Identificación de factores de la anadromía en trucha

Transcription

Departamento de Bioquímica, Genética e Inmunología
Área de Genética
Identificación de factores de la
anadromía en trucha
(Salmo trutta) y su aplicación a
programas de conservación
Memoria presentada por Francisco Marco Rius para optar al Grado
de Doctor por la Universidade de Vigo
Vigo, Mayo de 2013
!
Identificación+de+factores+de+la+anadromía+en+trucha+(Salmo&trutta)+
y+su+aplicación+a+programas+de+conservación
Francisco+Marco+Rius+
FINANCIACIÓN Y BECAS
Esta tesis doctoral ha sido parcialmente financiada por los siguientes
proyectos de investigación:
•
Título: “Bases ecológicas y genéticas para la recuperación de
poblaciones de reo (Salmo trutta)” financiado por el Ministerio de
Educación y Ciencia con referencia CGL2007-60572/BOS. Octubre
2007 - Septiembre 2010.
•
Título: “Estudios durante la etapa marina para la recuperación de
poblaciones de reo (Salmo trutta): Bases ecológicas e identificación
de secuencias reguladoras asociadas al comportamiento migrador”
financiado por el Ministerio de Educación y Ciencia con referencia
CGL2010-14964. Enero 2011 – Diciembre 2013.
Durante el desarrollo de la presente tesis doctoral, Francisco Marco Rius
ha disfrutado de las siguientes ayudas:
•
Ayuda Predoctoral de Formación de Personal Investigador con
referencia BES-2008-001973.
•
Contrato como investigador Predoctoral en el Departamento de
Genética de la Universidad de Vigo.
!
La presente tesis doctoral ha dado lugar a las siguientes publicaciones:
•
Marco-Rius, F., Caballero, P., Morán, P., & García de Leániz, C.
(2012). And the last shall be first: heterochrony and compensatory
marine growth in sea trout (Salmo trutta). PLoS ONE, 7(10),
e45528.
•
(2013). Mixed-effects modelling of scale growth profiles predicts the
occurrence of early and late fish migrants. PLoS ONE, 8(4),
e61744.
•
Morán, P., Marco-Rius, F., Megías, M., Covelo-Soto, L., & PérezFigueroa, A. (2013). Environmental induced methylation changes
associated with seawater adaptation in brown trout. Aquaculture,
392-395, 77–83.
•
Marco-Rius, F., Sotelo, G., Caballero, P., Morán, P. (2013). Insights
for planning an effective stocking program in anadromous brown
trout (Salmo trutta). Aceptado en Canadian Journal of Fisheries and
Aquatic Sciences.
•
Can migrants escape from density-dependence?. Aceptado en
Ecology and Evolution
!
ÍNDICE DE CONTENIDO
RESUMEN .......................................................................................................... 4!
INTRODUCCIÓN ................................................................................................ 8!
La trucha común .............................................................................................. 9!
Regulación de la pesca de trucha .................................................................. 10!
La anadromía en la trucha ............................................................................. 11!
El problema .................................................................................................... 12!
Trabajos incluidos en la presente tesis doctoral: unidad de coherencia
temática y objetivos........................................................................................ 14!
I. RELACIÓN ENTRE LA FASE DE AGUA DULCE Y AGUA MARINA ..... 15!
II. METODOLOGIA BASADA EN EL ANÁLISIS DE ESCAMAS ................ 15!
III. RELACIONES ENTRE LA ABUNDANCIA DE REOS Y LA
COMPETENCIA. ANÁLISIS TEMPORAL DE LAS POBLACIONES. ......... 16!
IV. DIRECTRICES PARA LA PLANIFICACIÓN DE UN PROGRAMA DE
REPOBLACIÓN DE REO ........................................................................... 17!
V. METILACIÓN Y ADAPTACIÓN AL AGUA DE MAR .............................. 19!
Referencias .................................................................................................... 20!
DISCUSIÓN ...................................................................................................... 31!
Introducción y antecedentes .......................................................................... 32!
Técnicas desarrolladas y utilizadas en la presente tesis doctoral ................. 35!
TÉCNICAS GENÉTICAS EMPLEADAS ..................................................... 36!
TÉCNICAS ECOLÓGICAS Y ESTADÍSTICAS EMPLEADAS ................... 38!
APORTACIONES DE LA PRESENTE TESIS DOCTORAL ....................... 39!
Perspectivas futuras....................................................................................... 44!
Referencias .................................................................................................... 48!
2!
!
CONCLUSIONES .............................................................................................. 57!
REPRINTS DE LAS PUBLICACIONES ............................................................ 60!
And the last shall be first: heterochrony and compensatory marine growth in
sea trout (Salmo trutta) .................................................................................. 61!
Mixed-effects modelling of scale growth profiles predicts the occurrence of
early and late fish migrants ............................................................................ 72!
Can migrants escape from density-dependence? ......................................... 80!
Insights for planning an effective stocking program in anadromous brown trout
(Salmo trutta) ................................................................................................. 92!
Environmental induced methylation changes associated with seawater
adaptation in brown trout ............................................................................. 110!
FACTOR DE IMPACTO Y CALIDAD DE LAS PUBLICACIONES .................. 118!
3!
!
RESUMEN
4!
!
RESUMEN
La trucha común, Salmo trutta, es una especie de salmonido que exhibe
diferentes comportamientos de migración dependiendo del ambiente. Algunas
poblaciones contienen juveniles que migran frecuente al mar mientras en otras,
los movimientos son muy restringidos y los individuos tienden a quedarse toda
la vida en agua dulce. El término migración parcial se usa para describir el
fenómeno por el cual la población se divide entre individuos migradores y
residentes. Normalmente, en ríos localizados en áreas costeras con acceso al
mar, la trucha anádroma o reo y la trucha residente viven en simpatría. Cuando
los adultos migradores vuelven al río de origen para el desove, las truchas y los
reos pueden reproducirse entre ellos y los juveniles crecen sin distinción alguna
en el mismo río. Cuando llega la época del esguinado, la decisión entre migrar
o permanecer en el río se toma cuando se excede cierto umbral de condiciones
ecológicas y genéticas, lo que representa un claro ejemplo de plasticidad
fenotípica.
Las poblaciones de trucha se someten continuamente a programas de
repoblación por el gran interés socio-económico de la especie. Lo que ocurre
es que estos programas de repoblación están destinados principalmente a las
poblaciones residentes. A pesar del gran interés del reo desde el punto de vista
recreativo, las repoblaciones no aumentan el numero de estos individuos
debido a que la domesticación ocurrida en la piscifactoría hasta la suelta
produce efectos sobre la capacidad de anadromía. Por ello, en los últimos años
la cantidad de reos se ha visto disminuida respecto a la cantidad de trucha.
Los objetivos principales de este trabajo han sido tratar de cuantificar las
condiciones genéticas y ecológicas que van a determinar la migración de la
trucha e intentar aplicar los resultados para llevar a cabo un programa de
5!
!
repoblación que aumente la fracción anádroma de los individuos soltados en
poblaciones naturales.
En primer lugar se realizó un control de los individuos migradores de seis
poblaciones naturales distintas con el objeto de observar diferencias entre ellas
y determinar la necesidad de condiciones particulares en la conservación de la
especie dependiendo de la cuenca. Además también se cuantificó la relación
en el crecimiento entre la fase de río y la de mar para conocer los efectos de la
migración sobre el crecimiento.
En segundo lugar, se observaron las diferencias en el crecimiento y las
relaciones denso-dependientes en una serie temporal de 13 años en dos
poblaciones. Esto permitió observar y cuantificar la relación del crecimiento con
la abundancia de individuos en la cuenca y su tendencia en los años
estudiados. La relación que existe entre el número de individuos y el
crecimiento resulta importante en la gestión de las repoblaciones, ya que
deben adaptarse los individuos repoblados a la capacidad de carga de la
cuenca donde son soltados. Además, a lo largo de este estudio se desarrolló
una técnica de análisis mediante el uso de escamas y métodos estadísticos
que permitió diferenciar los individuos migradores de los tardíos en función del
crecimiento juvenil.
Paralelamente, se realizó una repoblación mediante huevos plantados y
juveniles de un año de edad teniendo en cuenta ciertos parámetros genéticos,
como el tipo de reproductor usado en el cruce (migrador o residente) o el grado
de similitud entre el MHC (de las siglas en inglés Major Histocompatibility
Complpex) de los progenitores. Tras completar su ciclo vital, cierto número de
individuos fueron capturados antes de desovar en una estación de captura. Se
observó que los parámetros genéticos de los progenitores son clave para el
6!
!
aumento de la fase anádroma, aunque también se registraron individuos
residentes provenientes de todos los cruces.
Finalmente, como consecuencia de la necesidad de nuevas técnicas genéticas
para el estudio de la anadromía y su adaptación a técnicas de repoblación, se
desarrolló un estudio de metilación de los tejidos. Se comprobó como una dieta
rica en sal está relacionada con cambios en la metilación de las branquias, y a
su vez, como los individuos alimentados con esta dieta durante un tiempo
específico son capaces de sobrevivir mejor en agua salada. Por tanto, la
metilación es un proceso reversible que puede coordinarse con los programas
de repoblación para resultar en un diseño óptimo de estrategias para el
aumento de la fase anádroma.
7!
!
INTRODUCCIÓN
8!
!
INTRODUCCIÓN
La trucha común
La trucha común (Salmo trutta L.) es el pez más abundante en aguas
continentales de la región paleártica (Elliot 1994). La distribución geográfica de
esta especie se extiende de forma natural desde el océano Ártico (norte de
Noruega) y el mar Blanco (Rusia) hasta las montañas del Atlas en el norte de
África, y desde Islandia hasta el mar Aral en Afganistán y Pakistán.
La trucha muestra una considerable variación y plasticidad en muchos
aspectos de su morfología, ecología y comportamiento (Klemetsen y col. 2003;
Jensen y col. 2008; Fraser y col. 2011). La gran diversidad de adaptaciones
ecológicas y de comportamiento explica que durante mucho tiempo se haya
clasificado en más de 50 especies diferentes (Behnke 1972) atendiendo a su
forma, color, tamaño, etc. Todas las truchas tienen capacidad migradora,
aunque en función del lugar donde realizan su ciclo de vida (hábitat de agua
dulce o salada) se distinguen dos tipos: trucha residente y trucha migradora o
anádroma (reo; Jonsson y Jonsson 1993; Caballero y col. 2012).
Aunque todas las truchas que habitan en Europa pertenecen a una sola
especie, ésta presenta diferentes subespecies o variedades locales (GarcíaMarín y col. 1999; Bernatchez 2001; Ayllon y col. 2006). Parece claro que gran
parte de las poblaciones de trucha actualmente existentes en Europa se
originaron a partir de trucha migradora al mar, en una recolonización que tuvo
lugar hace unos 14.000 años, y que en los lugares en donde existen varios
tipos de trucha el estado ancestral reciente es la anadromía. Posteriormente
las poblaciones de los diferentes ríos y lagos evolucionaron de forma
independiente (Ferguson 2006).
9!
!
Las dos variedades de trucha que habitan en España, tradicionalmente
clasificadas erróneamente como subespecies y denominadas trucha común y
reo, son en realidad morfotipos (o fenotipos) que se han adaptado a unas
determinadas condiciones de vida y a unos ciclos biológicos concretos
(Caballero y col. 2012). La trucha marina o reo (tradicionalmente S.trutta trutta)
es un migrador anádromo que realiza la mayor parte de su ciclo vital en agua
dulce y en un momento dado de su ciclo de vida (en general a partir del
segundo año de vida en el río) esguina (se vuelve de color plateado) y se
desplaza a los estuarios para completar su crecimiento en un tiempo que oscila
entre unos pocos meses y años (Elliott 1994; Caballero y col. 2012). El reo se
puede encontrar en los ríos que drenan al Cantábrico y los que lo hacen al
Atlántico por encima de la latitud 42º N (río Limia en el norte de Portugal;
Bouza y col. 1999; Caballero 2006). La trucha común (tradicionalmente S. trutta
fario) es la variedad residente que desarrolla toda su vida en aguas dulces.
Esta variedad sólo migra aguas arriba durante la época de reproducción en
busca de lugares idóneos para la freza, pero siempre en aguas dulces. La
trucha permanece presente en muchos ríos de la península aunque se ha
extinguido en varios de ellos (Toledo y col. 1993; Hervella y Caballero 1999).
Regulación de la pesca de trucha
En Galicia, la trucha está sujeta a explotación regulada legalmente.
Únicamente está permitida la pesca con caña en el río, mientras que la pesca
en el mar bien con caña o con redes está totalmente prohibida y
económicamente penalizada. La ley que rige la pesca fluvial en Galicia es la
Ley 7/1992 de 24 de julio y el reglamento que la desarrolla, se rige por el
Decreto 130/1997 de 14 de mayo, por el que se aprueba el Reglamento de
Ordenación de la Pesca Fluvial y de los ecosistemas Acuáticos. El periodo
hábil de pesca oscila entre mediados de marzo y finales de agosto, existiendo
en los ríos zonas de veda (prohibición de pesca), zonas libres y cotos
10!
!
regulados. Para pescar es necesario estar en posesión de la correspondiente
licencia emitida por las diferentes comunidades autónomas. La diferencia entre
la trucha residente y la trucha anádroma es de tal magnitud que la mayoría de
los pescadores deportivos creen que se trata de dos especies diferentes, a lo
que contribuye sin duda la regulación de la pesca (a efectos de número de
capturas y tamaños mínimos) donde claramente se diferencia entre trucha y
reo a nivel administrativo.
La anadromía en la trucha
La anadromía es señal de cambios fisiológicos, ya que los requisitos para vivir
en agua dulce y agua salada son diferentes, así como los patrones de
alimentación y comportamiento (Caballero y col. 2006). Existen diferentes
evidencias de que existe una base genética para muchos de los cambios
mencionados, aunque la mejor explicación para la anadromía es considerarla
como un carácter cuantitativo controlado por múltiples genes y con influencia
del ambiente. Cuando la combinación de estos factores excede un valor
umbral, la consecuencia será la migración al mar (Jonsson y Jonsson 1993;
Ferguson 2006). Numerosos trabajos examinan y comparan la variabilidad
genética en muestras de trucha anádroma y residente del mismo río utilizando
alozimas, DNA mitocondrial y marcadores nucleares como mini y microsatélites
(Petersson y col. 2001; Hansen y col. 2002; Hovgaard y col. 2006). A pesar de
su gran interés, estos estudios presentan dos grandes problemas. El primero
de ellos es la posibilidad de que las truchas analizadas no sean siempre parte
de la misma población, ya que dentro de un río pueden coexistir varias
poblaciones, sobre todo en ríos con numerosos afluentes. El segundo de los
problemas recae en la dificultad para distinguir durante la fase juvenil a los
individuos residentes de los migradores (véanse criterios en Bagliniere 2000),
ya que al producirse el esguinado en diferentes épocas del año, una trucha
clasificada como residente en primavera podría ser migradora en otoño.
Aunque la mayoría de estos estudios concluyen que no existen diferencias
11!
!
genéticas entre las dos variedades de trucha, esto no se traduce de manera
directa en ausencia de base genética de la anadromía, ya que al tratarse de un
carácter cuantitativo umbral, los resultados obtenidos con marcadores
moleculares neutros no son contradictorios (Ferguson 2006). Además, existen
patrones de expresión génica diferenciados entre ambos tipos de trucha (Giger
y col. 2006) y también existen evidencias del control genético de
comportamiento anádromo en trucha y otras especies afines. Algunas de estas
evidencias son:
• Pérdida de anadromía en poblaciones con la construcción de barreras
físicas (Jonson 1982; Morita y col. 2009)
• Poblaciones de reo diferentes muestran diferentes patrones de
migración (Jonsson y L'Abee-Lund 1993; Jonsson y col. 2001)
• En estudios experimentales la progenie de reo produce más reo que la
progenie de trucha residente (Skrochowska 1969)
• Comportamiento migrador diferente entre machos y hembras de una
misma población (Fleming 1993; Caballero y col. 2012)
• Control genético de la dirección de migración (Nielsen y col. 2001)
• El patrón temporal de migración aguas arriba y abajo está determinado
genéticamente en el salmón Atlántico (Nielsen y col. 2001; Stewart y col.
2002)
El problema
En los últimos años las poblaciones de trucha en España han disminuido, tanto
en número como en tamaño e independientemente de la pertenencia a
poblaciones anádromas o residentes. La principales causas están envueltas en
esta diminución son la contaminación, las obras públicas (principalmente
construcción de presas y canalización de ríos), y la sobreexplotación. Esta
situación es general en los países industrializados, si bien hay que señalar que
12!
!
la situación de la trucha en la mayoría de los ríos españoles no es
preocupante. Con el objeto de restaurar o incrementar algunas de las
poblaciones, es habitual que en determinados ríos se realicen actuaciones
concretas como repoblaciones con individuos procedentes de piscifactoría,
transferencia de individuos de unas poblaciones a otras o cría en cautividad y
posterior suelta de alevines y juveniles del propio río. Estas actuaciones han
tenido un éxito desigual dependiente de los ríos y métodos utilizados (Morán y
col. 1991; Izquierdo y col. 2006; Almodóvar y col. 2006).
En las poblaciones de trucha que habitan en las partes bajas de los ríos
conviven y se reproducen individuos que completan todo su ciclo de vida en
agua dulce con aquellos que realizan migraciones al mar. En las repoblaciones
realizadas en estas zonas parece claro que las truchas que sobreviven
carecen, en su mayor parte, de componente migrador al mar, por lo que es
muy difícil con las metodologías utilizadas hasta la fecha, recuperar
poblaciones de reo. En muchos casos este fracaso es atribuible al stock
utilizado. Así por ejemplo en España durante muchos años se utilizó en la
repoblación un stock centroeuropeo de piscifactoría que, debido a su cría
continua en agua dulce, es posible que hubiera perdido su capacidad de
migración (Morán y col. 1991; Serrano y col. 2009). Lo sorprendente es que
cuando se reproducen en cautividad reos salvajes y sus alevines son criados
en piscifactoría durante un periodo inferior a un año, al utilizarlos para
suplementar los ríos, su capacidad de migración se ve enormemente
disminuida (Serrano y col. 2009). Está contrastado por experiencias en otros
países, que la repoblación, sobre todo si se utilizan stocks domesticados,
disminuye drásticamente el potencial de anadromía de las poblaciones salvajes
(Østergaard y col. 2003; Ruzzante y col. 2004) y que el simple hecho de
mantener las truchas en piscifactoría durante la etapa juvenil disminuye este
potencial, posiblemente debido a la alimentación (Glover y col. 2004). Por
tanto, parece claro que la trucha de repoblación no contribuye a aumentar la
fracción anádroma de las poblaciones de trucha con acceso al mar.
13!
!
Trabajos incluidos en la presente tesis doctoral: unidad de
coherencia temática y objetivos.
La presente tesis doctoral es un compendio de cinco publicaciones científicas,
de las cuales todas ellas han sido aceptadas. La tesis se realizó siguiendo la
normativa de la Universidad de Vigo y se centra en el estudio de las bases
ecológicas y genéticas de la anadromía en las poblaciones de trucha común.
Como se mencionó anteriormente, la relación entre ambas disciplinas (ecología
y genética) es clave para la obtención de nuevos conocimientos sobre el
fenotipo migrador de esta especie. Además, los estudios llevados a cabo en la
presente tesis doctoral sirven para sentar las bases de un programa de
repoblación enfocado a aumentar el fenotipo migrador de las poblaciones de
trucha. La tesis se puede dividir en dos bloques ligeramente diferenciados,
aunque totalmente dependientes entre ellos.
El primer bloque se centra en aspectos puramente ecológicos que ocurren
durante las primeras etapas de desarrollo de los individuos, tanto en el río
como en el mar. También se evalúan otros factores importantes como la
competencia denso-dependiente y su relación con la cantidad de individuos
presentes en el río o dispuestos a migrar. Ambos aspectos se estudiaron
utilizando técnicas de análisis de escamas. Paralelamente a los estudios
citados anteriormente, se desarrolló una nueva técnica para el análisis de las
escamas basado en el crecimiento reflejado en cada anillo depositado (Elliott y
Chambers 1996) y el análisis mediante modelos lineales de efectos mixtos
(Zuur y col. 2009). Con esta técnica analítica es posible diferenciar individuos
potencialmente migradores en las primeras etapas de agua dulce. Este método
se utilizó también para observar la variación en el crecimiento individual de los
14!
!
juveniles dependiendo de la cantidad de individuos adultos capturados antes
de la reproducción.
I. RELACIÓN ENTRE LA FASE DE AGUA DULCE Y AGUA MARINA
El crecimiento en etapas tempranas es, en muchas especies, un buen
indicador del crecimiento en etapas posteriores, ya que los juveniles con
tamaño superior a la media pueden tener ventajas competitivas (Mangel y
Stamps 2001). No obstante, en especies migradoras la relación entre el
crecimiento juvenil y adulto está poco claro. Varios estudios de campo revelan
diferentes presiones de selección para la talla en salmónidos anádromos en
diferentes ambientes (i.e. agua dulce y marina: Jonsson y col. 1991;
Scarnecchia y col. 1989). Estos datos sugieren la existencia de mecanismos de
compensación de crecimiento entre el río y el mar. Además, la relación entre la
talla de migración y la talla en la primera etapa marina (post-esguín) tampoco
está clara. Debido a que la selección actúa intensamente sobre la talla de los
salmónidos (Garcia de Leaniz y col. 2007) en el Reprint I: “And the last shall
be first: heterochrony and compensatory marine growth in sea trout
(Salmo trutta)” se explora el coeficiente de variación del tamaño corporal con
objeto de cuantificar la variación fenotípica (Agrawal 2001) a través del estudio
en diferentes fases vitales de individuos anádromos de seis poblaciones
diferentes (Einum y col. 2002; Jonsson y Jonsson 2007). Específicamente, la
hipótesis de partida es que el coeficiente de variación en el tamaño corporal
difiere entre el río y el mar, reflejando compensaciones en el tamaño a lo largo
de la ontogenia.
II. METODOLOGIA BASADA EN EL ANÁLISIS DE ESCAMAS
El crecimiento es usado comúnmente como un indicador de la fitness, pero es
sólo valido si la variación de este crecimiento se representa en relación con sus
15!
!
congéneres. Desafortunadamente, en condiciones naturales, evaluar la
variación de las tasas de crecimiento resulta problemático debido a que los
individuos tienen que ser marcados y a que la obtención de varias medidas de
un mismo individuo a lo largo del tiempo es difícil, ya que las recapturas de
individuos suelen ser muy limitadas. Por ello, en el Reprint II: “Mixed-effects
modelling of scale growth profiles predicts the occurrence of early and
late fish migrants” se utiliza un método basado en el análisis del espacio
entre anillos de crecimiento consecutivos de la escama (circuli) para reconstruir
perfiles individuales de crecimiento. Además, se utiliza un análisis estadístico
basado en las diferencias en la intersección y las pendientes con el objetivo de
aumentar la potencia, la exactitud y precisión de los resultados (van de Pol
2012). El método descrito se utilizó para predecir la migración temprana o
tardía de los individuos de dos poblaciones distintas en función del crecimiento
registrado durante la primera fase del crecimiento de agua dulce. Además,
también se comparó el modelo lineal de efectos mixtos utilizado para el análisis
de escamas con un modelado clásico como el de Von Bertalanffy (1938).
