TESIS DOCTORAL Spatio-Temporal Genetic Dynamics of European Hake
TESIS DOCTORAL Mención Internacional
Spatio-temporal genetic dynamics of European hake (Merluccius merluccius) populations from Atlantic fishing grounds
Alfonso Pita Bugallo Junio 2013
Departamento de Bioquímica, Xenética e Inmunoloxía
TESIS DOCTORAL Mención Internacional
Spatio-temporal genetic dynamics of European hake (Merluccius merluccius) populations from Atlantic fishing grounds
Memoria presentada por D. Alfonso Pita Bugallo para optar al grado de Doctor con Mención Internacional por la Universidad de Vigo. Junio 2013.
Cualquier forma de reproducción, distribución, comunicación pública o transformación de esta obra, sólo puede ser realizada con la autorización de sus titulares, salvo excepción prevista por la ley (Centro Español de Derechos Reprográficos, www.cedro.org).
Alfonso Pita Bugallo, 2009 – 2013 Disertación de Tesis Doctoral de la Universidad de Vigo Spatio-temporal genetic dynamics of European hake (Merluccius merluccius) populations from Atlantic fishing grounds Universidad de Vigo, Vigo, Pontevedra, España. Junio 2013 Campus Universitario de Vigo. Lagoas, Marcosende. 36310 Vigo www.uvigo.es – informació[email protected] – Tfno. 986 812 000 Impreso en España / Printed in Spain Faster Print, S.L. – Conde de Torrecedeira, 90 Bajo – 36208 Vigo (Pontevedra)
PABLO PRESA MARTÍNEZ, Dr. en Genética y Profesor Titular de Universidad en la Facultad de Ciencias del Mar de la Universidad de Vigo, y MONTSERRAT PÉREZ RODRÍGUEZ, Dra. en Biología y Científico Titular de OPIs del Instituto Español de Oceanografía ± Centro Oceanográfico de Vigo, en calidad de directores de investigación,
INFORMAN que el trabajo de Tesis Doctoral titulado ³Spatio-temporal genetic dynamics of European hake (Merluccius merluccius) populations from Atlantic fishing grounds", que presenta D. ALFONSO PITA BUGALLO para optar al Grado de Doctor por la Universidad de Vigo, ha sido realizado bajo su dirección en el Área de Genética del Departamento de Bioquímica, Genética e Inmunología de la Universidad de Vigo. El trabajo de Tesis cumple los requisitos científicos de esfuerzo, rigor, autenticidad y originalidad, por lo que autorizan su presentación ante el tribunal examinador.
En Vigo, el 25 de abril de 2013
Fdo.: Dr. Pablo Presa Martínez, Fdo.: Dra. Montserrat Pérez Rodríguez, Fdo.: Alfonso Pita Bugallo, Codirector Codirectora Autor
A Tere
AGRADECIMIENTOS
Estoy agradecido en primer lugar a mis directores, Pablo Presa y Montse Pérez, por su paciencia y esfuerzo en sacar las cosas adelante. A los miembros del grupo REXENMAR, tanto pasados (Anxela Seoane, Marta Miñambres y Adil Aghzar), como presentes (Yassine Ouagajjou, Angie Santafé, José Cuellar, Ana Mejuto, Nedia Remalia, Manuel Nande y Pablo Álvarez) y visitantes (Cecilia Corbella, María Inés Trucco, Susanne Tanner, Ilaria Milano, Marcela Astorga, Carolina Briones, Isabel Valdivia, Ricardo Guiñez), por sus aportaciones personales y profesionales y por el buen ambiente generado en el laboratorio.
Aprecio de una manera especial la colaboración de los investigadores del Instituto Español de Oceanografía en sus centros de Vigo (Carmen Piñeiro, Santiago Cerviño, Ana Leal y Antonio Gómez) y de Santander (Francisco Sánchez, Francisco Velasco y Antonio Punzón); los aportes de esta Institución han sido indispensables para abordar estudios histórico-genéticos y filogeográficos de un recurso marino vivo en todas sus dimensiones.
Agradezco igualmente la amable acogida y la ayuda prestada, tanto por las instituciones como por las personas colaboradoras, durante mis estancias en el extranjero: el Laboratorio GenMAP de la Universidad de Bolonia (Fausto Tinti, Ilaria Milano, Alessia Cariani, Serena Montanari, Nicola di Francesco, Eleonora Pintus), y el Instituto de Investigaciones Oceanológicas de la Universidad de Antofagasta (Ricardo Guiñez, Carolina Briones, Marcelo Oliva, Isabel Valdivia y Karla Pacheco).
Estoy también agradecido a la Universidad de Vigo por haberme financiado durante estos años de dedicación a lo que me gusta, a través de un contrato predoctoral (2011-2013), varias becas de asistencia a congresos (2009, 2010 y 2011) y una beca de estancia en el extranjero (septiembre 2012 – diciembre 2012). También ha sido crucial la financiación de la Xunta de Galicia a través del proyecto MERCAGAL (2006-2009), coordinado interinstitucional e impecablemente por la Fundación CETMAR (Julio Maroto, Elvira Abollo y Miguel Bao).
Mi más sincero agradecimiento a todos ellos por su simpatía y motivación profesional.
Sin el apoyo de mis padres no hubiese llegado hasta aquí, ¡gracias por soportar varias veces el “un año más” y hacer que todo parezca más fácil! Gracias a mi hermano Pablo, que enfados aparte, es sencillamente genial. Gracias a mis abuelos, en especial a Manuel y Pilar. Gracias a la tía Maruja y al resto de mis tíos y primos, por su constante interés en saber “cómo llevaba
la tesis”. A Tere, por su apoyo constante y su “poca” paciencia. A mis amigos, principalmente a Fer (“Titán”) y Cecilia, por soportar los momentos difíciles, y a Rigueiro por conocimiento de causa (ánimo). Quiero agradecer especialmente por la elaboración y el diseño de la portada a César, así como a Angie y José por los últimos retoques de la misma. A Carlos, Paula y Marta. Gracias por las cenas y el descanso de los fines de semana: ¡os debo una celebración!
TABLE OF CONTENTS
JUSTIFICACIÓN DE LA TESIS ...... i
RESUMEN ...... iii
ABSTRACT ...... iv
INTRODUCCIÓN - La merluza europea. Biología de la especie. Gestión pesquera. Gestión genética poblacional. Estructura genética y conectividad. Interés aplicado del estudio. Objetivos de la tesis y estructura de su presentación ...... 3
INTRODUCTION – The European hake. Hake biology. Fishery management. Population genetics and management. Genetic structure and connectivity. Interest of the study. Objectives and structure of the thesis ...... 15
CHAPTER I - What can gene flow and recruitment dynamics tell us about connectivity between European hake stocks in the Eastern North Atlantic? ...... 27
CHAPTER II - Out of the Celtic cradle: the genetic signature of European hake connectivity in the Atlantic North ...... 43
CHAPTER III – Gene flow quantification between European hake (Merluccius merluccius) Atlantic stocks over the last decade ...... 83
CHAPTER IV – How did the genetic effective population size relate to spawning stock biomass and fishing mortality of the Southern European hake population during the last decade? ...... 107
GENERAL DISCUSSION AND CONCLUSIONS ...... 141
GENERAL REFERENCES ...... 147
SCIENTIFIC PRODUCTION ...... 157
JUSTIFICACIÓN DE LA TESIS
Los cuatro capítulos de esta Tesis Doctoral corresponden con los cuatro manuscritos siguientes, publicados o presentados en revistas del SCI,
1. Pita, A., Pérez, M., Cerviño, S., Presa, P. 2011. What can gene flow and recruitment dynamics tell us about connectivity between European hake stocks in the Eastern North Atlantic? Continental Shelf Research, 31: 376-387.
2. Pita, A., Pérez, M., Balado, M., Presa, P. Out of the Celtic cradle: The genetic signature of European hake connectivity in the Atlantic North. Submitted, April 2013.
3. Pita, A., Leal, A., Santafé, A., Piñeiro, C., Presa, P. Gene flow quantification between European hake (Merluccius merluccius) Atlantic stocks over the last decade. Submitted, May 2013.
4. Pita, A., Pérez, M., Velasco, F., Presa, P. How did the genetic effective population size relate to spawning stock biomass and fishing mortality of the Southern European hake population during the last decade? Submitted, May 2013.
i
RESUMEN
La sobreexplotación pesquera es un problema actual que científicos y gestores tratan de evaluar para proponer planes estratégicos que mejoren la sostenibilidad de las pesquerías y eviten su colapso. El análisis genético espacio-temporal de los recursos pesqueros describe el patrón conectivo existente entre poblaciones y su estabilidad temporal, por lo que la inclusión de datos genéticos en los planes estratégicos es cada vez más demandada. Los datos moleculares obtenidos con marcadores microsatélite y secuencias de ADN mitocondrial de merluza europea (Merluccius merluccius), muestreada en todo su rango de distribución durante la última década, han permitido establecer importantes propiedades genético- poblacionales de esta pesquería. El patrón de conectividad a gran escala, obtenido tras la aplicación de un reloj molecular mitocondrial, sitúa en el Pleistoceno Medio (ca. 150.000 años) el comienzo de la divergencia genética entre las poblaciones de las dos principales vertientes europeas. La merluza atlántica parece constituir una población panmíctica, que se extiende desde el norte de Irlanda hasta el Mar Canario, y que únicamente muestra una reducción al flujo génico respecto a su población más septentrional del Mar del Norte. La migración latitudinal hacia el sur, planteada en esta tesis, sugiere un desplazamiento significativo de biomasa desde las principales áreas de freza de esta especie, del oeste y sur de Irlanda (Porcupine Bank y Great Sole) hacia latitudes septentrionales del llamado Stock Sur (Mar Cantábrico). El escenario panmíctico descrito para la merluza europea se alterna con episodios de divergencia metapoblacional y por tanto su gestión genética debería evitar visiones estructuralistas infundadas. El seguimiento interanual de la diversidad genética de esta especie, medida a través de su tamaño genético efectivo poblacional, parece ser una estrategia adecuada para evaluar su status genético, pues este indicador está estrechamente correlacionado con las estimas de biomasa registradas en el mismo espacio-tiempo. Los resultados presentados en esta tesis son relevantes para la gestión genética de largo plazo en esta especie, enfocada a su sostenibilidad ecológica y comercial.
iii
ABSTRACT
The overexploitation of fisheries is a major problem nowadays and therefore scientists and managers try to assess the risks of collapse and to propose strategic plans to improve the sustainability of fisheries. Spatio-temporal genetic analyses of marine resources help describing the connectivity pattern existing across populations and its temporal stability, so the inclusion of this type of data into strategic plans is increasingly demanded. Present molecular data obtained from microsatellite markers and mitochondrial DNA sequences on European hake (Merluccius merluccius) samples collected from its whole distribution range during the last decade, have allowed to establish important eco-biological properties of this fishery. The macroscale pattern of connectivity recovered upon a mitochondrial clock dates back the divergence between populations of the two main European basins to the Middle Pleistocene (ca. 150,000 yr bp). The Atlantic hake consists of a single panmictic population extending from Northern Ireland to the Canarian Sea and only the North Sea population exhibits a limited gene flow with the rest of Atlantic grounds. A southwards latitudinal migration pattern put forward in this thesis, suggests that a significant biomass displacement occurs systematically from the main spawning grounds of the species in South-western Ireland (Great Sole and Porcupine Bank grounds) to northern latitudes of the Southern Stock (Cantabrian Sea). The panmictic scenario proposed for the European hake alternates episodes of metapopulation divergence and therefore its genetic management should ignore unfounded structural criteria. The interannual monitoring of the genetic diversity of this species, measured through its effective genetic population size, appears to be a good predictor of its genetic status and correlates tightly with biomass estimates on the same spatio-tempo. The results presented in this thesis are of relevance for the long-term genetic management of this European species regarding its ecological and commercial sustainability.
iv
INTRODUCCIÓN
Introducción
La merluza europea
La merluza europea, Merluccius merluccius (Linnaeus, 1758) pertenece al Reino Animalia, Filo Chordata, Clase Actinopterygii, Subclase Neopterygii, Orden Gadiformes y Familia Merlucciidae. Es una de las 15 especies que componen actualmente el género Merluccius (Inada, 1981; Lloris y Matallanas, 2003; Matallanas y Lloris, 2006) y es la única del mismo que habita en las plataformas Europeas. Es la especie del género con una distribución más amplia, tanto longitudinal como latitudinal, lo que se refleja en su adaptación a diferentes escenarios oceanográficos, con variaciones muy amplias en temperatura y salinidad. Se distribuye desde la costa de Mauritania (18ºN) hasta la de Irlanda y Noruega (76ºN), incluyendo el Mar Mediterráneo y la costa sur del Mar Negro (42ºE) (Lloris y col., 2003).
Los análisis filogenéticos más recientes (Pérez y col., 2005; Pérez y Presa, 2008) indican que existe un mayor parentesco de esta especie con las merluzas del viejo mundo y en concreto con Merluccius senegalensis, con la cual solapa en aguas canario-saharianas, al igual que ocurre con el resto de especies de este género con distribución solapante (Pitcher y Alheit, 1995).
Biología de la especie
La merluza europea es una especie demersal que vive sobre fondos fango-arenosos y rocosos a una profundidad de 30 - 1000 metros (Lloris y col., 2003), aunque normalmente se encuentra a unos 70 - 400 metros de profundidad. Presenta reproducción sexual de tipo parcial (Domínguez-Petit, 2007), es decir, la estrategia sexual consiste en un amplio período de puesta con varias liberaciones de huevos. Esta dinámica permite la coexistencia de varias cohortes anuales, como las dos de primavera y otoño observadas en el Mediterráneo occidental, donde el periodo de puesta oscila entre diciembre y junio (Lloris y col., 2003). El periodo de puesta en el Atlántico se ajusta bien a las variaciones latitudinales de su rango de distribución (Casey y Pereiro, 1995), de modo que el máximo de puesta tiene lugar entre abril y julio en el caladero de Porcupine Bank (Hickling, 1927), y entre enero y marzo en el Golfo de Vizcaya (Murua y col., 2006). Se ha propuesto que estos periodos de puesta prolongados responderían a una estrategia de maximización de la supervivencia larvaria, ya que permitirían un mejor aprovechamiento de las condiciones ambientales favorables (Garvey y col., 2002).
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Introducción
La fecundidad estimada en la merluza europea oscila entre 2 y 7 millones de ovocitos por hembra (Lloris y col., 2003) y las principales zonas de puesta se encuentran en el talud de la plataforma francesa y en el suroeste de Irlanda (Álvarez y col., 2004; Murua, 2010). Además, se han registrado áreas de puesta a lo largo de la plataforma continental del noroeste de la Península Ibérica (Sánchez y col., 2003, Álvarez y col., 2004). La fecundación tiene lugar en la columna de agua y los huevos se concentran principalmente en sus primeros 100 metros (Álvarez y col., 2001). La eclosión se produce a los 4 - 6 días de la fecundación, dando lugar a una fase larvaria pelágica y por tanto las larvas, al igual que los huevos, están sujetas a fuerte deriva de los procesos oceanográficos de meso-escala. Las larvas se desarrollan entre los 50 y los 150 metros de profundidad (Coombs y Mitchell, 1982) y carecen de capacidad natatoria durante sus primeras semanas, hasta los 40 o 50 días, momento en que se asientan en el fondo para culminar la metamorfosis a juveniles (Arneri y Morales-Nin, 2000). Los juveniles ya son nadadores activos y se mueven hacia zonas más profundas, donde tiene lugar la incorporación de los mismos a la población adulta, mediante un proceso denominado reclutamiento (Sánchez y Gil, 2000).
Hay dos aspectos del ciclo vital de la especie que han sido especialmente estudiados: la alimentación y el crecimiento. El papel de la merluza en la cadena trófica es clave, ya que se trata de un depredador demersal que ocupa el vértice superior de la pirámide alimentaria. Este depredador activo realiza migraciones alimentarias tanto en la fase juvenil como en la adulta (Sánchez y Gil, 1995; Casey y Pereiro 1995). Durante la fase larvaria su alimentación se basa fundamentalmente en crustáceos de tipo copépodo. Los juveniles se alimentan de otros crustáceos planctónicos como los eufausiáceos y los anfípodos. En su etapa adulta la merluza es ictiófaga y consume principalmente bacaladilla, sardina, anchoa y otros gádidos (Velasco y Olaso, 1998; Velasco y col., 2003; Velasco, 2007). Además, la aparición de restos de merluza en el contenido estomacal de los adultos, pone de relieve la existencia de canibalismo en esta especie (Hickling, 1927; Velasco, 2007), el cual se ha llegado a estimar en un 30% de la mortalidad natural total (Piñeiro, 2013).
El crecimiento de la merluza es uno de los aspectos más inciertos de su biología. Uno de los métodos más utilizados para inferir el crecimiento, consiste en la estimación de la edad de cada individuo a partir de los anillos de carbonato cálcico depositados alrededor del núcleo del otolito sagitta. El análisis e interpretación de estos anillos es controvertido debido a la compleja estructura de crecimiento que presentan en esta especie. Sin embargo, la precisión ha ido mejorando paulatinamente en base al establecimiento de un criterio estandarizado de
4
Introducción
lectura de las mismas muestras por parte de varios laboratorios europeos. La última propuesta realizada en el seno del Workshop on Age estimation of European hake (WKAEH, Vigo, 2009) establece un nuevo sistema de lectura, que sugiere que la merluza crecería el doble de rápido de lo que se pensaba en las últimas décadas (Piñeiro y col., 2008; ICES, 2009). Una alternativa para el estudio del crecimiento son los trabajos de marcado y recaptura llevados a cabo tanto en el Océano Atlántico como en el Mar Mediterráneo (de Pontual y col., 2003; Piñeiro y col., 2007; Mellon-Duval y col., 2010). No obstante, los resultados obtenidos a partir de esta técnica deben tomarse con precaución debido a varios factores, entre los que destacan el bajo número de recapturas, la ausencia de recapturas con un periodo de tiempo significativo entre suelta y recaptura, y la ausencia de datos sobre el comportamiento de los individuos tras el marcado. Estos estudios requieren además una inversión costosa, tanto a nivel de información de los pescadores, como de recompensa por la devolución de los individuos recapturados (Mellon-Duval y col., 2010).
Gestión pesquera
La merluza es una de las especies comerciales más importantes de la costa atlántica europea, debido a su alto valor económico y organoléptico; buena prueba de ello son los esfuerzos políticos y técnicos que se han realizado para su explotación sostenible (ICES, 2009a, b). Para la gestión de esta especie se identifican tres stocks pesqueros dentro de la Unión Europea: dos stocks atlánticos, definidos por el International Council for the Exploration of the Sea (ICES/CIEM), y un stock mediterráneo, definido por la comisión dependiente de la FAO General Fisheries Commission for the Mediterranean (GFCM). Desde el año 1978 el ICES establece que los dos stocks atlánticos estarían separados por el Cañón de Cape Breton, un profundo valle submarino de la Bahía de Vizcaya, que recorre toda la plataforma continental francesa hacia el noroeste. El Stock Norte queda definido por la División IIIa, las Subáreas IV, VI y VII, y las Divisiones VIIIa, VIIIb y VIIIc del ICES, que se distribuyen desde la costa oeste de Noruega (76ºN) hasta el citado Cañón de Cape Breton (ICES, 2012a). El Stock Sur se extiende desde el sur de la Bahía de Vizcaya hasta el Golfo de Cádiz, lo cual comprende las Divisiones VIIIc y IXa del ICES (ICES, 2012b), aunque la especie se extiende además a lo largo de la costa norteafricana hasta el caladero del Sáhara Occidental (Lloris y col., 2003), en el que la regulación europea no es aplicable. Tanto los dos stocks atlánticos como el mediterráneo se gestionan de forma independiente, siendo el Stock Norte el más importante
5
Introducción desde el punto de vista de biomasa y de impacto económico en Europa (González-Laxe, 2008).
La evolución de las capturas y el descenso de la biomasa de reproductores (SSB) de los stocks atlánticos ha suscitado un intenso debate a escala europea durante las últimas décadas (Cerviño y col., 2009). Este debate ha ido desembocando en varios cambios importantes en la gestión de este recurso (ICES, 2009a, b). La biomasa de reproductores de ambos stocks atlánticos disminuyó drásticamente a mediados de la década 1980 - 1990, y continuó el descenso hasta su estabilización en la última década del Siglo XX. A raíz de estudios de evaluación poblacional, el ICES propuso la reducción de la mortalidad por pesca, dado que los niveles de biomasa de reproductores se situaban por debajo de los niveles de precaución y era deseable mejorar la sostenibilidad y alcanzar el rendimiento máximo sostenible (Jardim y col., 2010). A finales de la década de 1990 - 2000 se llegó a sopesar el colapso de la pesquería del Stock Norte si se mantenía el mismo esfuerzo pesquero. Esta sobreexplotación acarrearía una reducción del reclutamiento hasta dejar a los stocks sin la capacidad para reponer su biomasa de reproductores, lo que supondría el cierre de la pesquería a medio plazo, provocando unas pérdidas económicas inasumibles para los sectores dependientes de este recurso. A fin de evitar ese desenlace, se introdujeron una serie de medidas reguladoras (EC Reg. Núm. 1162/2001, 2602/2001 y 494/2002) orientadas a combatir la mortalidad por pesca, a través del aumento de la luz de malla y de la delimitación de las áreas para su aplicación. El plan de emergencia adoptado se sustituyó en 2004 por un plan de recuperación (EC Reg. Núm. 811/2004), con el objetivo prioritario de alcanzar las 140.000 toneladas de biomasa reproductora durante dos años consecutivos, y en ese momento comenzar un plan de gestión sostenible. Desde la instauración de estas medidas reguladoras en 2001, la mortalidad por pesca se ha situado en un nivel muy próximo al de precaución, y en la actualidad todo indica que el Stock Norte está plenamente recuperado y que se encuentra en el máximo de su capacidad reproductiva y en una situación de explotación sostenible (ICES, 2009a, b). Sin embargo, los resultados obtenidos tras la aplicación de estas medidas reguladoras no son plenamente satisfactorios, debido al alto grado de incertidumbre que presentan las series temporales de datos biológicos utilizadas en la evaluación y a los propios modelos predictivos utilizados (Moguedet y col., 2000; Van Hoof y col., 2008). Esta incertidumbre ocasiona que la merluza europea siga considerándose una especie sobreexplotada (FAO, 2010). A pesar de la tendencia positiva de los parámetros biológicos de los dos stocks atlánticos, tras la implantación de los planes de recuperación, hay una serie de cuestiones que permanecen sin resolver. Por ejemplo, es cuestionable el fundamento biológico que sustentaría la separación
6
Introducción de los stocks atlánticos y se requiere una explicación satisfactoria para los altos reclutamientos que se han observado en el Stock Sur a partir de 2003, cuando la biomasa de reproductores estaba en sus mínimos históricos.
Gestión genética poblacional
La evaluación de los stocks atlánticos de merluza europea ha sido muy controvertida en los últimos años debido a que: 1) las bases de datos anuales que se emplean son muy incompletas, 2) la metodología de estimación de la biomasa y los modelos predictivos podrían no ser los más adecuados, 3) existe una incertidumbre inherente a la estimación de clases de edad, 4) se desconoce cómo afectan los descartes realizados por la flota extractora y 5) no se comprende bien la relación entre biomasa de reproductores y reclutamiento (Moguedet y col., 2000; Santos y col., 2012). Por ello, la mejora en la toma de decisiones y en la implantación de una gestión más sostenible del recurso, implican la actualización de los datos de campo y la introducción de nuevas metodologías en la evaluación de los stocks (ICES, 2009a, b). Uno de los factores que se deberían incorporar a la evaluación son las estimas temporales de diversidad genética de los stocks y el estudio de su correlación con otros parámetros pesqueros (desembarques, biomasa de reproductores, reclutamiento, esfuerzo pesquero, etc.). El enfoque genético en la evaluación permitiría comprender mejor la dinámica de los stocks en el espacio y en el tiempo, así como efectuar una mejor evaluación de su estatus genético tras décadas de sobreexplotación (Enberg y col., 2009). Además, sabemos que la sobreexplotación de los recursos no sólo reduce el tamaño poblacional, sino que también erosiona el tamaño genético efectivo poblacional. Este parámetro es indicador del número mínimo de reproductores necesario para mantener la variabilidad genética de un stock, y puede constituir un dato clave para la gestión y el seguimiento del mismo (Waples y col., 2008).
