is violated (i. e. migration rates are not symmetrical, and effective population sizes are large) estimates of FST are biased and may not provide an accurate estimate of population differentiation (Neigel 2002;
Whitlock and McCauley 1999).
Coalescent methods (Kingman 2000) provide the opportunity to accurately estimate population genetics parameters by relaxing the assumptions imposed by the classical model of migration (Wakeley 2005). Both, maximum likelihood (ML, Beaumont 1999; Beerli and Felsenstein 1999, 2001; Griffiths and Tavare 1994;
Kuhner et al. 1995) and Bayesian inference (BI, Beaumont et al. 2002; Beerli 2006;
Drummond et al. 2002) approaches are used to calculate effective sizes and migration rates.
In addition, it is important to note that given the large population sizes and distributions of marine pelagic species, statistical power of inferences needs to ensure covering whole range distributions, obtaining representative sample sizes per population, considering temporally replicates samples, and testing a large number of genetic loci (Kuhner et al. 1998).
The marine environment presents many challenges to fully understanding the population dynamics of organisms, and hence to the successful management of marine fisheries (Waples 1998). Considering the above complexities, only complete genetic surveys paired with powerful statistical tools and direct observations can provide a more comprehensive picture of the population genetic structure patterns of marine organism, which is fundamental in defining stock structure. These results are
12
particularly important to prevent overexploitation, the loss of genetic variability, and avoid population collapse (Graves 1996; Graves 1998; Pauly et al. 2002; Utter 1991).
2. Patterns of ecologic and genetic diversity in marine pelagic fishes
Marine pelagic fishes include both oceanic epipelagic and mesopelagic species, such as tunas (e. g. genus Thunnus), swordfish (e. g. genus Xiphias), herrings (e. g. genus Clupea) and mackerel (e. g. genus Scomber), as well as smaller epipelagic species such as sardines (e. g. genus Sardina) and anchovies (e. g. genus Engraulis). These species differ in their biology from demersal fishes, which exhibit weaker migratory capacity, and more localized distributions off the continent platform.
Population genetic structuring in marine pelagic species may be explained by at least three alternative, but not exclusive hypotheses: 1) environmental factors including past sea level and temporal changes, as well as physical barriers to gene flow, that may mix or disrupt populations from different geographical locations; 2) isolation by distance among populations is expected to promote genetic differentiation; and 3) life-history traits (such as potential for dispersal, homing behaviour and local larval retention) may enhance or reduce gene flow (Zardoya et al.
2004, and references therein).
2.1 Environmental features of the marine realm.
One of the most evident characteristics of the pelagic habitat from a genetic point-of- view is the apparent lack of physical barriers to gene flow that allow passive drift of eggs and larvae as well as dispersal of adults of marine pelagic fishes (Hellberg et al.
2002; Palumbi 1994; Shulman 1998). However, dispersal is not completely free since other physical forces, such as depth and ocean currents, constrain and channel gene
13 flow across the oceans, and eventually promote population differentiation (Grant and
Bowen 1998). Moreover, temporary changes in temperature (e. g. during Pleistocene glacial maxima) and salinity can affect the distribution of fish populations (especially in long migratory, large pelagic species, with global distribution (Alvarado Bremer et al. 2005a; Graves and McDowell 1995; Viñas et al. 2004a), resulting in disruption of gene flow.
In recent years, growing evidence is accumulating in support of the Alboran-Oran front (Tintoré et al. 1998) and the Strait of Gibraltar as barriers for the dispersal of many marine demersal and pelagic species between the Atlantic Ocean and the Mediterranean Sea (Bargelloni et al. 2003; Chikhi et al. 1997; Kotoulas et al. 1995;
Naciri et al. 1999). For instance, a sharp phylogeographic break between the Atlantic Ocean and the Mediterranean Sea was reported for several sparid species including Dentex dentex, Lithognathus mormyrus, Spondyliosoma cantharus (Bargelloni et al.
2003) and Diplodus puntazzo and Diplodus sargus (Bargelloni et al. 2005). Similarly, a recent study detected a significant correlation between allele frequencies, and the spatial distribution of salinity and temperature values in the Atlantic and the Mediterranean genetic stocks of European hake, Merluccius merluccius (Cimmaruta et al. 2005). In these examples, hydrographical and environmental restrictions may limit the recruitment success of adults, separating populations and promoting genetic differentiation. Other known examples of phylogeographic breaks in the marine realm are the Florida Peninsula separating the Atlantic Ocean and the Gulf of Mexico (Gaffney et al. 2007), and the Peninsula of Baja California separating the Pacific Ocean from the Sea of Cortez (Huang and Bernardi 2001).