III. RELACIONES ENTRE LA ABUNDANCIA DE REOS Y LA COMPETENCIA.
ANÁLISIS TEMPORAL DE LAS POBLACIONES.
Entender las fluctuaciones temporales en el crecimiento, así como el número
de individuos que forman parte de una población es desde hace mucho tiempo
un desafío clave en ecología de poblaciones (Krebs 2009). La densodependencia es quizás uno de los reguladores endógenos clave en muchas
especies (Brook y Bradshaw 2006) y por el cual el crecimiento es controlado en
función de los recursos existentes y la cantidad de individuos que se
aprovechan de ellos. La consideración de la regulación denso-dependiente es
importante para la gestión de poblaciones explotadas como las poblaciones de
truchas, ya que la mortalidad asociada a la pesca puede tener importantes
efectos en la densidad (Minto y col. 2008). Los sistemas de agua dulce son
típicamente más limitados en recursos que los marinos (Ross 1986) y por tanto,
se considera que el crecimiento denso-dependiente en ríos está causado por la
16!
!
competencia en la obtención de alimento más que por la competencia por
interferencia del espacio (Grant e Imre 2005). A pesar de ello, ambos
mecanismos de competencia pueden operar y resultar en idénticas relaciones
denso-dependientes (Ward y col. 2007). Por el contrario, en el mar el efecto de
la densidad en salmónidos se considera que está mediada exclusivamente por
la competencia de alimento (Peterman 1984) ya que se asume que es muy
difícil para los individuos monopolizar el espacio o defender los recursos en el
mar (Snover y col. 2005). En el Reprint III: “Can migrants escape from
density-dependence?” (en revisión en Ecology and Evolution) se comprobó
que la variación del crecimiento en agua dulce de los reos muestra cambios
dependiendo de la abundancia anual de truchas debido al aumento de la
competencia. Contrariamente a lo esperado, la variación del crecimiento en el
mar también registró cambios dependientes de la abundancia de juveniles que
migran al mar, sugiriendo que las truchas migradoras no pueden ‘escapar’
completamente al efecto de la competencia.
El segundo bloque de esta tesis doctoral se centra en los aspectos genéticos
del fenotipo anádromo de la trucha.
IV. DIRECTRICES PARA LA PLANIFICACIÓN DE UN PROGRAMA DE
REPOBLACIÓN DE REO
La trucha común está a menudo sujeta a programas de repoblación debido a la
pesca recreativa a la que se ven sometidas ciertas poblaciones (Juanes y col.
2011). Por esta razón, es común utilizar truchas criadas en cautividad para
aumentar las poblaciones en los ríos o lagos (Jonsson y Jonsson 2011).
Numerosos autores han establecido pros y contras de esta práctica (Fleming y
col. 1997; Jonsson y col. 2003) y los resultados parecen estar íntimamente
ligados con el stock parental utilizado en los programas de repoblación, ya que
el éxito de la progenie se reduce cuando se utilizan truchas domesticadas
(Morán y col. 1991; Martínez y col. 1993; Dahl y col. 2006). Por otra parte, el
17!
!
ambiente de la piscifactoría normalmente favorece rasgos completamente
diferentes de los que la selección natural favorece en la vida salvaje (Rogell y
col. 2012). Uno de los rasgos más afectados por la piscifactoría es el
componente de la anadromía (Serrano y col. 2009).
En el Reprint IV: “Insights for planning an effective stocking program in
anadromous brown trout (Salmo trutta)” se proponen las bases para el
desarrollo de un programa enfocado a la repoblación de poblaciones
anádromas basado en la selección de los reproductores, el uso de diferentes
edades de repoblación y su relación con la tasa de retorno tras la migración
marina y el coste económico.
Las herramientas usadas para el estudio genético de estos stocks han variado
desde técnicas de paternidad mediante el uso de microsatélites hasta el
estudio del MHC clase II gen-β (de las siglas en inglés, Major Histocompatibility
Complex) o la metilación de tejidos. Además se relacionaron los resultados con
efectos maternos como el tamaño o número de huevos y con efectos paternos
como el fenotipo paterno.
Este experimento en el medio natural ha servido de base para la comparación
de las tasas de retorno de individuos repoblados pertenecientes a los mismos
cruces y criados en cautividad. Con estas comparaciones se han podido
vislumbrar ciertos patrones necesarios para la realización de protocolos que
sirvan para aumentar la abundancia de este fenotipo migrador. A su vez, sirvió
para comprobar que la de mayoría de los cruces se obtuvieron individuos
anádromos y a su vez individuos residentes, lo que sirvió como comienzo de la
búsqueda de nuevos aspectos genéticos en el estudio de la anadromía.
18!
!
V. METILACIÓN Y ADAPTACIÓN AL AGUA DE MAR
En condiciones naturales, los dos fenotipos de trucha se diferencian
claramente durante el segundo año de sus vidas debido a la habilidad de
algunos juveniles para llevar a cabo el esguinado (Økland y col. 1993). Este
proceso implica importantes adaptaciones fisiológicas y morfológicas al agua
marina, preparando así al individuo a la vida en mar abierto antes de haber
migrado. Los cambios en la morfología externa incluyen cambios en la forma
del cuerpo y en los patrones de coloración (Björnsson y col. 2011). Los
cambios fisiológicos están principalmente relacionados con el incremento de la
tolerancia al agua salada y cambios en sus tasas de crecimiento metabólico.
Además, el incremento de la tolerancia al agua marina requiere la
diferenciación y la activación del transporte epitelial, así como de la síntesis de
nuevas proteínas de transporte (McCormick 2001).
Como consecuencia de la necesidad de nuevas técnicas genéticas para el
estudio de la anadromía, la epigenética (i.e. metilación de los tejidos) puede
ayudar a entender el proceso de esguinado en la trucha. El esguinado es un
proceso reversible que puede ser estimulado por factores externos, ya que
individuos genotípicamente iguales pueden mostrar diferentes fenotipos. En el
Reprint IV: “Environmental induced methylation changes associated with
seawater adaptation in brown trout” se investigó si los cambios en la
metilación global del ADN pueden estar relacionados con la anadromía y si el
nivel de metilación puede cambiar en respuesta al estrés osmótico inducido por
estímulos externos como dietas ricas en sal. Entender los efectos de los
estímulos externos va a permitir un mejor manejo de los peces, y a su vez se
podrían incrementar las tasas de esguinado según la necesidad.
19!
!
Cada uno de los reprints en los que se divide esta tesis doctoral sirve para
avanzar en el conocimiento de la biología de la trucha migradora. El reo,
supone en Galicia un gran recurso natural y económico. Esta tesis aborda su
estudio desde un punto de vista multidisciplinar, donde aspectos metodológicos
variados y novedosos sirven para conocer aspectos genéticos y ecológicos de
esta especie.
Referencias
Agrawal, A. A. (2001). Phenotypic plasticity in the interactions and evolution of
species. Science, 294, 321–326.
Almodóvar, A., Nicola, G. G., Elvira, B., & García-Marín, J. L. (2006).
Introgression variability among Iberian brown trout Evolutionary Significant
Units: the influence of local management and environmental features.
Freshwater Biology, 51, 1175–1187.
Ayllon, F., Moran, P., & Garcia-Vazquez, E. (2006). Maintenance of a small
anadromous subpopulation of brown trout (Salmo trutta L.) by straying.
Baglinière,
J.-L.,
Ombredane,
D.,
&
Marchand,
F.
(2000).
Critères
morphologiques pour l'identification des deux formes (rivière et mer) de truite
(Salmo trutta) présentes sur un même bassin. Bulletin Français de la Pêche et
de la Pisciculture, 357/358, 375–383.
Bertalanffy, L. (1938). A quantitative theory of organic growth (inquiries on
growth laws. II). Human Biology, 10, 181–213.
20!
!
Behnke, R. J. (1972). The systematics of salmonid fishes of recently glaciated
lakes. Journal of the Fisheries Research Board of Canada, 29, 639–671.
Bernatchez, L. (2001). The evolutionary history of brown trout (Salmo trutta L.)
inferred from phylogeographic, nested clade, and mismatch analyses of
mitochondrial DNA variation. Evolution, 55, 351–379.
Björnsson, B. T., Stefansson, S. O., & McCormick, S. D. (2011). Environmental
endocrinology
of
salmon
smoltification.
General
and
Comparative
Endocrinology, 170, 290–298.
Bouza, C., Arias, J., Castro, J., Sanchez, L., & Martinez, P. (1999). Genetic
structure of brown trout, Salmo trutta L., at the southern limit of the distribution
range of the anadromous form. Molecular Ecology, 8, 1991–2001.
Brook, B. W., & Bradshaw, C. J. A. (2006). Strength of evidence for density
dependence in abundance time series of 1198 species. Ecology, 87, 1445–
1451.
Caballero, P., & Cobo, F. (2006). Life History of a Sea Trout (Salmo trutta L.)
Population from the North West Iberian Peninsula (River Ulla, Galicia, Spain). In
G. Harris & N. MIlner (Eds.), Sea Trout: Biology, Conservation and
Management (pp. 234-247). Blackwell Scientific Publications.
21!
!
Caballero, P., Morán, P., & Marco-Rius, F. (2012). A review of the genetic and
ecological basis of phenotypic plasticity in brown trout. In S. Polakof & T. W.
Moon (Eds.), Trout: from physiology to conservation (pp. 9-26). Nova Science,
Hauppauge, NY.
Dahl, J., Pettersson, E., Dannewitz, J., Järvi, T., & Löf, A.-C. (2006). No
difference in survival, growth and morphology between offspring of wild-born,
hatchery and hybrid brown trout (Salmo trutta). Ecology of Freshwater Fish, 15,
388–397.
Einum, S., Thorstad, E., & Næsje, T. (2002). Growth rate correlations across
life!stages in female Atlantic salmon. Journal of Fish Biology, 60, 780–784.
Elliott, J., & Chambers, S. (1996). A guide to the interpretation of sea trout
scales. National Rivers Authority, Bristol, UK. R&D Report, Vol. 22.
Elliott, J. M. (1994). Quantitative ecology and the brown trout. Oxford University
Press, Oxford.
Ferguson, A. (2006). Genetics of sea trout, with particular reference to Britain
and Ireland. In G. Harris & N. MIlner (Eds.), Sea Trout: Biology, Conservation
and Management (pp. 155–182). Blackwell Scientific Publications.
Fleming CC (1983). Population biology of anadromous brown trout (Salmo
trutta L.) in Ireland and Britain. PhD Thesis, The Queen’s University of Belfast.
475pp.
22!
!
Fleming, I., Lamberg, A., & Jonsson, B. (1997). Effects of early experience on
the reproductive performance of Atlantic salmon. Behavioral Ecology, 8, 470480.
Fraser, D. J., Weir, L. K., Bernatchez, L., Hansen, M. M., & Taylor, E. B. (2011).
Extent and scale of local adaptation in salmonid fishes: review and metaanalysis. Heredity, 106, 404–420.
Garcia de Leaniz, C., Fleming, I. A., Einum, S., Verspoor, E., Jordan, W. C.,
Consuegra, S., et al. (2007). A critical review of adaptive genetic variation in
Atlantic salmon: implications for conservation. Biological reviews of the
Cambridge Philosophical Society, 82, 173–211.
García-Marín, J.-L., Utter, F. M., & Pla, C. (1999). Postglacial colonization of
brown trout in Europe based on distribution of allozyme variants. Heredity, 82,
46–56.
Giger, T., Excoffier, L., Day, P. J. R., Champigneulle, A., Hansen, M. M.,
Powell, R., & Largiadèr, C. R. (2006). Life history shapes gene expression in
salmonids. Current biology, 16, 281-282.
Glover, K. A., Taggart, J. B., Skaala, Ø., & Teale, A. J. (2004). A study of
inadvertent domestication selection during start feeding of brown trout families.
Journal of Fish Biology, 64, 1168–1178.
23!
!
Grant, J. W. A., & Imre, I. (2005). Patterns of density-dependent growth in
juvenile stream-dwelling salmonids. Journal of Fish Biology, 67, 100–110.
Hansen, M. M., Ruzzante, D. E., Nielsen, E. E., Bekkevold, D., & Mensberg, K.L. D. (2002). Long-term effective population sizes, temporal stability of genetic
composition and potential for local adaptation in anadromous brown trout
(Salmo trutta) populations. Molecular Ecology, 11, 2523–2535.
Hervella, F., & Caballero, P. (1999). Inventario piscícola dos ríos galegos.
Conselleria de Medio Ambiente, Xunta de Galica, Santiago de Compostela.
Hovgaard, K., Skaala, Ø., & Naevdal, G. (2006). Genetic differentiation among
sea trout, Salmo trutta L., populations from western Norway. Journal of Applied
Ichthyology, 22, 57–61.
Izquierdo, J. I., Castillo, A. G. F., Ayllon, F., Hoz, J., & Garcia-Vazquez, E.
(2006). Stock transfers in spanish brown trout populations: a long-term
assessment. Environmental Biology of Fishes, 75, 153–157.
Jensen, L. F., Hansen, M. M., Pertoldi, C., Holdensgaard, G., Mensberg, K. L.
D., & Loeschcke, V. (2008). Local adaptation in brown trout early life-history
traits: implications for climate change adaptability. Proceedings of the Royal
Society B: Biological Sciences, 275, 2859–2868.
Jonsson, B. (1982). Diadromous and resident trout Salmo trutta: is their
difference due to genetics? Oikos, 38, 297–300.
24!
!
Jonsson, B., L'Abee-Lund, H., Heggberget, T., Jensen, A., Johnsen, B., Næsje,
T., & Sættem, L. (1991). Longevity, body size, and growth in anadromous
brown trout (Salmo trutta). Canadian Journal of Fisheries and Aquatic
Sciencies, 48, 1838–1845.
Jonsson, B., & L'Abee-Lund, J. H. (1993). Latitudinal clines in life-history
variables of anadromous brown trout in Europe. Journal of Fish Biology, 43, 1–
16.
Jonsson, B., & Jonsson, N. (1993). Partial migration: niche shift versus sexual
maturation in fishes. Reviews in Fish Biology and Fisheries, 3, 348–365.
Jonsson, B., Jonsson, N., Brodtkorb, E., & Ingebrigtsen, P. (2001). Life-history
traits of brown trout vary with the size of small streams. Functional Ecology, 15,
310–317.
Jonsson, N., Jonsson, B., & Hansen, L. (2003). The marine survival and growth
of wild and hatchery-reared Atlantic salmon. Journal of Applied Ecology, 40,
900–911.
Jonsson, N., & Jonsson, B. (2007). Sea growth, smolt age and age at sexual
maturation in Atlantic salmon. Journal of Fish Biology, 71, 245–252.
25!
!
Jonsson, B., & Jonsson, N. (2011). Ecology of Atlantic salmon and brown trout:
habitat as a template for life histories. Fish and Fisheries Series 33, Springer,
Dordrecht.
Juanes, F., Gephard, S., La Hoz, De, J., Moran, P., Dopico, E., Horreo, J. L., &
Garcia-Vazquez, E. (2011). Restoration of native Atlantic salmon runs in
northern Spain: do costs outweigh benefits? Knowledge and Management of
Aquatic Ecosystems, 402, 22.
Klemetsen, A., Amundsen, P., Dempson, J., Jonsson, B., Jonsson, N.,
O'Connell, M., & Mortensen, E. (2003). Atlantic salmon Salmo salar L., brown
trout Salmo trutta L. and Arctic charr Salvelinus alpinus (L.): a review of aspects
of their life histories. Ecology of Freshwater Fish, 12, 1–59.
Krebs, C.J. (2009). Ecology: The Experimental Analysis of Distribution and
Abundance. Benjamin Cummings, San Francisco, USA.
Mangel, M., & Stamps, J. (2001). Trade-offs between growth and mortality and
the maintenance of individual variation in growth. Evolutionary Ecology
Research, 3, 583–593.
Martinez, P., Arias, J., Castro, J., & Sanchez, L. (1993). Differential stocking
incidence in brown trout (Salmo trutta) populations from Northwestern Spain.
Aquaculture, 114, 203–216.
McCormick, S. D. (2001). Endocrine control of osmoregulation in teleost fish.
American Zoologist, 41, 781–794.
26!
!
Minto, C., Myers, R. A., & Blanchard, W. (2008). Survival variability and
population density in fish populations. Nature, 452, 344–347.
Morán, P., Pendás, A. M., Garcia-Vazquez, E., & Izquierdo, J. (1991). Failure of
a stocking policy, of hatchery reared brown trout, Salmo trutta L., in Asturias,
Spain, detected using LDH-5* as a genetic marker. Journal of Fish Biology, 39,
117–121.
Morita, K., Tsuboi, J., & Nagasawa, T. (2009). Plasticity in probabilistic reaction
norms for maturation in a salmonid fish. Biology Letters, 5, 628-631.
Nielsen, C., Holdensgaard, G., Petersen, H. C., Bjornsson, B. T., & Madsen, S.
S. (2001). Genetic differences in physiology, growth hormone levels and
migratory behaviour of Atlantic salmon smolts. Journal of Fish Biology, 59, 28–
44.
Økland, F., Jonsson, B., Jensen, A. J., & Hansen, L. P. (1993). Is there a
threshold size regulating seaward migration of brown trout and Atlantic salmon?
Østergaard, S., Hansen, M. M., Loeschcke, V., & Nielsen, E. E. (2003). Long
term temporal changes of genetic composition in brown trout (Salmo trutta L.)
populations inhabiting an unstable environment. Molecular Ecology, 12, 3123–
3135.
27!
!
Peterman, R. M. (1984). Density-dependent growth in early ocean life of
sockeye salmon (Oncorhynchus nerka). Canadian Journal of Fisheries and
Aquatic Sciencies, 41, 1825–1829.
Pettersson, J. C. E., Hansen, M. M., & Bohlin, T. (2001). Does dispersal from
landlocked trout explain the coexistence of resident and migratory trout females
in a small stream? Journal of Fish Biology, 58, 487–495.
Rogell, B., Dannewitz, J., Palm, S., Petersson, E., Dahl, J., Prestegaard, T., et
al. (2012). Strong divergence in trait means but not in plasticity across hatchery
and wild populations of sea-run brown trout Salmo trutta. Molecular Ecology,
21, 2963–2976.
Ross, S. T. (1986). Resource partitioning in fish assemblages: a review of field
studies. Copeia, 1986, 352–388.
Ruzzante, D. E., Hansen, M. M., Meldrup, D., & Ebert, K. M. (2004). Stocking
impact and migration pattern in an anadromous brown trout (Salmo trutta)
complex: where have all the stocked spawning sea trout gone? Molecular
Ecology, 13, 1433–1445.
Scarnecchia, D. L., Ísaksson, Á., & White, S. E. (1989). Oceanic and riverine
influences on variations in yield among icelandic stocks of Atlantic salmon.
Transactions of the American Fisheries Society, 118, 482–494.
28!
!
Serrano, I., Larsson, S., & Eriksson, L.-O. (2009). Migration performance of wild
and hatchery sea trout (Salmo trutta L.) smolts—Implications for compensatory
hatchery programs. Fisheries Research, 99, 210–215.
Skrochowska, S. (1969). Migrations of the sea-trout (Salmo trutta L.) brown
trout (Salmo trutta M. Fario L.) and their crosses. Polish Archives of
Hydrobiology, 16, 149–180.
Snover, M. L., Watters, G. M., & Mangel, M. (2005). Interacting effects of
behavior and oceanography on growth in salmonids with examples for coho
salmon (Oncorhynchus kisutch). Canadian Journal of Fisheries and Aquatic
Sciencies, 62, 1219–1230.
Stewart, D. C., Smith, G. W., & Youngson, A. F. (2002). Tributary-specific
variation in timing of return of adult Atlantic salmon (Salmo salar) to fresh water
has a genetic component. Canadian Journal of Fisheries and Aquatic
Sciencies, 59, 276–281.
Toledo, M. M., Lemaire, A. L., Baglinière, J.-L., & Braña, F. (1993).
Caractéristiques biologiques de la truite de mer (Salmo trutta L.) au Nord de
l'Espagne, dans deux rivières des Asturies. Bulletin Français de la Pêche et de
la Pisciculture, 330, 295–306.
van de Pol, M. (2012). Quantifying individual variation in reaction norms: how
study design affects the accuracy, precision and power of random regression
models. Methods in Ecology and Evolution, 3, 268–280.
29!
!
Ward, D. M., Nislow, K., Armstrong, J. D., Einum, S., & Folt, C. L. (2007). Is the
shape of the density-growth relationship for stream salmonids evidence for
exploitative rather than interference competition? Journal of Animal Ecology,
76, 135–138.
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. (2009).
Mixed Effects Models and Extensions in Ecology With R. Springer, NY.
30!
!
DISCUSIÓN
31!
!
DISCUSIÓN
Introducción y antecedentes
A continuación se discuten, de manera conjunta, los resultados obtenidos en la
presente tesis doctoral para dotar de coherencia y unidad el conjunto de los
trabajos presentados.
La migración de la trucha común se atribuye a la ventaja adquirida por los
individuos migradores sobre los congéneres que han permanecido en el río,
como por ejemplo una mayor fecundidad (Jonsson y Jonsson 1993), selección
de pareja (Revisado en Esteve 2005) o un aumento de la fitness de la progenie
debida a efectos maternos (Crespi y Teo 2002). No obstante, estos
mecanismos son poco conocidos. Numerosos estudios sugieren que la
migración en la trucha es el resultado de la interacción entre genética y
ambiente de modo que cuando se alcanza un determinado umbral el individuo
va a migrar (Ferguson 2006). Sin embargo, la cuantificación de ese umbral es
complicada y la gran mayoría de los trabajos se han centrado en aspectos
diferentes e independientes entre sí y que pueden ser agrupados y clasificados
según la propuesta de Rounsefell (1958) que descompone la anadromía en
seis partes:
(1) La distancia recorrida durante la migración marina, analizada comúnmente
mediante métodos de marcado-recaptura (Caballero y col. 2006).
(2) La duración de la estancia en el mar, que es muy variada en los reos,
pudiendo ir desde unos pocos meses hasta cuatro años antes de volver al río a
desovar por primera vez (Elliott 1994).
32!
!
(3) El estado de maduración y desarrollo alcanzado en el mar, que varía
dependiendo del tiempo que el individuo pasa en el agua marina. En hembras
reproductoras se traduce en una gran variabilidad en el tamaño y cantidad de
huevos (Jonsson y Jonsson 1999; Mousseau y Fox 1998).
(4) Los hábitos de desove y hábitats de los reos, que están íntimamente
ligados a la estrategia elegida por los individuos. Así, los reos que vuelven a
desovar al río tienen ventajas a la hora de elegir los sitios de desove debido a
su mayor tamaño. Por el contrario, pierden fitness, a medida que se desplacen
aguas arriba, cuando se comparan con los individuos residentes, ya que el
gasto energético es muy superior (Bohlin y col. 2001).
(5) La mortalidad tras el desove, es decir el porcentaje de iteroparidad (Crespi y
Teo 2002; Caballero y col. 2006).
(6) La aparición de formas residentes de la especie, donde la trucha puede
completar su ciclo sin tener que migar al mar. Esta forma residente se
desarrolló a partir de un ancestro anádromo que se refugió de la última
glaciación (Ferguson 2006) y evolucionó en los casos donde las poblaciones
perdieron su salida al mar. De la misma manera, las poblaciones actuales
residentes a las que, por mejoras de hábitat, se les proporciona una salida al
mar pueden tener la capacidad de desarrollar formas anádromas (KallioNyberg 2010).