Estructura genética y conectividad
La estructura genética de la especie se ha testado en diversos trabajos y con varios marcadores moleculares. Varios autores han descrito una diferenciación genética significativa entre el Atlántico y el Mediterráneo, las dos regiones biogeográficas más importantes de su distribución, tanto con marcadores proteicos (alozimas) (Pla y col., 1991; Roldán y col., 1998; Cimmaruta y col., 2005) como con marcadores de ADN nuclear (microsatélites y SNPs)
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Introducción
(Lundy y col., 1999; Castillo y col., 2005; Nielsen y col., 2012). A pesar de las serias limitaciones de tamaño muestral y de representatividad de los caladeros muestreados en esos trabajos, todo indica que dicha diferenciación es el mayor signo estructural de esta especie y que se mantiene en el tiempo, entre marcadores y entre estudios (González y col., 2010).
Una situación menos clara está determinada por el dudoso fundamento biológico de la separación de los dos stocks atlánticos reconocidos por el ICES. En primer lugar, se ha sugerido que dicha separación estaba influenciada por el cañón de Cape Breton (Casey y Pereiro, 1995) y por otras propiedades oceanográficas, tales como la forma y la extensión de la plataforma continental o la dependencia estacional de la dinámica de las corrientes marinas (Valdés y col., 1996). Además, una hipotética conexión entre stocks dependería del movimiento pasivo de larvas y pre-reclutas (Bartsch y col., 1996), implicaría aquellas fases más vulnerables del ciclo biológico y de dudosa dispersión a larga distancia. Por otra parte, dicha migración sigue siendo muy cuestionada debido a que las áreas de cría del Golfo de Vizcaya parecen estables en su tamaño y localización (Gil y Sánchez, 1996). En contra de dichos argumentos estructuralistas, está el hecho de que no se haya observado ningún patrón de estabilidad, ni en el espacio ni en el tiempo, que indique la existencia de un límite biológico entre ambos stocks, ni empleando marcadores microsatélite (Lundy y col., 2000) ni alozimas (Cimmaruta y col., 2005).
Otro aspecto estructural no resuelto en la mayoría de los trabajos realizados, es la complejidad observada dentro del Stock Norte de merluza. Por una parte, no se observaron diferencias en la variación genética de tres alozimas de 12 localidades distribuidas entre Noruega y el Golfo de Vizcaya (Mangaly y Jamieson, 1979). Además, se han observado características morfométricas unimodales en la merluza de todo el Atlántico Norte (Jones y Mackie, 1970). Por otra parte, se ha defendido la existencia de diferencias genéticas significativas entre muestras de la costa oeste de Noruega y del Mar Céltico, a partir de seis marcadores microsatélite (Lundy y col., 1999), o entre muestras de Irlanda y Francia, usando 21 alozimas (Roldán y col., 1998). Estos últimos trabajos sugerían la existencia de una mayor complejidad poblacional en el Atlántico Norte que la que suponía la visión clásica de una única población panmíctica. Es destacable que la mayoría de los estudios efectuados sobre el Stock Norte en las pasadas décadas carecían en general de un diseño de muestreo adecuado para testar la persistencia espacio-temporal de estructuras genéticas. Este problema se relaciona con el análisis de pocas muestras y de bajo tamaño muestral, con la ausencia de estructura geográfica o temporal adecuada en los muestreos, y con la falta de definición del estadío de vida preciso
8
Introducción
(juveniles, reclutas y adultos) sobre el que se efectuaban los muestreos o los análisis. Estas debilidades metodológicas conllevan una alta incertidumbre acerca de la estructura poblacional de la merluza atlántica y limitan el manejo de una información importante para conocer la conectividad entre caladeros y la gestión pesquera de los mismos.
Interés aplicado del estudio
Como ya se ha comentado anteriormente, la merluza europea es una de las especies demersales más importantes de las costas europeas, debido tanto a su abundancia y amplia distribución, como a su papel trófico dentro de los ecosistemas marinos (Casey y Pereiro, 1995; Oliver y Massutí, 1995; Papaconstantinou y Stergiou, 1995). Además, es una especie muy apreciada en el mercado que se emplea fundamentalmente para consumo humano, lo que la convierte en una de las especies prioritarias para la flota pesquera de varios países de la Unión Europea. La merluza europea es muy apreciada en mercados al por menor y, dado su alto valor gastronómico, se comercializa fundamentalmente en fresco (Lloris y col., 2003). Su importancia económica radica en la extracción anual de 21.500 toneladas en el Mediterráneo y 67.000 toneladas en el Atlántico (FAOSTAT, 2011). La especie se comercializa principalmente en los mercados de Gran Bretaña, Irlanda, España y Portugal (Casey y Pereiro, 1995; Lloris y col., 2003) y es España es el país comunitario con mayor porcentaje de desembarques (2011), i.e. 37% del total extraído, donde el 16.5% corresponde al Mar Mediterráneo y el 45% al Océano Atlántico (FishstatJ – FAO Fishery and Aquaculture Global Statistics). Concretamente, en el año 2011 en Galicia se subastaron cerca de 19.000 toneladas de merluza en primera venta, en lonjas de la comunidad, lo que representa un 10% del peso total subastado en lonja, siendo la tercera especie en este aspecto (Xunta de Galicia, 2012). La subasta total de merluza en 2011 alcanzó un valor final de 54 millones de euros (Xunta de Galicia, 2012), que representa el 11% del valor total alcanzado en la venta directa de pescado y la convierten en la especie más importante económicamente, representando un 0.1% del producto interior bruto gallego (Xunta de Galicia, 2012).
A pesar de esta importancia económica, el conocimiento de la dinámica poblacional y de la conectividad entre stocks de merluza es escaso y está lejos de poder resolverse con exactitud. Esto es debido a todas las limitaciones metodológicas descritas más arriba. Sin embargo, la información procedente de análisis genéticos poblacionales podría aportar datos clave para la gestión de esta especie, de modo similar a los trabajos desarrollados en otros recursos marinos (e.g. Díaz-Ferguson y col., 2012; Montes y col., 2012). Serviría, por ejemplo, para estimar el
9
Introducción riesgo de sobreexplotación por pérdida significativa del tamaño genético efectivo poblacional, para estimar la intensidad de la migración entre caladeros y el patrón global de conectividad de la especie, o para la identificación de áreas de especial relevancia, susceptibles de exportar biomasa y diversidad genética hacia áreas adyacentes más deprimidas.
Objetivos de la tesis y estructura de su presentación
El objetivo general de esta Tesis Doctoral es determinar el estatus genético y la estructura espacio-temporal de las poblaciones de merluza del caladero Cantábrico Noroeste. Para abordar este objetivo general se han planteado los siguientes objetivos específicos:
Inferir con marcadores genéticos el grado de conectividad entre poblaciones de merluza de la pesquería atlántica y valorar sus implicaciones en la definición entre stocks.
Definir la estructura poblacional de la merluza europea en todo su rango de distribución, especialmente la conectividad entre las dos áreas biogeográficas principales: el Océano Atlántico y el Mar Mediterráneo.
Medir la conectividad entre los principales caladeros atlánticos de desove de merluza, para conocer la dinámica espacio-temporal de esta pesquería.
Evaluar el status genético de la población de merluza del caladero Cantábrico Noroeste a través de una retrospectiva de la diversidad genética y del tamaño genético efectivo poblacional.
10
Introducción
Esta tesis doctoral se ha dividido en cuatro capítulos que responden a los objetivos anteriores:
El Capítulo I corresponde al artículo Pita y col., 2011 “What can gene flow and recruitment dynamics tell us about connectivity between European hake stocks in the Eastern North Atlantic?” Continental Shelf Research, 31: 376-387. En él se testa la separación de la pesquería atlántica de merluza en dos stocks, se analizan las correlaciones entre diversos parámetros de la evaluación de la pesquería y se interpretan en función de datos genéticos de migración entre caladeros.
El Capítulo II corresponde al artículo enviado: Pita y col. “Out of the Celtic cradle: The genetic signature of European hake connectivity in the Atlantic North”. En él se describe el patrón global de conectividad entre las poblaciones de merluza europea usando marcadores nucleares y mitocondriales. También se establece el foco potencial de radiación de biomasa en función de la diversidad genética y se data la separación entre las subpoblaciones atlántica y mediterránea mediante un reloj molecular mitocondrial.
El Capítulo III corresponde al artículo enviado: Pita y col. “Gene flow quantification between Atlantic hake stocks (Merluccius merluccius) over the last decade”. En él se analiza, mediante variabilidad de microsatélites, el patrón de conectividad espacio-temporal entre los dos principales caladeros atlánticos de merluza, se estima el grado anual de conectividad durante la pasada década y la dependencia de biomasa entre ambos caladeros, según la relación entre reclutamiento y biomasa de reproductores.
El Capítulo IV corresponde al artículo enviado: Pita y col. “How did the genetic effective population size relate to spawning stock biomass and fishing mortality of the Southern European hake population during the last decade?”. En él se fija la escala espacial del caladero Cantábrico Noroeste con gestión local, y se analizan 7 años consecutivos de esta pesquería, para distintos parámetros de diversidad genética, útiles en la gestión de este recurso.
11
INTRODUCTION
Introduction
The European hake
The European hake, Merluccius merluccius (Linnaeus, 1758), belongs to Kingdom Animalia, Phylum Chordata, Class Actinopterygii, Subclass Neopterygii, Order Gadiformes and Family Merlucciidae. It is one of the 15 hake species making up the Genus Merluccius (Inada, 1981; Lloris and Matallanas, 2003; Matallanas and Lloris, 2006), but the only one inhabiting European continental shelves. It is also the hake species with the broadest distribution in latitude and longitude. This fact denotes its physiological adaptation to various environments across ample ranges of temperature and salinity. The European hake is found from Mauritania (18ºN) to Ireland and Norway (76ºN), including the Mediterranean Sea and the southern coast of the Black Sea (42ºE) (Lloris et al., 2003). Recent phylogenetic analyses (Pérez et al., 2005; Pérez and Presa, 2008) indicate a higher evolutionary proximity of this species with the rest of Euro-African ones, namely with M. Senegalensis, a sister species cohabiting with the former at Saharan waters. This genetic similarity also occurs between any other pair of hake species with overlapping distributions (Pitcher and Alheit, 1995).
Hake biology
The European hake is a demersal species that dwells in muddy, sandy and rocky seabeds, between depths of 30 – 1,000 meters (Lloris et al., 2003), although it is normally fished around 70 - 400 meters in depth. It exhibits sexual reproduction with partial spawning (Domínguez-Petit, 2007), i.e. the reproductive strategy consists of a year-round sequential maturation with batch spawning periods. This spawning regime allows for the coexistence of two cohorts per year, such as those of spring and autumn observed in the Western Mediterranean Sea (Lloris et al., 2003) where the spawning period spans from December to June (Lloris et al., 2003).
Maximum spawning activity in the Atlantic varies upon latitude (Casey and Pereiro, 1995), occurring between January and March in southern Biscay Bay and between April and July in Porcupine Bank (Hickling, 1927; Murua et al., 2006). Estimates on hake fecundity range 2 - 7 million oocytes per female (Lloris et al., 2003). The principal spawning areas for this species are located in the French continental shelf as well as in south-western Ireland (Álvarez et al., 2004; Murua, 2010). In addition, some spawning activity has also been detected along the north-western continental shelf of the Iberian Peninsula (Sánchez et al., 2003, Álvarez et al., 2004). Oocyte fecundation occurs in the water column and the resulting eggs dwell within the
15
Introduction first one hundred meters of the water column (Álvarez et al., 2001). Hatching occurs 4 - 6 days after their fertilization and gives rise to the larval stage of the life-cycle. Larvae development is completely pelagic, so they are as much influenced as eggs by drift and mesoscale oceanographic phenomena. Larvae dwell between 50 meters and 150 meters (Coombs and Mitchell, 1982) and lack ability to swim until 40 - 50 days, when larvae move downwards to the seabed where they complete their metamorphosis into juveniles (Arneri and Morales-Nin, 2000). Juveniles are active swimmers and move towards deeper waters to join the adult population, a well-known process termed recruitment (Sánchez and Gil, 2000).
Feeding and growing have been two intensively addressed processes in the life-cycle of hakes. The leading role of the European hake in the trophic chain is unquestionable since this demersal predator occupies the very top of the food chain. This active predator experiences vertical feeding movements in the water column during both, juvenile stages and as adult (Sánchez and Gil, 1995; Casey and Pereiro 1995, de Pontual et al., 2012). During its larval phase it feeds principally from crustaceans such as copepods. Juveniles feed on other planktonic crustaceans such as euphausiaceans and amphipods. Finally, adult hakes are mainly ichthyophagous, feeding principally on blue whiting (Micromesistius poutassou) and other small pelagic fishes such as sardines (Sardine pilchardus), anchovies (Engraulis encrasicolus) and other gadoids (Velasco and Olaso, 1998; Velasco et al., 2003; Velasco, 2007). Additionally, the existence of hake remains in dissected hake stomachs has shown that cannibalism is not infrequent in this species (Hickling, 1927; Velasco, 2007) and reaches up to 30% of the total natural mortality in some instances (Piñeiro, 2013).
Growth is one of the most uncertain processes in hake biology. Age estimation of each individual is based on the study of calcium carbonate layers from the otolith saggita and age is then used to infer growth rates. The interpretation of otolith layers is controversial due to the complex structure of calcium accretion in this species. However, the interpretation accuracy has been improved with the standardization of the reading protocol across European laboratories. For instance, a new reading system for otoliths was implemented during the Workshop on Age estimation of European hake (WKAEH, Vigo, 2009). The application of this system indicates that the European hake could grow two times faster than previously thought (Piñeiro et al., 2008; ICES, 2009). One alternate approach to measure hake growth consists of marking and recapture studies that were carried out in the European hake in both European basins, the Atlantic Ocean and the Mediterranean Sea (de Pontual et al., 2003; Piñeiro et al., 2007; Mellon-Duval et al., 2010). Nevertheless, caution is needed to explaining
16
Introduction results obtained with this technique due to the low number of recaptured individuals, the absence of data from long-term recaptures, and the poor knowledge on the behaviour of marked individuals after their release. In addition, an important investment is required to inform fishermen on the aim of tagging and the proper way to return the marked fish, as well as to reward it (Mellon-Duval et al., 2010).
Fishery management
The European hake is one of the most important species among the European marine resources. This is due to its high economic impact and the large effort devoted to achieve a sustainable exploitation of this resource (ICES, 2009a, b). The management of the European hake relies on three regional stocks along the European coast, i.e. two Atlantic stocks defined by the International Council for the Exploration of the Sea (ICES/CIEM) and a Mediterranean stock managed by the General Fisheries Commission for the Mediterranean (GFCM) within FAO. Since 1978, ICES divides the Atlantic fishery in two stocks separated by the Cape Breton Canyon, a deep submarine canyon located in the Bay of Biscay that runs parallel to the French continental shelf. The Northern hake Stock is distributed from the western coast of Norway (76ºN) to the Cape Breton Canyon and occupies Division IIIa, Subareas IV, VI and VII, and Divisions VIIIa, VIIIb and VIIIc of ICES (ICES, 2012a). The Southern Stock spans from south Biscay Bay to the Gulf of Cadiz, a range comprised by Divisions VIIIc and IXa of ICES (ICES, 2012b), although the species is also found along North-African waters up to Western Saharan fishing grounds (Lloris et al., 2003). The two Atlantic stocks are independently managed from each other and from the Mediterranean one, the Northern hake Stock being the most important one in terms of biomass and economic impact in Europe (González-Laxe, 2008).
The evolution of hake catches and the decrease of its spawning stock biomass (SSB) during the last decades have become major concerns for European fishing managers (Cerviño et al., 2009). Consequently, some relevant regulatory changes of hake fisheries have been adopted as a result of those concerns (ICES, 2009a, b). The SSB of the two Atlantic stocks decreased drastically in the middle eighties and continued so until stabilization in the last decade of the 20th Century. Following assessment studies for this species, the ICES proposed a reduction of its fishing mortality provided that the SSB was below the precautionary level. The goal by that time was to reach the maximum sustainable yield (MSY) (Jardim et al., 2010). By the end of the nineties, fishing managers and scientists begun to spread the idea of a Northern
17
Introduction hake collapse due to the untenable levels of fishing pressure. This scenario of collapse would consist of a continuous reduction of recruitment until the population were unable to renew its SSB, bringing about the closure of the fishery and direct economic losses. Some regulatory measures were implemented to palliate this situation (EC Reg. Num. 1162/2001, 2602/2001 and 494/2002), i.e. those aiming to decreasing fishing mortality through modulation of the mesh size of nets and their application zones. The emergency plan implemented in 2004 was substituted by a recovery plan (EC Reg. Núm. 811/2004) with the main goal of reaching first, a SSB of 140,000 tonnes in two consecutive years, and implementing thereafter a management plan for sustainability. Since the implementation of the above mentioned regulations in 2001, the fishing mortality rate has been set close to the precautionary level and nowadays the Northern Stock seems to be fully recovered, approaching its maximum spawning capacity and exploited sustainably (ICES, 2009a, b). Nevertheless, the promising fishery recovery observed after applying the EU regulatory measures is still being questioned due to the high uncertainty of temporal biological datasets, the variability of methodologies used across countries on this fishery, and the suboptimal predictive methods used in the assessment (Moguedet et al., 2000, Van Hoof et al., 2008,). In fact all those uncertainties are behind the actual consideration of overexploited species for the European hake (FAO, 2010). Despite the positive trend observed after implementing the recovery plan, some issues remain elusive, such as the biological bases for the Atlantic population split into stocks, or the lack of a satisfactory explanation for the good 2003 recruitments when the adult hake population was dramatically sunk into its historical SSB minimum.
Population genetics and management
As outlined above, the assessment of Atlantic hake stocks has currently been controversial due to, among other, 1) incomplete annual datasets from this fishery, 2) inaccurate assessment methods and predictive models, 3) problems inherent to the estimation of growth and year classes and therefore, the demographic structure of this species, 4) the lack of knowledge on the impact of fishing discards, or 5) the unexplained irregular relationship trend between recruitment and spawning biomass (Moguedet et al., 2000; Santos et al., 2012). Therefore, a general concern exists nowadays among the European members concerned to update field data on this species and to introduce new methodologies for stock assessment in the near future if a sustainable exploitation of this species is an issue (ICES, 2009a, b). Spatio- temporal estimates of the genetic diversity and its correlation with other fishery parameters
18
Introduction
(landings, spawning stock biomass, recruitment, fishing effort, etc.) should be incorporated into a wiser more-effective management plan. Namely, the integration of genetic data in the assessment process would allow a better understanding of the spatio-temporal stock dynamics as well as a better assessment of this fishery’s genetic status after decades of overexploitation (Enberg et al., 2009). Besides reducing the population size, a sustained overexploitation also reduces the genetic effective size, i.e. the theoretical number of breeders explaining the genetic variation that remains in the population. Therefore, this poorly explored index in fishery management could be a long term management indicator of the genetic potential that a fishery holds for self-renewal (Waples et al., 2008).
Genetic structure and connectivity
The partial genetic structure of this species has been tested with various molecular markers. Several studies have reported a certain genetic differentiation between the Atlantic Ocean and the Mediterranean Sea, the main biogeographic areas of this species’ range. Such differentiation signal has been detected with protein markers (allozymes) (Plá et al., 1991, Pla and Roldán, 1994; Roldán et al., 1998; Cimmaruta et al., 2005), as well as with nuclear DNA markers (microsatellites and SNPs) (Lundy et al., 1999; Castillo et al., 2005; Nielsen et al., 2012). Despite the sound handicaps imposed by the low sampling sizes and the low representativeness of samples among fishing grounds in those studies, all available data indicate so far that such genetic differentiation is the major structural split of this species, that is temporally stable and consistent across studies and markers (Gonzalez et al., 2010).
A less clear situation is provoked by the putative existence of a real biological basis for the split of the Atlantic hake fishery into two stocks. This separation followed by ICES for management purposes has been traditionally based on the Cape Breton Canyon barrier (Casey and Pereiro, 1995), on the shape and width of the French continental shelf, and on seasonally- dependent water current dynamics (Valdés et al., 1996). In addition, it is believed that a putative migration between stocks would require the passive drift of larvae and pre-recruits (Bartsch et al., 1996) between distant grounds, a fact highly questioned regarding the weakness of these stages. This in turn makes uncertain the life stage involved in the biological exchange. Moreover, stock separation is also supported by the stability in time, size and location observed in nursery areas from the Bay of Biscay (Gil and Sánchez, 1996). Against such scenario of a hake population split in the Atlantic is that neither spatial nor temporal stability patterns of population structure support the existence of a biological limit between
19
Introduction stocks, neither using microsatellites (Lundy et al., 2000) nor allozymes (Cimmaruta et al., 2005). In addition, the genetic homogeneity between Atlantic stocks is congruent with recent studies performed in the Bay of Biscay on population dynamics of eggs and larvae (Álvarez et al., 2004).
Another controversial issue is the high population complexity observed within the Northern hake Stock during the last decades. For instance, significant differences have been reported between samples from Norwegian coasts and the Celtic Sea using six microsatellite markers (Lundy et al., 1999), or between samples from Ireland and France using 21 allozymes (Roldán et al., 1998). Those studies suggested a higher population complexity within the North Atlantic than the classical view of a single panmictic population unit. Nevertheless, a classic genetic homogeneity had also been observed across 12 hake grounds sampled between Norway and the Biscay Gulf (Mangaly and Jamieson, 1979). That population genetic homogeneity is also coherent with the unimodal morphometric characteristics so far observed within the North Atlantic (Jones and Mackie, 1970). Unfortunately, the majority if not all the genetic studies carried out on the Northern hake Stock, were devoid of a suitable sampling design to testing the temporal persistence of genetic structures, used a low number of samples and sites, consisted of punctual temporal windows, and did not discriminate clearly the life- stage (juveniles, recruits, adults) responsible for the reported structure. Therefore, the fuzzy population structure proposed for the Northern hake Stock determines for a high population management concern on its connectivity pattern around Europe.
Interest of the study
The European hake is one of the most important demersal species of European waters because of its large biomass, its wide distribution and its trophic role in marine ecosystems (Casey and Pereiro, 1995; Oliver and Massutí, 1995; Papaconstantinou and Stergiou, 1995). In addition, it is a high appreciated species for human consumption due to its excellent quality as fresh meat (Lloris et al., 2003). The European hake constitutes the flag species for several EU-Member’s fleets. Namely, its large economic impact is especially patent in the United Kingdom, Ireland, Spain and Portugal (Casey and Pereiro, 1995; Lloris et al., 2003). The last figures from the FAO database (2011) add up to 21,500 tonnes landed at the Mediterranean Sea and 67,000 tonnes at the Atlantic Ocean. Spain has the largest hake landings of the European Union, i.e. 37% of the total (16.5% from the Mediterranean Sea and 45% from the Atlantic Ocean) (FishstatJ – FAO Fishery and Aquaculture Global Statistics, 2011). Local fish markets in
20
Introduction
Galicia sold 19,000 hake tonnes in 2011, becoming the third most important species in abundance (10% of total weight). Hake sales reached 54 million euros in 2011 (11% of the gross profit), represented 0.1% of the Gross Domestic Product (GDP) and became the most important economic fishing resource in that year (Xunta de Galicia, 2012).
As outlined above, our poor knowledge on the European hake population dynamics seriously limits its proper management. Nevertheless, the information drawn from genetic analyses could be highly useful in fishery assessment, as advanced in other marine resources (Díaz- Ferguson et al., 2012; Montes et al., 2012), at estimating exploitation risks, or at establishing the genetic connectivity among fishing grounds. Moreover, molecular markers can definitively help identifying relevant areas for conservation, such as those radiating biomass and diversity to neighboring grounds showing a depleted spawning biomass.
Objectives and structure of the thesis
The main goal of this thesis is to determine the spatio-temporal genetic structure and the actual genetic status of the Atlantic hake population, with a special emphasis on the Northwestern Cantabrian fishing ground. Four specific objectives were worked out to achieve the main goal:
Testing the existence of a genetic divergence within the Atlantic hake population that justifies its splitting into different management units.
Deciphering the global genetic structure of the European hake across its range and establishing the connectivity pattern between the two main biogeographic zones: the Atlantic fishery and the Mediterranean fishery.
Testing the connectivity between the two main spawning grounds in the Atlantic to achieve a better knowledge on the spatio-temporal dynamics of this fishery.
Assessing the genetic status of the hake population from the Northwestern Cantabrian fishing ground through the historical analysis of both, its genetic diversity and its genetic effective population size.