2.2 Isolation by distance.
Despite the generally high levels of gene flow in marine pelagic fishes, isolation by distance has been reported for a number of fishes. Additional aspects of marine life history (such as local larval retention or reproductive homing behaviour) can lead to
14
limited genetic dispersal (Riginos and Nachman 2001). Isolation by distance describes the situation where populations becomes increasingly divergent as their geographic distance increases (Slatkin 1993). This occurs when gene flow between adjacent populations is significantly higher than between more distant populations, which is a commonly observed phenomenon. These geographical patterns of isolation by distance generally occur over larger geographical scales and are more common in small-medium size pelagic fish species that inhabit continental margins (Dannewitz et al. 2005; Knutsen et al. 2003; Ruzzante et al. 2006; Viñas et al. 2004a; Wirth and Bernatchez 2001). In other situations, discontinuities in the habitat or differences in dispersal abilities could lead to this pattern (Riginos and Nachman 2001). The case of the European eel, Anguilla anguilla in the North Atlantic Ocean and the Mediterranean Sea, is a classic example were population differentiation is detectable among eels sampled from a 4000 kilometres long stretch along European coastlines (Wirth and Bernatchez 2001).
2.3 Life-history traits
Adult marine pelagic species have developed dispersal mechanisms adapted to long distance migration (Hauser and Ward 1998). For instances, billfishes (Family Istiophoridae) have the capacity of disperse thousand of kilometers per day (Graves and McDowell 2003), and bigeye tuna in the Pacific Ocean is able to cover distances of 6500 kilometers per year (Grewe and Hampton 1998). This “roaming” lifestyle of adults may counteract genetic differentiation even on a large geographical scale.
Philopatric behaviour and its effect on population differentiation have been primarily studied in anadromous, e. g. salmon, (Tallman 1994), shad (Waters and Burridge 1999), sturgeon (Stabile et al. 1996), in catadromous, e. g. eel, Anguilla anguilla (Wirth and Bernatchez 2001), as well as freshwater fish species, e. g.
walleye, Stizostedion vitreum (Stepien and Faber 1998). Some instances of philopatry have been documented in marine pelagic fishes such as, e. g. herring, Clupea harengus
15 (McQuinn 1997), mackerel, Scomber scombrus (Nesbø et al. 2000) and scad mackerel,
Decapterus russelli (Rohfritsch and Borsa 2005); and it is likely that this behaviour is more common than previously thought in marine pelagic fishes, but difficult to document due to the inherent challenges in directly observing and assessing adult migration in the marine realm.
Another feature that characterizes marine pelagic fish species is their large population size, which determines the distribution of genetic variability among populations (Hauser and Ward 1998). The magnitude of genetic differentiation, in terms of number of genetic changes accumulated, is a function of the population size (Nei 1975). Hence, population genetics theory predicts that large populations should maintain high level of genetic variability at neutral loci, due to reduced genetic drift (Kimura 1979). Consequently, genetic diversity will be significantly reduced in small populations (Nei 1975).
On the other hand, effective population sizes (Ne) in marine pelagic fishes are often several orders of magnitude smaller than actual population sizes (census population sizes, N) probably because of life-history traits such as, e. g. strong bias in reproduction success, and size-dependant fecundity (Hauser et al. 2002). Thus, overfished marine pelagic fishes populations usually are more susceptible to undergo genetic bottlenecks (Hauser et al. 2002; Ryman et al. 1995).
3. The studied species
3.1 Small pelagic fish: the European sardine, Sardina pilchardus (Walbaum, 1792) (order Clupeiformes)
The European, or common, sardine (hereafter sardine) is a marine epipelagic and schooling species that forms shoals at a depth between ten and 90 meters (with a maximum depth of 150 m) and at isotherms of 13 to 25º C (Parrish et al. 1989). Its distribution ranges from the North Sea to Senegal, as well as the Mediterranean Sea,
16
the Sea of Marmara and the Black Sea (Parrish et al. 1989; Whitehead 1985) (Fig. 1).
Sardines are small plankton feeders (mostly microzooplankton and copepods, Olivar et al. 2001), which usually school with the anchovy Engraulis encrasicolus (Abad et al. 1998; Rodríguez et al. 1999). Sardines exhibit daily movements along the coasts as well as vertical movements within the water column (moving to shallow waters at night) (Whitehead 1985). On the other hand, adults and larvae displacements can be much greater and may be constrained by changes in ocean water temperatures, food availability, as well as other hydrogeographic factors (Olivar et al. 2001; Rodríguez et al. 1999; Somarakis et al. 2006). Juveniles reach the reproductive age at the second year (with a size of 10-20 cm). The spawning occurs in open waters when the water column is vertically homogeneous with a maximum concentration of eggs and larvae between ten and 30 meters (Olivar et al. 2001), remaining in the plankton for long periods of time (from two to three weeks in the case of the eggs and longer in the case of the larvae). The seasonality of spawning appears to vary with latitude as a result of latitudinal gradients in sea surface temperature regimes, but we can say that North Atlantic sardine spawns in spring (on the English Channel) and summer (North Sea and the Black Sea), while on the African coast and the Mediterranean Sea, sardines spawns mainly in winter (Olivar et al. 2001; Whitehead 1985).