Todos estos componentes de la anadromía deben unirse a otro rasgo propio
del género Salmo que está íntimamente ligado con su ciclo de vida, el homing
(i.e. retorno a desovar al río de origen; Stabell 1984; Jonsson y Jonsson 2012).
33!
!
Finalmente, hay que añadir el papel que tienen en la migración las diferencias
genéticas entre individuos. Una gran parte de los trabajos enfocados a la
búsqueda de diferencias genéticas entre individuos migradores y residentes de
una misma población concluyen que no hay diferenciación genética ente
individuos de ambos fenotipos (Cross y col. 1992; Pettersson y col. 2001), y
que la reproducción entre ambos fenotipos es habitual (Skaala y Naevdal
1989). Por otra parte, también se analizaron poblaciones separadas por
obstáculos naturales (Pettersson y col. 2001) y no se encontraron diferencias
genéticas entre truchas residentes y anádromas que conviven simpátricamente
por debajo del obstáculo. Además, los análisis de asignación, no demostraron
que truchas residentes fueran inmigrantes de la población establecida aguas
arriba del obstáculo, con lo que se descartó que los individuos hubieran
emigrado aguas abajo.
Un aspecto ecológico no relacionado directamente con el proceso de
anadromía y tratado en la presente tesis doctoral es el crecimiento y la
supervivencia
denso-dependiente
(Lobón-Cerviá
2007).
Este
sistema
endógeno de regulación adquiere una notable importancia en las poblaciones
parcialmente migradoras por diversas razones.
(1) El estado energético del individuo esta relacionado con la migración
(Forseth y col. 1999) e íntimamente ligado con el reparto de recursos en la
población.
(2) La variabilidad anual en el crecimiento de los juveniles es grande e influye
en la decisión de la táctica vital (Cucherousset y col. 2005).
(3) Los ambientes donde crecen los juveniles están relacionados con la táctica
vital (Morinville y Rasmussen 2006).
34!
!
El estado energético de los individuos supone que la asignación proporcional
de energía entre individuos migradores es menor que la asignación entre los
que permanecen en el río más tiempo. Esa reducción en la proporción de
energía entre el primer y el segundo año es mayor en los individuos anádromos
(Forseth y col. 1999) y es una explicación de por qué la mayoría de los
individuos migran con dos años. Por tanto, los individuos que crezcan más
rápido tenderán a cambiar su nicho antes que aquellos que lo hacen más
despacio. La tasa de crecimiento, dependiente entre otras cosas de las
relaciones de densidad que existe en la población, marca la tendencia en la
dispersión y migración de los juveniles (Kaspersson y Hojesjo 2009). Por otra
parte, es de gran importancia determinar cómo cambian esas relaciones en el
tiempo para poder gestionar mejor las poblaciones. Por ejemplo, la
competencia entre cohortes y el tipo de ambiente donde se desarrollan los
individuos influye en el cambio de las relaciones entre los individuos de una
población (Cucherousset y col. 2005), lo que sugiere que las variaciones en el
ambiente a pequeño nivel pueden determinar, a causa de la relación que existe
entre costes metabólicos y estrategia, la táctica del individuo (Morinville y
Rasmussen 2006).
Técnicas desarrolladas y utilizadas en la presente tesis doctoral
Durante el transcurso de esta tesis doctoral se han desarrollado y aplicado
nuevas técnicas para el estudio de la anadromía en las poblaciones de trucha.
La metodología utilizada tiene el objetivo de mejorar la gestión de las
poblaciones para favorecer el fenotipo migrador de la trucha. A continuación se
detallan estas técnicas y se señalan las mejoras respecto a trabajos anteriores
o bien su aplicación novedosa al campo del estudio de la trucha común.
35!
!
TÉCNICAS GENÉTICAS EMPLEADAS
Análisis de paternidad. Esta herramienta se emplea de forma habitual en
numerosos estudios de genética poblacional de trucha con diferentes objetivos.
Por ejemplo, para documentar el sistema de apareamiento en el medio natural
mediante el estudio de los potenciales reproductores de una población y la
descendencia con el objetivo de determinar cómo la proximidad y el tamaño
corporal influyen a la hora del apareamiento (Servezob y col. 2010) o para la
asignación de la progenie a cruces híbridos (Castillo y col. 2010).
El objetivo perseguido con el análisis de la paternidad en esta tesis doctoral ha
estado destinado a la asignación de los individuos capturados a un cruce
determinado, realizado en la piscifactoría. La asignación fue empleada
paralelamente como herramienta para el estudio del crecimiento, supervivencia
y dispersión en condiciones naturales de huevos plantados a lo largo de dos
años, aunque sirvió especialmente para el reconocimiento de adultos de
retorno tras completar su ciclo vital (Reprint IV: “Insights for planning an
effective stocking program in anadromous brown trout (Salmo trutta)”).
MHC. Los análisis del MHC (tanto clase I como II) son frecuentes en
salmónidos y en particular en trucha común (Campos y col. 2006; Coughlan y
col. 2006). El MHC clase I está implicado en la elección de la pareja y los
posibles efectos beneficiosos que tiene esta elección sobre la fitness de la
descendencia. Aunque algunos autores sostienen una relación positiva entre la
selección de la pareja y la fitness de la descendencia (Consuegra y Garcia de
Leaniz 2008), distinguir entre los efectos de la elección de pareja y otros
factores de la selección sexual es complicado (Kokko 2001). En salmón
Atlántico, los estudios sugieren que cuanto mayor es la variabilidad en el MHC
clase I de los padres, mayor es la resistencia a parásitos marinos de su
36!
!
descendencia (Consuegra y Garcia de Leaniz 2008). Por otra parte, en el
estudio del MHC clase II en poblaciones aisladas de trucha se observaron
variabilidades muy distintas dependiendo de la localidad (Campos y col. 2006),
lo que sugiere que en la gestión de las poblaciones debería incluirse esta
variable.
En consonancia con estos estudios, en el estudio de la tasa de retorno en
función de las familias empleadas y el tipo de repoblación llevado a cabo en el
Reprint IV: “Insights for planning an effective stocking program in
anadromous brown trout (Salmo trutta)”, se cuantificó la variabilidad en el
MHC de los cruces empleados con el objetivo de observar si esta variable está
relacionada con la supervivencia y migración de los descendientes.
Metilación global. La metilación es uno de los principales mecanismos
epigenéticos de regulación génica que existen junto con la modificación de
histonas y el ARN no codificante. La metilación consiste en la adición de un
grupo metilo a la citosina, situadas en las islas CpG. Esto está relacionado con
el silenciamiento de genes (De Smet y col. 1999; Jones y Takai 2001). Existen
evidencias que demuestran la importancia de factores epigenéticos en la
modulación de la plasticidad fenotípica (Johnson y Tricker 2010). Por tanto, el
estudio del grado de metilación global durante el proceso del esguinado
supone una novedosa técnica aplicada al estudio de la anadromía en especies
parcialmente migradoras como la trucha. Con el objetivo de cuantificar las
diferencias en metilación global, y si éstas pueden ser modificadas por
estímulos
ambientales,
en
el
Reprint
V:
“Environmental
induced
methylation changes associated with seawater adaptation in brown trout”,
se utilizó la técnica de MSAP (de las siglas en inglés methylation-sensitive
amplified polymorphism; Reyna-Lopez y col. 1997) para cuantificar los cambios
de metilación global en branquias de trucha alimentadas con dietas
enriquecidas en sal (Perry y Rivero-López 2012). Estas dietas, fáciles de
37!
!
preparar, podrían tener gran efecto en las políticas de gestión de poblaciones
migradoras.
TÉCNICAS ECOLÓGICAS Y ESTADÍSTICAS EMPLEADAS
Técnicas de análisis de escama. Las escamas son pequeñas placas rígidas
que crecen en la piel de muchos animales. En concreto, las escamas de las
truchas son cicloideas y contienen una serie de anillos que se van acumulando
a medida que el individuo crece. Con ello, es posible estudiar el crecimiento de
un individuo desde la fecha de nacimiento hasta su captura sin que el animal
sufra daños (i.e. como en el estudio de los otolitos). Otra de las características
que hacen a las escamas muy útiles para el estudio del crecimiento en
salmónidos es que se puede establecer su edad, ya que los crecimientos de la
escama en invierno son fácilmente diferenciables (Elliott and Chambers 1996).
Por tanto es posible identificar la edad total del individuo. Finalmente, también
es posible identificar de manera sencilla la fase de agua dulce de la marina y
cuándo se produce la transición entre ambos medios.
El estudio de escamas es común en muchas especies, incluso existen
protocolos descritos por la FAO para su análisis (Bilton 1975; Gayanilo y col.
2005). La mayoría de los métodos de estudio se basan en (1) el cálculo de la
edad (o punto especifico, i.e. esguinado) a partir de la escama y (2) el
retrocálculo del tamaño de la escama (revisado en Francis 1990) para obtener
una medida de longitud del individuo durante cada año de su vida en función
de lo que mide la escama en dicho punto (Morita y Fukuwaka 2006; Gagne y
Rodriguez 2008; Kuparinen y col. 2009). Esta técnica es ampliamente utilizada
en pesquerías para la obtención de curvas de crecimiento como la de Von
Bertalanffy (1938).
38!
!
Friedland y Haas (1996) sustituyeron el uso de tamaños de la escama a una
edad específica por el uso de los anillos de la escama, en concreto eso del
espacio existente entre dos anillos consecutivos. Estos anillos de crecimiento,
que se sitúan entre los puntos para determinar la edad, habían sido
desechados anteriormente para el estudio del crecimiento. Aún así, estos
autores, al igual que se ha hecho en el análisis de crecimiento basado en el
tamaño a diferentes edades, usaron un método de análisis enfocado en el
análisis de las medias de varios individuos.
La técnica desarrollada en la presente tesis doctoral para el análisis de las
escamas se basa en el espacio de crecimiento entre anillos consecutivos junto
con un modelado de efectos lineales mixtos (Zuur y col. 2009) en sustitución al
análisis de medias. Además se corrigieron aspectos técnicos como la
correlación de los errores. Este tipo de análisis, explicado en el Reprint II:
“Mixed-effects modelling of scale growth profiles predicts the occurrence
of early and late fish migrants”, se utilizó en el estudio de una serie temporal
de 13 años en la cual se analizaron escamas de truchas de dos ríos con el
objetivo de evaluar cómo la abundancia de juveniles en el agua dulce y de
esguines en el mar afecta al crecimiento (Reprint III: “Can migrants escape
from density-dependence?”). Las técnicas de modelado con efectos mixtos
proporcionan al estudio una potencia, precisión y exactitud mayor que los
análisis de medias (van de Pol 2012), ya que está basado en mediciones
múltiples de un mismo individuo y su variación en las intersecciones y
pendientes de crecimiento.
APORTACIONES DE LA PRESENTE TESIS DOCTORAL
Las aportaciones de la presente tesis doctoral se pueden dividir en cuatro
bloques diferentes, y todas ellas enfocadas a la gestión de las poblaciones.
39!
!
(1) Estudio del crecimiento en agua dulce y agua salada mediante el uso
de escamas. La gestión adecuada de un recurso como el reo pasa por
conocer con exactitud cómo es el desarrollo de los individuos en los dos
ambientes donde crecen y cuál es la relación entre ellos. Además, la gestión
óptima de las poblaciones puede ser diferente debido a las adaptaciones
locales de los salmónidos (revisado en Fraser y col. 2011) y sus diferencias a lo
largo de la ontogenia (Parra y col. 2011). En la presente tesis doctoral se
aportan las diferencias registradas en varias poblaciones de reo en relación
con el desarrollo en las distintas fases del ciclo vital y en relación al crecimiento
agua dulce-agua salada. La importancia del ambiente marino aparece como un
factor determinante en el desarrollo de los adultos, ya que va a permitir a
aquellos individuos mas desfavorecidos en el río (i.e. menores tallas), alcanzar
e incluso sobrepasar a los más favorecidos. Además, también se proporcionan
las diferencias existentes en la talla y edad de esguinado y las edades de
retorno al río con el objeto de complementar la información recogida mediante
pesca eléctrica en el río.
El conocimiento de las relaciones entre agua dulce y marina en distintas
poblaciones es necesario para su gestión. Estas diferencias se pueden
registrar anualmente, entre distintas cuencas. A su vez, si existe un registro de
años anteriores, se pueden observar tendencias y desequilibrios en las
poblaciones con objeto de poder actuar sobre el ambiente o sobre cierta parte
del ciclo vital donde se registra el problema (i.e. disminución de la talla de
esguinado, bajo crecimiento marino, reducción de la edad de maduración,
desequilibrio en la compensación de talla en el mar). En conjunto, la mejora de
la gestión puede ser notable mediante una observación constante de todos los
parámetros descritos.
(2) El desarrollo de una nueva metodología como herramienta para el
análisis de escamas. El estudio de las escamas supone en la gestión de las
poblaciones una importante fuente de información fácilmente aprovechable
40!
!
(Bilton 1975). En los ríos existen estaciones de captura que proporcionan,
información de los reproductores que remontan el río para el desove (Saura y
col. 2006; Caballero 2002). Es ahí donde se pueden recoger y almacenar las
escamas sin que los individuos sufran daño alguno. Con la recogida de datos
tales como peso, longitud y sexo, más la información presente en las escamas,
es posible obtener gran cantidad de datos que pueden aplicarse en la gestión
de poblaciones, tanto en el río como en el mar. Con el desarrollo de esta
técnica basada en un método estadístico de efectos mixtos y en la información
individual repetida a lo largo de la escama se mejoran los siguientes aspectos
en la gestión:
• La variación individual y la estacionalidad del crecimiento que registra
este nuevo método puede suplir carencias de otros métodos populares
como el de Von Bertalanffy (1938). La variación en los rasgos que
afectan a la fitness (i.e. crecimiento o talla) necesitan ser interpretados
en relación con lo registrado por los congéneres. El gran número de
puntos
comparados
en
cada
individuo
permite
que
dichas
comparaciones entre individuos tengan mayor robustez que aquellos
resultados donde sólo se registran dos puntos de cada individuo (como
normalmente ocurre en el marcado-recaptura; van de Pol 2012). Por
tanto,
la
fiabilidad
de
los
resultados
obtenidos
aumenta
considerablemente cuando se utilizan las escamas en lugar de
mediciones sobre el tamaño corporal.
• Se implementa la relación entre la competencia y el crecimiento entre
individuos de una misma población. La influencia sobre el crecimiento
del número de juveniles presentes en una cuenca o el número de
esguines que emigran hacia el mar se puede registrar mediante el uso
de las escamas y el método descrito. Es de gran importancia en los
programas de repoblación saber cuál va a ser el impacto del número de
individuos liberados en el río, ya que un número elevado de individuos
41!
!
soltados va a provocar, a parte de la interferencia con las poblaciones
salvajes, que el reparto de los recursos se vea perjudicado por el
aumento de la competencia (Lobón-Cerviá 2007). La aportación de esta
tesis doctoral mediante la técnica de escamas señala la importancia de
considerar en conjunto el número de juveniles que existen en el río y su
crecimiento.
Típicamente,
la
denso-dependencia
se
asocia
al
crecimiento en agua dulce (Jenkins y col. 1999; Bohlin y col. 2002;
Lobón-Cerviá 2007). En cambio, mediante el análisis de dos poblaciones
a lo largo de un periodo de 13 años, se observó cómo la densidad de
esguines afecta también al crecimiento marino. Esto aflora la
importancia de tener en cuenta no sólo la diferencia entre ríos en la
conservación de las poblaciones, sino que además hay que empezar a
tener en cuenta la perspectiva costera en su gestión.
(3) Comparación de métodos de repoblación y resultados positivos en el
aumento del fenotipo migrador. Los métodos de repoblación pueden variar
según la estación, meteorología y objetivos de la repoblación. La principal
aportación de esta tesis doctoral ha sido desarrollar un programa de
repoblación óptimo basado en protocolos clásicos de repoblación para
aumentar los individuos migradores de una población.
Este protocolo se basa en factores fácilmente manejables por los órganos de
gestión y con la consideración de aspectos económicos con el fin de reducir al
máximo los costes de la repoblación.
El método está basado en el estudio de dos rasgos observables en la
piscifactoría: el tamaño de los huevos de la hembra (Einum y Fleming 2002) y
el fenotipo del padre usado en el cruce (Skrochowska 1969). Además, en el
laboratorio se realizó un análisis genético de los reproductores para observar el
grado de similitud entre los alelos del MHC clase II (Campos y col. 2007).
42!
!
Por otro lado se compararon distintos métodos de repoblación: la plantación de
huevos y la repoblación de individuos de un año para maximizar la producción
de
esguines
(Jokikokko
y
Jutila
2004).
Tras
realizar
las
medidas
correspondientes en la piscifactoría del tamaño de los huevos, se identificó el
fenotipo paterno y se midió la variabilidad genética de los padres en el MHC.
Tras ello, se esperó a que los individuos completaran su ciclo vital en ambos
tipos de repoblación (i.e. plantado de huevos y suelta tras un año en la
piscifactoría).
La tasa de retorno entre los años 2010 y 2011 supuso casi un 10% del total de
la población de reos que retornaron a desovar. Además, esta tasa de retorno
se consiguió usando sólo una pequeña sección de un afluente del río principal.
Es importante indicar que los volúmenes tanto de huevos como de individuos
repoblados fueron sensiblemente inferiores a los que normalmente se utilizan
en programas de repoblación de salmón Atlántico. Con estos resultados se
abre un proceso para adoptar nuevos protocolos de repoblación
(4) Implementación de nuevas técnicas genéticas para la mejora frente al
estrés osmótico en individuos de repoblación. Uno de los problemas de las
repoblaciones, a parte de las interacciones con las poblaciones salvajes
(revisado en Naish y col. 2008), es la pérdida de los rasgos propios de las
poblaciones salvajes para completar el ciclo vital (Rogell y col. 2012). La
pérdida de la anadromía es una de las características que más afectan en la
conservación de las poblaciones parcialmente migradoras, ya que el uso de
individuos domesticados puede reducir el número de individuos migradores
(Serrano y col. 2009).
43!
!
En la presente tesis doctoral se aportan soluciones para la cría de truchas en
cautividad basadas en estudios de la interacción ente metilación y factores
ambientales. Concretamente, se han identificado y cuantificado los cambios de
metilación global en branquias provocados por una dieta rica en sal. Con los
resultados obtenidos es posible establecer un protocolo de alimentación
óptimo, para minimizar el estrés osmótico que se produce al inicio de la
migración.
Perspectivas futuras
La investigación relacionada con salmónidos anádromos necesita continuas
mejoras para la conservación de las especies. Debido a la explotación tanto del
recurso como del ambiente en el que se desarrollan, las dificultades de las
poblaciones salvajes se mantienen o incluso empeoran con el paso del tiempo.
Una de las principales herramientas que se deberían desarrollar en el campo
de la conservación está relacionada con la combinación entre las mejoras de
hábitat (Griffiths y col. 2011), la explotación sostenible del recurso (EIFAC
2008) y los programas de repoblación. Para tener una visión de la situación de
los últimos años en esta materia se puede tomar como ejemplo la región de la
provincia de Pontevedra:
• Cada año en función de la situación del recurso, hay regulaciones en
materia de pesca donde se limita el número de individuos que se
pueden capturar independientemente de las licencias expedidas (Xunta
de Galicia 2013).
• Los
programas
de
repoblación
realizados
en
ambas
especies
contribuyen a la recuperación de numerosos ríos tras su práctica
extinción (Saura y col. 2006).
44!
!
No obstante, en los los últimos diez años se han registrado numerosas
catástrofes indirectamente relacionadas con la destrucción del hábitat:
• Vertidos devastadores que afectaron tramos enteros de río (Umia en
2006, Lagares en 2007).
• Obras públicas que colmataron el lecho impidiendo la supervivencia de
peces (Barxa en 2009, Cabanelas en 2009).
En estos casos, los programas de repoblación junto con políticas de mejora de
hábitat pueden mitigar los efectos negativos. Por ello, es trascendente para la
conservación de las especies anádromas desarrollar un programa de
conservación que sea efectivo, fácil de realizar (a nivel de piscifactoría y en
medio natural) y económicamente viable.
El programa de repoblación mostrado en la presente tesis doctoral cumple,
inicialmente, los tres requisitos, aunque existe un gran margen de mejora en la
efectividad. Por ello:
(1) Se hace necesario la continua mejora de las estrategias de repoblación, en
especial las poblaciones de reo, ya que, al formar parte de una especie
parcialmente migradora, es necesario maximizar la proporción de individuos
anádromos. Debido a la existencia de numerosas estaciones de captura, sería
posible
analizar
si
los
métodos
propuestos
pueden
ser
utilizados
independientemente de la cuenca donde se llevan a cabo.
45!
!
(2) Es necesario comparar el método descrito en el Reprint IV con el método
de repoblación basado en individuos criados en la piscifactoría mediante
métodos de mejora frente al estrés osmótico Reprint V. Este último puede ser
más costoso pero a su vez puede proporcionar resultados más rápidos debido
al uso de individuos criados en piscifactoría en vez de plantados en el medio
natural como huevos. Su uso para zonas de urgente recuperación podría ser
más adecuado.
(3) El estudio del estrés osmótico demostró una supervivencia superior de los
individuos alimentados con dietas enriquecidas en sal al ser trasvasados a
agua salada. Esta mejora en la adaptación debe ser explorada en un futuro con
el objetivo de investigar qué genes en particular se activan o desactivan por
agentes externos, lo que permitiría criar truchas en condiciones óptimas que
faciliten la supervivencia durante los cambios de salinidad. Esto puede influir en
las estrategias de repoblación, ya que se podrían sustituir las repoblaciones en
río por las repoblaciones en aguas salobres o incluso en agua oceánica.
(4) Adaptar las repoblaciones, tanto en forma de huevo, como juvenil a las
densidades que existen de juveniles en el río como a la densidad de esguines
que emigran al mar. Las poblaciones de reo parecen ser sensibles a la densodependencia tanto en el río como en el estuario y por tanto, ese factor debe ser
considerado en los planes de repoblación. Un excesivo número de individuos
en las sueltas pueden llegar a producir una reducción en el crecimiento de los
individuos, tanto en poblaciones salvajes, repobladas como en ambos
ambientes. Por tanto, los mecanismos de adaptación relacionados con el
tamaño (esguinado, estado de maduración, etc.) pueden estar negativamente
afectados por el exceso de individuos sobre la capacidad de carga. El número
óptimo de individuos debe ponderarse mediante estudios específicos en cada
río y es necesario tener estimas tanto del número de individuos presentes en el
río (juveniles de un año específicamente) como del número de esguines.
46!
!
Considerar los mecanismos de adaptación a la osmorregulación y los
mecanismos de denso-dependencia pueden mejorar los planes de repoblación.
Junto con los análisis de los reproductores en la piscifactoría y una mejora de
la gestión del hábitat sería posible que la conservación de la especie estuviera
más cerca de un diseño óptimo.
47!
!
Referencias
Bertalanffy, L. (1938). A quantitative theory of organic growth (inquiries on
growth laws. II). Human Biology, 10, 181–213.