21
Introduction
Each of the above goals corresponds to one of the following chapters of this thesis:
Chapter I corresponds to the article Pita et al., 2011 “What can gene flow and recruitment dynamics tell us about connectivity between European hake stocks in the Eastern North Atlantic?” Continental Shelf Research, 31: 376-387. It checks the current ICES splitting of the Atlantic hake fishery in two management units, from a genetic perspective. Also, cross comparisons are made between distinct assessment parameters and migration data from genetic markers.
Chapter II corresponds to the submitted manuscript Pita et al. “Out of the Celtic cradle: The genetic signature of European hake connectivity in the Atlantic North”. The global pattern of connectivity among European hake populations is described herein using nuclear microsatellites and the mitochondrial DNA marker cytochrome b gene. The main Atlantic source of biomass radiation is identified after the molecular signatures of migration events. Also, a mitochondrial DNA clock is applied to date back the separation between Atlantic and Mediterranean populations.
Chapter III corresponds to the submitted manuscript Pita et al. “Gene flow quantification between Atlantic hake stocks (Merluccius merluccius) over the last decade”. Microsatellites are applied here to decipher the pattern of spatio-temporal connectivity between the two main Atlantic spawning grounds on a year basis during the last decade. Also the dependence of the Southern Stock is analyzed after the relationship between recruitment and the spawning stock biomass.
Chapter IV corresponds to the submitted manuscript Pita et al. “How did the genetic effective population size relate to the spawning stock biomass and fishing mortality of the southern European hake population during the last decade?”. The spatial scale of the Northwestern Cantabrian hake fishery is fixed to properly study the inter-annual evolution of various genetic diversity parameters that could be useful in the genetic assessment of fisheries.
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CHAPTER I
Chapter I
RESUMEN
El análisis genético sistemático de las poblaciones marinas explotadas permite comprobar la estabilidad temporal de su estructura genética. Los datos moleculares también permiten establecer los movimientos migratorios entre caladeros de una pesquería en el espacio y en el tiempo, y así definir su patrón de conectividad más regular. En este estudio se ha examinado el patrón del flujo génico entre los dos stocks Atlánticos de merluza europea (M. merluccius) reconocidos por el CIEM/ICES, durante el periodo 2000 - 2002. Los análisis basados en la variabilidad de microsatélites indicaron la existencia de una gran homogeneidad genética entre todas las poblaciones atlánticas en ese periodo. Además, las muestras procedentes de Porcupine Bank se agruparon sistemáticamente con las del margen cantábrico, lo cual es explicable por un flujo génico interanual desde los caladeros centrales del Stock Norte (Porcupine Bank y Great Sole) hacia los caladeros de la Península Ibérica que constituyen el Stock Sur de merluza europea (8 - 10 migrantes por generación y año). Los datos de migración estimados a partir de alelos de baja frecuencia coinciden con los altos índices de reclutamiento observados en el Stock Sur a partir de 2003, curiosamente en un momento en que la biomasa de reproductores se encontraba en uno de sus mínimos históricos. El conjunto de los resultados genéticos pone de manifiesto el papel central que juegan los caladeros del Stock Norte, Porcupine Bank y Great Sole, en la sostenibilidad de ambos stocks atlánticos de merluza. La ausencia de barreras migratorias entre stocks indica que es recomendable la gestión integral de esta pesquería, incorporando enfoques multidisciplinares en la evaluación de la misma.
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CHAPTER II
Chapter II
RESUMEN
La genética poblacional aplicada a la gestión de los recursos marinos permite establecer el modelo de conectividad que determina su gestión regional. En este estudio se ha caracterizado el patrón de conectividad global de la merluza europea (M. merluccius) mediante el mayor esfuerzo de muestreo efectuado hasta hoy en esta especie. La inferencia bayesiana efectuada a partir de genotipos multilocus de microsatélites, proporciona aquí una evidencia de que la homogeneidad genética entre los caladeros atlánticos de merluza se debe a migraciones sistemáticas desde los caladeros del Mar Céltico hacia el resto de caladeros adyacentes. Por tanto, la biomasa de reproductores de la población septentrional de merluza parece jugar un papel fundamental en la sostenibilidad de la población meridional. La única y mayor restricción al flujo génico observada en Almería, sugiere que el frente oceanográfico de Almería-Orán es una barrera efectiva que impide la mezcla entre las dos únicas unidades génicas dentro del rango de distribución de esta especie. La aplicación de un reloj molecular de ADN mitocondrial a los cambios nucleotídicos registrados en el gen citocromo b, indica que la divergencia entre la población atlántica y la mediterránea se situaría en torno al final del Pleistoceno Medio, i.e. hace unos 150.000 años. Por otra parte el uso de marcadores microsatélite de evolución rápida, indica que esa barrera regional entre vertientes permanece hoy activa. Además, se demuestra que el grado de estructuración de la especie está fuertemente influenciado por el modelo bayesiano utilizado para inferir el número de unidades genéticas (entre k = 1 y k = 8 en el rango de la especie), pero es mucho más insensible al parámetro de diferenciación poblacional que se maneje, esto es, flujo génico o diferenciación génica. Este estudio pone de manifiesto el interés que tiene la integración de metodologías de evaluación, los datos ecológicos y la genética poblacional, para comprender la dinámica poblacional y mejorar la gestión sostenible de las pesquerías de merluza.
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ABSTRACT
Population genetic analyses of marine genetic resources allow to establishing their pattern of connectivity. Such pattern determines the choice of the best suited management strategy to that resource in space and time. This study addresses the global pattern of connectivity among European hake populations upon the largest sampling effort so far developed in this species across European fishing grounds. Bayesian inference upon multilocus microsatellite data provides evidence that the large genetic homogeneity among Atlantic grounds is mediated by significant migration rates stepping up from the Celtic Sea towards all its adjacent Atlantic grounds. Therefore, the spawning biomass of the northern hake population seems to play a crucial role at ensuring the sustainability of the southern hake population. The single and deepest restriction to gene flow was observed in Almeria. This result suggests that the Almeria-Oran Oceanographic Front is an effective barrier preventing the admixture between the two unique discrete gene pools in this species’ range. A mitochondrial molecular clock applied to low-evolving signatures of the cytochrome b gene (0.3% divergence) indicates that the Atlantic - Mediterranean divergence might date back to the late Middle Pleistocene (150,000 years BP). Complementarily, fast-evolving memory-less microsatellites indicate that such barrier to gene flow is still active nowadays. We show that the degree of structuring found in this species is highly dependent of the Bayesian model enforced to infer the number of gene pools (from k = 1 to k = 8 in the species range), but much less dependent of the differentiation parameter considered, i.e. gene flow vs. genic differentiation. This study highlights the upmost interest of implementing an integrative effort to bring together assessment methodologies, ecological insights and population genetic data to better understand hake population dynamics and help management decisions for the sustainability of hake fisheries.
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Chapter II
Out of the Celtic cradle: The genetic signature of European hake connectivity in the Atlantic North
Introduction
The overexploitation of marine resources causes an irreversible loss of biomass that erodes the effective size of populations (Ludwig et al., 1993; Hutchings, 2005). Such erosion brings about not only the reduction of the long-term viability of the species but also collateral changes such as the reduction of size and age of first maturation (e.g., Botsford et al., 1997; Hutchings, 2000). Additionally, the depletion of a given species triggers disequilibrium changes in the trophic structure and therefore unpredictable ecological consequences for the aquatic ecosystem (Pauly et al., 1998).
The elaboration of effective policies towards a long-term sustainability of fisheries requires a prior definition of the fishery units as targets for management. Also indispensable for policy makers is to handle scientific knowledge on both, the actual exploitation and biological status of the fishery and the functioning of the ecosystems web where a fishery dwells. While management units can be defined upon diverse criteria (political, ecological, technical, etc.), the stock definition requires a more conspicuous profile of its biological properties (geographical, reproductive, genetic, etc…) in space and time (Waples et al., 2008). The spatio-temporal bio-identification of stocks is directly related to the connectivity pattern of the populations contributing effectively to its maintenance. Available strategies used to measure connectivity such as mark/recapture approaches (Piñeiro et al., 2007, de Pontual et al., 2003, 2006), or otolith biochemistry (Thorrold and Hare, 2002; Tanner et al., 2012), offer useful insights into fish movements but only partial qualitative estimates of the individual residence along its whole life-history. Complementarily to those techniques, population genetic studies have proved useful at deciphering migration histories (pathways in space and time), and migration rates between fishing grounds (Seeb et al., 1999; Beerli and Felsenstein, 2001; Wilson and Rannala, 2003). Migration rate is an essential property of fish stocks that, unlike other biochemical and physical tags, is affordable with genetic markers provided their undeletable chemical nature accompanying individuals along their whole life history.
The European hake, Merluccius merluccius, is a species of high ecological relevance and commercial value in Western Europe (Pitcher and Alheit, 1995). Three independent population units are considered for assessment and management: the Northern hake Stock
43
Chapter II
(ICES areas IIIa, IV, VI, VII and VIIIa,b,d), the Southern hake Stock (ICES areas VIIIc and IXa) and the Mediterranean population (GFCM - General Fisheries Commission for the Mediterranean). The sustainability of Atlantic hake stocks has raised much concern during the last two decades and therefore, pan-European fishing policies were implemented to reduce the possibility of recruitment failure caused by overexploitation (ICES, 2001; Council Reg. No 1162/2001, 2602/2001 and 494/2002; EC Reg. No 811/2004; EC Reg. No 51/2006). Probably helped by those policies, fishing mortality has been kept close to the precautionary level in the Northern hake Stock, which is presently near its carrying capacity and being sustainably exploited (ICES, 2009a, b). However, the Southern hake Stock has a reduced reproductive capacity since it has been intensively harvested in the past decades, so its long term sustainability seems to be compromised (ICES, 2007). Although the Southern Stock has less biomass than the Northern one, it has a high economical, ecological and social relevance for local communities (González-Laxe, 2008).
The genetic structure of the two Atlantic stocks has been addressed in the last decades using genetic markers (Mangaly and Jamieson, 1979; Roldán et al., 1998; Lundy et al., 1999, 2000; Castillo et al., 2004, 2005; Cimmaruta et al., 2005; Pita et al., 2010, 2011). Some genetic studies suggested the existence of a more complex population structure of hake in the North- eastern Atlantic than just a genetically homogeneous collection of populations. For instance, significant genetic differences were reported between Norwegian and Celtic samples in surveys of six microsatellites (Lundy et al., 1999; Castillo et al., 2004), or between Irish and French samples using 21 allozymes (Roldán et al., 1998). Opposite to the structuring hypothesis, classic studies have found unimodal morphometric characteristics throughout the Northern Stock (Jones and Mackie, 1970), and a non-significant allozymic differentiation in 12 samples ranging from Norway to the Bay of Biscay (Mangaly and Jamieson, 1979). More recently, none genetic structure was found either with mitochondrial DNA (Lundy et al., 1999) or with allozymes (Cimmaruta et al., 2005) from Norway to the Mediterranean, as well as among temporal samples from Biscay Bay using microsatellites (Lundy et al., 2000). Moreover, the genetic homogeneity of this species in the North-eastern Atlantic suggested after recent spatio-temporal population genetic studies (Pita et al., 2011) is congruent with present day knowledge on the dynamics of egg and larvae in the Bay of Biscay (Alvarez et al., 2004) as well as with a migration between stocks mediated by the passive drift of larvae and pre-recruits (e.g., Bartsch et al., 1996). However, a controversy between genetic data and population dynamics still exists because of various, any life-stage dependent migration has been identified, a high stability characterize hake nursery areas in the Bay of Biscay in time,
44
Chapter II size and location (Gil and Sánchez, 1996), and any clear pattern of global or local connectivity has been so far established for Atlantic populations.
A genetic homogeneity seems to exist among Mediterranean hake samples across studies (Lo Brutto et al., 1998, 2004) and a wider consensus exists regarding the main hake genetic differentiation between the two major biogeographical regions, the Atlantic and the Mediterranean, as depicted either with allozymes (Pla et al., 1991; Roldán et al., 1998; Lo Brutto et al., 1998, 2004; Cimmaruta et al., 2005) or with microsatellites (Lundy et al., 1999; Castillo et al., 2005; Pita et al., 2010). In this regard, the Almeria - Oran Oceanographic Front (AOOF) has been acknowledged as an effective barrier to gene flow in most population genetic studies performed on the biogeographical area of Gibraltar Strait (Patarnello et al., 2007; Sá-Pinto et al., 2012). However, the nature and stability of the factor maintaining apart the regional population pools between basins is uncertain. For instance, some genetic studies developed on hake have argued that such regional divide would better be explained by selective pressures after the high mathematical correlation found between salinity or temperature and genetic divergence at two protein-coding loci (Cimmaruta et al., 2005). More recently, a high allocation rate of hake individuals to its bio-geographical region was achieved using otolith chemistry signatures (Tanner et al., 2012) and high FST-SNPs, these latter assumedly affected by adaptive evolution (Nielsen et al., 2012). While otolith chemistry has a large assignment potential when combined with genetic markers (Tanner et al., unpublished) the FST-SNPs outlier markers could be useful for traceability related tasks, but quite useless in population genetics. This situation is caused by the non-random representativeness of outlier SNPs on the genomic status of hake populations, and the absence of temporal tests on its polymorphic information. To adequately address the unresolved structural tensions that exist regarding hake population heterogeneity in the Atlantic and the cause and extent of the regional divergence between basins, a multidisciplinary approach that combines oceanography, serial sampling, movement tracking and biological markers is needed. As long as such a multidisciplinary synergy is achieved, the combined use of genetic markers of different evolutionary pace (Ward, 2000) can provide a richer knowledge than present-day one regarding connectivity patterns in the European hake.
In order to elucidate the actual genetic status of all European hake populations we have enlarged a previous sampling set from the North-east Atlantic (Pita et al., 2011) to the whole species range. We have also reanalysed the molecular polymorphism of microsatellites and the mitochondrial DNA cytochrome b gene that were previously published as preliminary data
45
Chapter II
(Pita et al., 2010). Due to its fast evolving pace, microsatellites are shaped by contemporary processes and therefore, they are likely to reflect recent population events. Opposite to microsatellites, the intraspecific variation of cytochrome b gene better reflects the mitochondrial matrilineal composition of marine populations (e.g., Meyer, 1994) and therefore is far more suitable to infer past evolutionary histories than microsatellites. For instance, this marker has been useful to establish the genetic structure of the Indian mackerel (Perrin and Borsa, 2001), the existence of two stocks of sardine (Sardine pilchardus) in the Mediterranean (Tinti et al., 2002), or the phylogeography of the European chub (Leuciscus cephalus) in European rivers (Durand et al., 1999).
If the genetic scenario of the European hake is the result of consistent historical processes permanently shaping its gene pools, its actual pattern of connectivity as depicted with microsatellites is expected to be congruent with the nucleotide divergence exhibited by cytochrome b. However, the observation of divergent genetic scenarios between marker types would indicate that either present hake population divergence is a remnant of a structure blended in the far past (mitochondrial differentiation but none microsatellite differentiation) or it has been only recently shaped by oceanographic conditions (microsatellite differentiation but no mitochondrial divergence). Therefore, the aims of this study were two-fold, first to characterize the connectivity pattern among European hake populations in the Atlantic regarding its intensity and directionality. Second, to investigate on the coalescent time and maintenance of the genetic divergence existing between populations of the two main biogeographic regions of this species.
Material and methods
Sampling and DNA extraction
Twenty-seven populations of adult European hakes were sampled in 2000 - 2001 (Table 1). A piece of muscle tissue was removed from each of the 20 - 40 individuals collected per sample onboard of commercial vessels. Populations sampled spanned from the Tyrrhenian Sea to the Alboran Sea in the Mediterranean, from the Gulf of Cadiz to the Bay of Biscay in the Atlantic Iberia and south to the Canarian Sea, and from the Bay of Biscay to the Celtic Sea extending to the North Sea. Samples were kept in 96% ethanol upon collection until genomic DNA was extracted and purified with the method FENOSALT (Pérez and Presa, 2011). Purified DNA was resuspended in 500 µL of TE buffer and kept at –20 ºC until analysis.
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Chapter II
Microsatellite amplification and screening
Individuals were genotyped with five polymorphic microsatellites described for this species (Morán et al., 1999). Reaction conditions for PCR amplifications of microsatellite loci Mmer- UEAhk3b, Mmer-UEAhk9b, and Mmer-UEAhk20 were multiplexed in a PCR mixture consisting of 5 pmol of each primer, 1.5 mM MgCl2, and further co-amplified at 55 ºC of annealing temperature. The two other loci Mmer-UEAhk29 and Mmer-UEAhk34b were PCR- amplified at 55 ºC using 8 pmol per primer, and 1.0 mM and 1.7 mM MgCl2, respectively. The 15 µL amplification mixture used in all PCR reactions consisted of 30 ng DNA template, 200 µM of each dNTPs, 1% BSA, 0.50 U Flexi-Taq DNA polymerase (Promega), and the corresponding locus-dependent concentration of MgCl2 and primers. The forward primer of each locus was fluorescently labelled with Cy5 (5-N-N-diethyl- tetramethylindodicarbocyanine) for laser detection. PCR amplification conditions comprised one step at 95 ºC for 5 min, 40 cycles at 95 ºC for 1 min, annealing temperature for 1 min, 72 ºC for 55 s, and a final elongation step at 72 ºC for 30 min. Amplified fragments were separated by electrophoresis in an ALFexpressII automatic fragment analyser (GE Healthcare) and co-migrated with molecular ladders (80 bp, 180 bp, 230 bp, and 402 bp) for allele sizing. Putative genotyping errors were minimized in side-by-side comparisons of independent interpretations by two researchers. The consistency of the scored allelic series as well as the frequency of putative null alleles was calculated with MICRO-CHECKER 2.2.3 (Van Oosterhout et al., 2004).
47
Chapter II
Table 1. Geographic location and codes for hake fishing grounds sampled between autumn 2000 and spring 2001, ranging from the North Sea to the Mediterranean Sea. Na and Nb indicate the number of individuals sequenced and genotyped for the cytochrome b gene and microsatellites, respectively. Asterisks indicate populations also analysed in Pita et al. (2011).
Coordinates
Oceanic area Regional code Fishing ground Pop code Latitude Longitude Na Nb ATLANTIC OCEAN Atlantic North North Sea ATNOR North Sea AT1 * 55º30’N 04º36’E 31 2 Celtic Sea ATCEL Rockall AT2* 56º44’N 14º05’W 24 2 Great Sole AT3* 51º30’N 11º30’W 27 2 Little Sole AT4 48º00’N 08º20’W 27 2 South Biscay AT5* 43º99’N 02º25’W 23 2 South Biscay AT6 47º30’N 05º30’W 33 2
Atlantic Cantabric ATCAN Biscay Bay Kostarrenkala AT7 43º30’N 02º15’W 24 2 Labra de Laredo AT8* 43º32’N 03º26’W 44 4 Central Cantabric Sea La Carretera AT9* 43º42’N 05º40’W 13 2 Playa de Tapia AT10* 43º46’N 06º52’W 25 2 NW Cantabric Sea El Calvario AT11* 43º38’N 07º05’W 29 2 Praia de Ribadeo AT12* 43º50’N 07º35’W 26 1 Iberian Atlantic Ocean ATIBE Mar Mª Antonia AT13* 43º41’N 08º49’W 23 2 Pozo da Nave AT14* 43º04’N 09º31’W 21 3 Silleiro - Guardia AT15 41º14’N 08º50’W 31 2 Mar La Venia AT16 38º50'N 09º34’W 25 2 Sardaos AT17 37º35’N 08º50’W 20 2 Barra de Huelva AT18 36º21’N 07º06’W 20 3 Canarian Sea ATCAS Mogán AT19* 27º46’N 15º45’W 17 2
MEDITERRANEAN SEA Western Med. Sea MEDW Barra de Málaga ME20 36º33’N 04º22’W 25 2 Canto de Almería ME21 36º25’N 02º26’W 20 2 Cartagena ME22 37º30’N 01º05’W 35 2 Santa Pola ME23 38º01’N 00º22’W 27 2 Cullera ME24 39º13’N 01º56’E 24 2 Castellón ME25 39º58’N 01º33’E 19 2 Tarragona ME26 41º02’N 02º33’E 36 2 Central Med. Sea MEDC Castellammare ME27 38º03’N 12º56’E 36 2
OUTGROUP SPECIES Merluccius senegalensis Mauritania ME28 19º37’N 17º06’W 14 2
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Chapter II
Microsatellite data analysis
The number of alleles per locus and the allele frequencies were calculated with the program GENEPOP 4.0 (Rousset, 2008). The probability associated to the fixation index within populations (FIS) was calculated with the Markov Chain Method implemented in GENEPOP 4.0 using 100 batches and 5,000 iterations per batch. The model of Isolation by Distance was tested using ISOLDE as implemented in Genepop 4.0, which explores the regression between pair-wise values of the logarithm of geographic distance and the genetic distance [FST/(1-
FST)] between samples (Rousset, 1997). The observed and expected heterozygosities and the fixation index between populations (FST) were calculated with the algorithm of Wright implemented in FSTAT 2.9.3.2 (Goudet, 1995). The sequential correction of Bonferroni (Rice, 1989) was applied in multiple tests to minimize the type I error. The expected heterozygosity
HE was compared among samples, as well as among fishing grounds, using the non- parametric test of Friedman (SPSS 14.0). Fisher’s exact test and Pearson’s chi-square test were applied to estimate the statistical power of the sampling system when testing for genetic differentiation (50 generations of drift, effective size of 5,000, and one batch of 1,000 replicates) as well as to estimate the proportion of false significances (Type I error, P < 0.05) for combined test statistics (1,000 replicates and effective size of 5,000) as implemented in POWSIM (Ryman and Palm, 2006). The Analysis of Molecular Variance (Excoffier et al., 1992) as implemented in ARLEQUIN 3.11 (Excoffier et al., 2005) was used to test the global genetic structuring of the species at two hierarchical levels: between Atlantic and Mediterranean basins, and among the seven fishing grounds sampled (North Sea, Celtic Sea (North-western grounds of the Ireland Sea and Great Sole), Cantabrian Sea, Atlantic Iberian, Canary Islands, Western Mediterranean and Tyrrhenian Sea (See Regional code in Table 1)). The differentiation index D (Jost, 2008) and its confidence interval among samples and among grounds were calculated with DEMEtics 0.8-5 (Gerlach et al., 2010) using 1,000 bootstrap replicates. Corrections for multiple tests were performed using the false discovery rate approach (Benjamini and Hochberg, 1995). The correlation between the differentiation index (D) and the fixation index (FST) was carried out with SPSS 17.0. The number of structural units (k) present in the sample set was obtained after 10,000,000 iterations under the uncorrelated allele frequency model and the spatial model (Guillot et al., 2005a) implemented in GENELAND 3.2.4 (Guillot et al., 2005b). The k parameter was alternatively obtained under four population scenarios, e.g. spatial/non-spatial and mixture/admixture (Corander and Marttinen, 2006; Corander et al., 2008a) afforded from the Bayesian inference approach of
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Chapter II
BAPS 5.2 (Corander et al., 2008b). Also the best likelihood for k = 1 - 7 considering four population scenarios i.e. spatial/non-spatial and correlated/uncorrelated allele frequencies, was computed with STRUCTURE 2.3 (Pritchard et al., 2000). The migration rates m between fishing grounds were inferred after the Bayesian approach of 5,000,000 iterations of MCMC Iterations implemented in BAYESASS v1.3 (Wilson and Rannala, 2003).