Figure 1. Picture (right) and present geographic range (in blue, left) of sardine (Sardina pilchardus).
17 The genus Sardina consists of a single species (Grant and Bowen 1998),
and two subspecies have been accepted based on meristic studies (Andreu 1969; Parrish et al. 1989). The two subspecies are separated geographically into two areas: Sardina pilchardus pilchardus (Walbaum, 1792) is found in the northern Atlantic Ocean from the South of Portugal until the North Sea and S.
pilchardus sardina (Risso, 1826) is found in the Mediterranean Sea and near the Atlantic coast of Morocco and Mauritania (Andreu 1969; Parrish et al.
1989). Prior to this thesis, this morphological heterogeneity had not been tested with molecular data.
The family Clupeidae (order Clupeiformes, suborder Clupeoidei) comprises 72 species in 15 genera and includes herrings (e. g. Clupea harengus), sardines (e. g.
Sardinops sagax) and anchovies (e. g. Engraulis encrasicolus) (Whitehead 1985).
Most of the species are found in tropical or subtropical waters, but genera such as Clupea, Sprattus, Sardina and Sardinops occur in cool waters and high latitudes, extending the range of the family to about 70º N and 55º S (Whitehead 1985).
3.2 Large pelagic fish: the bigeye tuna, Thunnus obesus (Lowe, 1839) (Order Perciformes)
The bigeye tuna is a pelagic species inhabiting temperate and tropical waters between 50º N and 45º S (except in the Mediterranean Sea) (Fig. 2). Temperature and thermocline depth seem to be the main environmental factors governing the vertical and horizontal distribution of bigeye tuna (Collette and Nauen 1983). As shown by catch and tagging data young bigeye tunas spend their early lives in shallow, warm equatorial waters and as juveniles migrate into temperate feeding grounds (Fonteneau et al. 2005). Mature adults have a wider distribution than juveniles as they tolerate an oxygen-depleted habitat and live primarily in deep, cold waters (Chow et al. 2000), within the first 100 metres depth during the night-time and at depths between 400 and 500 metres during the daytime (Dagorn et al. 2000). As they grow, bigeye expand
18
their habitat as they move from feeding (temperate waters) to spawning areas (warm waters) and from surface to deeper waters (Chow et al. 2000).
Juveniles and small adults of bigeye tuna tend to school at the surface in monospecific groups or together with yellowfin tuna (T. albacares) and/or skipjack (Katsuwonus pelamis) while adults tend to be solitary. Catch data from surface gears indicate that the main breeding and nursery area of Atlantic bigeye tuna is located in the Gulf of Guinea (between 15º N and 15º S) (Fonteneau et al. 2005).
Figure 2. Diagram (right) and present geographic range (in blue, left) of bigeye tuna (Thunnus obesus).
The biology of the bigeye tuna allows long-distance migrations and a wide distribution. Tuna fishes are distinguished from other marine teleosts by their uniquely high metabolic rate resulting in rapid growth rates and body temperatures higher than that of ambient water. The high metabolic rate warms muscles allowing steady swimming, rapid bursts of speed, and long-distance movements (Hampton and Williams 2005; Miyabe and Bayliff 1998). However, horizontal movement data from conventional tagging studies should be considered cautiously due to the relative low
19 number of individuals tagged and recaptured and the low survivorship of released
bigeye tuna (Robinson 2006). Tuna fishes are opportunistic predators feeding primarily on other fishes, crustaceans and squids. Due to their large size, adult bigeye tunas have few natural predators apart from billfishes, sharks and toothed whales (Collette and Nauen 1983).
Species identification has been generally based upon patterns of body pigments at the larval stage (Nishikawa 1987) and in small juveniles (Matsumoto 1972; Nishikawa 1985), as well as based on the examination of variable external and internal morphological characteristics of adults or juveniles (Takeyama 2001). Today, genetic species identification can be used and presents several advantages over morphological identification, which is less accurate since pigmentation changes with different larval stages (Nishikawa 1987; Richards et al 1990) and some external and internal characteristics can be lost in the process of capture and fishing (Takeyama 2001; Ward 2000).
The genus Thunnus includes seven species (Table 1), of which only albacore (T.
alalunga), bigeye tuna (T. obesus) and yellowfin tuna (T. albacares) are found circumglobally in tropical and temperate waters (Fig. 2, Table 1). The bluefin tuna complex (Gibbs and Collette 1967), composed of the southern bluefin Thunnus maccoyii and the northern bluefin tuna Thunnus thynnus (further split into Atlantic and Pacific subspecies, T. thynnus thynnus and T. thynnus orientalis, respectively), was latter confirmed with molecular data (Alvarado Bremer et al. 1997; Chow and Inoue 1993; Chow et al. 2006; Elliott and Ward 1995; Takeyama et al. 2000).