Bilton, H. (1975). Factors influencing the formation of scale characters [in
Pacific salmon]. International North Pacific Fisheries Commission Bulletin, 32,
102–108.
Bohlin, T., Pettersson, J., & Degerman, E. (2001). Population density of
migratory and resident brown trout (Salmo trutta) in relation to altitude: evidence
for a migration cost. Journal of Animal Ecology, 70, 112–121.
Bohlin, T., Sundstrom, L., Johnsson, J., Höjesjö, J., & Pettersson, J. (2002).
Density-dependent growth in brown trout: effects of introducing wild and
hatchery fish. Journal of Animal Ecology, 71, 683–692.
Caballero, P. (2002). Ciclo vital reo (Salmo trutta Linnaeus, 1758) en la cuenca
del río Ulla. Memoria Diploma de Estudios Avanzados. Santiago de
Compostela.
Caballero, P., & Cobo, F. (2006). Life History of a Sea Trout (Salmo trutta L.)
Population from the North!West Iberian Peninsula (River Ulla, Galicia, Spain). In
G. Harris & N. MIlner (Eds.), Sea Trout: Biology, Conservation and
Management (pp. 234-247). Blackwell Scientific Publications.
48!
!
Campos, J. L., Posada, D., & Morán, P. (2006). Genetic variation at MHC,
mitochondrial and microsatellite loci in isolated populations of Brown trout
(Salmo trutta). Conservation Genetics, 7, 515–530.
Castillo, A. G. F., Beall, E., Morán, P., Martinez, J. L., & Garcia-Vazquez, E.
(2010). Indirect Benefits for Female Salmon from Mating with Brown Trout.
Journal of Heredity, 101, 461–468.
Consuegra, S., & Garcia de Leaniz, C. (2008). MHC-mediated mate choice
increases parasite resistance in salmon. Proceedings of the Royal Society B:
Biological Sciences, 275, 1397–1403.
Coughlan, J., McGinnity, P., O’Farrell, B., Dillane, E., Diserud, O., Deeyto, E., et
al. (2006). Temporal variation in an immune response gene (MHC I) in
anadromous Salmo trutta in an Irish river before and during aquaculture
activities. ICES Journal of Marine Science, 63, 1248–1255.
Crespi, B. J., & Teo, R. (2002). Comparative phylogenetic analysis of the
evolution of semelparity and life history in salmonid fishes. Evolution, 56, 1008–
1020.
Cross, T. F., Mills, C. P. R., & Courcy Williams, M. (1992). An intensive study of
allozyme variation in freshwater resident and anadromous trout, Salmo trutta L.,
in western Ireland. Journal of Fish Biology, 40, 25–32.
Cucherousset, J., Ombredane, D., Charles, K., Marchand, F., & Baglinière, J.-L.
(2005). A continuum of life history tactics in a brown trout (Salmo trutta)
49!
!
population. Canadian Journal of Fisheries and Aquatic Sciencies, 62, 1600–
1610.
De Smet, C., Lurquin, C., Lethé, B., Martelange, V., & Boon, T. (1999). DNA
methylation is the primary silencing mechanism for a set of germ line-and
tumor-specific genes with a CpG-rich promoter. Molecular and cellular biology,
19, 7327–7335.
EIFAC [European Inland Fisheries Advisory Commission]. (2008). EIFAC Code
of Practice for Recreational Fisheries. Rome: EIFAC. Occasional Paper No. 42,
45 pp.
Einum, S., & Fleming, I. A. (2002). Does within population variation in fish egg
size reflect maternal influences on optimal values? The American Naturalist,
160, 756–765.
Elliott, J., & Chambers, S. (1996). A guide to the interpretation of sea trout
scales. National Rivers Authority, Bristol, UK. R&D Report, Vol. 22.
Elliott, J. M. (1994). Quantitative ecology and the brown trout. Oxford University
Press, Oxford.
Esteve, M. (2005). Observations of spawning behaviour in Salmoninae: Salmo,
Oncorhynchus and Salvelinus. Reviews in Fish Biology and Fisheries, 15, 1–21.
50!
!
Ferguson, A. (2006). Genetics of sea trout, with particular reference to Britain
and Ireland. In G. Harris & N. MIlner (Eds.), Sea Trout: Biology, Conservation
and Management (pp. 155–182). Blackwell Scientific Publications.
Forseth, T., Nesje, T. F., Jonsson, B., & Harsaker, K. (1999). Juvenile migration
in brown trout: a consequence of energetic state. Journal of Animal Ecology,
68, 783–793.
Francis, R. (1990). Back-calculation of fish length: a critical review. Journal of
Fish Biology, 36, 883–902.
Fraser, D. J., Weir, L. K., Bernatchez, L., Hansen, M. M., & Taylor, E. B. (2011).
Extent and scale of local adaptation in salmonid fishes: review and metaanalysis. Heredity, 106, 404–420.
Friedland, K., & Haas, R. (1996). Marine post smolt growth and age at maturity
of Atlantic salmon. Journal of Fish Biology, 48, 1–15.
Gagné, S., & Rodríguez, M. (2008). Modelling seasonal increments in size to
determine the onset of annual growth in fishes. Journal of Fish Biology, 73,
153–168.
Gayanilo, F.C. Jr, Sparre, P., & Pauly, D. (2005). FAO-ICLARM stock
assessment tools II (FiSAT II). Revised version. User's guide. Computerized
Information Series: Fisheries, No. 8, rev. FAO, Rome.
51!
!
Griffiths, A. M., Ellis, J. S., Clifton-Dey, D., Machado-Schiaffino, G., Bright, D.,
Garcia-Vazquez, E., & Stevens, J. R. (2011). Restoration versus recolonisation:
The origin of Atlantic salmon (Salmo salar L.) currently in the River Thames.
Biological Conservation, 144, 2733–2738.
Jenkins, T. M. Jr, Diehl, S., Kratz, K. W., & Cooper, S. D. (1999). Effects of
population density on individual growth of brown trout in streams. Ecology, 80,
941–956.
Johnson, L. J., & Tricker, P. J. (2010). Epigenomic plasticity within populations:
its evolutionary significance and potential. Heredity, 105, 113–121.
Jokikokko, E., & Jutila, E. (2004). Divergence in smolt production from the
stocking of 1-summer-old and 1-year-old Atlantic salmon parr in a northern
Baltic river. Journal of Applied Ichthyology, 20, 511–516.
Jones, P. A., & Takai, D. (2001). The role of DNA methylation in mammalian
epigenetics. Science, 293, 1068–1070.
Jonsson, B., & Jonsson, N. (1993). Partial migration: niche shift versus sexual
maturation in fishes. Reviews in Fish Biology and Fisheries, 3, 348–365.
Jonsson, N., & Jonsson, B. (1999). Trade-off between egg mass and egg
number in brown trout. Journal of Fish Biology, 55, 767–783.
52!
!
Jonsson, B., & Jonsson, N. (2012). Naturally and hatchery produced European
trout Salmo trutta: do their marine survival and dispersal differ? Journal of
Coastal Conservation. doi:10.1007/s11852-012-0224-1.
Kaspersson, R., & Höjesjö, J. (2009). Density-dependent growth rate in an agestructured population: a field study on stream-dwelling brown trout Salmo trutta.
Kallio-Nyberg, I., Jutila, E., Koljonen, M. L., Koskiniemi, J., & Saloniemi, I.
(2010). Can the lost migratory Salmo trutta stocks be compensated with
resident trout stocks in coastal rivers? Fisheries Research, 102, 69–79.
Kokko, H. (2001). Fisherian and “good genes” benefits of mate choice: how
(not) to distinguish between them. Ecology Letters, 4, 322–326.
Kuparinen, A., Garcia de Leaniz, C., Consuegra, S., & Merilä, J. (2009).
Growth-history perspective on the decreasing age and size at maturation of
exploited Atlantic salmon. Marine Ecology Progress Series, 376, 245–252.
Lobón-Cerviá, J. (2007). Density-dependent growth in stream-living brown trout
Salmo trutta L. Functional Ecology, 21, 117–124.
Morinville, G. R., & Rasmussen, J. B. (2006). Does life-history variability in
salmonids affect habitat use by juveniles? A comparison among streams open
and closed to anadromy. Journal of Animal Ecology, 75, 693–704.
53!
!
Morita, K., & Fukuwaka, M. (2006). Does size matter most? The effect of growth
history on probabilistic reaction norm for salmon maturation. Evolution, 60,
1516–1521.
Mousseau, T. A., & Fox, C. W. (1998). Maternal effects as adaptations. Oxford
University Press, Oxford.
Naish, K. A., Taylor, J. E., Levin, P. S., Quinn, T. P., Winton, J. R., Huppert, D.,
& Hilborn, R. (2008). An evaluation of the effects of conservation and fishery
enhancement hatcheries on wild populations of salmon. Advances in marine
biology, 53, 61–194.
Parra, I., Almodóvar, A., Ayllón, D., Nicola, G. G., & Elvira, B. (2011).
Ontogenetic variation in density!dependent growth of brown trout through
habitat competition. Freshwater Biology, 56, 530–540.
Perry, S. F., & Rivero-Lopez, L. (2012). Does the presence of a seawater gill
morphology induced by dietary salt loading affect Cl− uptake and acid-base
regulation in freshwater rainbow trout Oncorhynchus mykiss. Journal of Fish
Biology, 80, 301–311.
Pettersson, J. C. E., Hansen, M. M., & Bohlin, T. (2001). Does dispersal from
landlocked trout explain the coexistence of resident and migratory trout females
in a small stream? Journal of Fish Biology, 58, 487–495.
Reyna-Lopez, G. E., Simpson, J., & Ruiz-Herrera, J. (1997). Differences in DNA
methylation patterns are detectable during the dimorphic transition of fungi by
54!
!
amplification of restriction polymorphisms. Molecular and General Genetics,
253, 703–710.
Rogell, B., Dannewitz, J., Palm, S., Petersson, E., Dahl, J., Prestegaard, T., et
al. (2012). Strong divergence in trait means but not in plasticity across hatchery
and wild populations of sea-run brown trout Salmo trutta. Molecular Ecology,
21, 2963–2976.
Rounsefell, G. A. (1958). Anadromy in North American Salmonidae. US Fish
Wildlife Service Bulletin, 58, 171–185.
Saura, M., Caballero, P., Caballero, A., & Morán, P. (2006). Genetic variation in
restored Atlantic salmon (Salmo salar L.) populations in the Ulla and Lérez
rivers, Galicia, Spain. ICES Journal of Marine Science, 63, 1290–1296.
Serbezov, D., Bernatchez, L., Olsen, E. M., & Vøllestad, L. A. (2010). Mating
patterns and determinants of individual reproductive success in brown trout
(Salmo trutta) revealed by parentage analysis of an entire stream living
population. Molecular Ecology, 19, 3193–3205.
Serrano, I., Larsson, S., & Eriksson, L.-O. (2009). Migration performance of wild
and hatchery sea trout (Salmo trutta L.) smolts—Implications for compensatory
hatchery programs. Fisheries Research, 99, 210–215.
Skaala, Ø., & Naevdal, G. (1989). Genetic differentiation between freshwater
resident and anadromous brown trout, Salmo trutta, within watercourses.
55!
!
Skrochowska, S. (1969). Migrations of the sea-trout (Salmo trutta L.) brown
trout (Salmo trutta M. Fario L.) and their crosses. Polish Archives of
Hydrobiology, 16, 149–180.
Stabell, O. (1984). Homing and olfaction in salmonids: a critical review with
special reference to the Atlantic salmon. Biological Reviews, 59, 333-388.
van de Pol, M. (2012). Quantifying individual variation in reaction norms: how
models. Methods in Ecology and Evolution, 3, 268–280.
Xunta de Galicia. (2013). Licencias expedidas para la pesca recreativa.
http://www.cmati.xunta.es/c/document_library/get_file?folderId=164453&name=
DLFE-21704.pdf.
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. (2009).
Mixed Effects Models and Extensions in Ecology With R. Springer, NY.
56!
!
CONCLUSIONES
57!
!
CONCLUSIONES
1. La compensación marina en el crecimiento de los esguines de menor
tamaño permite recuperar una talla óptima y por tanto eliminar la desventaja
inicial adquirida en agua dulce.
2. El estudio retrospectivo sobre el crecimiento de la trucha migradora en el
río y en el mar indica que los individuos de una misma edad alcanzan tallas
parecidas en el momento del desove pese a haber experimentado diferentes
trayectorias de crecimiento.
3. El nuevo método utilizado para examinar la variación en las trayectorias de
crecimiento de las escamas basado en un modelo lineal de efectos mixtos
permite establecer que las relaciones entre la abundancia de individuos y su
crecimiento son claves en la gestión de los hábitats tanto de agua dulce como
de agua salada.
4. Los experimentos de repoblación realizados indican que la repoblación
durante la fase de huevo es más eficaz para el aumento de la parte anádroma
de la población que la repoblación con individuos de mayor edad. Este método
reduce además sensiblemente el coste de las repoblaciones.
5. En la selección de reproductores para la reproducción artificial los aspectos
más importantes que deben considerarse son el fenotipo paterno y la
diversidad del MHC. La elección de hembras anádromas y machos anádromos
(siempre que sea posible) aumentan las posibilidades de migración de la
descendencia.
58!
!
6. Los experimentos realizados en piscifactoría indican que la dieta puede
regular, mediante mecanismos epigenéticos, la expresión de genes. La
alimentación con dietas ricas en sal mejora significativamente la supervivencia
de las truchas en agua de mar.
59!
!
REPRINTS DE LAS PUBLICACIONES
60!
!
And the last shall be first: heterochrony and compensatory
marine growth in sea trout (Salmo trutta)
61!
!
And the Last Shall Be First: Heterochrony and
Compensatory Marine Growth in Sea Trout (Salmo trutta)
Francisco Marco-Rius1, Pablo Caballero2, Paloma Morán1, Carlos Garcia de Leaniz3*
1 Departamento de Bioquı́mica, Genética e Inmunologı́a, Universidad de Vigo, Vigo, Spain, 2 Consellerı́a de Medio Rural, Servizo de Conservación da Natureza, Xunta de
Galicia, Pontevedra, Spain, 3 Department of BioSciences, Swansea University, Swansea, United Kingdom
Abstract
Early juvenile growth is a good indicator of growth later in life in many species because larger than average juveniles tend
to have a competitive advantage. However, for migratory species the relationship between juvenile and adult growth
remains obscure. We used scale analysis to reconstruct growth trajectories of migratory sea trout (Salmo trutta) from six
neighbouring populations, and compared the size individuals attained in freshwater (before migration) with their
subsequent growth at sea (after migration). We also calculated the coefficient of variation (CV) to examine how much body
size varied across populations and life stages. Specifically, we tested the hypothesis that the CV on body size would differ
between freshwater and marine environment, perhaps reflecting different trade-offs during ontogeny. Neighbouring sea
trout populations differed significantly in time spent at sea and in age-adjusted size of returning adults, but not on size of
seaward migration, which was surprisingly uniform and may be indicative of strong selection pressures. The CV on body size
decreased significantly over time and was highest during the first 8 months of life (when juvenile mortality is highest) and
lowest during the marine phase. Size attained in freshwater was negatively related to growth during the first marine
growing season, suggesting the existence of compensatory growth, whereby individuals that grow poorly in freshwater are
able to catch up later at sea. Analysis of 61 datasets indicates that negative or no associations between pre- and postmigratory growth are common amongst migratory salmonids. We suggest that despite a widespread selective advantage of
large body size in freshwater, freshwater growth is a poor predictor of final body size amongst migratory fish because
selection may favour growth heterochrony during transitions to a novel environment, and marine compensatory growth
may negate any initial size advantage acquired in freshwater.
Citation: Marco-Rius F, Caballero P, Morán P, Garcia de Leaniz C (2012) And the Last Shall Be First: Heterochrony and Compensatory Marine Growth in Sea Trout
(Salmo trutta). PLoS ONE 7(10): e45528. doi:10.1371/journal.pone.0045528
Editor: Casper Breuker, Oxford Brookes University, United Kingdom
Received April 20, 2012; Accepted August 20, 2012; Published October 1, 2012
Copyright: ! 2012 Marco-Rius et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was funded by grants from Ministerio de Ciencia y Tecnologı́a (CGL2007-60572 and CGL2010-14964), and Fondos FEDER
(PGIDIT03PXIC30102PN; Grupos de Referencia Competitiva, 2010/80). Francisco Marco-Rius was supported by a pre-doctoral fellowship from the Spanish
Government (BES-2008-001973). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: [email protected]
Introduction
size) and growth during the first marine season (post smolt growth)
are perhaps the traits that differ the most amongst populations
[7,13–14], probably because homing behaviour tends to result in
geographical isolation and locally adapted populations [15].
Maturation schedules also tend to differ greatly amongst
populations [16], even among fish inhabiting neighbouring rivers
[13,17] suggesting the existence of different and spatially localised
trade-offs.
Field studies have revealed contrasting selection pressures for
body size of anadromous salmonids in freshwater and marine
environments [18–21] suggesting the existence of different tradeoffs in rivers and sea. Yet, the relationship between pre-migratory
growth in freshwater (i.e. smolt size) and post-migratory growth in
the sea (i.e. post-smolt growth) is not clear. There appear to be as
many studies reporting a negative relationship between smolt
length and marine growth [16,22–24] as there are studies
reporting a positive or no relationship [25–27]. This suggests that
there can be considerable variation in the way individuals adapt to
environmental change during their transition from freshwater to
marine environments.
Here we used scale image analysis to reconstruct individual
growth trajectories of migratory brown (sea trout, Salmo trutta) in
Many animals pass through some migratory stage during their
lives [1–2], typically in relation to feeding or reproduction.
Migrations are thought to maximise age-specific fecundity and the
probability of surviving from one breeding season to the next [3–
4], and have been interpreted as a response to adversity [5].
Migrations are energetically costly and a trade off may be
expected to exist between the costs of migrations and the fitness
benefits accrued by a larger body sizes [1], as well as between
predator avoidance and feeding gains [6]. Migrants typically
achieve a larger body size than non-migrants, but may also sustain
higher mortality rates than resident individuals [7–8]. Such tradeoffs between growth and mortality are common in many species
and can reflect a balance between foraging and predation risk,
growth and maturation, and growth and resistance to diseases,
amongst others [9]. These may result in individuals achieving
similar fitness, despite having grown at widely different rates [10].
Amongst anadromous salmonids, which must migrate between
very different freshwater and marine environments, the risk of
predation increases at sea [11] and a relatively narrow optimum
size at migration appears to exist [12]. Yet, size at migration (smolt
PLOS ONE | www.plosone.org
1
October 2012 | Volume 7 | Issue 10 | e45528
Compensatory Marine Growth in Sea Trout
determine scale growth slopes, measured between the scale focus
and the scale edge [38].
order to examine the relationship between smolt size and post
smolt growth in six neighbouring populations. As selection can act
strongly on body size and size-related traits in juvenile salmonids
[15], we used the coefficient of variation on body size (CV) in
order to quantify the extent of phenotypic variation [28] across
different life stages. Specifically, we tested the hypothesis that the
coefficient of variation on body size of migratory trout would differ
between the freshwater and marine environment, perhaps
reflecting different trade-offs during ontogeny [22–23]. Furthermore, because selection in fishes tends to be strongest during the
early juvenile stages - when mortality is highest but when body size
is also smallest [29–30] - we also expected to find a negative
relationship between developmental stage and the extent of
individual variation in body size.
Reliability of Scale Analysis
A paired t-test was used to assess non-random deviations in scale
radii between the original scales and their acetate impressions
(n = 30) in order to quantify potential bias in scale measurements
arising from pressure from the hand roller. To ascertain the
precision of the scale analysis, we estimated the repeatability of the
point of entry into the sea and of the end of the first marine
growing season by measuring the scales of 30 individuals twice in
a double blind fashion and calculating the intra-class correlation
coefficient (a-Cronbach) as per [33]. The Pearson correlation
coefficient was used to evaluate the strength of the association
between scale radius and body size of fish in each river. The
coefficients of variation (CV = SD/mean) were then examined to
compare the precision of body size and scale measurements.
Precision in scale measurements (0.01 mm; CV = 13.9%) was
better than that of body size measurements (cm; CV = 15.3%), and
the former was therefore preferred to examine growth variation
among migratory trout.
In order to evaluate if the relationship between somatic growth
and scale radius changed with age or body size, we tested for
homogeneity of slopes in an ANCOVA model [39] using either
age (five age classes) or body size (four size quartiles) as covariates.
We also checked that the relationship between age and body size
(Log10) was linear within the limits of this study (F1,126 = 17.392,
P,0,001), and not different among rivers (River F5,126 = 0.323,
P = 0.898; River6Age interaction F5,126 = 0.060, P = 0.998).
Methods
Study Populations
Migratory sea trout were caught between August and October
2002 on their returning migration by government officials in
upstream traps or by angling by licensed sport fishermen in six
neighbouring rivers in NW Spain (Fig. 1). Study populations
differed in physical as well as in key demographic parameters,
including population abundance (as inferred from rod and line
caches), age and body size, and expected survival (as inferred from
incidence of multiple spawners and maximum longevity; Table 1).
Upstream migrants were assumed to have been caught on their
river of origin as these populations had shown isolation by distance
and restricted gene flow, which are suggestive of strong homing
behaviour [31].
Statistical Analysis
Ethics Statement
Analyses were carried out using SYSTAT 10.0 and the R
software [40]. The R MASS package [41] was used to model
variation in post smolt growth (PSG) in relation to smolt size, smolt
age and river identity, and the Akaike information criteria (AIC)
was used for model selection.
We employed the CV to quantify phenotypic variation in body
size [28] among populations and across four different key life
stages (first freshwater winter, moment of entry into the sea, first
marine growing season, and return as adult to the river). Only
individuals that had spent 2 years in freshwater (S2 smolts) were
used, as this was the dominant smolt age in the study populations
and the number of fish of other smolt ages was low. Approximate
95% confidence limits were constructed by bootstrapping 1,000
replicates, and the Fligner-Killen test (a non-parametric version of
Levene’s test which is robust to departures of normality [42]) was
used to compare differences in CV among stages of development
and among rivers.
We visualized growth reaction norms during the freshwater to
marine transition by plotting individual growth trajectories
between the moment of entry into the sea (smolt size) and the
growth of the first marine growing season (PSG). These trajectories
described how individuals responded to environmental change
according to population of origin and smolt age. Variation in
individual growth trajectories was analysed by repeated measures
ANCOVA using river of origin as a fixed factor and smolt age and
sea age as covariates. We then calculated the partial correlation
coefficient to test the strength of association between freshwater
and marine growth once the effects of freshwater and sea age had
been statistically partialled out. This was achieved by calculating
the correlation between the residuals of freshwater and marine
growth after each had been regressed on freshwater and sea age.
Finally, in order to estimate the extent and magnitude of
compensatory marine growth we computed the size rank of
Collection of scale samples was carried out by fisheries staff of
the Regional Government of Galicia (Wildlife Service) using a nonintrusive procedure and according to current Spanish Regulations.
No specific permits were required for the described field studies,
and these did not involve endangered or protected species.
Scale Analysis and Growth Profiles
Scales of 30 individuals per river were stored dry in paper
envelopes, along with information on their body size (fork length,
mm). Between three and five scales with a clear (non–regenerated)
nucleus were selected per individual to prevent bias due to loss of
growth rings [32]. Acetate impressions were made with the aid of
a pressure roller and the resulting impressions were then scanned
with a Minolta MS 6000 microfilm scanner at 23–506 magnifications and saved as high resolution TIFF images as in [33].