Cytochrome b amplification and data analysis
The PCR amplification of a 465 bp fragment spanning 37 bp from the 3’-end of the tRNA- Glu gene and 428 bp from the 5' end of cytochrome b gene was performed on total DNA from 57 hakes (Table 1) according to Pérez and Presa (2008). The 3' end of the mitochondrial tRNA-Glu gene was used to align the 57 cytochrome b sequences obtained, using the program SeqLab the GCG software package (Genetics Computer Group, Madison, Wisconsin). The number of synonymous and non-synonymous substitutions, the nucleotide diversity, and the diversity of silent substitutions were calculated with the program DnaSP 4.50 (Rozas et al., 2003). Global divergence between sequences was calculated using the gamma distance of the Kimura-2 parameters model (Kimura, 1980) implemented in the program MEGA 2.1 (Kumar et al., 2001). In order to explore putative departures from the mutation-drift equilibrium, either caused by selection or by population bottlenecks, the Tajima test for neutrality (Tajima, 1989) was applied to the molecular variation on cytochrome b gene. The probability values associated to this statistic were generated with 10,000 replicates of a Monte Carlo simulation algorithm of coalescence adapted from Hudson (1990) as implemented in ARLEQUIN 3.11. The molecular relationships between cytochrome b haplotypes were explored in a sequence network built with the Median-Joining method in absence of recombination, implemented in NETWORK 4.2.01 (Bandelt et al., 1999). The Global Disparity Index (Kumar and Gadagkar, 2001) between nucleotide sites was assessed with a Monte Carlo test (5,000 iterations) on the values of disparity between pairs of sites or sequences using the Homogeneity Pattern Test (Kumar and Gadagkar, 2001). The genetic divergence within or between oceanographic regions was estimated with the genetic distance of Kimura (2-parameters model) using MEGA 2.1. The hypothesis of random distribution of individuals among populations was assessed with the global exact test of differentiation enforcing 100,000 Markov chains iterations. The distribution of the molecular variance (AMOVA) was explored with ARLEQUIN 3.11, and the significance of the fixation indexes (Wright, 1965) was obtained from the null distribution of these statistics generated with a non-parametric method (10,000 permutations) by exchanging haplotypes, individuals or populations between the
50
Chapter II corresponding hierarchical levels enforced. The phylogenetic relationships between populations were explored upon the molecular variation of the 57 cytochrome b sequences recovered, and assessed with the Maximum likelihood method and heuristic search implemented in PAUP 4.0 (Swofford, 1998), starting from 100 bootstrap replicates of a Neighbor-Joining tree (Saitou and Nei, 1987), including the less divergent congeneric taxon Merluccius senegalensis, as outgroup. MODELTEST 3.7 program (Posada and Crandall, 1998) was used to select the substitution model of HKY85 + G (Hasegawa et al., 1985) with gamma distribution (α= 0.006), and the observed nucleotide frequencies and transition / transversion ratio (Ti / Tv = 7.96). The tree-bisection-reconnection (TBR) was the criterion used to contrast the topology of clusters (Branch-swapping), allowing the polytomic collapse of branch lengths shorter than 10-8. The number of structural units (k) present in the sample set was obtained after 10,000,000 iterations under the spatial model and the uncorrelated allele frequency model using a combined data matrix of microsatellites and cytochrome b as implemented in GENELAND 3.2.4.
Results
The statistical power of the sample system was 1.0 for an effective size of 2,000 irrespective of both, the number of replicates (200 or 500) or the calculation method used (chi-square or Fisher’s). Power estimate using an effective size of 5,000 with 200 or 500 replicates, was 0.870 or 0.876 (chi-square approach) and 0.815 or 0.796 (Fisher approach), respectively. Estimates of type I error using chi-square and Fisher’s method were 0.044 and 0.066, respectively, for an effective size of 500, and 0.046 and 0.050, respectively, for an effective size of 1,000.
Genetic diversity of microsatellites
All microsatellites were highly polymorphic, the number of alleles per locus ranging between 15 in locus Mmer-UEAhk3b and 38 in locus Mmer-UEAhk9b. One hundred and twenty nine alleles were scored in 704 individuals, as 120 in the Atlantic (482 individuals) and 105 in the Mediterranean (222 individuals). Genotypic adjustment to Hardy-Weinberg proportions was observed for loci Mmer-UEAhk20 and Mmer-UEAhk3b, and a significant deficit of heterozygotes was observed for locus Mmer-UEAhk9b (average FIS ± SD = 0.340 ± 0.143), locus Mmer-UEAhk34b (average FIS ± SD = 0.385 ± 0.129) and locus Mmer-UEAhk29
(average FIS ± SD = 0.538 ± 0.157), in most samples. Frequency estimates of putative null
51
Chapter II alleles were 0.05 for loci Mmer-UEAhk20 and Mmer-UEAhk3b, 0.21 for locus Mmer- UEAhk9b, 0.24 for locus Mmer-UEAhk34b and 0.38 for locus Mmer-UEAhk29. A similar number of alleles was observed among samples or among fishing grounds, except the Canarian Sea (ATCAS) and the Tyrrhenian Sea (MEDC), each consisting of a single sample. The average number of alleles did not differ across loci (t-test, P = 0.62) between the Atlantic basin (24.0 ± 8.15) and the Mediterranean basin (21.4 ± 7.57). Multimodal allelic series were observed in one locus for most Cantabrian (ATCAN) and Iberian (ATIBE) samples. None of the genetic variables, including A, HE or RS differed significantly among samples or among 2 fishing grounds (e.g. Friedman test for HE, χ = 12.686; P > 0.05). The expected heterozygosity was higher than 0.711 in all loci and samples and differed slightly (P = 0.0082) between the pool of Atlantic samples (0.883 ± 0.046) and the pool of Mediterranean samples (0.862 ± 0.089). Noteworthy, this difference could be due to the different sample size analyzed from each basin (Table 1).
Regional differentiation of microsatellites
Neither the global differentiation test among samples (30,000 Markov steps) nor the test of differentiation between pairs of samples (Markov chain length = 100,000 steps) were significant (P = 1.0). The Isolation By Distance (IBD) model for the whole species range was not rejected, since a weak but significant correlation was obtained between genetic distances and geographic marine distances (R2 = 0.176, N = 351, F = 74.54, P = 0). The largest variance from AMOVA partitions was observed within samples (FIS = 0.019, P < 0.01) and between
Atlantic and Mediterranean sample pools (FCT = 0.014, p < 0.01). The smallest and non- significant variation was observed among samples within groups (FSC = 0. 005, p > 0.01). The amount of variation among samples (FST = 0.011, p < 0.01) was similar to that among fishing grounds (FST = 0.009, p < 0.01) (Table 2). FST values between hake grounds ranged 0.002 – 0.039 (Table 3). Most pairwise distances between samples located north to Iberia were low and not significant. The higher FST values were observed in pairwise comparisons involving one Atlantic sample and one Mediterranean sample. All differentiation D values between grounds were one order of magnitude higher than the corresponding FST for that pair (Table 3). All D values were significant upon their 95% confidence intervals, but the Celtic Sea sample (ATCEL) was not differentiated either from the North Sea (ATNOR) or from the Atlantic Iberia (ATIBE) after the pBH correction. A tight correlation was observed between 2 -11 FST and D values between pairs of samples (R = 0.741, F = 199.79, P = 1.55 ) (Figure 1A) as well as between pairs of fishing grounds (R2 = 0.913, F = 995.92, P = 2.89-104) (Figure 1B).
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Chapter II
0.07 A ST y = 0.1338x - 0.0074 F R² = 0.7405 0.06
0.05
0.04
0.03
0.02
0.01
0 0.00 0.10 0.20 0.30 0.40 0.50 D
0.05
ST B y = 0.1165x - 0.0003 F 0.04 R² = 0.9132 0.04 0.03 0.03 0.02 0.02 0.01 0.01 0.00 0.00 0.10 0.20 0.30 0.40 D
Figure 1. Statistical regression between gene flow (FST) and genetic differentiation (D) for (A) 27 samples (R2 = 0.741, F = 199.79, P = 1.55-11) and (B) seven hake fishery grounds (R2 = 0.913, F = 995.92, P = 2.89-104) sampled in 2000-2001.
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Table 2. Hierarchical AMOVA for seven hake fishery grounds sampled and two main basins (Atlantic and Mediterranean) as indicated in Table 1. D.f.: degrees of freedom; SS: sum of squares; VC: variation of components; %: percentage of variation; F.i.: fixation index. * indicates that probability that the observed values were equal or smaller that those expected by random is P ≤ 0.01; ns: not significant.
Hierarchical Level Source of variation D.f. SS VC % F.i
* Whole dataset Among samples 26 74.427 0.02028 1.11 FST = 0.011
Within samples 1381 2496.202 1.80753 98.89
* Fishing grounds Among grounds 6 31.376 0.01624 0.89 FCT = 0.009
ns Among samples within grounds 20 43.051 0.00676 0.37 FSC = 0.004
* Within samples 1381 2496.202 1.80753 98.74 FIT = 0.013
* Atlantic & Among basins 1 16.847 0.02552 1.38 FCT = 0.014
ns Mediterranean Among samples within basins 25 57.580 0.00957 0.52 FSC = 0.005
* Within samples 1381 2496.202 1.80753 98.10 FIT = 0.019
Table 3. Pairwise estimates of differentiation (D, above diagonal) and gene flow (FST, below diagonal) between European hake grounds. The statistical significance of being different from zero is indicated with asterisks. Significance of D was derived from its 95% confidence interval or from the probability P (in parenthesis) calculated after the false discovery rate approach (pBH, 2,100 permutations, alpha = 0.002381). The significance threshold for FST was generated after 100 MC batches of 5,000 iterations each for alpha = 0.01; ns, not significant differentiation tests are bolded and the incongruences between statistical methods for D are bolded and italicized.
ATNOR ATCEL ATCAN ATIBE ATCAS MEDW MEDC
- ATNOR 0.053* 0.129* 0.076* 0.290* 0.164* 0.179*
(0.254ns) (0.002*) (0.040*) (0.001*) (0.001*) (0.001*)
ATCEL 0.002ns - 0.073* 0.029* 0.334* 0.118* 0.162*
(0.001*) (0.088ns) (0.001*) (0.001*) (0.001*)
ATCAN 0.012* 0.007ns - 0.050* 0.266* 0.073* 0.173*
(0.001*) (0.001*) (0.001*) (0.001*)
ATIBE 0.004ns 0.002ns 0.005ns - 0.326* 0.126* 0.197*
(0.001*) (0.001*) (0.001*)
ATCAS 0.033* 0.036* 0.024* 0.034* - 0.309* 0.282*
(0.001*) (0.001*)
MEDW 0.020* 0.015* 0.014* 0.020* 0.039* - 0.113*
(0.001*)
MEDC 0.023* 0.017* 0.024* 0.025* 0.037* 0.011* -
54
Chapter II
Molecular variation of cytochrome b gene
The 465 bp fragment of cytochrome b was characterized by a 40% GC content and a ratio Ti / Tv = 7.96. The haplotypic diversity was DhSD = 0.548 ± 0.005 and the nucleotide diversity was Pi (JC) = 0.0025. The 11 polymorphic positions consisted in seven unique substitutions (sites 125, 196, 212, 233, 268, 412, 451) where five of them were non-synonymous, and four synonymous and parsimonious substitutions (sites 181, 199, 217, 259) (Table 4). When polymorphic positions were normalized to the number of non-synonymous (dNs) and synonymous sites (dS), their substitution rate ω = dns / ds was 0.298. No departures from mutation-drift equilibrium were observed in the molecular variation on cytochrome b gene (Tajima’s D = -1.468, P > 0.10). The Global Disparity Index between sequences was ID = 0.001, and the probability associated with the assumption of homogeneity in the substitution pattern between sequences was P = 0.562 for 1,596 tests (CI 95%) between pairs of nucleotide sites and sequences. The exact test of differentiation in pairwise comparisons of samples was not significant (P = 0.629 ± 0.091; 20,000 Markov Chains steps). The gamma distance of Kimura was very low between haplotypes (K2P = 0.0001 ± 0.00001) and showed a similar score among samples within grounds (K2P = 0.0020 ± 0.0012) than between grounds (K2P = 0.0018 ± 0.0017). A main haplotype (Mmcytb.1) was present in 67% of the 57 individuals sequenced and in all the fishing grounds sampled. The rest of haplotypes (Mmcytb.2-11) appeared at an average frequency of 3.33 ± 2.54, some of them were common to both basins and some others were basin-specific (Table 4).
55
Chapter II
Table 4. Nucleotide polymorphisms, absolute and relative frequency of 57 cytochrome b haplotypes from M. merluccius and their distribution in seven fishery grounds. The haplotype MsencytbMS28 corresponds to the outgroup Merluccius senegalensis.
Nucleotide position
1 1 1 1 1 1 1 2 2 2 2 2 4 4
1 2 8 8 9 9 9 1 1 3 5 6 1 5 Fishing ground 6 5 1 4 3 6 9 2 7 3 9 8 2 1 Haplotype Frequency (%) ATNOR ATCEL ATCAN ATIBE ATCAS MEDW
Mmcytb.1 C T G A G A T G G G T A G A 38 (66.68) 1 9 9 8 2 7
Mmcytb .2 . . A ...... 5 (8.78) 1 2 2
Mmcytb .3 ...... C . . . C . A . 4 (7.02) 1 2 1
Mmcytb .4 ...... C . . . C . . . 2 (3.51) 1 1
Mmcytb .5 ...... A . . . . . 2 (3.51) 2
Mmcytb .6 . . A ...... G 1 (1.75) 1
Mmcytb .7 ...... C . . C C . . . 1 (1.75) 1
Mmcytb .8 ...... A ...... 1 (1.75) 1
Mmcytb .9 . . . . . G . . A . . . . . 1 (1.75) 1
Mmcytb .10 . C A . . . C ...... 1 (1.75) 1
Mmcytb .11 . . A ...... G . . 1 (1.75) 1
MsencytbMS28 A . . G A . . . A . . . . . -
Regional differentiation of cytochrome b gene
The Global Disparity Index between sequences was ID = 0.001, and the probability associated with the assumption of homogeneity in the substitution pattern between sequences was P = 0.562 for 1,596 tests (CI 95%) between pairs of nucleotide sites and sequences. The exact test of differentiation in pairwise comparisons of samples was not significant (P = 0.629 ± 0.091; 20,000 Markov Chains steps). The gamma distance of Kimura was very low between haplotypes (K2P = 0.0001 ± 0.00001) and showed a similar score among samples within grounds (K2P = 0.0020 ± 0.0012) than between grounds (K2P = 0.0018 ± 0.0017). Diversity of cytochrome b haplotypes within samples accounted for 99.86% of the whole molecular variance. A non-significant variation of 0.14% was observed among fishing grounds. The network relating the molecular relationships between the 11 cytochrome b haplotypes observed showed the ten low-frequent haplotypes (Mmcytb.2-11) diverging from the majority haplotype (Mmcytb.1) in 1 to 3 substitutions (Figure 2). The ML phylogenetic reconstruction for cytochrome b recovered a single tree (LnL = -809.85, Figure 2) characterized by a large polytomy and three main intraphyletic sub-clades. Two of them comprised samples from the two main basins, and one defined by synapomorphy A/217 was specific of the Mediterranean
56
Chapter II samples. The correspondence between the network of haplotypes and the phylogenetic tree indicates that the large polytomy is defined by the majority haplotype Mmcytb.1. All the remaining haplotypes could be classified into three groups, i.e. one group comprising haplotypes specific of individuals (i.e. Mmcytb.6,7,8,9,10,11), a second group comprising low-frequency haplotypes present in several individuals from the same ground (i.e. Mmcytb.4,5), and a third group comprising moderate frequency haplotypes shared by individuals from different regions (e.g. Mmcytb.2,3) (Figure 2).
57
Chapter II
Figure 2. Maximum likelihood phylogenetic tree (Ln = -809.85) showing the relationships among 57 cytochrome b sequences from 27 samples of M. Merluccius collected in 2000 - 2001. The Median-Joining network indicates the molecular relationships between 11 haplotypes and their correspondence with the intraphyletic clades. The tree was rooted with M. senegalensis (MS28).
58
Chapter II
Global population structure in the European hake
The representation of cytochrome b haplotypes on their sampling locations showed that haplotype Mmcytb.1 is ubiquitously present in all regions, haplotypes Mmcytb.4,6,7,8,11 are specific of the Atlantic, haplotypes Mmcytb.5,9,10 are specific of the Mediterranean, and haplotypes Mmcytb.2,3 are present at low frequency in both basins (Figure 3A). The spatial model with uncorrelated allele frequencies (GENELAND) implemented for microsatellite data, as well as for combined data of microsatellite genotypes and cytochrome b haplotypes, showed two most probable genetic clusters (k = 2) in the whole European distribution of the species. The map built upon the posterior probability of belonging to one of the two clusters showed a clear-cut divide of samples into a Mediterranean cluster and an Atlantic cluster. Such divide for microsatellites was situated in the middle of the Alboran Sea coinciding with the sample of Malaga (ME20). However, for combined data of microsatellites and cytochrome b, such divide between stocks was located at the easternmost side of the Alboran sea coinciding with the sample of Almería (ME21) (Figure 3B). The Bayesian reconstruction afforded from BAPS, as well as the likelihood approach from STRUCTURE, considering all affordable population models implemented therein, showed a single genetic pool for the whole European area sampled (k = 1). Significant unidirectional migration rates calculated with Bayesian inference on microsatellite data were only observed at two well defined locations: from Western Mediterranean (MEDW) to Central Mediterranean (MEDC) (m = 0.276), and from the Celtic sea (ATCEL) to the rest of Atlantic grounds (North Sea, m = 0.265; Cantabric Sea, m = 0.077; Iberian Atlantic, m = 0.156; and Canarian Sea, m = 0.109) (Table 5).
59
Chapter II
B
osatellite osatellite data.
upon the posterior probability of belonging to one of the two
) Map built
B
haplotypes of hake (represented with colours on the map) and their oceanographic locations sampled in
b
two gene pools andcoincides Alboran with Sea. the Malaga (ME20)sample in the
) Correspondence between cytochrome
A
= = 2) obtained when the spatial model with uncorrelated allele frequencies implemented in GENELAND 3.2.4 was enforced for micr
; their surface in pie charts on the map indicate their relative ( frequency.
k
. (
A
2001
-
Figure 3 2000 clusters ( Colour shift marks split betweenthe the
60
Chapter II
Table 5. Bayesian pairwise estimates of migration rates m between hake fishery grounds indicated in the first column (Donors) and those in the first row (Receptors). Asterisks indicate that the observed mean and 95% confidence interval of m are external to the background 95% confidence interval when there is no information in the data (not migration). The non-informative background threshold for the present system with seven fishery grounds was 0.0277 (1.15E-07, 0.144). Two asterisks indicate that the two observed confidence limits were higher than those obtained by simulation (m-values on the diagonal correspond to the within-ground migration rate). One asterisk indicates that only the inferior confidence limit observed lies within the simulated confidence interval. Migration rates were not different from zero where not signalled by asterisks.
From/To ATNOR ATCEL ATCAN ATIBE ATCAS MEDW MEDC
ATNOR 0.677** 0.002 0.002 0.002 0.013 0.001 0.004
ATCEL 0.265** 0.938** 0.077* 0.156* 0.109* 0.001 0.016
ATCAN 0.012 0.018 0.851** 0.033 0.116* 0.029 0.013
ATIBE 0.018 0.020 0.049 0.796** 0.029 0.003 0.009
ATCAS 0.005 0.002 0.002 0.002 0.692** 0.001 0.004
MEDW 0.018 0.018 0.016 0.010 0.026 0.955** 0.276**
MEDC 0.005 0.002 0.002 0.002 0.015 0.001 0.677**
Discussion
This study describes the global pattern of connectivity among European hake populations. The proposed population pattern is supported by the high statistical power of the sample/marker system under distinct priors and methods, by the consistency observed across genetic and statistical analyses, as well as by its congruence with some previous studies developed in the last decades.
Genetic parameters of present markers were consistent with those from previous studies but exhibiting an expected fluctuation in range and number of alleles, due to spatial variation (localities sampled), temporal variation (years) and sample sizes used across studies (Lundy et al., 1999, 2000; Pita et al., 2011). For instance, the average number of alleles per sample in Atlantic hake was not different across studies (F = 0.473; P = 0.705), being 28.4 ± 11.41 (142 alleles in 322 individuals) in Lundy et al. (1999), 30.8 ± 13.27 (154 alleles in 1017 individuals) in Pita et al. (2011) and 24.0 ± 7.91 (120 alleles in 483 individuals) in the present study. Likewise, no differences in allelic diversity were observed between subpopulations of the Atlantic and the Mediterranean neither in Lundy et al. (1999; t = 0.190; P = 0.854) nor in the present study (t = 0.611; P = 0.558). Also, Mediterranean samples did not differ between both studies (t = 0.947; P = 0.371), i.e. 135 alleles in 161 individuals with 27.0 ± 11.94 alleles
61
Chapter II per sample (Lundy et al., 1999) or 105 alleles in 222 individuals with 21.0 ± 7.62 alleles per sample. The consistency found between studies across hake grounds regarding genetic diversity (allele diversity, heterozygosity and allele richness) suggests that present samples from the Canarian Sea (ATCAS) and the Tyrrhenian Sea (MEDC), which show a reduced number of alleles (microsatellites) and haplotypes (cytochrome b), are probably downwards biased due to their small sample size. Nevertheless, such a sampling drift did not have any significant influence either in structural analyses or in the global connectivity pattern described.
The significant deficit of heterozygotes at three loci in most samples has also been observed in previous studies using these markers (e.g., Lundy et al., 2000). Showing that such deficit in hake is due to assortative mating (Lundy et al., 2000), or to hitch-hiking of a priori neutral microsatellites (e.g., Nielsen et al., 2006) remains speculative without further experimental evidence. Once excluded technically-related allelic artefacts such as overlapping bands and drop-out effects (Marshall et al., 1998), the existence of multiple null alleles co-segregating at low-frequency is a sufficient explanation to the absence of null-null homozygotes. In addition, a population containing unbalanced contributions from several cohorts will show heterozygote deficit when parental genotypes randomly differ from each other (Coombs and Mitchell, 1982; Lander, 1989). Such a population admixture is a well-known fact in hake during recruitment time (Sánchez and Gil, 2000; Sánchez et al., 2003). The multimodal allelic series in most Cantabrian (ATCAN) and Iberian (ATIBE) samples could result from cohort admixture, as occurs in other marine species with wide range, multiple spawning sites and early dispersal, such as Mytilus galloprovincialis (Diz and Presa, 2008). Cohort admixture has probably led to suggest the existence of a complex hake population substructure in the Atlantic (Roldán et al., 1998; Lundy et al., 1999). However, present data and recent studies (Pita et al., 2011) show that a wide genetic homogeneity exists in the Atlantic and that a vanishing admixture of cohorts at recruitment should not be confounded with the genetic concept of stable population structuring.
Correction of null alleles appeared to us inappropriate because current algorithms consider a single null allele as responsible for the whole heterozygote deficit. Such a correction increases the bias at estimating population scenarios since it creates a homogenizing effect among samples. Therefore, our simulations made with current algorithms for null alleles (e.g., Microchecker; FreeNa) suggest that raw genotypic frequencies are the least wrong estimate of diversity (Data not shown). Although diminishing the differentiation signal in exact tests (see
62
Chapter II
Chapuis and Estoup, 2007) we have observed that heterozygote deficits are assumable biases in large-scale population studies, when inter-population genetic divergence is low and molecular diversity is evenly sampled and underestimated in all conspecific populations containing null alleles (e.g., Diz and Presa, 2008).
Neutrality tests made on the whole variation of the cytochrome b gene indicates a non- departure from the mutation-drift equilibrium. However, while the polymorphism of synonymous substitutions was strictly neutral (parsimonious), the polymorphism of non- synonymous changes was quasi-neutral and consisted of singletons specific of individuals. Evidence of purifying selection on non-synonymous changes was patent by the higher diversity of silent substitutions (Pi = 0.0079) as compared to the total nucleotide diversity (Pi = 0.0027), by the ratio dns / ds = 0.298 that was skewed towards purifying selection, although compatible with drift (Nei and Gojobori, 1986; Yang and Nielsen, 2002), and by the short separation among haplotypes, branch length being at most three substitutions distant from the majority sequence Mmcytb.1. While neutrality of silent positions is expected, it is less clear why some polymorphisms are maintained in non-synonymous sites. Several hypotheses can be mentioned for this phenomenon, such as frequency-dependent selection, the neutral behaviour of non-synonymous substitutions, or mitochondrial DNA heteroplasmy. Any of these explanations is plausible given that all individuals carrying polymorphisms were viable adults.
The lack of differentiation among samples and the weak correlation between the genetic distance and the geographic distance suggest a scenario of connectivity in the Atlantic, that is only marginally compatible with a model of Isolation by Distance (IBD) (Kimura and Weiss, 1964). Several marine fishes with high-dispersal fit this model (Castric and Bernatchez, 2003) and its poor fit in hake is probably due to the low gene flow reaching the limits of the species range to the north (North Sea) and to the south (Canary-Saharan grounds). The large residual variance of the genetic-geographic correlation indicates that factors other than the geographic distance can better explain the population genetic scenario in the Atlantic. For instance, a consistent migration between Porcupine Bank and Biscay Bay has recently been proposed to explain the violation of IBD model in hake from the Biscay Bay (Pita et al., 2011). Since the IBD model develops as genetic drift and gene flow approach equilibrium (Wright, 1943), this model is hard to accept in hake due to the large inter-annual variance of both, reproduction and environment, that determine primarily the extent of gene flow among fishing grounds.