All molecular phylogenies using allozymes data (Elliott and Ward 1995) and mtDNA (Alvarado Bremer et al. 1997; Bartlett and Davidson 1991; Block et al. 1993;
Chow and Inoue 1993; Chow and Kishino 1995; Finnerty and Block 1995) support the monophyletic origin of the genus Thunnus and are concordant with morphological data (Collette 1979; Gibbs and Collette 1967). However, at the subspecies level, the status of the two northern bluefin tunas requires further examination. Molecular surveys based on mitochondrial sequence data (Alvarado Bremer et al. 1997; Chow
20
and Kishino 1995) and enzyme restriction analyses of the mtDNA genome (Chow and Inoue 1993) have been conclusive regarding the extremely divergent separation of the northern Pacific and Atlantic bluefin tunas. It was shown that genetic differences between these two subspecies are as great as those found between other species of Thunnus, and that Pacific northern bluefin tuna is more similar to albacore than to Atlantic bluefin tuna. Alternative hypotheses have been proposed that could explain mitochondrial evidence (introgression of albacore mtDNA into the Pacific bluefin tuna populations or retention of ancestral mitochondrial polymorphism by the Pacific bluefin tuna and albacore, but not by the Atlantic bluefin tuna), but additional data (nuclear) is needed in order to determine which scenario is more likely.
Table 1. Species of the genus Thunnus, its distribution and estimates of catches (F. A. O. 2005)
Species Common name Distribution
a
Estimated catches (1000
metric tonnes) T. thynnus thynnus (Linnaeus, 1758) Atlantic northern bluefin tuna A 36 T. thynnus orientalis (Temminck
and Schlegel, 1844) Pacific northern bluefin tuna P 4.3 T. maccoyii (Castelnau, 1872) Southern bluefin tuna A, P, I 16
T. alalunga (Bonnaterre, 1788) Albacore A, P ,I 209
T. obesus (Lowe, 1839) Bigeye tuna A, P ,I 403
T. albacares (Bonnaterre, 1788) Yellowfin tuna A, P, I 1296
T. atlanticus (Lesson, 1831) Blackfin tuna A 1.9
T. tonggol (Bleeker, 1851) Longtail tuna P, I 228
a A= Atlantic Ocean, P= Pacific Ocean, I= Indian Ocean
In the case of T. obesus, two highly divergent mitochondrial groups have been reported, Clade I (that is almost exclusive to the Atlantic Ocean) and Clade II (that is present in both the Indian Ocean and Pacific oceans) (Alvarado Bremer et al. 1998;
Chow et al. 2000; Grewe and Hampton 1998). The origin of the two mitochondrial clades has been related to past isolation during the Pleistocene glacial maxima and later unidirectional gene flow of Clade I from the Indian and Pacific oceans into the Atlantic Ocean, across the Cape of Good Hope and favoured by the strong Agulhas
21 current (Alvarado Bremer et al. 1998; Chow et al. 2000; Grewe and Hampton 1998;
Martínez et al. 2006).
The family Scombridae (Order Perciformes, suborder Scombroidei) comprises 55 species in 15 genera and includes comercially important fishes such as, e. g. mackerel (Scomber scombrus), bonito (Sarda sarda) and billfish (Makaira nigricans). The genus Thunnus is included within the tribe Thunnini, which also includes two species in Auxis, three species in Euthynnus and one species in Katsuwonus (Collette and Nauen 1983). All are epipelagic or mesopelagic (>500 metres) depending on the species (Collette and Nauen 1983) and constitute important commercial and recreational fisheries throughout the world (Ward 1995; Table 1).
3.3 Management of pelagic fisheries
The difficulties in managing fisheries of marine pelagic fishes are exacerbated by a number of factors related to their biology including their broad dispersal patterns which span international boundaries, and difficulties associated with their study and observation (Graves 1996; Ward and Grewe 1995). For important commercial marine fish species such as tuna and sardines, management is even more challenging as countries with conflicting objectives compete for the same dwindling resources.
Several international organizations have been formed to manage these species, such as the International Commission for the Conservation of Atlantic Tunas (ICCAT), in order to organize comprehensive and consistent international collaboration in gathering catch-per-effort data which is used to estimate population status (Graves 1996; Ward and Grewe 1995). In this regard, the use of genetics studies to better understand uncertainties in marine pelagic fish population distribution and dynamics is critical (Avise 1998).