The software Image-J v. 1.4.1 [34] was employed to digitize the
position of each growth ring (circuli), to identify the annual growth
rings (annuli), and to measure the inter-circuli spacing along the
360u scale axis with reference to a calibrated scale bar in order to
derive measures of scale growth [35]. The freshwater and marine
ages were determined based on the number of annuli [36], and the
points of entry of smolts into the sea (beginning of marine phase)
and end of the first marine growing season (post-smolt growth,
PSG) were noted [16]. Twenty three finnocks (individuals which
had returned to freshwater before completing one full winter at sea
[37]) were excluded from analysis as these provided no comparable data on post-smolt growth.
Individual growth profiles were obtained by plotting circuli
number against scale size at four key life stages: (a) first freshwater
winter, (b) moment of entry into the sea, (c) end of first marine
growing season, and (d) return of adults into freshwater from the
sea. Ordinary Least Squares (OLS) regression was then used to
2
Figure 1. Study sea trout (Salmo trutta) populations in Galicia, NW Spain.
doi:10.1371/journal.pone.0045528.g001
(F5,136 = 1.002, P = 0.419) allowing us to use scale measurements to
reconstruct changes in body size regardless of river identity.
Testing of interactions terms in ANCOVA indicated that the
relationship between somatic growth and scale radius was not
affected by age or body size (age6scale radius F7,102 = 0.983,
P = 0.447; body size6scale radius F14,91 = 1.344, P = 0.197), i.e.
slopes were homogeneous across age and size classes.
individual fish before and after migrating into the sea, and
calculated Spearman rank correlation coefficients (rs) between
freshwater and marine growth for each river. In the absence of
compensatory marine growth, we would expect to find a positive
correlation between smolt size and postsmolt growth, as larger
than average smolts would continue to be larger than average at
sea. On the other hand, if fish exhibited compensatory marine
growth, we would expect to find no association between smolt size
and postsmolt growth, as smaller than average fish would be able
to catch-up (and move up the size rank) at sea.
Variability in Life Histories Among Populations
Sea trout populations differed significantly in sea age
(F5,132 = 5.39, P,0.001), but not on smolt age (F5,132 = 0.55,
P = 0.736). Populations did not vary in the size of smolts
(F5,126 = 1.87, P = 0.104), once the overriding effect of smolt age
(F1,126 = 110.53, P,0.001) had been statistically controlled for, but
there was an interaction between smolt age and river of origin on
smolt size (F1,126 = 110.53, P = 0.029) suggesting that different
populations experienced different freshwater growth patterns
before migrating to sea. Sea trout populations also differed in
size of returning adults (F5,126 = 3.20, P = 0.009) once the
important effect of sea age had been statistically accounted for
(F1,126 = 25.51, P,0.001).
Results
Reliability of Scale Measurements
There was no significant distortion of scale radius due to the
impression process (t29 = 0.547, P = 0.465), indicating that acetate
impressions gave an accurate, unbiased representation of scale
size. Repeatabilities of scale size were high, both for smolt scale
length (a-Cronbach = 0.879) and for scale size attained at the end
of the first marine growing season (a-Cronbach = 0.918). Scale
radius and fork length were positively correlated (r = +0.654,
P = 0.001), and the relationship was not different among rivers
3
4
We concentrated on modelling post-smolt growth (PSG) during
the first marine growing season, as this was the stage where there
was greatest variation among individuals, and we could obtain
data from all fish regardless of time spent at sea. We used as
predictors the age and size of smolts, as well as the river of origin,
to test the prediction that early growth performance during
freshwater life was a good predictor of growth performance later in
life at sea. Analysis of individual growth reaction norms (Fig. 4)
indicated that smolt size at the moment of entry into the sea was
negatively correlated with subsequent growth during the first
marine growing season, once the effects of sea age and freshwater
age had been statistically partialled out (partial correlation
r = 20.233, df = 134, P = 0.006).
Following stepwise multiple regression, variation in post-smolt
growth (PSG) was accounted for by river of origin (F5,106 = 11.079,
P,0.001), smolt scale length (F1,106 = 7.838, P,0.001) and smolt
age (F2,106 = 3.975, P = 0.048), in addition to a significant 3-way
interaction among river, smolt age and smolt scale length
(F7,106 = 2.967, P = 0.007). The minimal adequate model explained about 53% of the variance in PSG (F31,106 = 3.68,
P,0.001, AIC = 2474.72) and provided evidence of a negative
relationship between size attained in freshwater and subsequent
growth at sea.
Compensatory marine growth (revealed by the frequency of fish
moving up the size rank following entry into the sea) was
substantial and widespread. Thus, all populations exhibited
compensatory growth, as suggested by non-significant rank
correlation coefficients between smolt size and post-smolt growth
(these ranged from rs = 20.442, P = 0.051 in the R Tambre to
rs = 0.043, P = 0.846 for the R. Eume). The results also indicate
that between 45% (R. Tambre) and 54% (R. Ulla) of fish displayed
a gain in size rank at sea, depending on population of origin.
Average change in post-migratory size rank of those fish displaying
compensatory growth ranged from 7.6 positions in the R. Ulla to
10.4 positions in the R. Lerez, suggesting that size rank changes
are likely to be biologically meaningful.
480
351
6
doi:10.1371/journal.pone.0045528.t001
4
3.3360.13
Compensatory Marine Growth
238
2803.6
102.3
79.7
Ulla
Individual variation in reconstructed growth profiles was high
among individuals (Fig. 2; CV = 71.2%) and increased significantly
over time (Fligner-Killen Test, x2 = 59.91 df = 3, P,0.001) as fish
followed diverging growth trajectories. The CV on body size, as
inferred from variation in scale size and calculated for those
returning adults that had spent two winters in freshwater and one
winter at sea (the dominant age class), varied significantly among
life stages (Fig. 3;Fligner-Killen test x2 = 55.51, df = 3, P,0.001),
being highest during the first 8 months of life (when juvenile
mortality is highest) and lowest when adults returned from the sea.
Populations differed significantly in CV for body size only during
the first winter in freshwater (Fligner-Killen test x2 = 14.50, df = 5,
P = 0.013), but not at later stages (smolt x2 = 5.33, df = 5,
P = 0.376; PSG x2 = 9.036, df = 5, P = 0.107; returning adults
x2 = 1.678, df = 5, P = 0.891).
220612.4
2.1260.12
5
3.5560.11
2.3060.12
1530
54.2
Tambre
5.7
133
6
356
550
205611.4
3.4360.15
2.0460.13
6
4.1060.19
2.1460.09
17469.1
490
540
479
6
5
5
449.5
64
456.9
21.3
Lerez
25.1
14.2
Mandeo
20.7
99
21067.1
3.3560.13
2.2060.11
4
3.6960.14
2.1260.08
21267.5
450
440
537
113
5
5
269.6
10
470.2
7.2
Landro
22.6
19.3
Eume
13.3
64
Rod and
line catch
Stream order
Watershed
(Km2)
Mean annual
flow (m3/s)
River
Accessible reach
(Km)
Estuary
(Km2)
22465.8
Mean age at
return (yr)
5
Individual Variation in Reconstructed Growth Profiles
Mean
smolt
age
(yr)
Mean smolt
size
(mm)
Max.
body
size
(mm)
Table 1. Physical and demographic parameters (means 6 SE) of study populations of migratory brown trout (Salmo trutta).
Max. longevity
(yr)
Discussion
Our study on migratory trout indicates that there is a negative
relationship between the size of juveniles in freshwater prior to
migration and their subsequent growth at sea, once the effects of
age on growth are controlled for. In general, a positive relationship
between freshwater and marine growth is expected if marine food
resources are patchily distributed, and dominant individuals can
monopolize resources, as they tend to do in freshwater [43]. In
4
Figure 2. Individual scale growth profiles of migratory sea trout. Shown are estimated scale sizes (a proxy for body size) at each circuli
number. Dark line represents mean values (95 CI) adjusted for a common smolt age and sea age at four key life stages (first winter in freshwater, entry
into the sea – dotted line, end of first marine growing season, and adult returning to freshwater).
comparisons), though not statistically so (x2 = 3.0 df = 1,
P = 0.083). Our study, like most other studies, suggests that
juvenile size is a poor predictor of subsequent growth at sea
because individuals that grow slowly in freshwater are able to
compensate with enhanced post-migratory growth later in life.
There are various possible reasons for this.
Firstly, it is possible that gender differences (which were not
measured in our study) may introduce a source of variation in the
relationship between pre- and post-migratory body size, for
example if males and females achieve different sizes [45] or are
under different selection pressures [46–47]. However, studies
where gender has been controlled for [22] failed to find a positive
relationship between freshwater size and marine growth, or found
contrast, when marine resources are evenly distributed, resource
monopolization is not possible, or the costs of resource defence
simply outweigh its benefits, a negative or no correlation between
freshwater and marine growth can be expected [44].
Negative correlations between pre and post-migratory growth
have been reported in many studies of anadromous salmonids and
suggest the existence of trade-offs, whereby traits that promote fast
growth in one environment do not translate into rapid growth in
other environments. Indeed, analysis of 61 datasets representing
four salmonid species (Table 2) indicates that there is no significant
association between smolt size and marine growth in the majority
of studies (55.7%), and that negative relationships (29.5% of cases)
tend to be more likely to occur than positive ones (14.8% of
5
Figure 3. Temporal trends in the coefficient of variation (CV) for scale size (a proxy for body size) of three year old sea trout (2.1.
age class) at four key life stages, stratified by population of origin. Shown are mean values and approximate 95% confidence intervals
derived from 1,000 bootstrap replicates.
an inverse relationship, suggesting that the lack of association is
a common phenomenon. Lack of association between pre- and
post-migratory growth may also be due to low statistical power.
For example, with our sample size (n = 138) we were able to detect
a correlation greater than 0.21 or lower than 20.21 with 80%
power, but power to detect weaker associations (and to reject the
null hypothesis of no correlation) may have been too low in
previous studies [48].
Thirdly, random measurement error would tend to blur any
relationship between freshwater and marine growth. This may be
particularly true if back-calculation of body size (instead of scale
growth) is used because in situ measurements of fish size may lack
precision in the field [49]. On the other hand, scale size measured
in the laboratory has been shown to be a reliable indicator of smolt
size in salmonids [50], and our study shows that repeatability in
scale size was high and that no bias due to the scale impression
process could be detected. Therefore, we are confident that our
estimates of scale growth are reliable, and that these allow us to
reconstruct changes in growth of migratory trout and to compare
growth trajectories of individuals.
Using size comparisons to infer rate of growth implicitly
assumes that individuals have grown over the same period of time.
This would be true only if all fish had emerged and smolted at the
same time, and grown over the same length of time in the marine
environment. Otherwise, variation in growth rates may be
confounded by variation in the length of the growing season,
and this may mask the detection of size trade-offs. All adults used
in our study were caught in freshwater over a relatively short
period of time (August–October), and we excluded from analysis
those fish that had spent less than one full winter at sea to reduce
additional sources of variation. Information on timing of alevin
emergence was not available in our study, but development in
southern brown trout populations is rapid and emergence is likely
to be short and less protracted than in more northern latitudes
[51–52]. Data from a downstream smolt trap in one of the study
rivers (R. Ulla) indicates that the timing of smolt migration is
highly clumped, with 50% of smolts moving downstream over
a relatively narrow time window (average during 1998–2001 was
16 days, range = 6–28 days). This suggests that the observed
variation in the size of individuals cannot solely be explained by
differences in the timing of emergence, timing of smolting, or in
length of the growing season, which are thought to have been
similar among individuals in our study. Other studies have also
shown that such differences are small in relation to variation in
smolt size and post-smolt growth [22].
Compensatory growth, where individuals that grow poorly
during periods of nutritional deficit are then able to accelerate
their growth and ‘‘catch-up’’ when conditions improve [10,53], is
the most plausible explanation for the observed inverse relationship between freshwater and marine growth shown in our study.
Migration has been viewed as a strategy to ‘‘escape’’ from harsh
conditions, typically caused by predation and competition from
increasingly larger conspecifics [54]. Among facultative anadromous salmonids, migration is thought to represent a trade-off
between better growth opportunities at sea, but also greater risk
from predation [7,55]. Although there is some evidence for
density-dependence in salmonid marine survival [56], the evidence
is not compelling. In contrast, evidence of density-dependent
marine growth is much more common [57–59], though this is
most readily apparent during the late marine phase, presumably
because the costs of reduced growth are less likely to have an
impact on survival later in life [60]. Often the mean scale radius of
salmonid migrants is significantly smaller than that of returning
adults from the same cohort, suggesting that small migrants sustain
high mortality at sea [49]. More generally, large individuals often
have a survival advantage over small conspecifics, both in
freshwater and in the sea [50,61–64] adding some support to
the ‘bigger is better’ hypothesis. However, there are also many
cases when no such size advantage is apparent [65], or when sizeselective mortality favours a large body size in some years and
a small size in others [66–67], perhaps because phenotypic
adjustment is the norm in salmonid populations [15,68].
6
Figure 4. Individual growth reaction norms during the freshwater to marine transition in six sea trout populations, stratified by
smolt age (# 1 yr, n 2 yr. +3 yr). Shown are matched comparisons between scale size at the moment of entry into the sea and subsequent scale
growth increment during the first marine growing season (PSG) for each individual fish.
During the first stages of their lives, brown trout juveniles tend to
be subjected to strong selection mediated by both densitydependent and density-independent processes [73], the relative
strengths of which may differ markedly from site to site, and also
from year to year [70,74–75]. Early density dependent processes
are thought to decrease at smolting, when territoriality in
migratory salmonids disappears and the strength of intra-specific
competition weakens in preparation for the marine migration [55].
Osmoregulation amongs smolts is accompanied by tissue differentiation of gut, gill and kidney [55,76], and juveniles must reach
a minimum threshold smolt size or will not smolt [55]. Smolt size,
not age, is the primary determinant of marine survival in
anadromous salmonids [76], and strong selection for smolt size
may therefore be expected to exist. Indeed, our study indicates
that there was relatively little variation for smolt size in migratory
brown trout, and a significant decrease in CV from the first
freshwater winter onwards. Individual variation in salmonids body
size has been shown to decrease after periods of intense selection,
for example following size-selective predation [77–78] or poor
feeding conditions at sea [61].
Whatever the precise direction of selection, a recent metaanalysis [69] has shown that fish are subjected to extreme selection
on body size during early life, the strength of which typically
decreases over time. This is consistent with our results on
migratory trout, which indicate that the CV for body size (as
inferred from variation in scale size) varies markedly over the life
time of individuals, decreasing with time as fish migrated from
freshwater into the sea. With the exception of the first freshwater
winter, no differences were found among six neighbouring
populations, suggesting that once the critical time for survival
has passed [70–71], selection probably operates in a similar way in
neighbouring rivers.
The coefficient of variation (CV) is useful for quantifying
phenotypic variation [28] and for examining ontogenetic size
changes in longitudinal studies [51]. CV is expected to decrease
when stabilizing selection acts upon a continuous trait, and can be
useful as a preliminary step towards more detailed selection
analysis [72]. Individual variation in growth rates decreases with
increasing competition in brown trout [29,46], suggesting that
changes in the CV could track changes in selection intensity.
7
Table 2. Studies on anadromous salmonids investigating the relationship between smolt size and post-smolt growth.
Relationship between smolt size
and post-smolt growth
Type of
Fish
Species
Location
2
NS
+
Period
S. salar
Matre Aq St.
0
0
1
1981
MR
H
[27]
S. salar
R. Narcea
5
1
0
1986–1990
BC
W
[16]
S. salar
R. Esva
3
2
0
1986–1990
BC
W
[16]
S. salar
R. Cares
1
1
0
1987–1988
BC
W
[16]
S. salar
R. Penobscot
0
0
1
1973–1990
SG
H
[18]
S. salar
Finland
0
0
1
1980–1991
MR
H
[26]
S. salar
R. Imsa
1
0
0
1981–2003
MR
H
[23]
S. salar
R. Alta
1
0
0
1993–1995
MR
W
[22]
S. salar
Gulf St. Lawrence
0
1
0
1982–1984
SG
W
[25]
S. salar
R. Asón
1
0
0
1948–2003
BC
W
[33]
S. salar
R. Miramichi
0
1
0
1956–2003
BC
W
[87]
S. trutta
Finland
1
0
0
1915–1989
Cline
W
[14]
O. kisutch
Oregon
0
9
2
1991–2000
BC
W
[44]
O. kisutch
Washington
0
8
2
1991–2000
BC
W
[44]
O. kisutch
British Col.
2
9
0
1991–2000
BC
W
[44]
O. kisutch
British Col.
1
0
0
1990
MR
H
[88]
O. mykiss
British Col.
0
0
1
1990
MR
H
[88]
S. laucomaenis
R. Nairo & Haraki
1
2
1
1990–1991
BC
W
[24]
S. trutta
6 rivers, NW Spain
1
0
0
2002
SG
W
This study
Total
18
34
9
Method
Reference
Studies that found a negative relationship (2), no significant relationship (NS), and a positive relationship (+) are indicated. Method: MR mark and recapture, BC backcalculation of juvenile size, SG scale growth. Type of fish : H hatchery, W wild.
Some authors have found that variation in smolt size and age
decrease with increasing stream size (e.g. [79]), apparently because
the success of large juveniles is more variable and less predictable
in small than in large streams [46]. However, no such relationship
was apparent in our study populations, which displayed the same
narrow variation in smolt size in spite of relatively large differences
in stream size and in demographic parameters (Table 1), again
suggesting that smolt size is probably under strong selection.
In summary, our study indicates that there is an inverse
relationship between pre- and post-migratory size in migratory
trout, which we interpret as indicative of marine compensatory
growth. The CV on body size was highest during the first
freshwater winter and decreased during the marine phase, and this
appears to track changes in juvenile mortality. In addition to
heritable variation, phenotypic plasticity and genotype6environment interactions, ontogenetic variation due to changes in the
timing or rate of developmental events (growth heterochrony [80])
can be an important source of body size variation [81–82]. This is
particularly true in fishes, which have indeterminate growth, and
where even small changes in heterochrony can result in large
morphological differences among individuals [83–84]). Gene
duplication may have also allowed large phenotypic diversification
amongst the teleosts [85], as it provides ‘‘extra genetic material
freed from the need to function in only one way, and therefore
available for experimental [evolutionary] change’’ [81].
We suggest that despite a widespread selective advantage of
large body size in freshwater, freshwater growth is a poor predictor
of final body size amongst migratory fish because selection may
favour growth heterochrony leading to marine compensatory
growth. Marine compensatory growth allows size-depressed
individuals to catch-up later in life and may, therefore, negate
any initial size advantage acquired in freshwater. Such a mechanism could be responsible for the heterogeneity in growth
trajectories observed in our study, a pattern not readily detected
in captivity [86], where food is generally plentiful and natural
selection relaxed. Ultimately, growth heterochrony could help
maintain phenotypic variation in sea trout because anadromous
individuals could attain similar sizes at spawning (and hence have
similar fecundities) despite having experienced very different
growth trajectories early in life.
Acknowledgments
We wish to thank Pilar Alvariño and Diego Figueroa for technical
assistance and Sonia Consuegra and four anonymous referees for useful
comments on previous versions of this manuscript.
Author Contributions
Conceived and designed the experiments: FMR PC PM CGL. Performed
the experiments: FMR PM CGL. Analyzed the data: FMR CGL.
Contributed reagents/materials/analysis tools: PC PM CGL. Wrote the
paper: FMR CGL. Commented on the manuscript: PM PC.
8
References
34. Abramoff MD, Magalhães PJ, Ram SJ (2004) Image processing with ImageJ.
Biophot Int 11: 36–42.
35. Fisher JP, Pearcy WG (1990) Spacing of scale circuli versus growth-rate in young
coho salmon. Fish Bull 88: 637–643.
36. Rifflart R, Marchand F, Rivot E, Baglinière JL (2006) Scale reading validation
for estimating age from tagged fish recapture in a brown trout (Salmo trutta)
population. Fish Res 78: 380–384.
37. Maisse G, Mourot B, Breton B, Fostier A, Marcuzzi O, et al. (1991) Sexual
maturity in sea trout, Salmo trutta L., running up the River Calonne (Normandy,
France) at the ‘finnock’ stage. J Fish Biol 39: 705–715.
38. Schröder V, Garcia de Leaniz C (2011) Discrimination between farmed and
free-living invasive salmonids in Chilean Patagonia using stable isotope analysis.
Biol Invasions 13: 203–213.
39. Shafer DJ (2000) Evaluation of periodic and aperiodic otolith structure and
somatic-otolith scaling for use in retrospective life history analysis of a tropical
marine goby, Bathygobius coalitus. Mar Ecol-Prog Ser 199: 217–229.
40. R Development Core Team (2010) R: A language and environment for
statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
41. Venables WN, Ripley BD (2002) Modern applied statistics with S. New York:
Springer. 495 p.
42. Jacobs MW, Podolsky RD (2010) Variety is the spice of life histories: comparison
of intraspecific variability in marine invertebrates. Integr Comp Biol 50: 630–
642.
43. Tiira K, Laurila A, Enberg K, Piironen J (2009) Short-term dominance: stability
and consequences for subsequent growth. J Fish Biol 74: 2374–2385.
44. Snover ML, Watters GM, Mangel M (2005) Interacting effects of behavior and
oceanography on growth in salmonids with examples for coho salmon
(Oncorhynchus kisutch). Can J Fish Aquat Sci 62: 1219–1230.
45. Martin Smith KM, Armstrong JD (2002) Growth rates of wild stream-dwelling
Atlantic salmon correlate with activity and sex but not dominance. J Anim Ecol
71: 413–423.
46. Klemetsen A, Amundsen PA, Dempson JB, Jonsson B, Jonsson N, et al. (2003)
Atlantic salmon Salmo salar L., brown trout Salmo trutta L. and Arctic charr
Salvelinus alpinus (L.): a review of aspects of their life histories. Ecol Freshw Fish
12: 1–59.
47. Consuegra S, Garcia de Leaniz C (2007) Fluctuating sex ratios, but no sexbiased dispersal, in a promiscuous fish. Evol Ecol 21: 229–245.
48. Nicieza AG, Álvarez D (2009) Statistical analysis of structural compensatory
growth: how can we reduce the rate of false detection? Oecologia 159: 27–39.
49. Anderson RO, Neumann RM (1996) Length, weight, and associated structural
indices. In: Fisheries techniques. American Fisheries Society, Bethesda, Maryland.
50. Henderson MA, Cass AJ (1991) Effect of smolt size on smolt-to-adult survival for
Chilko Lake sockeye salmon (Oncorhynchus nerka). Can J Fish Aquat Sci 48: 988–
994.
51. Garcia de Leaniz C, Fraser N, Huntingford FA (2000) Variability in
performance in wild Atlantic Salmon, Salmo salar L., fry from a single redd.
Fish Mgmt Ecol 7: 489–502.
52. Parra I, Almodóvar A, Ayllón D, Nicola GG, Elvira B (2011) Ontogenetic
variation in density-dependent growth of brown trout through habitat
competition. Freshw Biol 56: 530–540.