63
Chapter II
The overall genetic distance between hake samples (FST = 0.011) was in the range reported for other North-east Atlantic gadoids (FST = 0.000 – 0.016; Ryan et al., 2005) including hake from the Bay of Biscay and Portuguese waters (FST = 0.013; Lundy et al., 1999) or from the whole
Northern Stock (FST = 0.018; Pita et al., 2011). In the Eastern Atlantic, the high gene flow deducted from the low FCT-values observed among hake grounds is indicative of a negligible spatial divergence. This is especially patent north to Portuguese waters where most pairwise
FCT-values were low and not significant. Despite D showed a less conservative behaviour than
FST, that is patent by the statistical signification of all D pairwise values, the implementation of the false discovery rate approach (pBH correction) to test the significance of D clearly found homogeneity between the Celtic Sea (ATCEL) and either the North Sea (ATNOR) or
Atlantic Iberia (ATIBE), the same scenario inferred from FST. Moreover, Bayesian estimates of mutation rates showed significant and unidirectional migrations from the Celtic Sea (ATCEL) to all the rest of Atlantic grounds, without apparent exceptions.
Previous genetic studies performed in hake with allozymes (Roldán et al., 1998; Cimmaruta et al., 2005) and microsatellites (Lundy et al., 1999; Castillo et al., 2004) highlighted the existence of complex substructures in the Atlantic. However, present results and recent ones performed on otolith chemistry (Tanner et al., 2012) further enlarge the lack of genetic structuring described in the North-east Atlantic with allozymes (Mangaly and Jamieson, 1979) and microsatellites (Pita et al., 2011) to at least the Eastern Atlantic Iberian shelf (IXa). Noteworthy, a similar lack of structuring has been reported for the horse mackerel T. trachurus over a larger geographical range comprising the present one (Kasapidis and Magoulas, 2008) or between assumed discrete populations of Atlantic herring Cuplea harengus (Smith et al., 1990).
The Atlantic - Mediterranean population split was characterized by the lowest gene flow (FST) and the highest differentiation (D) of the present sampling set. Noteworthy, the genetic variation between those populations (1.4%) was half of that reported for microsatellites
(2.9%, Lundy et al., 1999) and allozymes (3.08%, Roldán et al., 1998). Such a FST-halving can be due to an episodic migration between basins in the period 1999 – 2001, but it is more likely due to a reduction of the genetic variance as sampling coverage was raised on the same range, as done in the present study as compared to previous ones. Opposite to microsatellites, the absence of both, molecular covariance within regions and genetic differentiation among samples for the cytochrome b gene, does not support any population structure in the European hake. This result agrees with the genetic scenario proposed for three distant samples of this
64
Chapter II species (Norway, Tunisia and Bay of Biscay) after other mitochondrial DNA regions, such as the D-loop, the subunits VI and VIII of the ATPase gene, and the ND1 gene (Lundy et al., 1999). Neither microsatellites showing too much homoplasy nor the large ML polytomy defined by haplotype Mmcyb.1, that was present in 67% of individuals, allowed to endorse panmixia between basins or to assess the extent of their connectivity. Nevertheless, a more informative scenario was afforded from low-frequent cytochrome b haplotypes defining subclades outside the mitochondrial polytomy. For instance, cytochrome b haplotypes Mmcytb.4,6,7,8,11 were specific to the Atlantic while haplotypes Mmcyb.5,9,10 were specific to the Mediterranean (the only regional subclade defined by synapomorphy A/217), indicating a highly restricted gene flow between basins.
Nevertheless, if recurrent mutation is assumed improbable, the ubiquity of the rare haplotypes Mmcyb.2 and Mmcyb.3 is likely the result of a historical connection between basins. This biological connection probably flowed from the Atlantic to the Mediterranean, what is in agreement with well-known patterns of oceanic currents in Gibraltar Strait and Alboran Sea (Tintoré et al., 1988; Millot, 1999; Robinson et al., 2001). For instance, the rare haplotype Mmcyb.3 is more frequent in the Atlantic and reached up to the Alboran Sea while haplotype Mmcyb.2 clearly showed a higher frequency in the Atlantic. Also, while Bayesian simulations on microsatellite data did not detect any significant migration between basins, a significant and unidirectional migration was patent from Western Mediterranean (MEDW) to Central Mediterranean (MEDC). Altogether, these data suggest that on-going gene flow exists in the Atlantic as well as in the Mediterranean and that their episodic connection might have been predominantly unidirectional. Assuming that cytochrome b of vertebrate fishes evolved at a rate of 0.94% to 3.05% per 1,000,000 years (Van Houdt et al., 2003) and taking into account the standard mitochondrial clock (2% per 1,000,000 years) (Brown et al., 1979), present 0.3% dates back the divergence between basins to 150,000 years BP. This figure fits into the late Middle Pleistocene geological time when connectivity between Atlantic and Mediterranean basins was limited by a spectacular reduction of the sea level depth in 130 m below the current level. Such oceanographic feature is thought to be causative of a significant gene flow restriction between those basins in many marine species (Patarnello et al., 2007). The map built upon the posterior probability of belonging to one of the two basins, identified the divide between two most probable genetic clusters (k = 2) at the easternmost side of the Alboran sea, which coincides with the sample site of Almería (ME21). That oceanographic site lies within the area where the well characterised oceanic front AOOF (Tintoré et al., 1991) has been reported responsible for the unique phylogeographic divide in the European hake (Pita et al.,
65
Chapter II
2010) as well as occurs in many other marine species (see Bargelloni et al., 2003). Noteworthy, a recent study performed on Serranus cabrilla using particle dispersion simulations and population genetic data has shown a similar pattern of dispersion in 2001 as in 2004, which supports the temporal stability of the AOOF as a consistent biogeographical barrier (Schunter et al., 2011).
Worth mentioning is the selective explanation to the Atlantic – Mediterranean genetic divergence given in some studies developed on hake. It has suggested a causal relationship between salinity and temperature with interpopulation divergence based on allozyme variation between both sides of the AOOF (Cimmaruta et al., 2005). Obviously, a mathematical correlation of genetic and physical factors does not imply their functional relationship. Moreover, in a recent study, a priori non-neutral SNPs markers were calibrated to discriminate the origin of commercial hakes (Nielsen et al., 2012). The subjacent molecular variation of some FST-outliers from-EST-derived SNPs showed a significant and diagnostic divergence between hakes from both basins. Provided that selected SNPs had been drawn from a cDNA library, authors “unambiguously” did correspond causally the genotypic divergence to selective forces acting on those polymorphisms, in a distinct way in the Atlantic than in the Mediterranean. Obviously, and as a precaution, extra-care should be taken before concluding selective scenarios from microsatellite polymorphisms. In fact, the heads-up comparison of a priori neutral microsatellites vs non-neutral microsatellites should provide nearly identical macro-phylogeographic hake scenarios if genetic drift instead of selection is responsible for the interbasin divergence (Santafé et al., in preparation). A conservative explanation should be adopted when a more-parsimonious, energetically-cheaper and biogeographically-congruent, neutral explanation for population divergence can be enforced without conceptual handicaps. In fact, present genetic data on hake, together with recent genetic and geochemical studies in this species (Tanner et al., 2012) as well as previous temporal studies in other marine taxa, indicate that the AOOF is a consistent barrier over time (e.g. see Sanjuan et al., 1994 vs. Diz and Presa, 2008). Such oceanographic barrier explains satisfactorily the biological split in many species with very different physiological requirements (e.g. Chthamalus montangui, C. Stellatus, Meganyctiphanes norvegica and Sepia officinalis) without the need to invoking selection.
The two type of markers used in this study offer distinct evolutionary views of this species. Fast mutating microsatellites inform on the recent past of this species, i.e. the Celtic Sea as the main focus of an on-going multidirectional radiation, a wide Atlantic connectivity, and a
66
Chapter II main subpopulation split that is coincident with the AOOF. Meanwhile, the more conservative cytochrome b gene informs on the dispersive pathway of ancient mutations across the Alboran Sea, indicating a long history of low connectivity between those subpopulations. Intensity and direction defining the connectivity pattern in the Atlantic are estimated here for the first time. This genetically-based dispersive pattern is congruent with previous studies reporting the Celtic Sea as the main spawning area for hake (Fives et al., 2001) as well as with some patterns previously established for dispersal and recruitment of hake juveniles in the Cantabrian Sea (Sánchez and Gil, 2000). Assumedly, causal forces of hake dispersal are oceanographic phenomena such as hydrographic macroscale structures connecting spawning grounds and fisheries (Le Cann, 1990), geostrophic currents enforcing spatial segregation between spawning and nursery grounds (Sánchez, 1994), and an active pursuit of the optimal habitat related with seasonally-bounded phytoplankton blooming episodes (Valencia et al., 1989). Therefore, temporal variability of migration figures (Pita et al., 2011) is likely caused by uncoupling between life-cycle stages and season-dependent oceanographic conditions for dispersal (Parrish et al., 1981), by yearly-dependent spawning success (Palumbi, 1994) and/or by prey/feed related migrations (Olaso et al., 1994). Since hake is a species with large population sizes, high fecundity, year-round spawning dynamics (Murua and Motos, 2006) and an unknown potential for long-distance dispersal of eggs, larvae, juveniles or adults, age- class of migrants must be deciphered. Provided the large migration rate from the Celtic Sea to Atlantic Iberia, hake dispersal between these grounds might involve connection pathways other than a passive dispersal of larvae and recruits following the continental shelf (Casey and Pereiro, 1995), i.e. characterized by short-intermediate distance recruitment processes (Alvarez et al., 2004), and driven by microscale oceanic features acting on the Western European continental shelf (Kloppmann et al., 1996; Sánchez and Gil, 2000). In fact, migration of pelagic larvae and adults has been proposed to explain the high levels of genetic homogeneity of marine species over large (Lessios et al., 1998) or intermediate (Knutsen et al., 2003) oceanic distances, a dynamics that is not excluded for hake radiating in all directions from the Celtic Sea.
67
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Chapter III
RESUMEN
El epicentro del debate pesquero a escala Europea durante los últimos veinte años, ha sido la sobreexplotación de los recursos marinos y su sostenibilidad. A pesar de que un enfoque multidisciplinar podría mejorar los planes de gestión de las pesquerías amenazadas, la colaboración entre evaluadores pesqueros y genetistas aún está en ciernes. Una vez abordada la estructura espacial de la merluza europea los conocimientos pendientes sobre su estabilidad temporal, el número efectivo de poblaciones explotadas y su patrón conectivo, son factores clave para la comprensión de la dinámica de los stocks de merluza y su gestión adecuada. Con los cinco marcadores microsatélite comúnmente utilizados en la merluza europea, se ha intentado establecer el patrón de conectividad y la intensidad de la migración entre los dos caladeros más relevantes para la reposición de biomasa de la merluza atlántica, Porcupine Bank y Vizcaya, durante la última década. Los datos disponibles sugieren un flujo migratorio unidireccional desde los caladeros de Great Sole y Porcupine Bank hacia el Cantábrico, con una intensidad interanual variable. La confirmación de la estabilidad temporal de este patrón migratorio, de su sentido e intensidad, es de vital importancia para el manejo de esta pesquería.
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ABSTRACT
Overexploitation of marine resources and its sustainability remain the main focus of the fishery debate in the European Union after twenty years of common fishing policies. Despite it is generally accepted that a multidisciplinary approach can improve the management plans for endangered fisheries, the cooperation between fishing managers and geneticists is still pending. Once the genetic structure of the European hake has been well established, the temporal stability of both, the number of populations and their connectivity pattern, are key factors to understand and manage Atlantic hake stocks. Using five classic microsatellite markers of the European hake, we tested here the connectivity pattern between the two most relevant Atlantic spawning grounds of this species (Porcupine Bank and Biscay Bay) over the last decade. Available data suggest that genes flow unidirectionally from Great Sole and Porcupine Bank grounds to the Cantabrian Sea, yet at a variable inter-annual intensity. Confirmation of such systematic migration over time, as well as its directionality and intensity are key elements for a more rational management of this important European fishery.
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Chapter III – Gene flow quantification between Atlantic hake stocks (Merluccius merluccius) over the last decade
Introduction
The European hake (Merluccius merluccius, Linnaeus, 1758) is one of the most important species in marine ecosystems of Atlantic and Mediterranean continental shelves, in terms of trophic role, biomass, and distribution range (Casey and Pereiro, 1995; Oliver and Massutí, 1995; Papaconstantinou and Stergiou, 1995). The European hake occupies the widest range in longitude and latitude among hake species, ranging from Mauritanian coasts to Irish and Norwegian ones, including the Mediterranean Sea and the southern coast of the Black Sea (Lloris et al., 2003). This species is highly appreciated for human consumption and therefore it has experienced a strong fishing pressure since the 1960s (Casey and Pereiro, 1995; Oliver and Massutí, 1995; ICES, 2009a, b).
Since 1978 the European Atlantic hake fishery has been divided in two different stocks by the International Council for the Exploration of the Sea (ICES). The Northern hake Stock is distributed upwards the Cape Breton Canyon, including the French coast, the British Isles and the west coast of Norway. The Southern hake Stock ranges from the Bay of Biscay to the Gulf of Cadiz. The Northern Stock is more important than the Southern one in both, fishing biomass and economic impact throughout European markets (González-Laxe, 2008). The separation of this Atlantic hake fishery in two different stocks was firstly supported by a putative barrier effect of the Cape Breton Canyon (Casey and Pereiro, 1995), by the shape and width of the continental shelf, and by water current dynamics in the southern Bay of Biscay (Valdés et al., 1996). However, some population genetic studies suggested a more complex genetic structure in the North Atlantic than a merely existence of two stocks. For instance, such population ultrastructure was based on differences found between Celtic Sea and Norwegian samples genotyped with six microsatellites (Lundy et al., 1999), or between French and Irish samples analysed with 21 protein-loci (Roldán et al., 1998). Contrary to the previous arguments, other classical studies had suggested a wide genetic homogeneity of the Atlantic hake, based on: 1) non-significant differences in morphometric characteristics within the Northern hake Stock (Jones and Mackie, 1970), 2) non-significant differentiation in protein-loci among samples taken from Norway to the Bay of Biscay (Mangaly and Jamieson, 1979) and up to the Mediterranean Sea (Cimmaruta et al., 2005), 3) no genetic structuring was observed using several mitochondrial DNA markers (Lundy et al., 1999), and 4) absence
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Chapter III of microsatellite genetic differentiation among temporal hake samples from the southern part of the Bay of Biscay (Lundy et al., 2000). This controversy about the number of gene pools, populations, stocks and management units of hake inhabiting European Atlantic fishing grounds were largely addressed in this thesis whereby the classic idea of a single genetic pool in the North Atlantic hake population is reinforced (Jones and Mackie, 1970; Mangaly and Jamieson, 1979; Cimmaruta et al., 2005; Lundy et al., 1999; Lundy et al., 2000; Pita et al., 2010, 2011).
Accuracy of annual fishing figures, inferences on the size of the spawning stock biomass (SSB) and estimates of recruitment (R) figures of both Atlantic hake stocks have been some of the main topics of the European fishing debate for decades (Cerviño et al., 2009). The spawning stock biomass of both Atlantic stocks sank drastically in the middle eighties and stayed there stagnant during the last decade of the 20th century. This little hopeful scenario pointed to by fishery parameters brought about the implementation of different management changes on this fishery just to avoid a collapse (ICES, 2009a, b) similar to that experienced by the Canadian cod in the early 90's (Hutchings, 2005). Such EU measures focused on the need to reduce fishing mortality to ensure the sustainability of this fishery, which SSB was well below its precautionary level (Cerviño et al., 2009). The Northern hake Stock seems to be fully recovered nowadays and being managed according to sustainable levels of exploitation. Meanwhile, the situation of the Southern hake Stock seems to be improving as judging from recent recruitment figures, although it is yet far from being considered as a sustainable fishery. Population genetics analyses are useful tool to clarify one of the most important issues in the management process: the definition of what a stock is. In general, stock was defined either as a self-sustainable population over time in a definable area (Booke, 1981) or as an intraspecific group of individuals with random reproduction and a spatial or temporal integrity (Ihssen et al., 1981). Some modifications to the previous stock concept were made upon availability of genotypic data (a population in Hardy-Weinberg equilibrium over time), or phenotypic information (expression of different characteristics in different environments) (Booke, 1981). Therefore, the stock concept has changed through history, but especially since the development of DNA-based molecular markers (Booke, 1999). Obviously in modern times, linking the genetic integrity of populations to the stock definition makes feasible the long term conservation of genetic diversity (Jamieson, 1973; Ovenden, 1990).
Incomplete knowledge on key bio-ecological aspects of the species managed introduces a level of uncertainty in the assessment process. Genetics comes at this point to improve our
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Chapter III understanding of spatio-temporal bio-ecological patterns in stock dynamics (Enberg et al., 2009). Apart from establishing the genetic structure of the exploited species across its full distribution range (space level), precise data on the temporal stability of both, genetic structure and gene diversity parameters, are badly needed to incorporate genetics into the management process. The first approaches to test for the temporal stability of genetic structures in the Atlantic hake were done by Lundy et al. (2000) and Pita et al. (2011). Noteworthy, any significant genetic differentiation was observed either among samples or among years. However, the number of cohorts or years addressed in those studies was not large enough to confirm the Atlantic hake genetic stability at large time scales.
In order to test for the temporal stability of both, genetic structuring (or lack of it) and gene diversity parameters, in this study we have employed the usual microsatellite set applied in previous population studies on the Atlantic hake. Such markers have shown a reasonably large molecular variance to solve both, gene flow patterns (Pita et al., 2011) and genetic structures in this species (Pita et al., 2010; Pita et al, unpublished data). We hypothesize that both phenomena should be stable over the last decade if any systematic migration occurs between the two main hake spawning grounds in the Atlantic, the Porcupine Bank and the Bay of Biscay fishing grounds. If this were the case, any interanual variability of migration rates should have a transient effect on the heterogeneity of cohorts between northern and southern Atlantic areas, disappearing as migration is recovered under favourable oceanographic conditions.
Material and methods
Hake samples used in this study were collected over the last decade in the most important spawning areas of the European hake in the Atlantic North, i.e. Great Sole and Porcupine Bank grounds for the Northern hake Stock, and southern Bay of Biscay for the Southern hake Stock. Sampling the Northern Stock was feasible through close collaboration with the Spanish Institute of Oceanography (IEO) throughout the annual prospective campaign “Porcupine”. The Southern Stock was sampled by the authors on commercial vessels between 2000 and 2002, and on the IEO collection from the annual fishing survey “Demersales” between 2003 and 2010. Samples consisted of gills or otoliths, upon sampling year.
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DNA extraction from otoliths
Otoliths were individually preserved dry in test tubes for DNA extraction. The most important handicap found in some year samples was the cleansing of otoliths upon collection, so the amount of tissue remaining was very limiting. The otolith was immersed in digestion buffer (100 mM Tris-HCl, 10 mM NaCl, 10 mM EDTA, 1% SDS and 150 µg/ml of proteinase-K) and incubated for 48 hours at 40 ºC. The supernanant was transferred to a clean tube and salts were added (40 µl of 5 M NaCl and 10 µl of 1 M MgCl). After vortexing this solution, 1 ml of absolute cold ethanol was added and incubated for one hour at -20 ºC. An 11,000 rpm centrifugation was run for 20 minutes at 4 ºC, and the supernatant was removed carefully to avoid losing the pellet. The tubes were laid with the lids opened until excess of ethanol was evaporated. DNA was resuspended in 100 µl of TE (10 mM Tris-HCl, 1 mM EDTA) and 5 µl of each extract were visualized in a 1% agarose gel. Finally, the tubes containing the DNA were stored at -20 ºC before PCR amplification.
DNA extraction from gills
Gill tissue was preserved in absolute ethanol immediately after sampled. A piece of gill arch was cut and dipped in distilled water to remove ethanol. DNA extraction followed the protocol FENOSALT (Pérez and Presa, 2011) and several random individual’s DNA from each sample were tested in 1% agarose gels. DNA was stored at -20 ºC before PCR amplification of microsatellite markers.
Microsatellite analysis
Five hundred and ninety hakes were amplified for five microsatellite markers described by Morán et al. (1999). Microsatellites termed Mmner-hk3b, Mmer-hk9b and Mmer-hk20b were combined in a single multiplex reaction using 10 pmol of each primer, 1.5 mM of MgCl2, and 55 ºC as annealing temperature. Mmer-hk29b and Mmer-hk34b were amplified independently at 55 ºC, using 10 pmol of each primer and a MgCl2 concentration of 1.0 mM and 1.7 mM, respectively. DNA was previously diluted 1:15 and 1 µl used in a final 15 µl PCR composed by 50 ng DNA, 200 µM dNTPs, 0.75 U Taq-polymerase (Bioline) and the same primer concentrations as described above. PCR cycling consisted of 5 min at 95 ºC, 35 cycles of 50 s at 95 ºC, 50 s at 55 ºC, and 50 s at 72 ºC, followed by a final extension at 72 ºC for 15 min. Amplification results from PCR were tested in 2 % agarose gels. The forward primer of each
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Chapter III marker was labelled with HEX (Mmer-hk9b), NED (Mmer-hk34b and Mmer-hk20b) and FAM (Mmer-hk29b) fluorophores, using ROX fluorophore as internal marker (GeneScan-500, Applied Biosystems). The genotyping process was carried out in an ABI-3130 Analyzer at the Sequencing Service of the Scientific and Technological Research Assistance Centre (CACTI) from Vigo University.
Statistical analyses
Row ABI genotypic data from each individual was read in GeneMarker V1.97 (Softgenetics). Errors in this step were minimized by independent readings of three researchers and a final validation of results. Multilocus genotypes were registered in an Excel sheet and treated as input file for statistical and genetic analyses. The statistical power of the dataset was tested in POWSIM (Ryman and Palm, 2006) by simulating 10 generations of drift in a population of effective size 1,000 and 500 iterations. Associated type I error was calculated with the same program using an effective size of 1,000 individuals and 1,000 iterations. Probability values were obtained with the exact test of Fisher (Fisher, 1922) and the chi-square test of Pearson
(Pearson, 1900). Allele frequencies, expected heterozigosity (HE), observed heterozygosity
(HO) and Hardy-Weinberg equilibrium per sample and year were calculated in GENEPOP 4.0
(Rousset, 2008). Genetic parameters such as number of alleles (A), allelic richness (RS), and fixation indices FIS, FCT and FST were calculated in FSTAT 2.9.3.2 (Goudet, 1995).
Significant differences of genetic parameters (HO, HE, RS, FIS, FST) between samples or years were assessed through 15,000 permutations in FSTAT. The differentiation index between pairwise samples D (Jost, 2008) and its confidence interval were calculated with DEMEtics 0.8-5 (Gerlach et al., 2010) using 1,000 bootstrap replicates. The statistical regression between D and FST was carried out with SPSS 17.0. Null allele frequencies were inferred per sample and locus using the EM algorithm (Dempster et al., 1977) implemented in FreeNA (Chapuis and Estoup, 2007). The Analysis of Molecular Variance (Excoffier et al., 1992) as implemented in ARLEQUIN 3.11 (Excoffier et al., 2005) was used to split the genetic variance of the whole dataset between groups (FCT) and between populations within groups
(FSC). Nominal statistical levels for each index were determined through 1,023 permutations. Sample relationship upon variance components were visualized in a bi-dimensional space using a principal components analysis (PCA) available in the statistical package SSPS 17.0. The Isolation by Distance model (IBD) (Wright, 1943) was tested with program ISOLDE (Rousset, 1997) implemented in GENEPOP. Migration rate between years was calculated using BayesAss 1.3 (Wilson and Rannala, 2003). The number of effective migrants per
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generation (Nem) between consecutive years was obtained after the private alleles method (Barton and Slatkin, 1986) implemented in Genepop. The most probable number of gene pools in the dataset was inferred with Geneland 3.2.4 (Guillot et al., 2005; Guillot et al., 2008).
Results
Samples included in this study belonged to 6 years of the last decade, 2000-2002 and 2008- 2010, and consisted of 590 individuals in all (Table 1). The statistical power estimated in the sample dataset was 1.0 and type I error was 0.043 and 0.066 depending on the method used, Chi-square test or Fisher exact test, respectively. None of the genetic parameters inferred differed significantly over fishing grounds and years, i.e. between Biscay Bay and Porcupine Bank or between periods 2000 - 2002 and 2008 - 2010. Microsatellites Mmer-hk9b, Mmer- hk29b and Mmer-hk34b showed significant deviations from Hardy-Weinberg equilibrium (FIS ± SD = 0.194 ± 0.083, 0.432 ± 0.147 and 0.153 ± 0.090 respectively) in all cases.