Regarding the species studied in this work, sardines are heavily fished with their greatest abundance and current catches in the coastal waters of Morocco, as well as in the Atlantic waters of the Iberian Peninsula. Catches for Morocco in 2005 were over
22
600,000 tones showing no sign of decline, whereas catches for Spain and Portugal peaked in the 1960s at around 250,000 and declined to current rates of around 104,000 tones in 2005 (F. A. O. 2005). However, the stock of Safi and Cap Bojador (from 32º N to 26º N) experienced a severe bottleneck in 1970 due to overfishing (Belveze and Erzini 1983). Morphologic data has been used in attempts to differentiate populations, and leading to the designation of four stocks from the North Sea to Mauritania: the septentrional Atlantic stock, (which extends from 57º N to 43º N), the meridional Atlantic group (from 43º N to 36º N), the Moroccan stock (from 36º N to 28º N), and the Saharian stock, which is distributed from Cap Juby to Cap Blanc in Mauritania (Parrish et al. 1989). Some more localized studies have identified more stocks (Belveze and Erzini 1983). However, the structure of sardine populations in the European Atlantic waters is still not well understood with the sardines considered by some authors (Silva 2003) to belong to a single stock. For management purposes, a more thorough understanding of stock structure is necessary.
The second studied species, bigeye tuna, is considered one of the most valuable species of tuna with their meat reaching very high values on the Japanese sashimi market (Fonteneau et al. 2005). The total catches of bigeye tuna doubled in the past thirteen years and in 2005 ranked second among tuna captures with an average of 403,000 tones (approximately 20% of total tuna captures, Table 1; F. A. O. 2005;
Graves 1996). The status of tuna fisheries is considered over-exploited in most regions, and particularly in the Atlantic Ocean (Graves 1996). Nearly 58% of bigeye tuna catches in 2005 came from the Pacific Ocean, with about 14% and 28% coming from the Atlantic and Indian oceans, respectively (F. A. O. 2005). Based on catch-per- effort data, three different stocks have been identified, one per ocean (Atlantic, Pacific and Indian), and have been managed independently by the ICCAT, the IATTC (Inter- American Tropical Tuna Commission) and the IOTC (Indian Ocean Tuna Commission), respectively.
Given the documented historical lost of species diversity in marine pelagic fishes caused by overfishing (Hauser et al. 2002), genetic studies can contribute to better
23 characterizing the true status of pelagic fisheries allowing for more effective management and conservation programs (Hutchings 2000; Myers and Worm 2003;
Pauly et al. 2002).
4. Genetic markers
A considerable number of genetic markers and molecular techniques have been applied for the estimation of population structure of marine pelagic fishes over the last decades (Graves 1998; Graves and McDowell 2003). The choice of the most appropriate molecular approach has been largely dependent on the ease of use, the level of marker polymorphism, and the genetic and evolutionary characteristics of markers including mode of inheritance (biparental or maternal), genome ploidy level, marker expression (dominant or co-dominant), and mutation and evolutionary rates of divergence (reviewed in Avise 2004; Hillis et al. 1996; Schlotterer 2004; Zhang and Hewitt 2003).
Different classes of molecular markers should yield complementary views of population structure within a species, which allows investigating how evolutionary processes at different time scales had shaped patterns of genetic heterogeneity (Buonaccorsi et al. 2001). While in some cases patterns are similar, discordance in outcomes may result from differential effects of genetic drift and mutation on a marker class (Buonaccorsi et al. 2001) or may detect real patterns such as sex-biased dispersal or homing behaviour.
Among all the molecular markers available for population genetics, those most commonly used for population structure studies are described here.
4.1 Allozymes
In 1970s and 1980s, allozymes were a popular molecular marker used as a diagnostic for distinguishing between species of marine pelagic fishes (such as e. g. Thunnus
24
species, Elliott and Ward 1995; Graves 1998; Ward 2000) and testing biogeographic hypotheses. Allozyme electrophoresis has the advantage of being a relatively cheap technique with the ability to generate a large amount of data in a relatively short period of time (Utter 1991). However, its main limitation is that sampling is usually invasive (meaning the sacrifice of the specimen) due to the fact that some enzyme systems are specific to certain tissues such as the liver, kidney, etc. In addition, this technique requires fresh samples (or preservation in liquid nitrogen) because allozymes degrade very quickly making collection more difficult. Furthermore, recent studies have shown that some allozyme variants are not neutral markers limiting their utility in demographic studies. In addition, interpretation of allozyme allele size variation is often subjective and difficult to replicate. For all these reasons, allozyme data are currently barely used.
4.2 Mitochondrial DNA
Most genetic studies of marine pelagic fishes to date have relied upon data from mtDNA. Originally these studies used restriction fragment length polymorphism (RFLP) of the whole mtDNA molecule (Graves and McDowell 1995). The advent of the Polymerase Chain Reaction (PCR, Saiki et al. 1985) allowed amplification of portions of the mtDNA for either RFLP or nucleotide sequence analyses. The latter have been also facilitated by the development of automated sequencing and the availability of highly conserved primers in fishes (Meyer 1994; Normark et al. 1991;
Ostellari et al. 1996) that allowed the amplification of a specific sequence across a wide range of species.