53. Ali M, Nicieza AG, Wootton RJ (2003) Compensatory growth in fishes:
a response to growth depression. Fish Fisher 4: 147–190.
54. Hebblewhite M, Merrill EH (2009) Trade-offs between predation risk and forage
differ between migrant strategies in a migratory ungulate. Ecology 90: 3445–
3454.
55. McCormick SD, Hansen LP, Quinn TP, Saunders RL (1998) Movement,
migration, and smolting of Atlantic salmon (Salmo salar). Can J Fish Aquat Sci 55:
77–92.
56. Emlen JM, Reisenbichler RR, McGie AM, Nickelson TE (1990) Densitydependence at sea for coho salmon (Oncorhynchus kisutch). Can J Fish Aquat Sci
47: 1765–1772.
57. Mathisen OA, Fair L, Beamish RJ, Bugaev VF (2007) Density-dependent
growth of sockeye salmon in the North Pacific Ocean. N Pacif Anad Fish Com 4:
299–310.
58. Martinson EC, Helle JH, Scarnecchia DL, Stokes HH (2008) Density-dependent
growth of Alaska sockeye salmon in relation to climate-oceanic regimes,
population abundance, and body size, 1925 to 1998. Mar Ecol-Prog Ser 370: 1–
18.
59. Hansen LP, Quinn TP (1998) The marine phase of the Atlantic salmon (Salmo
salar) life cycle, with comparisons to Pacific salmon. Can J Fish Aquat Sci 55:
104–118.
60. Ruggerone G, Nielsen J, Farlet E, Ignell S, Hagen P, et al. (2006) Long-term
trends in annual Bristol Bay sockeye salmon scale growth at sea in relation to
sockeye abundance and environmental trends, 1955–2000. NPAFC Tech Rep 4:
56–58.
61. Zabel RW, Williams JG (2002) Selective mortality in Chinook salmon: what is
the role of human disturbance? Ecol Appl 12: 173–183.
62. Cross AD, Beauchamp DA, Moss JH, Myers KW (2009) Interannual variability
in early marine growth, size-selective mortality, and marine survival for Prince
William Sound Pink salmon. Mar Coast Fish 1: 57–70.
1. Milner-Gulland EJ, Fryxell JM, Sinclair ARE (2011) Animal migration. Oxford
University Press, USA. 269 p.
2. McKeown, BA. 1984. Fish migration. London Croom Helm.
3. Alerstam T, Hedenström A, Åkesson S (2003) Long-distance migration:
evolution and determinants. Oikos 103: 247–260.
4. Bohlin T, Pettersson J, Degerman E (2001) Population density of migratory and
resident brown trout (Salmo trutta) in relation to altitude: evidence for a migration
cost. J Anim Ecol 70: 112–121.
5. Taylor LR, Taylor RA (1977) Aggregation, migration and population
mechanics. Nature 265: 415–421.
6. Brönmark C, Skov C, Brodersen J, Nilsson PA, Hansson LA (2008) Seasonal
migration determined by a trade-off between predator avoidance and growth.
PLoS ONE 3: e1957.
7. Jonsson B, Jonsson N (1993) Partial migration: niche shift versus sexual
maturation in fishes. Rev Fish Biol Fish 3: 348–365.
8. Kinnison MT, Unwin MJ, Quinn TP (2003) Migratory costs and contemporary
evolution of reproductive allocation in male chinook salmon. J Evol Biol 16:
1257–1269.
9. Mangel M, Stamps J (2001) Trade-offs between growth and mortality and the
maintenance of individual variation in growth. Evol Ecol Res 3: 583–593.
10. Metcalfe NB, Monaghan P (2001) Compensation for a bad start: grow now, pay
later? Trends Ecol Evol 16: 254–260.
11. Friedland KD, Chaput G, MacLean JC (2005) The emerging role of climate in
post-smolt growth of Atlantic salmon. ICES J Mar Sci 62: 1338.
12. Nicieza AG, Braña F (1993) Compensatory growth and optimum size in oneyear-old smolts of Atlantic salmon (Salmo salar). Can Spec Publ Fish Aquat Sci
118: 225–237.
13. Cucherousset J, Ombredane D, Charles K, Marchand F, Baglinière JL (2005) A
continuum of life history tactics in a brown trout (Salmo trutta) population.
Can J Fish Aquat Sci 62: 1600–1610.
14. Jonsson B, L’Abee-Lund JH (1993) Latitudinal clines in life-history variables of
anadromous brown trout in Europe. J Fish Biol 43: 1–16.
15. Garcia de Leaniz C, Fleming IA, Einum S, Verspoor E, Jordan WC, et al. (2007)
A critical review of adaptive genetic variation in Atlantic salmon: implications
for conservation. Biol Rev Camb Philos Soc 82: 173–211.
16. Nicieza AG, Braña F (1993) Relationships among smolt size, marine growth, and
sea age at maturity of Atlantic salmon (Salmo salar) in northern Spain. Can J Fish
Aquat Sci 50: 1632–1640.
17. Hutchings JA, Jones M (1998) Life history variation and growth rate thresholds
for maturity in Atlantic salmon, Salmo salar. Can J Fish Aquat Sci 55: 22–47.
18. Friedland KD, Haas R (1996) Marine post-smolt growth and age at maturity of
Atlantic salmon. J Fish Biol 48: 1–15.
19. Jonsson N, Hansen LP, Jonsson B (1991) Variation in age, size and repeat
spawning of adult Atlantic salmon in relation to river discharge. J Anim Ecol: 60:
937–947.
20. Scarnecchia DL (1984) Climatic and oceanic variations affecting yield of
Icelandic stocks of Atlantic salmon (Salmo salar). Can J Fish Aquat Sci 41: 917–
931.
21. Scarnecchia DL, Ísaksson Á, White SE (1989) Oceanic and riverine influences
on variations in yield among Icelandic stocks of Atlantic salmon. Trans Am Fish
Soc 118: 482–494.
22. Einum S, Thorstad E, Næsje T (2002) Growth rate correlations across life-stages
in female Atlantic salmon. J Fish Biol 60: 780–784.
23. Jonsson N, Jonsson B (2007) Sea growth, smolt age and age at sexual maturation
in Atlantic salmon. J Fish Biol 71: 245–252.
24. Yamamoto T, Ueda H, Higashi S (1998) Correlation among dominance status,
metabolic rate and otolith size in masu salmon. J Fish Biol 52: 281–290.
25. Friedland KD, Clarke LM, Dutil J, Salminen M (2006) The relationship
between smolt and postsmolt growth for Atlantic salmon (Salmo salar) in the Gulf
of St. Lawrence. Fish Bull 104: 149–155.
26. Salminen M (1997) Relationships between smolt size, postsmolt growth and sea
age at maturity in Atlantic salmon ranched in the Baltic Sea. J Appl Ichthyol 13:
121–130.
27. Skilbrei O (1989) Relationships between smolt length and growth and
maturation in the sea of individually tagged Atlantic salmon (Salmo salar).
Aquaculture 83: 95–108.
28. Valladares F, Sanchez Gomez D, Zavala M (2006) Quantitative estimation of
phenotypic plasticity: bridging the gap between the evolutionary concept and its
ecological applications. J Anim Ecol 94: 1103–1116.
29. Elliott JM (1994) Quantitative ecology and the brown trout. Oxford: Oxford
University Press.
30. Einum S, Fleming IA (2007) Of chickens and eggs: diverging propagule size of
iteroparous and semelparous organisms. Evolution 61: 232–238.
31. Campos JL, Posada D, Caballero P, Morán P (2007) Spatio-temporal genetic
variability in sea trout (Salmo trutta) populations from north-western Spain.
Freshw Biol 52: 510–524.
32. Shearer WM (1992) Atlantic salmon scale reading guidelines. ICES Coop Res
Rep 188: 46.
33. Kuparinen A, Garcia de Leaniz C, Consuegra S, Merilä J (2009) Growth-history
perspective on the decreasing age and size at maturation of exploited Atlantic
salmon. Mar Ecol-Prog Ser 376: 245–252.
9
76. McCormick SD, Saunders RL (1987) Preparatory physiological adaptations for
marine life of salmonids: osmoregulation, growth, and metabolism. Am Fish Soc
Symp 1: 211–229.
77. Parker RR (1971) Size selective predation among juvenile salmonid fishes in
a British Columbia inlet. J Fish Res Bd Can 28: 1503–1510.
78. Post JR, Prankevicius AB (1987) Size-selective mortality in young-of-the-year
yellow perch (Perca flavescens): evidence from otolith microstructure. Can J Fish
Aquat Sci 44: 1840–1847.
79. Jonsson B, Jonsson N, Brodtkorb E, Ingebrigtsen PJ (2001) Life-history traits of
brown trout vary with the size of small streams. Funct Ecol 15: 310–317.
80. Smith KK (2001) Heterochrony revisited: the evolution of developmental
sequences. Biol J Linn Soc 73: 169–186.
81. Gould SJ (1977). Ontogeny and phylogeny. Cambridge, MA. Belknap Press of
Harvard University Press.
82. West-Eberhard MJ (2003). Developmental plasticity and evolution. New York.
Oxford University Press.
83. Holtmeier CL (2001) Heterochrony, maternal effects, and phenotypic variation
among sympatric pupfishes. Evolution 55: 330–338.
84. Parsons KJ, Sheets HD, Skúlason S, Ferguson MM (2011) Phenotypic plasticity,
heterochrony and ontogenetic repatterning during juvenile development of
divergent Arctic charr (Salvelinus alpinus). J Evol Biol 24: 1640–1652.
85. Santini F, Harmon LJ, Carnevale G, Alfaro ME (2009) Did genome duplication
drive the origin of teleosts? A comparative study of diversification in ray-finned
fishes. BMC Evol Biol 9, 194.
86. Björklund M, Hirvonen H, Seppä T, Peuhkuri N, Piironen J (2003) Phenotypic
variation in growth trajectories in the Arctic charr Salvelinus alpinus. J Evol Biol
16: 543–550.
87. Friedland KD, Moore D, Hogan F (2009) Retrospective growth analysis of
Atlantic salmon (Salmo salar) from the Miramichi River, Canada. Can J Fish
Aquat Sci 66: 1294–1308.
88. Johnsson JI, Blackburn J, Clarke WC, Withler RE (1997) Does presmolt growth
rate in steelhead trout (Oncorhynchus mykiss) and coho salmon (Oncorhynchus kisutch)
predict growth rate in seawater?. Can J Fish Aquat Sci 54: 430–433
63. Duffy EJ, Beauchamp DA (2011) Rapid growth in the early marine period
improves the marine survival of Chinook salmon (Oncorhynchus tshawytscha) in
Puget Sound, Washington. Can J Fish Aquat Sci 68: 232–240.
64. Einum S, Fleming IA (2000) Selection against late emergence and small offspring
in Atlantic salmon (Salmo salar). Evolution 54: 628–639.
65. Hendry AP, Letcher BH, Gries G (2003) Estimating natural selection acting on
stream-dwelling Atlantic salmon: implications for the restoration of extirpated
populations. Cons Biol 17: 795–805.
66. Good SP, Dodson JJ, Meekan MG, Ryan DA (2001) Annual variation in sizeselective mortality of Atlantic salmon (Salmo salar) fry. Can J Fish Aquat Sci 58:
1187–1195.
67. Carlson SM, Olsen EM, Vøllestad LA (2008) Seasonal mortality and the effect of
body size: a review and an empirical test using individual data on brown trout.
Funct Ecol 22: 663–673.
68. Niva T, Jokela J (2000) Phenotypic correlation of juvenile growth rate between
different consecutive foraging environments in a salmonid fish: a field
experiment. Evol Ecol 14: 111–126.
69. Perez KO, Munch SB (2010) Extreme selection on size in the early lives of fish.
Evolution 64: 2450–2457.
70. Elliott JM (1990) Mechanisms responsible for population regulation in young
migratory trout, Salmo trutta. III. The role of territorial behaviour. J Anim Ecol
59: 803–818.
71. Lorenzen K, Enberg K (2002) Density-dependent growth as a key mechanism in
the regulation of fish populations: evidence from among-population comparisons. Proc R Soc B 269: 49–54.
72. Arnold SJ (1986) Limits on stabilizing, disruptive, and correlational selection set
by the opportunity for selection. Am Nat 128: 143–146.
73. Vøllestad LA, Olsen EM (2008) Non-additive effects of density-dependent and
density-independent factors on brown trout vital rates. Oikos 117: 1752–1760.
74. Elliott JM (1986) Spatial distribution and behavioural movements of migratory
trout Salmo trutta in a Lake District stream. J Anim Ecol 55: 907–922.
75. Elliott JM, Hurley MA (1998) Population regulation in adult, but not juvenile,
resident trout (Salmo trutta) in a Lake District stream. J Anim Ecol 67: 280–286.
10
Mixed-effects modelling of scale growth profiles predicts the
occurrence of early and late fish migrants
72!
!
Mixed-Effects Modelling of Scale Growth Profiles Predicts
the Occurrence of Early and Late Fish Migrants
Francisco Marco-Rius1, Pablo Caballero2, Paloma Morán1, Carlos Garcia de Leaniz3*
1 Departamento de Bioquı́mica, Genética e Inmunologı́a, Universidad de Vigo, Vigo, Spain, 2 Consellerı́a de Medio Rural, Servizo de Conservación da Natureza, Xunta de
Galicia, Pontevedra, Spain, 3 Department of BioSciences, Swansea University, Swansea, United Kingdom
Abstract
Fish growth is commonly used as a proxy for fitness but this is only valid if individual growth variation can be interpreted in
relation to conspecifics’ performance. Unfortunately, assessing individual variation in growth rates is problematic under
natural conditions because subjects typically need to be marked, repeated measurements of body size are difficult to obtain
in the field, and recaptures may be limited to a few time events which will generally vary among individuals. The analysis of
consecutive growth rings (circuli) found on scales and other hard structures offers an alternative to mark and recapture for
examining individual growth variation in fish and other aquatic vertebrates where growth rings can be visualized, but
accounting for autocorrelations and seasonal growth stanzas has proved challenging. Here we show how mixed-effects
modelling of scale growth increments (inter-circuli spacing) can be used to reconstruct the growth trajectories of sea trout
(Salmo trutta) and correctly classify 89% of individuals into early or late seaward migrants (smolts). Early migrants grew
faster than late migrants during their first year of life in freshwater in two natural populations, suggesting that migration
into the sea was triggered by ontogenetic (intrinsic) drivers, rather than by competition with conspecifics. Our study
highlights the profound effects that early growth can have on age at migration of a paradigmatic fish migrant and illustrates
how the analysis of inter-circuli spacing can be used to reconstruct the detailed growth of individuals when these cannot be
marked or are only caught once.
Citation: Marco-Rius F, Caballero P, Morán P, Garcia de Leaniz C (2013) Mixed-Effects Modelling of Scale Growth Profiles Predicts the Occurrence of Early and Late
Fish Migrants. PLoS ONE 8(4): e61744. doi:10.1371/journal.pone.0061744
Editor: Josep V. Planas, Universitat de Barcelona, Spain
Received September 30, 2012; Accepted March 13, 2013; Published April 16, 2013
Copyright: ! 2013 Marco-Rius et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was funded by grants from Ministerio de Ciencia y Tecnologı́a (CGL2007-60572 and CGL2010-14964), and Fondos FEDER
(PGIDIT03PXIC30102PN; Grupos de Referencia Competitiva, 2010/80). Francisco Marco-Rius was supported by a doctoral scholarship from the Spanish
Government (BES-2008-001973). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: Carlos Garcia de Leaniz is a member of the Editorial Board of PLOS ONE. This does not alter the authors’ adherence to all the PLOS ONE
policies on sharing data and materials.
* E-mail: [email protected]
Introduction
and migratory individuals commonly coexist and alternative
reproductive tactics are often size-dependent [18,19] reflecting
considerable phenotypic plasticity [20,21]. Thus, the choice to
remain in freshwater or to migrate to sea is often determined by
size and/or growth thresholds [22] and these may in turn depend
on metabolic efficiency [23]. However, movement can be
triggered by two very different and seemingly opposing conditions
in relation to energy acquisition and individual performance. Poor
growers may be forced to move in response to competition and
low food acquisition [24,25], while fast growers may move because
they have increasingly high food demands, as high metabolic rates
are difficult to maintain in freshwater [26]. Thus, although
movement can be viewed as a generalized response to adversity
[27], the underlying causes can be very different and will likely
have different fitness consequences.
Individual growth is often estimated as the difference in body
size between two or more arbitrarily chosen time events, but in
most field studies not all marked individuals can be recaptured, or
even sampled at the same times [28], making individual
comparisons difficult. More commonly, researchers are limited
to inferring growth from changes in the average size at successive
ages or time events [29–31], often based on different individuals.
Growth is then analyzed using models based in the Von
Bertalanffy [32] approximation [33–35], but this ignores individ-
Body size is often the direct target of natural selection [1–3] and
examining how different individuals grow can reveal much about
how they respond to competition and adapt to environmental
change [4,5]. However, modelling animal growth has proved
challenging because there is considerable heterogeneity among
species and individuals, and because accounting for such diversity
is inherently difficult at the analytical level [6]. For example,
homoeothermic and poikilothermic organisms show markedly
different constraints on evolution of body size [7], and hence on
growth. Growth can also vary markedly throughout the lives of
organisms, and regional-scale processes can affect individuals very
differently depending on season [8,9]. Individuals may cease
feeding or augment food intake depending on temporal cues but
environmental thresholds may vary markedly among individuals
giving rise to divergent growth trajectories even among neighbouring conspecifics exposed to the same cues [10–12]. In
addition, growth is intimately linked to many life history traits
[13], and cannot be considered in isolation. For example, rate of
growth has a pervasive effect on age at maturity in fishes [14–16],
and on longevity in mammals [17].
Anadromous salmonids are good models to examine the fitness
consequences of individual variation in growth because resident
1
April 2013 | Volume 8 | Issue 4 | e61744
Mixed-Effects Modelling of Fish Growth
precision of body size and scale measurements. Precision in scale
measurements (0.01 mm; CV = 17.1%) was better than that of
body size measurements (cm; CV = 19.2%), and the former was
therefore preferred to examine growth variation among migratory
trout. Thus, and in common with other recent studies [44–45], we
did not convert scale measurements into body size estimates, as
this would have merely introduced additional errors resulting from
(1) low precision of body size measurements taken on live fish in
the field, and (2) uncertainty about the precise nature of the
function linking scale growth to body growth.
ual variation in growth trajectories [36] and assumes that age is
accurately measured [37], something that is not always possible.
Here we used a method based on the analysis of the spacing
between consecutive growth rings found in the scales (growth
circuli) to reconstruct individual growth trajectories of migratory
brown trout (sea trout, Salmo trutta) before they migrated to sea. We
specifically compared the growth trajectories of individuals that
migrated to sea after only one year in the river (early migrants)
with those that delayed seaward migration for one or more
additional years (late migrants). We hypothesized that early
migrants would have grown faster than late migrants during their
first year of life if migration was triggered by high food demands
(i.e. intrinsic drivers), but would have grown more slowly if
migration was the result of extrinsic constraints, for example
competition resulting in low food acquisition.
Data analysis
In order to model individual variation in freshwater growth, we
examined the spacing between consecutive growth circuli (intercirculi spacing), the number of growth circuli deposited in the
scales, and the growth of the scales (scale length) attained at the
end of the first year. We only modelled variation in freshwater
growth as we are interested in explaining drivers of seaward
migration, and this allowed us to compare the common part of the
scales of all migratory individuals, irrespective of the time they had
spent at sea. We employed binary logistic regression to model the
likelihood of early vs. late seaward migration as a function of
growth during the first year of life, and assessed model fit by the
log-likelihood ratio using SYSTAT 10.0. The first three circuli of
the scale were not taken into account due to the possibility of
missing growth rings during early life [44].
We used REML to model individual variation in inter-circuli
spacing with the ‘nlme 3.1–86’ package [46] on the R 2.14
language [47]. A preliminary analysis of 20 randomly chosen
individuals per river suggested that the inclusion of random effects
was required to account for individual variation in slopes and
intercepts (Figure S1, Supporting information). The inclusion of
higher order polynomials allowed us to examine differences in the
seasonal growth of one and two year old smolts, according to the
following expression (Equation 1):
Methods
Study populations
Migratory brown trout (sea trout) were caught in upstream traps
or by angling in the rivers Ulla and Lerez (Galicia, NW Spain)
during 1999–2010. The two populations differ in key physical and
demographic parameters, including accessible length (Lerez = 25.1 Km, Ulla = 102.3 Km), watershed area (Lerez = 449.5 Km2, Ulla = 2803.6 Km2) and stream order (Lerez = 5,
Ulla = 6). Mean angling catch of migratory trout during (used as a
proxy for population size) was 101 individuals in the R. Lerez and
1,934 individuals in the R. Ulla. Sea trout smolts in the R. Ulla
tend to be older (mean smolt age 2.260.45 yrs) and larger (mean
smolt size 216644 mm) than those in the R. Lerez (mean smolt
age 2.160.58 yrs; mean smolt size 191652 mm), while mean age
at return shows the opposite pattern (R. Ulla, 3.1560.84 yrs; R.
Lerez, 3.5160.78
Scale analysis and reconstruction of growth profiles
Scales of 60 individuals per river and year were stored dry in
paper envelopes, along with information on their body size (fork
length, mm). Between three and five scales with a clear nucleus
were selected per individual to minimize bias due to scale
regeneration [38]. Acetate impressions were made with the aid
of a pressure roller and the resulting impressions scanned at 23–
506 magnifications (Minolta MS 6000) as in [39]. The software
Image-J v. 1.4.1 [40] was employed to digitize the position of each
growth ring (circulus) and to measure inter-circuli spacing with
reference to a calibrated scale bar [41]. Freshwater and marine
ages were determined based on the number of annuli [42], and the
point of entry of smolts into the sea identified by a change from
concave to convex circuli (i.e. the point where circuli open
outwards on the posterior zone of the scale) and presence of
broken growth rings [43].
2
Ri,j ~b0 zb1 Pi zb2 Yi,j zb3 Ai,j zb4 Ci,j zb5 Ci,j
zb6 (Ai {1)|
3
4
zb7 Ci,j
zb8 Ri |Ci,j zb9 Li zai zbi Ci,j zei,j
Ci,j
where R is the scale radius of individual i at circulus j, P is the
population (R. Lerez, 0, R. Ulla 1), Y represents the different smolt
years, A is the freshwater age, C is the scale circuli and L is the fork
length of the fish returning from the sea. Random slopes (b) and
intercepts (a) were assumed to be independent and normally
distributed with zero means and variances s2a and s2b,
respectively; errors ei,j were also assumed to be independent and
normally distributed. We allowed for autocorrelation in intercirculi spacing by considering an autoregressive (AR) model of
order one in the autocorrelation structure, as this provided a better
fit to the data than a model without correlated serial errors. The
Bayesian Information Criteria (BIC) was used for model selection
of fixed effects, mixed effects and autocorrelation structure of
errors terms [48]. We employed the coefficient of variation (CV) to
quantify individual variation in inter-circuli spacing at each
circulus [21].