Table 1. Geographical location and codes of the samples analyzed in this study. N indicates the number of individuals genotyped per sample.
Year Fishery ground ICES code Coordinates N
2000 Biscay Bay VIIIc 43º40´N 02º11´W 64 2001 Great Sole – Porcupine VIIc,j,k 52º34´N 13º00´W 93 Biscay Bay VIIIc 43º29´N 02º09´W 34 2002 Great Sole – Porcupine VIIc,j,k 52º21´N 13º27´W 102 Biscay Bay VIIIc 43º26´N 02º09´W 55 2008 Great Sole – Porcupine VIIc,j,k 51º58´N 14º11´W 42 Biscay Bay VIIIc 43º17´N 02º44´W 53 2009 Great Sole – Porcupine VIIc,j,k 51º44´N 13º58´W 53 2010 Great Sole – Porcupine VIIc,j,k 51º39´N 13º29´W 50 Biscay Bay VIIIc 43º28´N 02º04´W 44
The analysis of molecular variance showed a large variation within samples (28%), while the variance observed between samples represented 0.28% (Table 2). Only the variance between samples and years was significant (1.13%) among all the hierarchical levels assayed.
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Table 2. Hierarchical AMOVA per samples and years. D.f.: degrees of freedom; %: percentage of variation; F.i.: fixation index. *: P ≤ 0.01; ns: non significant.
Hierarchical level Source of variation D.f. % F.i.
ns Global Between samples 9 0.28 FST = 0.003
Within samples 1170 99.72
ns Samples Between groups 1 0.13 FCT = 0.001
ns Between samples within groups 8 0.20 FSC = 0.002
ns Within samples 1170 99.67 FIS = 0.003
ns Years Between groups 5 0.01 FCT = 0.000
ns Between samples within groups 6 0.14 FSC = 0.001
ns Within samples 1402 99.85 FIS = 0.001
* Samples & years Between groups 9 1.13 FCT = 0.011
ns Between samples within groups 10 -0.85 FSC = -0.009
ns Within samples 2340 99.72 FIS = 0.003
FST-values between samples and years were significant among Biscay Bay samples, but were not significant among Porcupine Bank samples or between Porcupine Bank and southern Biscay Bay samples (Table 3). The majority of D-values between samples were statistically different from 0 (Table 3). The behaviour of both statistics used to infer the differentiation 2 between samples was fairly similar between FST and D according to their high correlation (R = 0.8787; F = 311.44; P = 0.00) (Figure 1).
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Table 3. Pairwise D-values (above diagonal) and pairwise FST-values (below diagonal) between fishing grounds and years of the last decade.
Biscay Biscay Biscay Biscay Biscay Porcu Porcu Porcu Porcu Porcu
2000 2001 2002 2008 2010 2001 2002 2008 2009 2010
Biscay 2000 - 0.157* 0.130* 0.131* 0.240* 0.097* 0.106* 0.159* 0.125* 0.148*
Biscay 2001 0.012* - 0.021 0.159* 0.157* 0.086* 0.092* 0.120* 0.120* 0.177*
Biscay 2002 0.010* 0.001 - 0.100* 0.115* 0.095* 0.058* 0.095* 0.024 0.086*
Biscay 2008 0.011* 0.015* 0.008* - 0.051 0.109* 0.115* 0.072 0.070* 0.046*
Biscay 2010 0.022* 0.016* 0.010* 0.004 - 0.139* 0.136* 0.107* 0.082* 0.084*
Porcu 2001 0.005* 0.008 0.008 0.010 0.014* - 0.042* 0.027 0.031 0.110*
Porcu 2002 0.007* 0.010 0.006 0.012* 0.013 0.001 - 0.106* 0.051* 0.135*
Porcu 2008 0.010* 0.013 0.008 0.005 0.010 0.002 0.009* - 0.000 0.031
Porcu 2009 0.010* 0.014* 0.001 0.006 0.010 0.004 0.006 0.000 - 0.027
Porcu 2010 0.012* 0.016 0.006 0.000 0.006 0.010* 0.015 0.002 0.002 -
) 0.03
ST F
0.02
0.02 Fixation ( Fixation index
0.01
0.01
0.00 0.00 0.05 0.10 0.15 0.20 0.25 0.30
Differentiation parameter (D)
Figure 1. Correlation between the FST-distance and the differentiation parameter of Jost (D) performed on European hake samples from the Atlantic Ocean.
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A weak temporal aggregation was observed in the first component of the PCA plot, grouping together all samples from the beggining of the decade and all samples from its end, independently of their sampling location (Figure 2). Mantel test showed a lack of correlation between the genetic distance and the geographical distance in the sample system (R2 =
0.0005; P = 0.27) (Figure 3).
1.5 1.0 Porcupine 09 Porcupine 08 Biscay 01 0.5 Biscay 10 Biscay 02 0.0 Biscay 08 Porcupine 01
-0.5 Porcupine10 Porcupine 02 component (15.5%) component
-1.0 nd 2 -1.5 -2.0 Biscay 00 -2.5 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0
st 1 component (18%)
Figure 2. Principal component analysis showing the spatial relationship of the temporal samples included in this study
0.03
Fst)] - 0.02
0.02
0.01
Genetic [Fst/(1 Genetic Distance 0.01
0.00 0 200 400 600 800 1,000 1,200 1,400
Geographic Distance (km)
Figure 3. Mantel correlation between the genetic distance and the geographic distance performed on European hake samples from the North Atlantic in the last decade.
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The number of population units (k) inferred with Geneland was k = 1 over all models tested, either between fishing grounds (Figure 4) or within fishing grounds. Migration rates calculated from Biscay Bay to Porcupine Bank were low and non-significant, while significant m-rates were detected from Porcupine Bank to Biscay Bay in years 2002 and 2008 (Table 4; Figure 5).
Figure 4. Posterior probability of European hake samples to belong to a single gene pool.
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0.25 8
7 0.20 6
5
0.15
4 (Nem) 0.10 3
Tasa de migración migración de (m) Tasa 2 0.05
1 Número de migrantes por generación generación migrantes de por Número
0.00 0 2000/2001 2001/2002 2002/2008 2008/2009 2009/2010 Años
Figure 5. Temporal estimates of m (migration rate) between two major hake fishing grounds in the North Atlantic: Porcupine and southern Bay of Biscay. Nem is the effective number of migrants (▲); m, is the migration rate, northwards (□) and southwards (◊). Only those migration rates between stocks and between consecutive years or samples per year are represented.
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Table 4. Migration rates from fishing ground per year in the first row to fishing ground per year in the first column. The interval 3.12e-11 – 0.121 show the area of no information in the data according to Wilson and Rannala, 2003 and therefore the asterisks indicate those values with a higher 95% confidence interval than previous one and denote significant information.
Biscay Biscay Biscay Biscay Biscay Porcu Porcu Porcu Porcu Porcu 2000 2001 2002 2008 2010 2001 2002 2008 2009 2010
Biscay 2000 0.914* 0.003 0.004 0.003 0.004 0.013 0.035 0.015 0.004 0.004
Biscay 2001 0.043 0.680* 0.007 0.006 0.006 0.048 0.142* 0.054 0.007 0.005
Biscay 2002 0.052 0.005 0.683* 0.005 0.007 0.017 0.103 0.116 0.008 0.005
Biscay 2008 0.018 0.004 0.005 0.681* 0.009 0.008 0.032 0.230* 0.007 0.006
Biscay 2010 0.007 0.004 0.004 0.006 0.686* 0.008 0.052 0.216* 0.010 0.006
Porcu 2001 0.057 0.003 0.004 0.004 0.006 0.699* 0.144* 0.074 0.006 0.003
Porcu 2002 0.057 0.002 0.004 0.004 0.006 0.014 0.871* 0.029 0.009 0.004
Porcu 2008 0.006 0.004 0.004 0.006 0.006 0.048 0.014 0.897* 0.010 0.005
Porcu 2009 0.021 0.004 0.005 0.005 0.005 0.014 0.032 0.228* 0.682* 0.004
Porcu 2010 0.017 0.004 0.006 0.007 0.010 0.017 0.022 0.229* 0.007 0.680*
Discussion
The majority of studies carried out on the genetic structure of the North Atlantic European hake did not reach any clear scenario on its structure (see Introduction). Some common drawbacks across population genetic studies may explain this result: 1) a low number of individuals and/or samples, 2) a lack of temporal samples from the same fishery, and 3) use of samples from randomly chosen years, depending on its availability. The present work along with Chapter II and Chapter IV of this thesis are the first studies performing long term spatio- temporal genetic studies in this species with a sample size adequate to the molecular markers applied. Although the connectivity pattern of this area was firstly described in Pita et al. (2011), the inclusion of more years from the 2000 decade is now improving our knowledge on hake connectivity pattern for management purposes.
Despite the main goal of the study was to testing the genetic structure and connectivity of Atlantic hake populations in the last decade (2000 - 2010), some technical problems were encountered during PCR amplification of molecular markers used in this study, specifically
94
Chapter III from cleansed otoliths from the central years of the decade. Consequently only 590 hakes from 6 years (2000 - 2002 and 2008 - 2010) from the two major fishing grounds of the Atlantic North (Porcupine Bank and Biscay Bay) were analysed, instead of the 1,500 hake individuals from the whole decade initially planned.
The statistical power of present microsatellite markers and sampling systems appears bearing power enough to test the genetic differentiation between basins or among years from the Southern hake Stock. In this study, the sampling system was also suitable to test the null hypothesis of genetic homogeneity among temporal samples of the two major fishing grounds of the Atlantic.
Gene diversity parameters showed similar values to those reported in previous studies on this species (Lundy et al., 1999, 2000; Pita et al., 2011; Tanner et al., 2012), and the absence of significant differences in gene diversity parameters among fishing grounds and among years was congruent with the single Atlantic hake population unit proposed in this thesis. The high percentage of null alleles assumed to exist in two markers deviating significantly from Hardy- Weinberg equilibrium could underly the heterozygote deficit observed. In addition, other technical issues such as the presence of outlier alleles or the overlapping of allele bands could affect equilibrium estimates (Marshall et al., 1998). However, the bias produced by the presence of null alleles can be neglected when all samples are genotyped with the same criteria (Diz and Presa, 2008) or when samples belong to closely related populations (Estoup et al., 1995).
The low genetic variance observed between samples (0.28%) confirmed that a large genetic homogeneity exits over the North Atlantic hake as proposed in Chapter II (Pita et al., 2011). This result contrasts with the idea of an undisclosed complex genetic structure in this area (i.e., Roldán et al., 1998; Castillo et al., 2005). The lack of genetic differences between samples from Porcupine Bank indicates a temporal stability of allele frequencies and suggests its self-sustainability from the major spawning area in Irish waters (Casey and Pereiro, 1995). However, the significant genetic differences observed among Biscay Bay samples over time, pointed out that the genetic load of this area varies temporally in spite of the existing spawning grounds over the Cantabrian coast (Alvarez et al., 2001). Finally, the absence of temporal differentiation between samples from Porcupine Bank and Biscay Bay is an additional evidence of the high connectivity that exists in the whole Atlantic North. Gene flow is likely the factor explaining the homogeneity of allele frequencies between Biscay Bay and
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Porcupine Bank. However, the weak differentiation observed between year samples from southern Biscay Bay is probably a result of drift in years of low spawning activity.
The fact that Porcupine Bank and Biscay Bay were closely grouped in the Principal Component Analysis despite a temporal aggregation of samples, seems indicative that a common gene pool is shared between fishing grounds over time, i.e. at the beginning of the decade (Pita et al., 2011) as well as in its end. Therefore, the molecular variation synthetized in the PCA that is responsible for the temporal grouping is shared across Atlantic hake grounds due to their high connectivity. In addition, the non-significant Isolation By Distance pattern also points towards the same behaviour in the temporal stability of the genetic structure, i.e. the low genetic differentiation estimated in the dataset was nearly the same within fishing grounds as among them, and no apparent differences due to the temporal comparisons were detected. The connectivity pattern of the European hake in the Atlantic North, described in the first two chapters of this thesis, seems to have been fairly constant along the last decade as also supported by the landscape analysis of year samples (see Figure 4).
Although present data require completion with those from the whole decade, gene flow estimates seem to be unidirectional from Porcupine Bank and Great Sole to southern Biscay Bay, as proposed before (Pita et al., 2011). The intensity of such gene flow is probably related to the environmental facilitation, spawning success and/or feeding behaviour of the species, and its temporal variation (i.e. migration rates in the range of 0.00 – 0.20) is crucial to understand the dynamics of this species and to improve actual management plans. Thus, the results of this study go on with the idea that a multidisciplinary approach is badly needed to improve stock assessment in hake and manage stocks according to their true dynamics.
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Chapter IV
RESUMEN
Algunos estudios recientes sobre la genética poblacional de la merluza europea (Merluccius merluccius) han sugerido la existencia de un patrón de conectividad global en toda la fachada atlántica europea. Una de las propiedades principales de tal patrón es la radiación multidireccional de biomasa desde los principales caladeros de reproducción de la especie en el Stock Norte, i.e. Porcupine Bank y Great Sole. Siguiendo ese modelo se ha llegado a hipotetizar que los caladeros del norte de la Península Ibérica, que corresponden con el Stock Sur de merluza, experimentarían un enriquecimiento genético debido a la inmigración procedente del Stock Norte. En este trabajo se ha fijado la escala espacial en la pesquería del noroeste peninsular, al objeto de estudiar la composición temporal de su diversidad genética. Se han analizado diversos parámetros genéticos a lo largo de la historia reciente de esta pesquería (1976 - 2007), que pueden resultar útiles en su evaluación genética, tales como el tamaño genético efectivo poblacional. Se muestra que a pesar de la sobrepesca efectuada durante décadas sobre el Stock Sur de merluza, su diversidad genética permanece estable tanto en polimorfismo de microsatélites como en variación nucleotídica del gen mitocondrial citocromo b. Esa estabilidad genética ha sido testada con un referente temporal externo, tomado de esta pesquería en 1976. Dicha estabilidad genética de largo plazo se interpreta en términos de compensación entre la deriva génica ocasionada por sobrepesca y el gran tamaño efectivo del Stock Sur de merluza. Además, todo indica que el mantenimiento de un tamaño efectivo elevado es probablemente debido al aporte de biomasa proporcionado por el Stock Norte de merluza europea.
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ABSTRACT
Recent population genetic studies on the European hake have suggested a global connectivity pattern in the Atlantic that radiates multi-directionally from the source spawning grounds of Porcupine Bank and Great Sole. According to that population model, northern Iberian fishing grounds inhabited by the Southern hake Stock would experience a genetic enrichment brought about by southward migrations from the Northern hake Stock. In this study we have fixed the spatial scale of the North-western Cantabrian hake fishery to properly study the inter-annual composition of its genetic diversity. Some genetic parameters that could be useful in the genetic assessment such as the genetic effective population size have been tracked along the recent history of this fishery (1976 - 2007). We show that in spite of the large fishing pressure exerted on this population during decades, its genetic diversity remains nowadays stable regarding nuclear microsatellite polymorphisms as well as mitochondrial cytochrome b gene nucleotide variation. Such genetic stability has been verified against a temporal outlier sample from 1976. Such a long-term stability is explained here in terms of compensation between the genetic drift induced by overfishing and the large population genetic effective size of the Southern hake Stock. Moreover, present data together with previous population dynamics models point to the maintenance of such a large genetic effective size by consistent biomass dislodgements from the Northern hake Stock.
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Chapter IV - How did the genetic effective population size relate to spawning stock biomass and fishing mortality of the southern European hake population during the last decade?
Introduction
The European hake (Merluccius merluccius) is a demersal species that is widely distributed along the NE Atlantic from Norwegian and Icelandic coasts to Mauritania and also up to the eastern Mediterranean Sea (Lloris et al., 2003). The International Council for the Exploration of the Sea (ICES) divides the Atlantic hake fishery in two stocks located on each side of the Cape Breton Canyon, i.e. a Northern hake Stock (ICES Division IIIa, Sub-areas IV, VI and VII, and Divisions VIIIa,b, and d) (ICES, 2012a) and a Southern hake Stock (ICES Divisions VIIIc and IXa) (ICES, 2012b). Those stocks have been heavily overexploited over the last decades and therefore, two respective recovery plans were required to reboot them: one for the Northern hake Stock (EC Reg. No. 811/2004) and the other for the Southern hake Stock (EC Reg. No. 2166/2005) . More precisely, the main goal of the recovery plan for the Southern Stock was the recovery of the spawning stock biomass (SSB) up to 35,000 tonnes by 2016. The implementation of regulatory measures on this fishery since 2005 has led to good recruitments and to increase SSB estimates close to the objective. However, new perceptions on the dynamics of this stock (Pita et al., 2011) as well as on the bias underlying current methods for SSB calculation, make such a goal no longer realistic (ICES, 2011). The SSB is one of the biological parameters used by fishery managers to work out annual assessment reports on the population status and its potential of regeneration. Estimates of SSB are highly dependent of the algorithm used and this has been recently changed within the Working Group on the Assessment of the Southern Shelf Stocks of Hake, Monk and Megrim (WGHMM). For instance, XSA (age structured model) was used for SSB calculation before 2008, the Bayesian statistical CATCH-AT-AGE model was used in 2008-2009, and GADGET (an age-length structured model) was used since 2010 (see ICES-WGHMM Reports 2009-2011). Moreover, growth rate of the European hake is still uncertain since the methodology used to measure it seemed highly skewed so far (Piñeiro et al., 2007). Therefore, it is expected that SSB figures could dramatically change in the near future. The extent of the uncertainty associated to the calculation of SSB could be better estimated if a parameter independent of fishery data were available as external control of SSB estimates.
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Population genetic parameters are biological candidate indices to assess the goodness of population estimates from fishery data. Fishery genetics is called to be one of the main research fields in fisheries research since the onset of molecular markers. Incorporation of genetics to fishery science has gone from the description of intrapopulation genetic variation of neutral markers to the traceability of species (Pérez and Presa, 2008) or environmental- dependent gene expression (Waples and Naish, 2009). Nowadays, fishing scientists and fishing policy makers emphasize the need for a better temporal knowledge of both: the genetic structure and the genetic diversity of the species, in order to improve their management plans. Such temporal studies are now affordable due to improved methods of ancient DNA extraction (Shapiro and Hofreiter, 2012) and the development of Next- Generation Sequencing tools that provide more statistical power to population analyses (Santafé et al., unpublished).
The very basic concern on the sustainability of an overexploited fishery is the loss of its adaptive potential and final collapse. In this regard, such a low-fitness risk in a fishery is higher when the genetic effective population size (Ne) is low, and both are closely related to the loss of genetic diversity (Zarraonaindia, 2011). Nowadays, the genetic erosion of exploited fisheries can be addressed through friendly population parameters drawn from large scale genotyping approaches, such as the genetic effective population size (Ne). This parameter was defined by Wright (1931) as the size of an ideal population which had the same rate of change of allele frequencies or heterozygosity as the observed population. Since Ne is a critical parameter in evolutionary biology, but very difficult to measure in natural populations, various indirect statistical estimators have been worked out so far (Waples, 2005). However, a general consensus exists on the need of more precise knowledge on real population characteristics, such as bottlenecks and sex ratio (Wang, 2005) to properly explain temporal fluctuations of Ne.
There are several methods to infer Ne from a wide range of molecular markers. However, each method usually makes use of distinct population parameters which in turn are taxon- dependent. For instance, Ne in random-mating systems can be estimated from the heterozygote excess method (Pudovkin et al., 1996), but its use should better be applied to marine invertebrates such as shellfish, due to their reproduction type. The temporal method (Nei and Tajima, 1981) is based on the variation of allele frequencies observed in at least two temporal samples drawn from the same population. It is assumed that the bias introduced in this method when we assume discrete populations instead of a more realistic scenario of an
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Chapter IV age-structured natural population with overlapping generations, could be minimized if the elapsed time between temporal samples is long (Waples and Yokota, 2007). Other methods are less demanding, such as that based on linkage disequilibria that require a single sample to infer Ne (Waples, 2006). This latter method is also biased when the sample size is smaller than Ne (England et al., 2006), but a renewed edition of this method seems to reduce successfully its own original bias (Waples, 2006).
A wide variety of molecular markers can be applied to extract molecular variation from hake populations, useful in the estimation of Ne. However, microsatellites are the most informative ones for a fine description of the intraspecific variation and inferring diverse population phenomena affecting it (Estoup et al., 1993). Microsatellites have been profusely applied during the last decades to depict the genetic diversity of various European hake populations (e.g., Lundy et al., 1999; Castillo et al., 2005; Pita et al., 2010). Some studies have reported significant genetic differences among hake fishing grounds from the Atlantic North, using either microsatellites (Lundy et al., 1999) or allozymes (Roldán et al., 1998). Also a high- complex population structure was suggested after microsatellites and mitochondrial DNA data in hake samples collected during autumn 1997 and spring 1998 - 1999 (Lundy et al., 2000). However, more recent studies carried out with microsatellites on temporal samples (2000, 2001 and 2002) from the Bay of Biscay (Pita et al., 2011) as well as with otolith chemistry (Tanner et al., 2012) have suggested a high genetic homogeneity of hakes over the whole Atlantic North. Such scenario implies a large interpopulation connectivity that blurs any trace of biological boundary between the two classically considered Atlantic hake stocks. This actual absence of genetic differentiation is highly congruent with the classic population homogeneity observed with allozymes (Mangaly and Jamieson, 1979) as well as with morphometric characteristics (Jones and Mackie, 1970) over the whole European Atlantic hake grounds.
In this study we have addressed the spatial scale of the North-western Cantabrian fishery that corresponds to the area where the largest biomass of the Southern hake Stock dwells. The existence of a single hake population over the whole Atlantic implies that the Iberian fishing grounds inhabited by the Southern hake Stock would have undergone a genetic enrichment brought about by southward migrations from the Northern hake Stock. If this were the case, we hypothesize that the genetic diversity of the Southern hake Stock should not exhibit any genetic erosion despite decades of overexploitation. Moreover, we also launched the hypothesis that if SSB estimates have been realistic then they should somehow correlate with
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Chapter IV the genetic effective size estimated on the same temporal samples. To test those hypotheses, we have tracked the inter-annual composition of genetic variation in microsatellites from the Southern hake Stock, in period 2000 - 2007. We have also tracked the genetic effective population size, and tested its relationship with SSB estimates in that period. Experimental controls to microsatellites and temporal samples consisted of the nucleotide variation of the mitochondrial DNA cytochrome b gene and the molecular variation of an outlier hake reference sample from 1976, respectively.
Material and methods
Sampling and molecular analyses
Eighty-two samples comprising 1,833 mature hakes (fork-length > 35 cm) were taken from commercial vessels and Official IEO (Spanish Institute of Oceanography) autumn campaigns on ICES Areas VIIIc and IXa. Samples belonged to seven years (1976, 2000 - 2005 and 2007) and were ascribed to three adjacent grounds of the Southern hake Stock in the north coast of Spain, i.e. southern Biscay Bay, central-west Cantabrian Sea and Atlantic Iberia) (Table 1). Gill and muscle tissues from each specimen were collected on board and preserved in absolute ethanol, while otoliths stored dried in envelops from 1976 were individualized in test tubes and moisturized by immersion in pure water.
Few picograms of tissue were recovered after dipping otoliths at 40 ºC 24 hours in a lysis solution composed of 100 mM Tris (ph = 8), 100 mM NaCl, 10 mM EDTA, 2% SDS and 200 µg/ml of proteinase-k. DNA was extracted as described for Atlantic cod otoliths (Hutchinson et al., 1999). About 25 mg of gill or muscle tissue were washed in miliQ water, air-dried for 10 minutes to remove excess of ethanol and immersed in miliQ water for 30 minutes to hydrate the tissue. DNA was extracted and purified using the method FENOSALT (Pérez and Presa, 2011).
A DNA aliquot was diluted 1:15 before PCR amplification of five polymorphic microsatellites to genotype all individuals. Using those conditions described in Pita et al. (2011). The forward primer of each marker was labelled with Cy5 fluorophore (5-N-N-diethyl- tetramethylindodicarbocyanine) for laser detection and amplified fragments were electrophoresed in an ALFexpress II fragment analyzer (GE Healthcare). Molecular ladders (80 bp, 180 bp, 230 bp, and 402 bp) were co-migrated with samples for allele sizing.