Mitochondrial genomes are haploid and non-recombining, features that make them very useful for reconstructing recent phylogenetic history (Avise 2004). The inheritance of mtDNA is predominantly maternal and therefore, the effective population size of mtDNA is one quarter of that for nuclear autosomal DNA (Moore 1995). This makes mtDNA particularly susceptible to stochastic fluctuations arising
25 from drift and consequently highly useful for the detection of bottleneck events at the
population level (Avise 2004).
In most fishes examined to date, mtDNA comprises 37 functional genes that encode 22 tRNAs, 13 mRNAs and two ribosomal RNAs (Miya et al. 2001). Probably the most common region for analyzing stock structure in fishes is the control region, which exhibits the fastest mutation rates and therefore is useful in high resolution analyses of shallow population genetic structure in marine pelagic fishes (Buonaccorsi et al. 2001; Magoulas et al. 2006). The control region is a non-coding region of the circular mitochondrial organelle that contains the heavy-strand origin of replication and the promoters for heavy and light strand transcription (Desjardins and Morais 1990). The variation in length of the control region between species has been attributed to variation in tandem repeats usually in the 5’ and 3’ ends (reviewed in (Berg et al. 1995; Moritz et al. 1987).
Although usually less variable, the cytochrome b gene (Martin and Palumbi 1993) has also been largely used in population genetics studies (Liu et al. 2006; Rohfritsch and Borsa 2005).
4.3 Microsatellites
Since their discovery (Litt and Luty 1989; Tautz 1989; Weber and May 1989), and due to the higher mutation rates, microsatellites have been increasingly used as molecular markers in population genetic and conservation studies in the last decade, and in particular, applied for detecting fine-scale population structure in marine pelagic fishes (Buonaccorsi et al. 2001; Durand et al. 2005; Ruzzante et al. 2006) Microsatellites are tandem repeats (typically between 9 to 50) formed by 1-6 base pairs (Fig. 3) found at high frequency in the nuclear genome of most taxa (review in Gonzalez 2003; Selkoe 2006). They are generally considered selectively neutral markers (but see Kashi and Soller, 1999 and Li et al., 2002 for some exceptions), co- dominant, and with a Mendelian mode of inheritance (Schlötterer and Tautz 1992).
26
Recent advances such as multilocus genotyping using color fluorescent detection with automated DNA sequencers and multiplexing techniques (i. e. amplification of several loci in the same reaction, O'Reilly et al. 1996), have simplified the screening of large sample sizes (50-100 individuals) needed for population genetics studies of marine fishes. However, automated scoring of alleles is not without technical problems such as PCR artefacts (also called “stutter bands”), which can create difficulties for population genetic analyses.
Figure 3. Example of a sequence chromatogram where each peak corresponds to a nucleotide (numbered along the top of the graph) and shows a microsatellite from position 169 to 220, composed of twenty-six tandem copies of the CA motif (CA)26, surrounded by a flanking region that could be used for the design of a PCR primer to amplify the microsatellite locus.
Microsatellites normally have higher mutation rates than other markers (between 10-2 to 10-5 mutations per locus and generation, Chakraborty et al. 1997), making them very suitable for detecting changes in the recent past or present day demography of marine fishes. Microsatellites gain or lose repeat units by DNA replication slippage (Ellegren 2000; Schlötterer 2000) with a complex mutation rate pattern that it is still not well understood. For many of the applications, knowing the exact process of mutation is not necessary, but several statistics rely explicitly on the mutation model.
27 Two extreme mutation models have been developed: the infinite-allele mutation
(IAM, Weir and Cockerham 1984) and the stepwise mutation model (SMM, Michalakis and Excoffier 1996). The latter is the most widely applied in the analyses of microsatellite allele size variation and assumes that each mutation creates a novel allele (u) either by adding or deleting a single repeat unit of the microsatellite, with an equal probability u/2 in both directions. Consequently, alleles of very different sizes will be more distantly related than other alleles of similar sizes. It can be said that the SMM has a “memory” of allele size (Balloux and Goudet 2002). A variant of this model is the two-phase model (TPM, Di Rienzo et al. 1994) where mutations increase or decrease allele size by one repeat with probability p, and increase or decrease allele size by k repeats with probability (1-p). In the IAM, each mutation creates a novel allele at a given rate (u). Finally, the K-allele model (KAM, Crow and Kimura 1979) postulates k possible number of alleles in a population with a probability to mutate of u/(k-1) to any of the other k-1 allelic states. Finally, the Brownian mutation model (Beerli and Felsenstein 2001) is approximationto the SMM which ismuch faster, but may be inaccurate when polymorphism is low.