For the classical Von Bertalanffy growth model, a nonlinear
least-squares (nls) regression was used to estimate the growth
parameters (L‘, k and t0) from the scales, using all the scale circuli
(Equation 2):
Reliability of scale analysis
A paired t-test was used to assess non-random deviations in scale
radii between the original scales and their acetate impressions
(n = 30) in order to quantify potential bias in scale measurements
arising from pressure from the hand roller. To ascertain the
precision of the scale analysis, we estimated the repeatability of the
point of entry into the sea and of the end of the first freshwater
growing season by measuring the scales of 30 individuals twice in a
double blind fashion and calculating the intra-class correlation
coefficient (a-Cronbach) as per [21]. The Pearson correlation
coefficient was used to evaluate the strength of the association
between scale radius and body size of fish in each river. The
coefficients of variation (CV) were then examined to compare the
!
"
L(t) ~L? 1{e{k(t{t0 )
2
Effect of first year growth on age of seaward migration
Individual growth curves were fitted to each fish and comparisons
were made between fitted and observed values.
Results
The minimal adequate model that best explained age of
seaward migration included scale growth (estimate = 24.5,
SE = 0.96, t = 24.66, P,0.001) and mean inter-circuli spacing
during the first year (estimate = 565.5, SE = 85.20, t = 6.64,
P,0.001) as predictors. This provided a reasonably good fit to
the data (McFadden rho2 = 0.138, x2 = 60.263, df = 2, P,0.001)
and correctly classified 89.4% of fish into early and late migrants.
The inclusion of other terms and their interactions did not
improve model fit. In both rivers, early migrants (i.e. one year old
smolts) were those that had attained higher rates of scale growth
until their first winter (Fig. 1a; R. Lerez F3,448 = 13.123, P,0.001;
R. Ulla F3,486 = 6.328, P,0.001). This was chiefly achieved by
depositing more growth rings for a given scale length (Fig. 1b; R.
Lerez F3,448 = 14.279, P,0.001; R. Ulla F3,486 = 5.906, P = 0.001).
Thus, scale length and number of growth rings were positively
correlated in both early and late migrants, but inter-circuli spacing
and number of growth rings were not (Figure 2).
Individual variation in freshwater growth
The impression process produced no significant distortion of
scale radius (t29 = 0.547, P = 0.465), indicating that acetate
impressions gave an accurate, unbiased representation of scale
size. Repeatabilities of scale size were high, both for smolt scale
length (a-Cronbach = 0.879) and for scale size attained at the end
of the first freshwater growing season (a-Cronbach = 0.898). Scale
radius and fork length were positively correlated (r = 0.747,
P = 0.001), and the relationship was not different between rivers
(F1,636 = 0.542, P = 0.589) allowing us to examine individual
variation in scale growth regardless of river identity.
The minimal adequate model for inter-circuli spacing in
freshwater included an autocorrelation term as well as random
slopes and intercepts (Table 1). Although random slopes and
intercepts were relatively small, their inclusion significantly
improved model fit, indicating that growth rates during early
development, as well as initial size differences, differed significantly
among individuals and affected subsequent growth. The positive
and significant effect of fork length reflects the positive association
found between body size and scale size, whereas the four
polynomial terms of the model reflect the seasonality in intercirculi spacing that was found to be necessary to include in order
to capture seasonal growth stanzas (Figure 3), and which differed
significantly between rivers. Thus, inter-circuli spacing for the
Ethics Statement
We made use of scales samples collected routinely for ageing
purposes by trained fisheries staff of the Regional Government of
Galicia (Wildlife Service). Scales were collected from anaesthetized
fish (clove oil) using a small fish knife and according to current
Spanish Regulations, as described in a previous study [21]. After
the fish had fully recovered from the anaesthesia (approx. 30 min),
they were returned live to a point immediately upstream of the
point of capture. No specific permits were required for scale
collection, as these did not involve endangered or protected species
and the work was carried out by government fishery officers under
the supervision of one of the co-authors (PC), who is a government
fish veterinarian.
Figure 1. Marginal means (± SE) of (a) scale growth and (b) no. of growth circuli deposited during the first year in sea trout of
different smolt ages (age of seaward migration).
3
Figure 2. Scatterplot matrix showing relationships between
scale growth parameters (no. of growth circuli, mean interciculi spacing, and scale length during the first year in
freshwater) of returning sea trout migrating to sea as early
migrants (1 year old smolts) or late migrants (2–4 year old
smolts). Each variable is compared against the other two and the
relationship between pairs of variables is shown by a solid red line
representing a locally weighted scatterplot smoothing (LOWESS) and by
the strength of the Pearson correlation coefficient (***P,0.001;
**P,0.01). Frequency histograms for each variable are shown along
the diagonal.
River Ulla was significantly higher than for the River Lerez. In
contrast, inter-circuli spacing did not vary significantly over the 11
years of study. Trout which emigrated to sea after only one year of
growth in freshwater (early migrants) tended to have smaller than
average inter-circuli spacing during the first months of life than
those that delayed migration for one or two additional years. Such
an effect was evident in both populations (Figure 1), and was
largely the consequence of having deposited more growth rings for
a given scale length. Analysis of temporal trends in CV indicates
that variability in scale length increments increased until the first
winter in both rivers (Figure S2), suggesting that initial differences
in growth performance among individuals became amplified
Figure 3. Fitted values of the mixed-effects model of intercirculi spacing in the freshwater phase of migratory brown
trout. Growth trajectories of one ( ) and two-year old smolts ( ) are
indicated. Correspondence between circuli number and calendar
month is only approximate and is used to visualize the timing of
seasonal growth stanzas.
N
during early life. Model checks indicate a moderately good fit
between observed and predicted values (Supporting information,
Figure S3) and well behaved residuals. On the other hand, model
fitting using individual Von Bertalanffy growth curves was very
4
regulation during early life may determine the extent of individual
variability in growth, so that fish which achieve high growth rates
during their first year are able to maintain a size advantage later in
life [57]. Small individuals within a cohort are more influenced by
intra-specific competition than large ones [58]. In this sense,
incorporating random effects in the model, allows a better insight
into the nature of within-individual variation in growth patterns.
Egg size has been found to have a large effect on early growth
and survival of salmonids [59,60] and it is possible that the large
individual differences revealed by our analysis might be related to
variation in egg size (maternal effect; [61]) in addition to
differences in early rearing [62]. In addition, we found significant
differences in the freshwater growth of sea trout inhabiting two
neighbouring rivers. This serves to highlight the large influence
that, in addition to maternal effects [62], local-scale rearing
conditions may have on juvenile salmonids during early life
[53,63]. The significant effect of body size in our model further
suggests the growth experienced in freshwater has a positive effect
on length at maturity. Marine survival of anadromous fish is
thought to depend not just on growth attained at sea (e.g. [64]),
but also on the size attained by juveniles before seaward migration
(smolt size). Indeed, large smolts typically survive better than
smaller ones, particularly on bad years [65–67], and as our
analysis illustrates, the retrospective analysis of inter-circuli spacing
may reveal periods when differences among individuals become
most pronounced.
An additional advantage of modeling growth based on the
seasonal deposition of growth circuli is to remove bias caused by
an arbitrary choice of sampling events. For example, growth of
softwoods varies markedly between wood formed in spring and
wood formed in summer and autumn [68], and an analysis of nonannual growth rings can provide valuable information on
individual growth variation that would be lost if only annual
growth rings are examined (as it is often the case in fishery science).
Accounting for individual variation in fitness traits is becoming
increasingly important in ecological and evolutionary studies [69–
72], because fitness is essentially a relative concept that needs to be
interpreted in relation to performance shown by conspecifics [73].
Growth is commonly used as a proxy for fitness [74], yet
comparing large number of serially correlated points, typical of
growth studies, presents a considerable challenge [75]. Mixed
effects modelling [76,77] is useful in this respect, as it allows for the
inclusion of random slopes and intercepts that can be used to
describe the growth of individuals, as well as for the incorporation
of an auto-correlation structure. Recent studies have highlighted
the advantages of such an approach, and have stressed the
importance of avoiding sampling individuals only twice [78], a
condition common to many growth studies.
In summary, we have illustrated how a new method based on
mixed-effects modelling can be used to examine individual
variation in growth trajectories, reconstructed from multiple
repeated measurements of the spacing between consecutive
growth rings, thereby affording greater statistical power than
simple growth estimates based on two or few arbitrarily chosen
points in time. Fish scales can be collected non-destructively and
stored dry for considerable time, providing unique archival
material to address long-term population changes (e.g. [79–81]).
Our approach makes it possible to quantify individual variation
and seasonality in growth stanzas, something that popular
methods such as the Von Bertalanffy growth model cannot do.
The method has shown good reproducibility, and can be readily
extended to the analysis of growth of other aquatic organisms
having hard structures where growth rings can be visualized, such
as otoliths, vertebrae, shells, or bones. We illustrate the application
Table 1. Parameter estimates in mixed-effects modelling of
inter-circuli spacing of sea trout in two study rivers.
Effects
Estimate
Std. Error
t-value
P-value
Fixed
Intercept
0.0389
0.00071
54.54
,0.0001
FW age
22.667 1024
3.520 1024
20.75
0.449
River [Ulla]
0.0395
7.599 1024
52.07
,0.0001
3.754 1025
272.52
,0.0001
1.815 1026
67.33
,0.0001
0.453 1028
249.72
,0.0001
0.190 1027
0.024 1028
53.01
,0.0001
27
1.401 1028
7.22
,0.0001
River [Ulla]6Circuli
6.200 1027
8.256 1026
0.07
0.946
Fork length
3.480 1026
1.234 1026
2.82
0.004
22.722 10
Circuli
1.222 1024
Circuli2
Circuli
3
20.453 10
Circuli4
FW age6Circuli
23
3
1.001 10
27
Random (SD)
Intercept
2.683 1023
Slope (Circuli)
8.706 1025
Residual
5.796 1023
Correlation structure
corr
0.742
Random effects are indicated by the standard deviation of slopes and
intercepts.
poor (R2 = 0.04), and gave exponential or even negative growth for
many fish (data not shown).
Discussion
How does one examine individual variation in growth rates
when subjects cannot be marked or few are ever recaptured? We
believe that the analysis of growth rings may provide an answer.
Fish scales are routinely collected in fisheries for ageing, but it has
long been recognised that the spacing between growth circuli
(inter-circuli spacing) can also reveal much about the growth of
individuals [49]. However, this information has traditionally
proven difficult to analyze [50]; often researchers simply backcalculate body size at a given age, work on average size increments
at particular times, or apply corrections to the degrees of freedom
in an attempt to account for autocorrelations in inter-circuli
spacing (e.g. [45]). We employed mixed effects modelling to
compare inter-circuli spacing in the scales of migratory brown
trout, and used this information to reconstruct juvenile growth
trajectories in freshwater. To model seasonal changes in consecutive length increments, we included first to fourth order
polynomial terms as a smoothing function of time [51,52] and
compared the results to those obtained by applying individual Von
Bertalanffy equations.
Our results indicate that variation in early summer growth
differed significantly among individuals (as evidence by the
existence of random intercepts and slopes), and that individual
differences in early growth became amplified over the first months
of life (as evidenced by an increase in the coefficient of variation).
Large individual differences in early growth have been documented in salmonids soon after emergence from the redd [53] and these
appear to be closely related to dominance status [54], metabolic
rate [55], and timing of hatching in relation to prior residency
effects [56]. It is likely that the strength of density-dependent
5
of the method by examining growth of migratory brown trout
from two populations, based on digitized impressions of fish scales.
Analysis of inter-circuli spacing during the first year of life
accurately predicted whether trout migrated to sea as early (one
year old) or late (2–4 year old) smolts, highlighting the profound
effect that early growth can have on age at migration of
anadromous fish. The fact that early migrants grew faster than
older ones suggests that seaward migration was primarily triggered
by ontogenetic (intrinsic) drivers, possibly in response to changes in
energetic state [26], rather than by extrinsic forces such as
competition with conspecifics or low food availability [24,25].
Figure S2 Coefficient of variation (CV = SD/mean) of
inter-circuli spacing of freshwater growth in two
populations of sea trout. Grey bands represent point-wise
95 CI envelopes derived from bootstrapping.
(TIFF)
Supporting Information
We thank Pilar Alvariño and Laura Cal for technical assistance, and Sonia
Consuegra, Josep V Planas, and two anonymous reviewers for helpful
comments on the manuscript.
Figure S3 Observed versus fitted values for the mixedeffects model of sea trout inter-circuli spacing in two
populations of sea trout (R. Ulla, R. Lerez).
(TIFF)
Acknowledgments
Figure S1 Variation in random slopes (time) and
intercepts of the mixed-effects model of inter-circuli
spacing during the first year of freshwater growth for a
random sample of 20 sea trout from each of the two
study rivers (R. Lerez, R Ulla).
(TIFF)
Author Contributions
Conceived and designed the experiments: FMR CGL. Performed the
experiments: FMR PC PM CGL. Analyzed the data: FMR CGL.
Contributed reagents/materials/analysis tools: PM CGL PC. Wrote the
paper: FMR CGL.
References
21. Marco-Rius F, Caballero P, Morán P, Garcia de Leaniz C (2012) And the last
shall be first: heterochrony and compensatory marine growth in sea trout (Salmo
trutta). PLoS ONE 7(10): e45528.
22. Aubin-Horth N, Dodson JJ (2002) Impact of differential energy allocation in
Atlantic salmon (Salmo salar) precocious males on otolith–somatic size
proportionality: a longitudinal approach. Can J Fish Aquat Sci 59: 1575–1583.
23. Morinville GR, Rasmussen JB (2003) Early juvenile bioenergetic differences
between anadromous and resident brook trout (Salvelinus fontinalis). Can J Fish
Aquat Sci 60: 401–410.
24. Thorpe JE, Mangel M, Metcalfe NB, Huntingford FA (1998) Modelling the
proximate basis of salmonid life-history variation, with application to Atlantic
salmon, Salmo salar L. Evol Ecol 12: 581–599.
25. Wysujack K, Greenberg LA, Bergman E, Olsson IC (2009) The role of the
environment in partial migration: food availability affects the adoption of a
migratory tactic in brown trout Salmo trutta. Ecol Freshw Fish 18: 52–59.
26. Forseth T, Nesje TF, Jonsson B, Harsaker K (1999) Juvenile migration in brown
trout: a consequence of energetic state. J Anim Ecol 68: 783–793.
27. Taylor LR, Taylor RAJ (1977) Aggregation, migration and population
mechanics. Nature 265: 415–421.
28. Letcher BH, Coombs JA, Nislow KH (2011) Maintenance of phenotypic
variation: repeatability, heritability and size-dependent processes in a wild brook
trout population. Evol Appl 4: 602–615.
29. Jacobsen JA, Lund RA, Hansen LP, O’Maoileidigh N (2001) Seasonal
differences in the origin of Atlantic salmon (Salmo salar L.) in the Norwegian
Sea based on estimates from age structures and tag recaptures. Fish Res 52: 169–
177.
30. Jonsson N, Jonsson B (2002) Migration of anadromous brown trout Salmo trutta in
a Norwegian river. Freshw Biol 47: 1391–1401.
31. Beechie T, Buhle E, Ruckelshaus M, Fullerton A, Holsinger L (2006) Hydrologic
regime and the conservation of salmon life history diversity. Biol Conserv 130:
560–572.
32. Von Bertalanffy L (1938) A quantitative theory of organic growth (inquiries on
growth laws. II). Hum Biol 10: 181–213.
33. Velasco G, Reis EG, Vieira JP (2007) Calculating growth parameters of Genidens
barbus (Siluriformes, Ariidae) using length composition and age data. J Appl
Ichthyol 23: 64–69.
34. Gagné S, Rodrı́guez MA (2008) Modelling seasonal increments in size to
determine the onset of annual growth in fishes. J Fish Biol 73: 153–168.
35. Anderson JH, Faulds PL, Atlas WI, Pess GR, Quinn TP (2010) Selection on
breeding date and body size in colonizing coho salmon, Oncorhynchus kisutch. Mol
Ecol 19: 2562–2573.
36. Wang YG, Thomas MR (1995) Accounting for individual variability in the von
Bertalanffy growth model. Can J Fish Aquat Sci 52: 1368–1375.
37. Britton JR, Cowx IG, Peirson G (2004) Sources of error in the ageing of stocked
cyprinids. Fisheries Manag Ecol 11: 415–417.
38. Shearer WM (1992) Atlantic salmon scale reading guidelines. ICES Coop Res
Rep 188: 46.
39. Kuparinen A, Garcia de Leaniz C, Consuegra S, Merilä J (2009) Growth-history
perspective on the decreasing age and size at maturation of exploited Atlantic
salmon. Mar Ecol-Prog Ser 376: 245–252.
40. Abramoff MD, Magalhães PJ, Ram SJ (2004) Image processing with ImageJ.
Biophot Int 11: 36–42.
1. Rundle HD, Schluter D (2004) Natural selection and ecological speciation in
sticklebacks. In: Dieckmann U, Doebeli M, Metz JAJ, Tautz D, editors.
Adaptive Speciation. Cambridge University Press. pp.192–209.
2. Garcia de Leaniz C, Fleming IA, Einum S, Verspoor E, Jordan WC, et al. (2007)
A critical review of adaptive genetic variation in Atlantic salmon: implications
for conservation. Biol Rev Camb Philos Soc 82: 173–211.
3. Takahashi T, Watanabe K, Munehara H, Rüber L, Hori M (2009) Evidence for
divergent natural selection of a Lake Tanganyika cichlid inferred from repeated
radiations in body size. Mol Ecol 18: 3110–3119.
4. Kearney M, Simpson SJ, Raubenheimer D, Helmuth B (2010) Modelling the
ecological niche from functional traits. Philos T Roy Soc B 365: 3469–3483.
5. Angilletta MJ, Sears MW (2011) Coordinating theoretical and empirical efforts
to understand the linkages between organisms and environments. Integr Comp
Biol 51: 653–661.
6. Nicholson JK, Holmes E, Lindon JC, Wilson ID (2004) The challenges of
modeling mammalian biocomplexity. Nat Biotechnol 22: 1268–1274.
7. Albert JS, Johnson DM (2011) Diversity and evolution of body size in fishes. Evol
Biol 39: 324–340.
8. Fukaya K, Okuda T, Nakaoka M, Hori M, Noda T (2010) Seasonality in the
strength and spatial scale of processes determining intertidal barnacle population
growth. J Anim Ecol 79: 1270–1279.
9. English S, Bateman AW, Clutton-Brock TH (2011) Lifetime growth in wild
meerkats: incorporating life history and environmental factors into a standard
growth model. Oecologia 169: 143–153.
10. Ali M, Nicieza AG, Wootton RJ (2003) Compensatory growth in fishes: a
response to growth depression. Fish Fisher 4: 147–190.
11. Gurney WSC, Jones W, Veitch AR, Nisbet RM (2003) Resource allocation,
hyperphagia, and compensatory growth in juveniles. Ecology 84: 2777–2787.
12. Dieterman DJ, Hoxmeier RJH, Staples DF (2012) Factors influencing growth of
individual brown trout in three streams of the upper Midwestern United States.
Ecol Freshw Fish 21: 483–493.
13. Stearns SC (1992) The evolution of life histories. New York: Oxford Univ. Press.
249 p.
14. Scott RD, Heikkonen J (2012) Estimating age at first maturity in fish from
change-points in growth rate. Mar Ecol-Prog Ser 450: 147–157.
15. Bono LM, Rios-Cardenas O, Morris MR (2011) Alternative life histories in
Xiphophorus multilineatus: evidence for different ages at sexual maturity and growth
responses in the wild. J Fish Biol 78: 1311–1322.
16. Morris MR, Rios-Cardenas O, Lyons SM, Tudor MS, Bono RM (2012)
Fluctuating asymmetry indicates the optimization of growth rate over
developmental stability. Funct Ecol 26: 723–731.
17. Kopp EB, Medzhitov R (2009) Perspective: infection and inflammation in
somatic maintenance, growth and longevity. Evol Appl 2: 132–141.
18. Gross MR (1996) Alternative reproductive strategies and tactics: diversity within
sexes. Trends Ecol Evol 11: 92–98.
19. Gross M, Repka J (1998) Stability with inheritance in the conditional strategy.
J Theor Biol 192: 445–453.
20. Caballero P, Morán P, Marco-Rius F (2012) A review of the genetic and
ecological basis of phenotypic plasticity in brown trout. In: Polakof S, Moon
TW, editors. Trout: from physiology to conservation. Hauppauge: Nova
Science. pp.9–26.
6
41. Schröder V, Garcia de Leaniz C (2011) Discrimination between farmed and
free-living invasive salmonids in Chilean Patagonia using stable isotope analysis.
Biol Invasions 13: 203–213.
42. Rifflart R, Marchand F, Rivot E, Baglinière JL (2006) Scale reading validation
for estimating age from tagged fish recapture in a brown trout (Salmo trutta)
population. Fish Res 78: 380–384.
43. Baglinière JL (1985) La détermination de l’age par scalimétrie chez le Saumon
Atlantique (Salmo salar) dans son aire de répartition méridionale: utilisation
pratique et difficultés de la méthode. Bull Fr Pêche Piscic 298: 69–105.
44. McCarthy JL, Friedland KD, Hansen LP (2008) Monthly indices of the postsmolt growth of Atlantic salmon from the Drammen River, Norway. J Fish Biol
72: 1572–1588.
45. Hogan F, Friedland KD (2010) Retrospective growth analysis of Atlantic salmon
Salmo salar and implications for abundance trends. J Fish Biol 76: 2502–2520.
46. Pinheiro JC, Bates DM (2009) Mixed-effects models in S and S-Plus. Berlin:
Springer-Verlag. 530p.
47. R Development Core Team (2010) R: A language and environment for
statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
48. Konishi S, Kitagawa G (2008) Information criteria and statistical modeling. New
York: Springer. 276p.
49. Bagenal TB, Tesch FW (1978) Age and growth. In: Bagenal TB, editor. Methods
for assessment of fish production in fresh waters. Oxford: Blackwell Scientific
Publications. pp.101–136.
50. Hinrichsen HH, Buehler V, Clemmesen C (2007) An individual-based model for
the direct conversion of otolith into somatic growth rates. Fish Oceanogr 16:
207–215.
51. Wood SN (2006) Generalized additive models: an introduction with R. Boca
Raton: Chapman Hall/CRC.
52. Köhn F, Sharifi AR, Simianer H (2007) Modeling the growth of the Goettingen
minipig. J Anim Sci 85: 84–92.
53. Garcia de Leaniz C, Fraser N, Huntingford FA (2000) Variability in
performance in wild Atlantic salmon, Salmo salar L., fry from a single redd.
Fish Mgmt Ecol 7: 489–502.
54. Nakano S (1995) Individual differences in resource use, growth and emigration
under the influence of a dominance hierarchy in fluvial red-spotted masu salmon
in a natural habitat. J Anim Ecol 64: 75–84.
55. Régnier T, Bolliet V, Labonne J, Gaudin P (2010) Assessing maternal effects on
metabolic rate dynamics along early development in brown trout (Salmo trutta): an
individual-based approach. J Comp Physiol B 180: 25–31.
56. Huntingford FA, Garcia de Leaniz C (1997) Social dominance, prior residence
and the acquisition of profitable feeding sites in juvenile Atlantic salmon. J Fish
Biol 51: 1009–1014.
57. Crozier LG, Zabel RW, Hockersmith EE, Achord S (2010) Interacting effects of
density and temperature on body size in multiple populations of Chinook
salmon. J Anim Ecol 79: 342–349.