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Genotyping scores were checked twice by independent researchers and dubious samples were reamplified and re-genotyped. The consistency of the scored allelic series and the frequency of putative null alleles were calculated with MICRO-CHECKER 2.2.3 (Van Oosterhout et al., 2004).
The PCR amplification of a 465 bp fragment spanning 37 bp from the 3’-end of the tRNA- Glu gene and 428 bp from the 5' end of cytochrome b gene was performed on total DNA from 55 hakes (Table 1) using the primers L14735 and H15149AD described by Wolf et al. (2000). The amplification mixture of 25 µL consisted of 50 ng DNA template, 15 pmol of each primer, 200 µM of each dNTPs, 1.5 mM MgCl2, 1.5 U of Taq DNA polymerase (Promega) and 5 ul of 10 x reaction buffer. Amplifications were carried out as follows: (1) 3 min at 95 ºC, (2) 40 cycles of 30 s at 95 ºC - 1 min at 62 ºC - 1 min at 72 ºC, and (3) a final extension at 72 ºC for 20 min. PCR products were cleaned and sequenced as described in Pérez and Presa (2008).
Statistical analyses
Genetic differentiation among samples
The exact test of population differentiation was computed with 100,000 Markov chains iterations for all the hierarchical levels tested, using the software ARLEQUIN 3.11 (Excoffier et al., 2005). The analysis of variance that tests the differential distribution of the genetic variance among hierarchical levels, was partitioned among hake grounds (FCT) and between samples within hake grounds (FSC) using the analysis of molecular variance (AMOVA; Excoffier et al., 1992) as implemented in ARLEQUIN 3.11. The statistical significance of
FCT, a measure of genetic distance between groups, was obtained with FSTAT 2.9.3.2 (Goudet, 1995) after 1,023 permutations of multilocus genotypes among samples. The nominal level considered was the proportion of permuted F-values that were larger than or equal to the observed value. The differentiation index D (Jost, 2008) and its confidence interval in pairwise comparisons between samples, between grounds, between years and between grounds per year, was calculated after 1,000 iterations with DEMEtics 0.8-5 (Gerlach et al., 2010). The regression between the differentiation index (D) and the interpopulation fixation index (FST) was carried out in SPSS 17.0.
An exact test of differentiation was performed to check for pair-wise genetic divergence of cytochrome b haplotypic sequences using 30,000 Markov chain steps implemented in
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ARLEQUIN 3.11. A haplotypic network was built to represent possible mutational routes between cytochrome b sequences using the median-joining method (Bandelt et al., 1999) implemented in Network 4.6 (http://www.fluxus-engineering.com, 5th April 2006).
The number of gene pools (k) present in the whole dataset was calculated to check for the genetic homogeneity expected to exist among samples from the Southern hake Stock. Such k- parameter was estimated either per year or for the whole period using 107 Bayesian iterations under the uncorrelated allele frequency model (Guillot, 2008) and the spatial model (Guillot et al., 2005a) implemented in GENELAND 3.2.4 (Guillot et al., 2005b). The k-parameter was also explored for the same time frames both, under the spatial population analyses considering a mixture model (Corander et al., 2008a) or an admixture model (Corander et al., 2008b), as implemented in BAPS 5.2 (Corander et al., 2004).
Genetic diversity and population genetic effective size
Fisher’s exact test and Pearson’s chi-square test were used to estimate the statistical power of the sampling system at refuting the hypothesis of genetic homogeneity (50 generations of drift, effective size of 5,000, and one batch of 1,000 replicates), as well as to estimate the proportion of false significant tests (Type I error, P < 0.05) in combined test statistics (1,000 replicates and effective size of 5,000) as implemented in POWSIM (Ryman and Palm, 2006).
The number of alleles (A), the allele frequencies and the allele range of each locus were calculated with FSTAT 2.9.3.2. The observed heterozygosity (HO) and the expected heterozygosity (HE) were estimated with GENEPOP 4.0 (Raymond and Rousset, 1995). Gene diversity related parameters, such as allelic richness (RS), and fixation indices within (FIS) or between (FCT) hake grounds were also estimated with FSTAT 2.9.3.2. Differences in the average values of HO, HE, RS, FIS, and FCT were assessed with 1,000 permutations of samples among hake grounds, years, and grounds per year with FSTAT. Hardy-Weinberg equilibrium was tested with GENEPOP 4.0, using a Markov chain (MC) algorithm of 20 batches and 5,000 iterations per batch to estimate the exact P-value for each locus per population (Guo and Thompson, 1992). Estimated frequencies of putative null alleles were calculated per locus using the Expectation Maximization algorithm of Dempster et al. (1977), and carried out with FreeNA (Chapuis and Estoup, 2007) upon 1,000 iterations. Recent changes of the population genetic effective size were explored with Bottleneck (Cornuet and Luikart, 1997) using 1,000 replicates of the infinite allele model and the two phase mutation model.
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Mitochondrial DNA sequences of 465 bp in length were evaluated with Chromas Lite 2.01 (http://www.technelysium.com.au, 27th August 2010) to detect possible errors during the sequencing process. Sequences were aligned with BioEdit (Hall, 1999) using the 3´-end of the mitochondrial tRNA-Glu gene from each sequence. Nucleotide frequencies and transition/transversion rates (Tamura et al., 2004) were calculated in MEGA 4.0.2 (Tamura et al., 2007). The number of synonymous and non-synonymous substitutions, the number of polymorphic sites, the number of haplotypes, and the haplotype and nucleotide diversity, were calculated with DnaSP 4.50.3 (Rozas et al., 2003). Four additional statistics were calculated in DnaSP 4.50.3, i.e. D (Tajima, 1989), D* and F* (Fu and Li, 1993) and Fs (Fu, 1997) to test whether nucleotide polymorphisms were compatible with a neutral segregation.
The population genetic effective size was calculated on grouped samples per year under three different methodologies: (1) the moment and likelihood method implemented in MNe 1.0 (Wang and Whitlock, 2003), (2) the linkage disequilibrium method (Waples, 2006) implemented in LDNe (Waples and Do, 2008), and (3) the Fs estimator implemented in TempoFs (Jorde and Ryman, 2007). Putative correlations between those Ne scenarios recovered with the three methods as well as those performed against the spawning stock biomass (SSB) estimates were performed in SPSS 17.0.
Results
The 82 samples comprising 1,833 mature hakes collected from the Southern hake Stock were classified per year (1976, 2000 - 2005, and 2007) and per fishing ground (southern Bay of Biscay, central-western Cantabric Sea and Atlantic Iberia) (Table 1).
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Table 1. Southern hake Stock grounds sampled per year (1976, 2000 - 2005, and 2007) and sampling locations for the 1,833 hakes studied. N is the number of individuals genotyped per sample (last column) or per year (first column, between parentheses).
Year Zone Fishery ground ICES Area Coordinates N
1976 Cantabric Sea West Cantabric VIIIc 43º54´N 07º21´W 12 (295) West Cantabric VIIIc 44º03´N 07º35´W 18 Iberian Atlantic North Galician VIIIc 43º53´N 08º04´W 22 North Galician VIIIc 44º03´N 08º17´W 26 North Galician VIIIc 43º47´N 08º31´W 17 North Galician VIIIc 43º33´N 08º32´W 21 North Galician VIIIc 43º37´N 08º46´W 23 North Galician VIIIc 43º32´N 09º00´W 26 North Galician VIIIc 43º28´N 08º52´W 27 Central Galician VIIIc 43º19´N 09º25´W 23 Central Galician IXa 42º58´N 09º30´W 15 Central Galician IXa 42º42´N 09º18´W 17 Central Galician IXa 42º39´N 09º30´W 20 South Galician IXa 42º17´N 09º12´W 28
2000 Cantabric East East Cantabric VIIIb 44º00N 02º25W 23 (370) East Cantabric VIIIc 43º28N 02º10W 17 Cantabric Sea Central Cantabric VIIIc 43º32´N 03º26´W 20 Central Cantabric VIIIc 43º32´N 04º03´W 16 Central Cantabric VIIIc 43º41´N 04º41´W 16 West Cantabric VIIIc 43º35´N 06º22´W 16 West Cantabric VIIIc 43º38´N 06º42´W 16 West Cantabric VIIId2 44º04´N 07º31´W 16 West Cantabric VIIIc 43º50´N 07º35´W 26 Iberian Atlantic North Galician VIIIc 43º44´N 08º50´W 16 North Galician VIIIc 43º41´N 08º49´W 23 North Galician VIIIc 43º15´N 09º06´W 25 North Galician VIIIc 43º04´N 09º31´W 21 Central Galician IXa 42º34´N 09º24´W 31 Central Galician IXa 42º21´N 09º23´W 40 Central Galician IXa 42º20´N 09º27´W 23 South Galician IXa 42º11´N 09º00´W 25
2001 Cantabric East East Cantabric VIIIc 43º34N 02º00W 19 (276) East Cantabric VIIIc 43º31N 02º00W 19 East Cantabric VIIIc 43º27N 02º16W 20 Cantabric Sea Central Cantabric VIIIc 43º32´N 03º26´W 24 Central Cantabric VIIIc 43º32´N 03º38´W 24 Central Cantabric VIIIc 43º30´N 04º10´W 20 Central Cantabric VIIIc 43º42´N 05º40´W 13 West Cantabric VIIIc 43º46´N 06º52´W 25 West Cantabric VIIIc 43º38´N 07º05´W 29 Iberian Atlantic North Galician VIIIc 43º47´N 08º28´W 24 Central Galician IXa 42º32´N 09º21´W 24 South Galician IXa 42º05´N 09º14´W 15 South Galician IXa 41º53´N 09º00´W 20
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2002 Cantabric East East Cantabric VIIIc 43º31N 02º05W 15 (141) East Cantabric VIIIc 43º27N 02º19W 20 East Cantabric VIIIc 43º22N 02º04W 20 Cantabric Sea Central Cantabric VIIIc 43º32´N 04º18´W 21 West Cantabric VIIIc 43º48´N 06º52´W 20 Iberian Atlantic Central Galician IXa 42º34´N 09º29´W 20 Central Galician IXa 42º32´N 09º29´W 25
2003 Cantabric Sea West Cantabric VIIIc 43º50´N 07º48´W 25 (150) West Cantabric VIIIc 43º48´N 07º48´W 25 Iberian Atlantic North Galician VIIIc 43º26´N 08º22´W 12 North Galician VIIIc 43º14´N 09º05´W 25 North Galician VIIIc 43º10´N 09º13´W 36 South Galician IXa 42º21´N 08º55´W 27
2004 Cantabric Sea West Cantabric VIIIc 43º51´N 07º41´W 16 (160) West Cantabric VIIIc 43º48´N 07º57´W 18 Iberian Atlantic North Galician VIIIc 43º30´N 09º00´W 18 North Galician VIIIc 43º30´N 08º45´W 19 North Galician VIIIc 43º16´N 09º00´W 18 North Galician VIIIc 43º15´N 09º05´W 23 North Galician VIIIc 43º15´N 09º02´W 31 Central Galician IXa 42º35´N 09º06´W 17
2005 Iberian Atlantic North Galician VIIIc 43º30´N 09º00´W 32 (167) North Galician VIIIc 43º16´N 09º00´W 15 North Galician VIIIc 43º15´N 09º07´W 26 North Galician VIIIc 43º15´N 09º05´W 32 North Galician VIIIc 43º13´N 09º05´W 16 Central Galician IXa 42º43´N 09º18´W 21 South Galician IXa 42º20´N 09º11´W 25
2007 Cantabric Sea Central Cantabric VIIIc 43º44´N 05º40´W 24 (274) Central Cantabric VIIIc 43º42´N 05º40´W 25 West Cantabric VIIIc 43º46´N 07º34´W 30 West Cantabric VIIIc 43º58´N 07º37´W 14 Iberian Atlantic North Galician VIIIc 43º27´N 08º47´W 29 North Galician VIIIc 43º18´N 09º05´W 30 North Galician VIIIc 43º11´N 09º23´W 30 Cecntral Galician IXa 42º52´N 09º20´W 46 South Galician IXa 42º15´N 09º03´W 14 South Galician IXa 42º07´N 09º15´W 32
The statistical power estimated in the sample system using an effective size of 5,000 and 1,000 iterations was 1.00 irrespective of the statistical methods enforced, i.e. chi-square test or Fisher’s test. Estimates of type I error using any of both, chi-square method or Fisher’s method, were 0.0580.
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Genetic differentiation among samples
The test of global differentiation among samples (30,000 Markov chain steps) resulted in P = 1.00 for the hypothesis of no differentiation among populations. The amount of molecular variance was low but significant (0.90%) among the whole set of samples (Table 2). The molecular variance was also significant “among years” (0.17%) and “among grounds per year” (0.24%), but it was not significant “among grounds”. Most pairwise FST-distances (range 0.0003 – 0.0062) and differentiation D-parameter values (range 0.0139 - 0.0619) between years were low but significant. Pair-wise FST-distances between grounds per year were low (range 0.0000 - 0.0247) and apparently randomly significant. The range of D- parameter between grounds per year was 0.0100 - 0.2715 and most of them were significant.
A high significant correlation was observed between the FST-distance and the D-parameter, either per year (r2 = 0.47 - 0.97; F = 16.78 - 886.69; P = 0.001 - 0.000) or globally (r2 = 0.797; F = 1722.9; P = 0.000).
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Table 2. Hierarchical AMOVA per ground and year as indicated in Table 1. SS: Sum of squares; VC: variance components; %: Percentage of variation; F.i.: Fixation index. Asterisks indicate the probability that the observed values were equal or smaller than those expected by chance, *: P ≤ 0.01; ns: non significant.
d.f. SS VC % F.i. Hierarchical level Source of variation
Whole dataset Among samples 81 242.393 0.01930 0.90 FST = 0.009* Within samples 3584 7635.843 2.13054 99.10 ns Among grounds Among grounds 2 5.865 -0.00003 0.00 FCT = 0.000 Among samples within grounds 79 236.528 0.01931 0.90 FSC = 0.009* Within samples 3584 7635.843 2.13054 99.10 FST = 0.009* * Among years Among grounds 7 31.762 0.00363 0.17 FCT = 0.002 Among samples within grounds 74 210.631 0.01614 0.75 FSC = 0.008* Within samples 3584 7635.843 2.13054 99.08 FST = 0.009* Per zone & year Among grounds 17 64.464 0.00507 0.24 FCT = 0.002* Among samples within grounds 64 177.929 0.01457 0.68 FSC = 0.007* Within samples 3584 7635.843 2.13054 99.09 FST = 0.009* ns 1976 Among samples 13 39.971 0.02049 0.92 FST = 0.009 ns Among grounds 1 3.004 0.00118 0.05 FCT = 0.001 Among samples within grounds 12 36.967 0.02025 0.91 FSC = 0.009* Within samples 576 1275.626 2.21463 99.08 ns 2000 Among samples 17 38.563 0.00519 0.25 FST = 0.003 ns Among grounds 2 4.142 -0.00096 -0.05 FCT = 0.000 ns Among samples within grounds 15 34.422 0.00580 0.28 FSC = 0.003 Within samples 770 1572.721 2.04249 99.75
2001 Among samples 11 36.747 0.02953 1.38 FST = 0.014* ns Among grounds 2 7.334 0.00300 0.14 FCT = 0.001 Among samples within grounds 9 29.413 0.02760 1.29 FSC = 0.013* Within samples 492 1036.182 2.10606 98.62 ns 2002 Among samples 6 16.525 0.01319 0.59 FST = 0.006 ns Among grounds 2 5.856 0.00257 0.11 FCT = 0.001 ns Among samples within grounds 4 10.669 0.01120 0.50 FSC = 0.005 Within samples 275 611.741 2.22451 99.41 ns 2003 Among samples 5 5.736 -0.01732 -0.87 FST = 0.000 ns Among grounds 1 2.220 0.01065 0.54 FCT = 0.005 ns Among samples within grounds 4 3.516 -0.02309 -1.16 FSC = -0.000 Within samples 294 587.924 1.99974 100.87
2004 Among samples 7 33.503 0.06576 2.93 FST = 0.029* ns Among grounds 1 7.390 0.03038 1.34 FCT = 0.013 Among samples within grounds 6 26.113 0.05405 2.39 FSC = 0.024* Within samples 312 678.728 2.17541 97.07 a ns 2005 Among samples 6 17.615 0.01611 0.74 FST = 0.007 Within samples 327 711.604 2.17616 99.26 ns 2007 Among samples 9 21.971 0.00522 0.24 FST = 0.002 ns Among grounds 1 2.757 0.00143 0.07 FCT = 0.001 ns Among samples within grounds 8 19.214 0.00450 0.21 FSC = 0.002 Within samples 538 1161.317 2.15858 99.76 a Only one ground was sampled in 2005.
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None of the exact tests of differentiation performed on pair-wise comparisons between samples for cytochrome b sequences were significant (P = 1.000 ± 0.000; 30,000 Markov Chain steps) at any hierarchical level: grounds, years, and grounds per year. The network relating the molecular relationships among the 13 cytochrome b haplotypes observed in the whole dataset, showed eight secondary haplotypes differing in 1 to 3 substitutions from the majority haplotype (Mmcytb.1), and four tertiary haplotypes derived from secondary haplotypes (Figure 1).
Figure 1. Median-Joining network illustrating the molecular relationships among the 13 cytochrome b haplotypes so far observed in the whole sampling set.
The number of gene pools inferred from Bayesian simulations upon multilocus genotypes was k = 1 (Figure 2) with a frequency of 0.6. The analysis of the number of gene pools per year sampled (among grounds) resulted in k = 1 over years, except 2004 (k = 2), its frequency being 0.6 in all cases. Tests on the number of gene pools calculated with BAPS for the whole period, as well as per year, also resulted in k = 1, except k = 2 observed in 2004.
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Figure 2. (A) South hake fishing grounds sampled in period 1976, 2000 - 2005 and 2007; (B) posterior probability of samples belonging to a single gene pool (k = 1).
Genetic diversity and population trends
All microsatellites assayed were highly polymorphic and the number of alleles per locus ranged between 18 in locus Mmer-UEAhk3b and 54 in locus Mmer-UEAhk9b (Table 3). One hundred and sixty one alleles were scored in the 1,796 individuals composing the sample set. The average number of alleles per locus did not differ at any level tested: among years (χ = 1.850; P = 0.968), among grounds (χ2 = 1.926; P = 0.382) or among grounds per year (χ2 =
13.672; P = 0.690). None of the genetic parameters calculated (A, HE and RS) differed significantly among years (1976 - 2007) or between the three fishing grounds studied (south
Biscay Bay, central-western Cantabrian Sea and Atlantic Iberia). Significant differences in HE and RS were observed per ground and year, i.e. between Cantabric 2004 and the rest of year samples. Also significant A and RS differences were observed within years between grounds, i.e. between Atlantic Iberia and south Biscay Bay in 2002. Putative null allele frequencies across samples were lower than 5% in loci Mmer-hk9b, Mmer-hk20b and Mmer-hk3b, and averaged 12.28% in locus Mmer-hk34b and 18.18% in locus Mmer-hk29b. The estimated amount of null alleles was less than 10% in loci Mmer-hk9b, Mmer-hk20b and Mmer-hk3b per year, per ground or per ground and year. Genotypic adjustment to Hardy-Weinberg expected proportions was observed for loci Mmer-hk9b, Mmer-hk20b and Mmer-hk3b. Markers Mmer- hk29b and Mmer-hk34b showed a significant deviation from Hardy-Weinberg expectations in all samples (Locus Mmer-hk29b, average FIS ± SD = 0.415 ± 0.162; locus Mmer-hk34b, average FIS ± SD = 0.292 ± 0.119). Hardy-Weinberg deviations of those two loci were also
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Chapter IV observed in all the hierarchical levels tested, e.g. ground and year (Table 3). The Wilcoxon sign-rank test implemented in Bottleneck (Luikart et al., 1998) showed that pooled samples from 2000 - 2002 (two tailed P = 0.09) and from 2005 - 2007 were at mutation drift equilibrium under the two-phase-mutation model. Opposite, samples from years 1976, 2003 and 2004 showed a high heterozigosity excess. None year sample was at mutation drift equilibrium under an infinite allele model.
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Hardy size(Range range A, thein bp), theallelic(Rs), richness expected andareaFor each Note: locus individuals arethe given number of (N), genotyped allelesthe number of mean allelethe (A), s 2000 Table 3.
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Genetic parameters for five microsatellite loci analyzed on 1,833 hakes from three fishing fishing three from hakes 1,833 on analyzed loci five microsatellite for parameters Genetic
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Chapter IV
The 428 bp fragment of cytochrome b was characterized by a 46.5% GC content and a ratio Ti / Tv = 5.61. The 12 polymorphic positions consisted in six unique substitutions (sites 49, 125, 212, 217, 233 and 268) and six parsimonious substitutions (sites 92, 181, 199, 259, 291 and 412). Those polymorphic sites detected in the fifty-five sequences dataset resulted in thirteen different haplotypes (Table 4). The highest frequency (52.7%) corresponded to haplotype Mmcytb.1 while the others showed an average frequency of 3.93 ± 3.63 (Table 4). Haplotype
Mmcytb.1 was detected in all the samples analysed in this study. Haplotype diversity (Hd) and nucleotide diversity (Pi) were 0.702 ± 0.063 and 0.00328 ± 0.00052, respectively. Fu & Li and Tajima´s statistics showed that nucleotide polymorphisms were compatible with a neutral segregation (Fu and Li´s D* = -1.29, K > 0.10; Fu and Li´s F* = -1.55, P > 0.10; Tajima´s D = -1.36, P > 0.1). However, Fu´s parameter was significant under the null hypothesis of neutrality (Fs = -6.93, P = 0.001).
Table 4. Variable nucleotide positions and global frequencies for thirteen haplotypes of the cytochrome b gene (465 bp) observed in the Southern hake Stock in period 2000 – 2007.
Variable nucleotide position
1 1 1 2 2 2 2 2 2 4 4 9 2 8 9 1 1 3 5 6 9 1 Haplotype 9 2 5 1 9 2 7 3 9 8 1 2
Mmcytb.1 C G T G T G G G T A A G Mmcytb.2 . . . A ...... Mmcytb.3 . . . . C . . . C . . A Mmcytb.5 ...... A . . . . . Mmcytb.12 . T ...... Mmcytb.8 . . . . . A ...... Mmcytb.11 . . . A . . . . . G . . Mmcytb.13 . T . . C . . . C . . A Mmcytb.14 . T ...... G . Mmcytb.15 . . C ...... Mmcytb.16 G . . A ...... Mmcytb.17 ...... G . Mmcytb.18 ...... A . . . .
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Chapter IV
Effective population size
Estimates of population genetic effective size (Ne) performed with three methods used did not differ significantly among each other (Table 5) (F = 2.906; P = 0.079). The ratio between Ne and the census size (N) was also calculated for each Ne method (Table 5). A similar evolution trend was observed between Ne and several assessment indices or the allele diversity (Figure 3). A significant correlation was observed between LDNE and fishing mortality or allele diversity (Table 6).
Table 5. Estimates of the population genetic effective size (Ne) (MLNe (Wang and Whitlock, 2003), TempoFs (Jorde and Ryman, 2007) and LDNe (Waples and Do, 2008) in the period (1976, 2000 - 2005 and 2007), their 95% confident intervals (between parentheses) and their ratio to census size (Ne/N). SSB (in metric tons): spawning stock biomass (ICES, 2011). Census size (N) was estimated after SSB taking as a proxy to fresh body weight of 2 kilograms per adult hake (0.5-3.5 kg).
Interval MLNe MLNe/N TempoFs Fs/N LDNe LDNe/N SSB
1976-2000 2909 (2085 – 4221) 2.24*10-4 1294 (758 –4409) 9.97*10-5 12480 (1599 - ∞) 6.16*10-4 40500
2000-2001 261 (189 – 403) 5.30*10-5 78 (54 – 144) 1.58*10-5 3019 (1255 - ∞) 6.22*10-4 9700
2001-2002 321 (181 – 1082) 6.29*10-5 160 (83 – 2114) 3.14*10-5 3201 (1292 - ∞) 6.04*10-4 10000
2002-2003 302 (212 – 494) 5.84*10-5 231 (103 - ∞) 4.46*10-5 933 (500 – 5022) 1.79*10-4 10400
2003-2004 226 (133 – 580) 4.37*10-5 104 (59 – 457) 2.01*10-5 611 (380 - 1430) 1.19*10-4 10300
2004-2005 314 (170 – 1328) 5.90*10-5 78 (46 – 271) 1.46*10-5 291 (227 - 395) 5.60*10-5 10400
2005-2007 278 (191 – 452) 4.39*10-5 112 (56 – 37084) 1.77*10-5 1203 (620-10493) 2.21*10-4 10900
2007 - - - - 3191 (1296 - ∞) 4.34*10-4 14700
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Chapter IV
0.9 16000
F
0.8 14000
NA) SSB
02 - 0.7 12000
0.6
10000
0.5 8000
0.4 size size (LDNe) 6000 0.3 Na 4000 0.2
0.1 LDNe 2000
Fishing mortality (F) and number Fishing mortality and (F)number allelesof(10 Spawning Stock Biomass (SSB) and Effectivepopulation
0 0 2000 2001 2002 2003 2004 2005 2006 2007 Years
Figure 3. Estimates of assessment parameters (SSB, spawning stock biomass ◊; F, fishing mortality
∆) and assessment- free genetic parameters (NA, number of alleles ○; Ne, population genetic effective size □) over the last decade.