4.3.1.Microsatellite characterization
It has been shown that some microsatellite loci can be cross-amplified in different related species within the same genus or even at higher taxon levels (Rico et al. 1997;
Scribner and Pearce 2000; Zardoya et al. 1996). Nevertheless, finding suitable working microsatellites for a study species is normally difficult, and it is often necessary to prepare a genomic library enriched in microsatellites, and to subsequently characterize a range of specific microsatellites for the organism of study.
Previous to this work, there were only published microsatellites for four clupeid species: the Pacific herring, Clupea pallasii (Olsen et al. 2002), the Pacific sardine, Sardinops sagax sagax (Pereyra et al. 2004), the allis shad, Alosa alosa (three loci) and the twaite shad A. fallax (five loci) (Faria et al. 2004). The divergence times
28
between these genera and Sardina suggested that these loci were unlikely to amplify in this species.
In the case of Thunnus, there are microsatellite characterized for T. thynnus, (Clark et al. 2004), T. t. thynnus (McDowell et al. 2002) and T. albacares (Appleyard et al. 2001), some of which has been previously used in bigeye tuna (Appleyard et al.
2002; Grewe et al. 2000).
There exists a wealth of available protocols for isolating microsatellites (Glenn and Schable 2005; Gonzalez 2003; Zane et al. 2002). The success rate of microsatellite isolation largely depends on their frequency in the genome of the species (Tóth et al. 2000). A relatively simple method that might be valid for species with abundant microsatellite loci is the isolation of microsatellites from a complete genomic library (Gonzalez 2003; Rassmann et al. 1991; Tautz 1989, Fig. 4), which basically consists of cloning and screening thousands of positives by hybridization with repeat containing probes after blotting bacterial colonies onto nylon membranes (Fig. 4).
However, a more effective and extensively used method involves libraries enriched for microsatellite regions (reviewed in Brown et al. 2001; Carleton et al.
2002; Glenn and Schable 2005; Gonzalez 2003; Hamilton et al. 1999; Ostrander et al.
1992; Toonen 1997). This requires a previous step of DNA restriction and posterior ligation to adapters following a hybrid-capture enrichment process by magnetic field application (Fig. 4). Thanks to the adapters (the most commonly used are the SNX, Hamilton et al. 1999), the PCR product obtained after enrichment can be cloned directly and sequenced. In theory, this protocol will work for the majority of eukaryotic organisms with minor modifications depending on the species of study as is demonstrated by the growing number of studies that use it for the isolation of microsatellites in many different species.
29
Genomic DNA
Restriction enzyme digestion
DNA ligation to linkers microsatellite
Primer extension DNA size selection
(optional)
Vector ligation
Transformation into competent cells and colony transfer to the nylon membranes
Hybridization of the biotine labelled colonies.
Chemiluminiscent detection of positives
Hybridization and selective biotine capture with streptavidin-coated magnetic beads
Biotine (CA)12probe
Magnetic beads Streptavidin Genomic DNA
Restriction enzyme digestion
DNA ligation to linkers microsatellite
Primer extension DNA size selection
(optional)
Vector ligation
Transformation into competent cells and colony transfer to the nylon membranes
Hybridization of the biotine labelled colonies.
Chemiluminiscent detection of positives
Hybridization and selective biotine capture with streptavidin-coated magnetic beads
Biotine (CA)12probe
Magnetic beads Streptavidin
Figure 4. Schematic representation of the microsatellite isolation methods. Left: method based on complete libraries (Rassmann et al. 1991). Right: method based on enriched libraries (Brown et al. 2001; Carleton et al. 2002; Hamilton et al. 1999; Ostrander et al. 1992; Toonen 1997). From Gonzalez (2003)
30
5. Population genetic analyses
Population genetics could be defined as the mathematical study of the dynamics of genetic variation within species. Its main purpose is to understand the ways in which the forces of mutation, natural selection, random genetic drift, and population structure interact to produce and maintain the complex patterns of genetic variation that are observed among individuals within a species (Wakeley 2005). Despite the outstanding advancement of molecular biology and bioinformatics, many analytical methods nowadays still rely on theoretical foundations built more than half a century ago.
Beginning with Wahlund (1928), genetic surveys have been used for estimating metapopulation subdivision and gene flow. Wright (1951) introduced the F-statistic as a way of utilizing allele frequency data to quantify the extent of population subdivision and estimate amounts of gene flow.
However, as mentioned above, to achieve a better understanding of population genetic structure and dynamics of marine pelagic fishes, it is necessary to incorporate a variety of methodological and analytical approaches with complementary strengths, and to move beyond using only classic statistics (Neigel 2002). New coalescent methods share the ability to relax several assumptions of classical statistics that are typically violated by real data (Beerli 1998; Pearse and Crandall 2004; Wakeley 2005). Classic F-statistics based approaches are still very useful for estimating current allele distributions within and among population, nevertheless, ML and BI approaches are expanding the analytical power available to population genetics (Shoemaker et al.
1999; Williamson and Slatkin 1999) by providing estimates of effective population size and past demography rather than the current allele frequency distribution (Beerli 1998; Pearse and Crandall 2004; Wakeley 2005; Williamson and Slatkin 1999).
Coalescent-based methods can use stochastic reduction in lineage numbers looking backwards though time to infer the past demographic history of the populations based on a model of evolution for the marker used (Pearse and Crandall 2004).
31 This approach has been shown to be a very good approximation for randomly
mating populations and neutral genes without recombination. In a population of fixed size under a Wright-Fisher model, by chance, some of the individuals may fail to produce offspring and therefore some copies of the genes will be passed on to the next generation while others will not. As time passes, more and more gene lineages become extinct and looking backwards in time, all gene copies must ultimately be descended from a single ancestral gene since, given enough time, all other gene lineages will have become extinct (Fig. 5).
Figure 5. Gene genealogy of a Wright-Fisher population, where the gene copies in small population (A) share a common ancestor more recently than those in a large population (B) (Hein et al 2005).
The genealogy of genes in the present population is said to “coalesce” to a common ancestral gene copy. In particular, we are interested in the first of such ancestors, that is, the Most Recent Common Ancestor (MRCA) (review in Hein et al.
2005).
It should be noted that the distribution of coalescence times follows an exponential distribution that depends on the population size. Thus, if a population has been
32
growing in size, the value of N is actually decreasing when going back in time (and expectations of coalescence decreases with N) and therefore, the obtained tree will have a “star-like” shape. On the contrary, if the population decreases, coalescence times will be moved towards the present and the tree will have a comb-like shape.
While traditional estimators in population genetics could be calculated using simple analytical calculations, modern population genetic analyses rely heavily on computer power. This is especially true for methods based on ML or BI (Pearse and Crandall 2004). In likelihood, we have L (D | H), the likelihood L of the data D given the hypothesis H, and a specific model. The Bayesian procedure requires that we provide a prior on all parameters of the model. According to the Bayes theorem, we can define the posterior probability P (H | D) as the probability P of the hypothesis H given the data D. In the BI approach, the prior probability of the hypothesis P(H) is combined with the likelihood and conditioned on the known data:
(2) ( )
) ( ) (
D P
H xP H D ) P D (H
P | = |
The posterior probability is easy to formulate conceptually but it is almost impossible to calculate analytically in population genetic models. Therefore, posterior probabilities of population genetic parameters for large data sets are approximated by using Markov Chain Monte Carlo (MCMC) methods and the Metropolis-Hastings algorithm (Hastings 1970; Metropolis et al. 1953). A key issue in MCMC simulation is to determine when equilibrium has been reached, that is, when to stop the simulation in order to have a reasonable approximation of the posterior or likelihood curve. This is a serious problem, since even very long runs which appear to have converged may in fact be misleading (see Stephens and Donnelly 2000 for examples).
When a MCMC is run it starts from a random position in the parameter space.
Consequently, it is often required to discard the initial steps of the MCMC and only use the points sampled from the distribution of interest (called the burn-in). The starting point will influence the output of the MCMC, and thus, several runs are needed.
33 Coalescent methods are not free from methodological problems and pitfalls,
including limited computational power (which is progressively increasing) (Neigel 2002) and the need for using large data sets and large number of loci to simultaneously estimate multiple parameters with accuracy (Kuhner et al. 1998;
Pearse and Crandall 2004). It is also important to evaluate the performance of these methods when population structure levels are low or levels of gene flow are high, as in the case of marine pelagic fishes (Waples 1998).
Below is a summary of some of the statistical methods usually employed to estimate population differentiation, gene flow and other demographic parameters.
5.1 Estimation of population differentiation and gene flow
Different analytical approaches can be used to define genetic variation between populations (see also Beerli 1998; Neigel 1997) from both sequence and allele frequencies data, including 1) classic measures of population structure based on Wright’s (1951) F-statistics, 2), coalescent-based methods to estimate migration rates and effective population size (Kingman 2000) and 3) Bayesian clustering approaches based on allele frequencies (Corander et al. 2003; Huelsenbeck et al. 2007; Pritchard et al. 2000; Rannala and Mountain 1997).
5.1.1.Classic measures of population structure based on Wright’s (1951) F-statistics
The most commonly used genetic estimator of population structure is derived from the statistic FST, which describes the amount of genetic differentiation among pre-defined subpopulations (Beerli 1998). It is defined as the probability that two alleles drawn randomly from a fragmented population are identical by descent. FST is commonly transformed into a more direct measure of migration according to formula (1) given by Wright (1951). It assumes an infinite number of equally sized island populations that