124
Chapter IV
F* F LDNe
2000 period Table 6.
*These regressions wereregressions performed historical fromon*These data period (1982
F R 16920xy = - LDNe
Linear regression between population between regression Linear
2
= 10.1441; =
= 0.6698 =
-
2007.
–
11064
P
= = 0.0244
- F R 4E y = F
2
= 10.1441; =
= 0.6698 =
-
05x + 0.6886
P
genetic
= = 0.0244
effectiveor (LDNe) size
F R y = F R 9194.7x 3935.5y = + F R 0.4387x 10134 y= + SSB
2 2
2
=0.6481; =0.6283;
= 67.6536; =
-
=0.1147 =0.1116
= 0.7224 =
2008) after ICES 2008) Advice2011 (ICES, 2011)
-
52.599x + + 52.599x 57.058
P P
P
=0.4573 =0.4639
= = 0.0000
fishing mortality (F) with either assessment parameters or number of alleles in the the ofalleles in or number parameters assessment either with (F) mortality fishing
F R 29.721x 59.065y = + F R 28.353x 66.109y = + F R 0.006x+ y= 77.038 R
2 2 2
=0.9575; =0.0099; =0.1940;
=0.0355 =0.002 =0.0374
P P P
=0.3368 =0.9245 =0.6780
F R 12.904x y = F R y= F R 6E y= R/SSB
2 2
2
=0.1925; =0.0050;
= 29.8551; =
=0.0371 =0.001
= 0.5345 =
-
7.6861x 7.6861x 13.643 +
-
05x 7.7004 05x +
P P
–
P
3.2747
=0.6791 =0.9463
= = 0.0000
F R y= F R + 0.0009x y = 23.176 Na
= 0.4653 2
2
=4.3510;
= 12.4728; =
= 0.7138 =
14.972x 14.972x 13.408 +
P
P
=0.0914
= = 0.0167
125
Chapter IV
Discussion
This study was focused on the temporal composition of the genetic diversity and the differentiation among temporal samples of European hake from the Southern Stock. Several estimators of the population genetic effective size were addressed too, as well as their implications in the management of this species. For those purposes, 1) a historical reference sample was included in the sample dataset, 2) a large spatio-temporal sampling effort was done, making use of 82 samples collected in the last decade (22.35 ± 6.30 individuals per sample; 10.25 ± 3.99 samples per year), and 3) two distinct types of DNA markers were used, i.e. nuclear DNA microsatellites and mitochondrial DNA cytochrome b nucleotide sequence. Despite the statistical power of the present dataset to rejecting panmixia was reasonably high under distinct priors and methods, a large genetic homogeneity dominated the Cantabrian scenario. Therefore, such scenario was incompatible with an IBD model due to a high gene flow and the short oceanographic distances among samples.
Genetic differentiation
The genetic differentiation observed among 82 temporal samples of the Southern hake Stock from northern Iberia (FST = 0.011) was less than the average reported for 57 fish species (FST = 0.062 ± 0.011) (Ward, 2006) but in the range reported for other northeast Atlantic gadoids
(e.g., Ryan et al., 2005), including the European hake (i.e., four Atlantic samples, FST = 0.011, Lundy et al., 1999). Such molecular variation among Atlantic Iberian samples was also congruently similar to the divergence reported among samples of the two Atlantic hake stocks using microsatellites (FST = 0.013, Pita et al., 2011). Therefore, the present level of genetic variation suggests a high interpopulation connectivity all across northern Iberian hake fishing grounds. The weak but significant molecular variation (0.90%) observed among samples was due to the divergence of outliers, such as sample 83 from 2004. In addition, the significant molecular variation between grounds and years was likely due to sample 83 too, as judging from its statistical departure in several parameters. That divergence of sample 83 was made patent in various analyses, e.g. its highest pairwise FST values or two gene pools (k = 2) in 2004 (see below). Aside from that exception, the absence of any significant variation among grounds suggests a wide connectivity among them. Therefore, present data support the connectivity previously reported to exist between the two Atlantic hake stocks, i.e. the Northern Stock (i.e. Porcupine Bank and Great Sole) and the Southern Stock (i.e. Biscay Bay) (Pita et al., 2011) or throughout the Atlantic façade (Chapter II). The two parameters used to
126
Chapter IV measure genetic differentiation are being widely tested nowadays regarding the meaning of its differential magnitude when applied to large biological datasets. Although the highest values of D were not coincident with those of FST, both parameters correlated highly significantly under any spatial or temporal frame, thus appearing to be equivalent at least at the intraspecific level (Schunter et al., 2011).
Genetic diversity
The heterozygote deficit observed in loci Mmer-hk29b and Mmer-hk34b was congruent with that reported in previous studies (e.g., Lundy et al., 2000, Pita et al., 2011). However, the putative causal explanations for such deficit in hake provided by Lundy et al. (2000) (assortative mating) or Nielsen et al. (2006) (hitch-hiking) remain difficult to endorse without further evidence. Once excluded technical allele artefacts, the existence of multiple null alleles co-segregating at low-frequency is a parsimonius explanation to the absence of null- null homozygotes. Although a heterozygote deficit diminishes the differentiation signal in exact tests (Chapuis and Estoup, 2007), it is an assumable bias in population studies provided that the interpopulation genetic divergence is low and the molecular variation is evenly underestimated for the same null alleles in close populations (e.g., Diz and Presa, 2008).
Parameters of genetic variation showed range values consistent with previous studies on this species. The average number of alleles across loci (32.20 ± 13.31) was the highest value observed among studies, probably due to the large number of individuals analyzed (1,796). Nevertheless, no significant differences were observed in the number of alleles either across studies (χ2 = 0.880; P = 0.830) or among Atlantic samples analyzed in different studies (χ2 = 1.145; P = 0.766) (see Lundy et al., 1999; Pita et al., 2011; Pita et al., see previous chapters). The absence of significant differences among samples pooled per year, confirms the consistent homogenization of the population among grounds of the Southern Stock as suggested by Pita et al. (2011). Nevertheless, some significant differences between grounds per year might be due to a low number of individuals per sample, to the dispersal mode of early-life stages in this species (Casey and Pereiro, 1995), to the recruitment processes in the Iberian shelf (Alvarez et al., 2004), or to specific oceanographic conditions off the Cantabrian coast (Alvarez et al., 2010). Those causal factors could also underlie the differences observed between the 2004 sample and all the rest of samples, but are unaffordable here. Apart from that outlier sample, the absence of significant genetic differences among years suggests a consistent genetic homogenization of annual cohorts in the Bay of Biscay.
127
Chapter IV
As hypothesized above, no genetic erosion was expected in the Southern under recurrent and significant migration from the Northern hake Stock (Pita et al., 2011). Opposite, if the present Southern hake Stock is self-contained and has been intensively overexploited for decades, we would expect a significant loss of genetic diversity. Similarly to the present lack of temporal genetic erosion in hake has also been found in Raja clavata samples from the Irish Sea that were collected in the last 40 years (Chevolot et al., 2008), as well as in Baltic cod, Gadus morhua, populations (Poulsen et al., 2006). Nevertheless, the large gap of 24 years of the present study (1976 - 2000) together with evidence of historical overexploitation of the Southern hake Stock, advises against ruling out the findings of Hutchinson et al. (2003) on Atlantic cod, consisting of a previous collapse of the genetic diversity and its posterior recovery.
Observed gene diversity in pooled samples per years 1976, 2003 and 2004, was higher than the expected one under the mutation-drift equilibrium. This observation could represent an evidence of a recently bottlenecked population under the assumption of a constant-size population model (Luikart et al., 1998). Observed differences among models regarding the mutation-drift equilibrium can be due to the better fit of most microsatellite datasets to the TPM model rather than to SMM or IAM models (Di Renzo et al., 1994). No other model test was applied since the present one provided high power using at least 4 polymorphic markers and any given number of individuals. In addition, the global result obtained with cytochrome b sequences supported a mutation-drift equilibrium status for this Atlantic hake population and its demographic expansion in last decade, as judging from the significance of the Fu-test on the neutrality of this mitochondrial polymorphism.
Management implications
The consistent single gene pool (k = 1) observed across years and grounds in the southern population of the European hake fits well the most common definitions of stock (Ihssen et al., 1981; Smith et al., 1990). The outlier result observed in 2004 (k = 2) could be due to specific oceanographic trends in that year. However, the significant genetic divergence of this sample is likely due of its low sample size. The single genetic pool recovered with all Bayesian methods applied so far is in agreement with previous studies on the dispersal pattern of hake juveniles along the Cantabrian shelf (Sánchez and Gil, 2000), indicating a large connectivity over the whole Bay of Biscay. In fact, stable population differences are only expected to accumulate in response to strong and consistent barriers to gene flow (Palumbi, 2003) but
128
Chapter IV very low gene flow is required to homogenize populations (Hartl and Clark, 1989). The global connectivity and panmixia found in the whole Atlantic range of this species (Pita et al., 2011) suggests that hake populations should be managed as a unique stock in the NE Atlantic. This view is congruent with the definition of stock after Ihssen et al. (1981) as well as with those proposed by Jamieson (1973) and Ovenden (1990) who take into consideration the real population structure. Noteworthy, as in hake, other fish stocks from the NE Atlantic, such as Gadus morhua, Merlangius merlangus or Clupea harengus, are not being managed according to its true genetic population structure but under other non-genetic criteria (Reiss et al., 2009).
The no significant differences observed among estimates of Ne across models had to be taken with care, i.e. their cross-comparison would be improperly done since they refer to different time frames and spatial scales. Those estimates had also been computed upon different measures of genetic change, such as loss of heterozygosity or changes in allele frequencies (Schwartz et al., 1998; Luikart et al., 2010). No consensus exists on the minimum population genetic effective size to ensure maintaining the genetic diversity of a fish population. Primarily, 50 and 500 breeders were suggested to ensure short term and long term variability, respectively (Soulé and Wilcox, 1980). However, Lynch and Lande (1998) proposed a Ne of 1,000 - 5,000 to maintain evolutionary flexibility as well as to avoid the accumulation and fixation of mildly deleterious mutations. Following those figures, the genetic variability of the Southern hake Stock could be considered sustainable in the short term. Noteworthy, the high reduction of this parameter estimated between the historical sample of 1976 and year 2000 could represent an important evidence of overfishing. The ratio Ne/N used a N value inferred upon SSB estimates (SSB/adult medium weight) and could bear a large bias. Nonetheless, Ne appears to be at least 5,000 times less than N, what is consistent with Ne values estimated in Pagrus auratus (Hauser et al., 2002). Otherwise, the variation of the ratio Ne/N in natural fish populations is an important pending question (Kalinowski and Waples, 2002). The significant positive regression between LDNe and Na was expected because the two parameters were estimated from the same dataset. However, that between LDNe and fishing mortality F although expected too, is a very interesting observation between two independent estimates, one from genetics and the other from assessment. This relationship is first described in hake and tells about the potential application of Ne estimates in management due to its realism at predicting harmful mortalities for the sustainability of this fishery. Systematic and temporal estimates of the population genetic effective size in the Southern hake Stock should be incardinated into its genetic monitoring program (Schwartz et al., 2007) and be considered in the management of this fishery.
129
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General discussion and conclusions
General discussion
Population dynamics is the branch of life sciences that addresses short-term and long-term changes in size and age composition of populations, and the biological and environmental processes influencing those changes. Population dynamics deals with the way populations are affected by birth and death rates and by immigration and emigration, and it studies topics such as ageing populations or population decline. The concept of metapopulation was proposed by Richard Levins in 1970 as a “population of populations” and came to fix some problems of classic models of population dynamics, such as the existence of gene flow between adjacent populations, revealing that the majority of populations are more variable entities and susceptible to change. Population dynamics is especially relevant in marine fished resources because some of the parameters studied within this discipline are used in the assessment and management of fisheries. Although population dynamics is still dominated by ecologists, other disciplines such as genetics have a huge potential to solve many concerns of this discipline.
This PhD thesis aimed to take a step forward on some recurrent problems in population dynamics of the European hake. Therefore, the use of genetic metrics is badly vindicated as a tool to shed light on the still many unclear aspects of population assessment processes. For instance, the effective population size, which is far more informative than the census population size regarding the spatio-temporal sustainability of a given fishery. The application of population genetics to marine resources also allows to delimitating the spatial distribution of their populations, even if its limits are only temporally stable, representing a new dimension in classic population approaches. In addition, population genetic tools allow to testing the metapopulation concept, e.g. does a fishery consist of a swarm of isolated populations or is there a wide connectivity pattern homogenizing the whole fishery? Questions like this one can be solved quickly upon the right choice of both, molecular markers and appropriate sampling designs. Genetic answers will surely improve present knowledge on fishery dynamics as well as the understanding of how fisheries are affected by human overexploitation. Sustainable management plans for most overexploited fisheries are nowadays sound challenges in Fishery Science. Its implementation should allow to reaching a balance between the maintenance of gene diversity and the sustainability of local fishing economies.
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General discussion and conclusions
Counter intuitively, one of the most important aspect to achieve the goals put forward in this thesis is the experimental design. On the one hand, the large sampling effort carried out on this species from the North Sea to the Canary Islands in the Atlantic, and from Gibraltar Strait to the Aegean Sea in the Mediterranean, covers most fishing grounds of this species in Europe. In addition, the largest fishing ground of Spanish waters, i.e. the Northwestern Cantabrian fishery, was sampled thorough and through over the last decade to obtain a valuable temporal scale supportive of significant conclusions. On the other hand, the two type of molecular markers used in this thesis allowed to analyzing distinct evolutionary scales. Rapidly mutating microsatellites inform about recent population processes (i.e. gene diversity swifts and gene flow trends), while the slow mutation pace of cytochrome b sequence tells on historical differentiating events. Altogether, the large sampling effort and range covered, and the temporal frame focussed have made possible a finer resolution of the genetic structure of the European hake. The genetic structure of the European hake can be decomposed into two main basin-related axes. The use of both type of molecular markers, microsatellites and mitochondrial cytochrome b have unveiled significant differences between the Atlantic Ocean and the Mediterranean Sea (Roldán et al., 1998; Lo Brutto et al., 1998, 2004; Lundy et al., 1999; Castillo et al., 2005; Cimmaruta et al., 2005; Pita et al., 2010; Nielsen et al., 2012) at the Almeria – Oran Oceanographic Front, a well-known natural barrier to gene flow between basins (see examples in Patarnello et al., 2007). Also microsatellites made feasible the calculation of the approximate coalescent time since that regional divergence begun (150,000 years before present) and allowed to determining the current validity of this barrier. The combined use of microsatellites and saturated sampling designs has put forward an important change in previous scenarios for the Atlantic hake, i.e. from a complex population structure in the Atlantic (Roldán et al., 1998; Lundy et al., 1999; Castillo et al., 2004) to a wide genetic homogeneity. Opposite to classic views, the spatio-temporal gene flow analyses performed have unveiled a consistent migration between Porcupine Bank and Great Sole to adjacent fishing grounds and reaching up to Canary-Saharan waters. Congruently to those results, the new Bayesian landscape algorithms clustered all Atlantic samples in a single gene pool, independently of their membership to areas, subareas or other hierarchical levels enforced according to their geographical origin.
The relevance of having determined the existence of a single gene pool in the Atlantic Ocean resides in the improvements that could be enforced in management plans for this heavily exploited species (ICES WGAGFM, 2012). The Atlantic genetic homogeneity contrasts with the Cape Breton Canyon being an active barrier to gene flow, a fact considered causative of
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General discussion and conclusions the separation between Atlantic stocks (Casey and Pereiro, 1995). By the same token, the identification of core sources of biomass exportation in south-western Ireland waters advocates for a multidisciplinary approach where ecological data, genetic information and oceanographic patterns were enforced in a wiser assessment, together with current fishing parameters (Pita et al., 2011). The maintenance of gene diversity in Cantabrian fishing grounds during the last decade, when recruitment and spawning stock biomass were sank into their historical minimums, was a good proof for migration being enforced on the Southern Stock. In our view, new management policies should consider this new reality of a Northern hake Stock sustaining the whole Atlantic population. Also, the population recovery from 2004 on, after a dramatic depletion of the population genetic effective size in the beginning of last decade, was another evidence of such ongoing gene flow and a major result put forward in this thesis.
Noteworthy, the change to a management model based on the real number of gene pools, requires a temporal stability of its genetic structure. Such temporal stability in the whole Atlantic range has not been fully addressed in this thesis because it exceeded its main goals. Nevertheless, it has been tested over the main fishing grounds of the Southern stock. The effective genetic population size of the Southern hake Stock correlated quite tightly with other assessment parameters, but perhaps the relationship between LDNe and fishing mortality F is a very interesting observation between two independent estimates, one from genetics and the other from assessment. This relationship is first described in hake and tells about the potential application of Ne estimates in management due to its realism at predicting harmful mortalities for the sustainability of this fishery.
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General discussion and conclusions
Conclusions
This thesis on the spatio-temporal population dynamics of the European hake can be summarized in the following conclusions:
The microsatellite markers used showed a large connectivity among Atlantic hake populations, deciphering for the first time in this species the direction and intensity characterizing its connectivity pattern. It is also remarkable the divergence time established upon the mitochondrial clock in 150,000 yr BP, providing molecular evidence of the barrier to gene flow imposed by the Almeria – Oran Oceanographic front. This divide can have important diversions on domestic stocks of this species regarding putative beneficial effects of heterozygosis.
The absence of genetic differentiation between Atlantic hake populations together with other collateral insights, such as the panmictic scenario, the migration pattern, the recruitment dynamics, or the recovery of the population genetic effective size, suggest that any biological distinction should be made between stocks of this fishery. Therefore, a single management plan should be implemented to properly protect its reproduction areas in the Atlantic, which are responsible for the dissemination of biomass towards the rest of Atlantic fishing grounds.
The gene diversity of European hake population from the Northwestern Iberian Peninsula seems to have remained high enough during the last forty years despite the overexploitation exerted on this fishing ground. This phenomenon is congruent with the existence of systematic migrations from the Northern hake Stock and is well depicted throughout temporal estimates of the population genetic effective size. Therefore this genetic parameter is proposed here to be incorporated into assessment and the genetic management of this fishery.
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SCIENTIFIC PRODUCTION
Scientific production
Articles indexed in the SCI
Pita, A., Presa, P., Pérez, M. 2010. Gene flow, multilocus assignment and genetic structuring of the European hake (Merluccius merluccius). Thalassas: An International Journal of Marine Sciences, 26(2): 129-133.
Pita, A., Pérez, M., Cerviño, S., Presa, P. 2011. What can gene flow and recruitment dynamics tell us about connectivity between European hake stocks in the Eastern North Atlantic? Continental Shelf Research, 31: 376-387.
Articles submitted to SCI-indexed journals
Pita, A., Pérez, M., Balado, M., Presa, P. Out of the Celtic Cradle: The Genetic Signature of European Hake Connectivity in the Atlantic North. Submitted, April 2013.
Pita, A., Pérez, M., Velasco, F., Presa, P. How did the genetic effective population size relate to spawning stock biomass and fishing mortality of the southern European hake population during the last decade? Submitted, May 2013.
Pita, A., Leal, A., Santafé, A., Piñeiro, C., Presa, P. Gene flow quantification between European hake (Merluccius merluccius) Atlantic stocks over the last decade. Submitted, May 2013.
Proceedings
Pérez, M., Pita, A., Presa, P. 2011. Utilidad del análisis genético de poblaciones en la definición de unidades reproductivas y de gestión de la merluza europea (Merluccius merluccius). En: Saborido-Rey et al., (Eds.), Actas I Simposio Iberoamericano de Ecología Reproductiva, Reclutamiento y Pesquerías. Vigo, p. 386-389.
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Scientific production
Symposia
II International Symposium in Marine Sciences. Vigo (Spain), 27-30 April 2009.
o Pita, A., Presa, P., Pérez, M. 2009. Gene flow, individual assignment and genetic structuring of SW European hake (Merluccius merluccius) stocks, p. 142-143.
XXXVII Congreso de la Sociedad Española de Genética. Torremolinos (España), 29 de septiembre a 2 de octubre de 2009.
o Pita, A., Pérez, M., Presa, P. 2009. La compensación entre deriva por sobrepesca y migración entre caladeros explica la estabilidad interanual de la diversidad genética en la merluza europea, Merluccius merluccius, ref. PS156.
Symposium on Rebuilding Depleted Fish Stocks – Biology, Ecology, Social Science and Management Strategies. Warnemünde (Germany), 3-6 November 2009.
o Pérez, M., Pita, A., Presa, P. 2009. Ongoing gene flow between fisheries of the Southern hake stock (Merluccius merluccius), p. 70.
o Pita, A., Presa, P., Pérez, M. 2009. Management implications of gene flow estimates between fisheries of the Northern hake Stock (Merluccius merluccius), p. 71.
I Simposio Iberoamericano de Ecología Reproductiva, Reclutamiento y Pesquerías. Vigo (Spain), 24-28 November 2009.
o Pérez, M., Pita, A., Presa, P. 2009. Utilidad del análisis genético de poblaciones en la definición de unidades reproductivas y de gestión de la merluza europea (Merluccius merluccius), p. 78-79.
o Pita, A., Pérez, M., Saborido-Rey, F., Presa, P. 2009. Evolución interanual de la diversidad genética de la merluza europea (Merluccius merluccius) en el caladero Cantábrico Noroeste de la Península Ibérica, p.79-80.
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XVIII Seminario de Genética de Poblaciones y Evolución. Guitiriz (España), 5-7 de Mayo de 2010.
o Pérez, M., Pita, A., Cerviño, S., Presa, P. 2010. Hacia la integración de los enfoques poblacional, genético y ecosistémico en la gestión de pesquerías de merluza europea, p. 27.
ICES Annual Science Conference. Nantes (France), 20-24 September 2010.
o Pita, A., Pérez, M., Presa, P. 2010. The historical genetic drift due to overfishing is compensated by the large effective sizes and migration rates of southern hake stock populations, ref. M: 26.
ICES Annual Science Conference 2011. Gdansk (Poland), 19-23 September 2011.
o Pita, A., Pérez, M., Velasco, F., Presa, P. 2011. The pattern of genetic structuring in the European hake is highly dependent on the Bayesian model enforced but insensitive to the differentiation parameter used, p. 93.
o Pita, A., Leal, A., Piñeiro, C., Presa, P. 2011. Shifts in the genetic structure of the European hake metapopulation as fingerprints of migration between fishery grounds, p. 123.
ICES/NAFO Decadal Symposium 2011. Santander (Spain), 10-12 May 2011.
o Pérez, M., Pita, A., Presa, P. 2011. Population differentiation and gene flow in the North Atlantic range of the European hake (Merluccius merluccius), ref. 115.
o Pita, A., Pérez, M., Velasco, F., Presa, P. 2011. Global connectivity and interanual fluctuation of genetic diversity in the southern hake population during the last decade, ref. 116.
International Symposium in Marine Science 2012. Cádiz (Spain), 24-26 January 2012.
o Pita, A., Presa, P., Pérez, M. 2012. Conectividad de la merluza europea: de macro a micro, p. 56.
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XIII International Symposium on Oceanography of the Bay of Biscay, ISOBAY13. Santander (Spain), 11-13 April 2012.
o Pita, A., Pérez, M., Velasco, F., Presa, P. 2012. Migration explains the maintenance of the southern hake population despite overexploitation, p. 134.
o Pérez, M., Pita, A., Presa, P. 2012. The Celtic Sea is the multidirectional focus for the expansive dispersal of hake (Merluccius merluccius) in the Atlantic, p. 135.
o Pita, A., Leal, A., Piñeiro, C., Presa, P. 2012. Genetic pattern of connectivity in the hake metapopulation of Biscay Bay, p. 158.
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