58. Einum S, Forseth T, Finstad AG (2011) Individual variation in response to
intraspecific competition: problems with inference from growth variation
measures. Method Ecol Evol 3: 438–444.
59. Elliott JM (1994) Quantitative ecology and the brown trout. Oxford: Oxford
University Press. 286 p.
60. Rollinson N, Hutchings JA (2010) Why does egg size increase with maternal
size? Effects of egg size and egg density on offspring phenotypes in Atlantic
salmon (Salmo salar). Evol Ecol Res 12: 949–960.
61. Einum S, Fleming IA (2002) Does within-population variation in fish egg size
reflect maternal influences on optimal values? Am Nat 160: 756–765.
62. Rollinson N, Hutchings JA (2011) Body size-specific maternal effects on the
offspring environment shape juvenile phenotypes in Atlantic salmon. Oecologia
166: 889–898.
63. Einum S, Nislow KH (2005) Local-scale density-dependent survival of mobile
organisms in continuous habitats: an experimental test using Atlantic salmon.
Oecologia 143: 203–210.
64. Peyronnet A, Friedland KD, Maoileidigh NÓ, Manning M, Poole WR (2007)
Links between patterns of marine growth and survival of Atlantic salmon Salmo
salar, L. J Fish Biol 71: 684–700.
65. Jonsson N, Jonsson B, Hansen LP (2003) The marine survival and growth of wild
and hatchery-reared Atlantic salmon. J Appl Ecol 40: 900–911.
66. Saloniemi I, Jokikokko E, Kallio-Nyberg I, Jutila E, Pasanen P (2004) Survival of
reared and wild Atlantic salmon smolts: size matters more in bad years.
ICES J Mar Sci 61: 782–787.
67. Kallio-Nyberg I, Jutila E, Jokikokko E, Saloniemi I (2006) Survival of reared
Atlantic salmon and sea trout in relation to marine conditions of smolt year in
the Baltic Sea. Fish Res 80: 295–304.
68. Downes GM, Drew D, Battaglia M, Schulze D (2009) Measuring and modelling
stem growth and wood formation: an overview. Dendrochronologia 27: 147–
157.
69. Bolnick DI, Svanbäck R, Fordyce JA, Yang LH, Davis JM, et al. (2003) The
ecology of individuals: incidence and implications of individual specialization.
Am Nat 161: 1–28.
70. DeAngelis DL, Mooij WM (2005) Individual-based modeling of ecological and
evolutionary processes. Annu Rev Ecol Evol Syst 36: 147–168.
71. Moyes K, Morgan BJT, Morris A, Morris SJ, Clutton-Brock TH, et al. (2009)
Exploring individual quality in a wild population of red deer. J Anim Ecol 78:
406–413.
72. Clutton-Brock TH, Sheldon BC (2010) Individuals and populations: the role of
long-term, individual-based studies of animals in ecology and evolutionary
biology. Trends Ecol Evol 25: 562–573.
73. Wilson DS (2004) What is wrong with absolute individual fitness? Trends Ecol
Evol 19: 245–248.
74. Schluter D (1994) Experimental evidence that competition promotes divergence
in adaptive radiation. Science 266: 798–801.
75. Zens MS, Peart DR (2003) Dealing with death data: individual hazards,
mortality and bias. Trends Ecol Evol 18: 366–373.
76. Zuur AF, Ieno EN, Walker NJ, Saveliev AA, Smith GM (2009) Mixed effects
models and extensions in ecology with R. New York: Springer. 574p.
77. Zuur AF, Ieno EN, Elphick CS (2010) A protocol for data exploration to avoid
common statistical problems. Method Ecol Evol 1: 3–14.
78. van de Pol M (2012) Quantifying individual variation in reaction norms: how
models. Method Ecol Evol 3: 268–280.
79. Consuegra S, Garcia de Leaniz C, Serdio A, González Morales M, Straus LG,et
al. (2002) Mitochondrial DNA variation in Pleistocene and modern Atlantic
salmon from the Iberian glacial refugium. Mol Ecol 11: 2037–2048.
80. Consuegra S, Verspoor E, Knox D, Garcia de Leaniz C (2005) Asymmetric gene
flow and the evolutionary maintenance of genetic diversity in small, peripheral
Atlantic salmon populations. Cons Genetics 6: 823–842.
81. Ciborowski KL, Consuegra S, Garcia de Leaniz C, Wang J, Beaumont MA, et
al. (2007) Stocking may increase mitochondrial DNA diversity but fails to halt
the decline of endangered Atlantic salmon populations. Cons Genetics 8: 1355–
1367.
7
Can migrants escape from density-dependence?
80!
!
Contemporary ocean warming and freshwater
conditions are related to later sea age at maturity
in Atlantic salmon spawning in Norwegian rivers
Can
migrants
from density-dependence?
Jaime Otero
, Arne J. Jensen escape
, Jan Henning L’Abée-Lund
, Nils Chr. Stenseth , Geir O. Storvik, &
1
2
3
1,4
1,5
Leif Asbjørn Vøllestad1
Centre for Ecological and Evolutionary Synthesis
(CEES), Department of Biology,
of Oslo, P.O. Box 1066
N-0316, Oslo,
Francisco
Marco-Rius1, Pablo
Caballero2, University
Paloma
Morán1, Blindern,
Carlos
Garcia de
Norway
3
Norwegian Institute for Nature Research, P.O. Box 5685 Sluppen, N-7485, Trondheim, Norway
Leaniz
Norwegian Water Resources and Energy Directorate, P.O. Box 5091 Majorstua, N-0301, Oslo, Norway
1
2
3
4
Institute of Marine Research, Flødevigen Marine Research Station, N-4817, His, Norway
15
Department
of Mathematics,
University
of Oslo, P.O. Box de
1066Bioquímica,
Blindern, N-0316, Genética
Oslo, Norway
Universidad
de Vigo,
Departamento
e Inmunología, Vigo 36310,
Spain.
2
Xunta de Galicia, Consellería de Medio Rural, Servizo de Conservación da Natureza,
Pontevedra 36071, Spain
3
Swansea University, Department
of BioSciences, Swansea SA2 8PP, UK
Keywords
Abstract
discharge, maturation, Norway, Salmo salar,
sea surface temperature.
Keywords:
Correspondence
Individual approach, brown trout,
Jaime Otero, Centre for Ecological and
statistical modelling, growth, density,
Evolutionary Synthesis (CEES), Department of
migration.
Biology, University of Oslo, P.O. Box 1066
Blindern, N-0316 Oslo, Norway. Tel: +47
Correspondence
22854400; Fax: +47 22854001;
Carlos Garcia de Leaniz, Swansea
E-mail: [email protected]
University, Department of BioSciences,
Swansea SA2 8PP, UK;
Funding Information
E-mail:
This study was funded by the Research
c.garciadeleaniz@swansea
Council of Norway project nº 183989/S30.
.ac.uk
Received: 6 February 2012; Revised: 20 June
Atlantic salmon populations are reported to be declining throughout its range,
raising major management concerns. Variation in adult fish abundance may be
due to variation in survival, growth, and timing of life history decisions. Given
Abstract
the complex
life history, utilizing highly divergent habitats, the reasons for
declines may be multiple and difficult to disentangle. Using recreational angling
is thought
to maximize
by enabling
data 1.
of twoMigration
sea age groups,
one-sea-winter
(1SW) and growth
two-sea-winter
(2SW)
fish originated
fromtothe
same smolt
yeardensity-dependence,
class, we show that sea agebut
at maturity
individuals
escape
from
this has
of the
returnsbeen
has increased
rivers over level
the cohorts
1991–
rarely
testedin 59
at Norwegian
the individual
in natural
2005.populations.
By means of linear mixed-effects models we found that the proportion of
1SW2.
fish spawning
in Norwaylinear
has decreased
We employed
mixedconcomitant
modellingwith
of the
theincreasing
spacing
sea surface temperature experienced by the fish in autumn during their first
between consecutive scale growth rings to reconstruct
year at sea. Furthermore, the decrease in the proportion of 1SW fish was influindividual growth profiles of a paradigmatic fish migrant, the
enced by freshwater conditions as measured by water discharge during summer
sea1 year
troutahead
(Salmo
trutta)
and related
thesesuggest
to estimates
months
of seaward
migration.
These results
that part of of
year class
strength
overcan
a 13-year
period.
the variability
in age
at maturity
be explained
by the large-scale changes
3. Variation
in scaleAtlantic
growthpelagic
was food
1.3 times
greater
among
occurring
in the north-eastern
web affecting
postsmolt
individuals
than in
within
individuals
in freshwater,
and
growth,
and by differences
river conditions
influencing
presmolt growth
rate10
times
greater
at sea. Scale growth was inversely related to
and later
upstream
migration.
Funding Information
2012; Accepted: 22 June 2012
Funding for this work was
provided by grants from
Ministerio de Ciencia y
Ecology and Evolution 2012; 2(9): 2192–2203
Tecnología (CGL200760572 and CGL2010doi: 10.1002/ece3.337
year class strength, but density-dependence was c. 2.5
14964),
and Fondos
FEDER
times stronger in freshwater (before migration) than at sea
but is also highly plastic (Hutchings 2011). This is also
(PGIDIT03PXIC30102PN;
Introduction
(after migration).
the case for the Atlantic salmon (Salmo salar) (Garcı́a de
Grupos de Referencia
4. effects
Competition
distributed
Competitiva,
2010/80).
Recent climate
change is promoting multiple
at
Leániz for
et al.patchily
2007), a highly
charismaticresources
species with is
greatthe
most
plausible
explanation
for It isthe
negative
densityFrancisco
Marco-Rius
was and ecosystem levels
population,
community,
inducing
economic
and social value.
therefore
of great concern
supported
by aecological
doctoral changes well documenteddependent
observed
freshwater
and,
to a across
lesser
extensive
from vari- growth
that Atlantic
salmon in
production
has been
declining
scholarship from the
extent,
in the most
marine
environment.
ous terrestrial, freshwater, and marine systems
(Letcher
of the
species’ distributional range over the past
Spanish Government (BES2009). The impacts of climate variability on 5.
aquatic
ecodecadessome
(Hindar
al. 2011).
Our
studyrecent
provides
ofetthe
strongest evidence for a
2008-001973).
systems are diverse and affect key life history
processes,
The life history ofinAtlantic
salmon ispartial
complexmigrations
(Thorstad
role
of density-dependence
determining
including
reproduction and maturation of fishbecause
(Rijnsdorpalthough
et al. 2011).
Spawning
in freshwater
October–
Received:
11 February
migrants
can occurs
maximize
growthinby
moving
2013.
et al. 2009). Age at reproduction is among the most
January. After hatching in spring the juveniles (parr) stay
into the sea, they do not appear to become free from
important life history traits, having profound fitness
in freshwater 1–6 years before transforming into smolt.
density-dependence
constraints completely. This has
effects and also important demographic implications
The smolts then leave their rivers to pursue oceanic
implications for conservation and suggests that sea trout
(Stearns 1992). In fish, age at maturity has a significant
feeding migrations during spring and early summer. Postand other
fish
displaying
partial
may
additive genetic component (Carlson and Seamons
2008), anadromous
smolt Atlantic
salmon
spend 1–4
years migrations
at sea until the
2192
Introduction
not be best managed on a river by river basis, but rather
from a broader, coastal perspective.
ª 2012 The Authors. Published by Blackwell Publishing Ltd. This is an open access article under the terms of the Creative
Commons Attribution Non-Commercial License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited and is not used for commercial purposes.
Understanding temporal fluctuations in the
abundance and growth of organisms has
long been a key challenge in population
ecology (Krebs 2009) and density-
dependence is perhaps one of the most
ubiquitous endogenous regulators (Brook &
Bradshaw 2006). Consideration of densitydependence regulation is also important for
managing exploited populations because
harvest mortality can have markedly
1
different effects depending on density
(Minto, Myers & Blanchard 2008). Yet, the
effects of density-dependence may not
become noticeable unless one repeatedly
samples at the individual level (Vøllestad &
Olsen 2008) because most organisms
become more mobile as they grow, and
ontogenetic changes in per capita resource
requirements can cause the relationship
between density and resource abundance
to change over time (Begon, Mortimer &
Thompson 1996). This is particularly the
case for migratory species, where
individuals may be able to ‘escape’
competition by moving between habitats
(Poethke, Pfenning & Hovestadt 2007;
Mobæk et al. 2009), thereby making it
difficult to detect density-dependence
regulation.
Salmonids are well suited for studying
density-dependence because juveniles
pass through a critical time for survival
soon after emergence from spawning
redds (Elliott 1994; Garcia de Leaniz,
Fraser & Huntingford 2000) when
competition for food and space is intense
(Van Zwol, Neff & Wilson 2012), and many
populations include both resident and
migratory individuals (partial migration,
Wysujack et al. 2009; Acolas et al. 2012).
Most density-dependence studies have
made use of time series based on stockrecruitment relationships to infer densitydependent regulation at the early juvenile
stage. For example, researchers have
compared egg densities against juvenile
survival (Nicola et al. 2008), or against
body size (Einum, Sundt-Hansen & NIslow
2006). However, this approach assumes
that density is a valid metric of the intensity
of competition experienced by individuals,
which may not be the case if resources are
patchily distributed and sampling area is
unrelated to the spatial scale of the species
(Berryman 2004). An effect of density on
growth may also be difficult to detect in
such studies if resident fish have a larger
than average size or emigration is sizedependent (Elliott 1994; Jenkins et al.
1999). Density needs to be measured in
relation to the mobility of the organism
under study (Jenkins et al. 1999) and the
spatial distribution of resources (Berryman
2004; Finstad et al. 2009), but these may
not always be known. A second limitation
of observational studies is that fitness
metrics, such as survival or growth, are
often collected for a small subset of
individuals, typically over a short time
period. For example, density estimates of
juvenile salmonids are typically inferred
(and scaled up) from a necessarily limited
number of small sampling stations, which
can restrict statistical inferences. Similar
limitations also exist for growth studies
because the number of individuals that can
be recaptured is typically small, which
seriously limits the number of repeated
measures that can be obtained. In addition,
small alevins are usually difficult or even
impossible
to
mark
individually
(Kaspersson et al. 2009), which is
unfortunate since this the stage where the
impact of density on fitness is most likely to
be manifested (Elliott 1994). Fitness is a
relative concept that needs to be
interpreted in relation to conspecifics’
performance and which is useful to the
extent that one can quantify trait variation
among individuals (Wilson 2004).
The analysis of the spacing between
consecutive growth rings (circuli) found in
bone structures such as scales or otoliths
can be used to reconstruct the growth
trajectories of individuals with much more
detail than is usually possible through mark
and recapture (Campana & Thorrold 2001).
In addition, because the analysis of growth
circuli represents repeated measures on
the same individuals, linear mixed effects
models can be used to increase the power
and accuracy of statistical inferences (van
de Pol 2012). Here we used a 13-year time
series with estimates of year class strength
for two populations of migratory brown trout
(Salmo trutta) to test the hypothesis that
early freshwater growth, but not marine
growth, is suppressed at high population
densities. We employed upstream trap
records of sea trout returning to rivers to
spawn to estimate year class strength
based on the number of adults caught each
year, standardized to a common body size.
We tested for density-dependence by
examining changes in juvenile growth
reconstructed from scale analysis in
relation to parental abundance.
Freshwater systems are typically more
resource limited than marine environments
(Ross 1986), and density-dependent
2
Table 1. Parameter estimates of mixed-effects modelling of intercirculi spacing (square root) during the first year
in freshwater. Random effects are indicated by the standard deviation of slope and intercept. Minimal adequate
0.5
linear mixed effects model (LMM): Intercirculi spacing) = River + Year class strength + Circuli No.
Effects
Estimate
SE
t-value
P-value
Fixed
Intercept
Circuli No.
River
Year class strength
0.167
-0.002
-2
0.305 10
-6
-6.310 10
Random (SD)
Intercept
Slope (Circuli)
Residual
0.011
0.001
0.018
Corr
0.267
growth is thought to be caused by
exploitative competition for food, rather
than by interference competition for space
in freshwater (Grant & Imre 2005), though
both mechanisms may operate and result
in identical density-growth relationships
(Ward et al. 2007). In the sea, in contrast,
the effect of density on salmonid growth is
thought to be mediated exclusively through
completion for food (Peterman 1984) as it
is assumed that it would be difficult for fish
to monopolize space or defend marine
resources (Snover, Watters & Mangel
2005). Our expectation, therefore, was that
variation in freshwater growth would track
changes in year class strength, whereas
variation in marine growth would be largely
independent of density if juveniles migrate
to ‘escape’ competition.
Materials and methods
Study populations
We examined temporal variation in densitydependent growth in two contrasting
populations of migratory brown trout (sea
trout) from Galicia (NW Spain), one from a
large watershed (R. Ulla – 2,804 km2) and
one with a much smaller catchment (R.
Lerez – 449 Km2). Sea trout were caught in
upstream traps on their returning marine
migration each year during 1999-2010, the
body size was measured (fork length, mm)
and a sample of scales collected before the
fish were returned upstream by fishery
officers. The two rivers differ markedly in
accessible stream length for sea trout (R.
Lerez – 25 Km, R. Ulla – 102 Km) and
-3
0.813 10
-5
3.703 10
-4
8.567 10
-6
2.711 10
207.70
-47.78
3.56
-2.32
<0.001
<0.001
<0.001
0.020
estuary size (R. Lerez – 99 Km2, R. Ulla –
238 Km2), and also likely in the strength of
density-dependent
effects
(Caballero,
Cobo & Gonzalez 2006; Marco-Rius et al.
2012). Thus, sea trout from the much
smaller River Lerez migrate to sea at a
smaller size (mean 174 ± 9.1 mm) and
younger age (mean 2.04 ± 0.13 yrs) than
those from the larger R. Ulla (mean smolt
size 220 ± 12.4 mm, mean smolt age 2.12
± 0.12 yrs; Marco-Rius et al. 2012).
Scale analysis and growth profiles
We randomly chose scales from 60
individuals per river and year from the
historical scale collection, and selected 2-5
scales with clear (non–regenerated) nuclei
for each fish to prevent bias due to loss of
the first few growth rings. We made acetate
impressions of the scales withthe aid of a
pressure roller, scanned these with a
Minolta MS 6000 microfilm scanner at 2350x magnification, and saved them as high
resolution TIFF images as in Kuparinen et
al. (2009).
Image-J v. 1.4.1 (Abramoff, Magalhães &
Ram 2004) was employed to digitize the
position of each growth ring, to identify the
annual growth rings (annuli), and to
measure the inter-circuli spacing along the
360º scale axis with reference to a
calibrated scale bar in order to derive
measures of scale growth (Marco-Rius et
al. 2012). The freshwater and marine ages
were determined based on the number of
annuli and the point of entry of smolts into
the sea (beginning of marine phase) was
3
Table 2. Parameter estimates of mixed-effects modelling of intercirculi spacing (square root) during the first
marine growing season (post-smolt growth). Random effects are indicated by the standard deviation of slope
0.5
and intercept. Minimal adequate linear mixed effects model (LMM): Intercirculi spacing) = River + Smolt Year
strength + Circuli No.
Effects
Estimate
SE
Fixed
Intercept
Circuli No.
River
Smolt Year strength
0.020
-4
1.504 10
-2
1.797 10
-6
-2.550 10
0.727 10
-5
1.011 10
-3
0.036 10
-6
1.278 10
Random (SD)
Intercept
Slope (Circuli)
Residual
2.718 10
-5
3.949 10
-3
8.565 10
Corr
0.564
-3
t-value
P-value
27.57
14.88
4.98
-1.98
<0.001
<0.001
<0.001
0.048
-3
noted based on the change from a concave
to a convex curvature of the first marine
circulus (Marco-Rius et al. 2012). We
considered scale growth patterns between
the scale focus and the first freshwater
winter as a measure of early juvenile
growth in freshwater, and between the
point of entry into the sea and the first
marine winter as a measure of early marine
growth (post smolt growth, PSG; Friedland
et al. 2006). Using only the first part of the
freshwater and marine phases (common to
all individuals) avoids problems due to age
effects and assumes that inter-cohort
density-dependent effects are similar for
yearling and under-yearling individuals in
terms of growth potential (Kvingedal &
Einum 2011). The first three scale circuli
were not taken into account in the analysis
due to the possibility of scale regeneration
during early growth. In total we analysed
scales of 453 sea trout from the R. Lerez
and 490 sea trout from the R. Ulla.
A paired t-test was used to assess nonrandom deviations in scale radii between
the original scales and their acetate
impressions (n = 30) in order to quantify
potential bias in scale measurements
arising from pressure from the hand roller.
To ascertain the precision of the scale
analysis, we estimated the repeatability of
the point of entry into the sea and of the
end of the first freshwater growing season
by measuring the scales of 30 individuals
twice in a double blind fashion and
calculating the intra-class correlation
coefficient (α-Cronbach) as per Kuparinen
et al. (2009). The Pearson correlation
coefficient was used to evaluate the
strength of the association between scale
radius and body size of fish in each river.
The coefficients of variation (CV) were then
examined to compare the precision of body
size and scale measurements. Precision in
scale measurements (0.01 mm; CV =
17.1%) was better than that of body size
measurements (cm; CV = 19.2%), and the
former was therefore preferred to examine
growth variation among migratory trout.
Scale measurements were not converted
to body size measurements as this
introduces additional errors resulting from
relatively crude measurements of body size
taken in the field.
Data analysis
We modelled variation in year class
strength by considering the annual number
of returning sea trout caught in two
upstream traps at the end of the fishing
season (R. Lerez, mean trap catch = 347
sea trout/year, range: 203-610; R. Ulla
mean trap catch = 350 sea trout/year,
range: 212-596), standardized to a
common body size (Lerez, 347.3 mm; Ulla,
350.3 mm) in order to factor in variation in
size-dependent fecundity. We assumed
that annual variation in trap catches
reflected variation in spawning escapement
(and thus on egg deposition) and derived
4
Figure 1. Freshwater scale growth profiles
(cumulative scale growth, mm) of sea trout from the
R. Lerez (n = 453) and the R. Ulla (n = 490) stratified
by year of seaward migration (smolt year).
Lerez, 1997
Lerez, 1998
Lerez, 1999
Lerez, 2000
Lerez, 2001
Lerez, 2002
Lerez, 2003
Lerez, 2004
Lerez, 2005
Lerez, 2006
Lerez, 2007
Lerez, 2008
Lerez, 2009
Ulla, 1997
Ulla, 1998
Ulla, 1999
Ulla, 2000
Ulla, 2001
Ulla, 2002
Ulla, 2003
Ulla, 2004
Ulla, 2005
Ulla, 2008
Ulla, 2009
50
50
50
50
50
50
4
3
2
1
0
4
3
Scale length (mm)
2
1
0
4
3
2
1
0
4
3
2
1
0
0
100 1500
100 1500
100 1500
100 1500
100 1500
100 150
Circuli No.
indices of year class strength for each
cohort and smolt year (i.e. year of seaward
migration) taking into account the age
structure of these populations (Marco-Rius
et al. 2012) to factor in the relative
contribution of overlapping age years
classes.
We employed linear mixed modeling to
assess individual variation on the spacing
between consecutive growth circuli (intercirculi spacing) using the protocol
described in Zuur et al. (2009) for nested
data using the Bayesian Information
Criteria (BIC). The effects of river and year
class strength on freshwater inter-circuli
spacing was examinedwith the following
fixed effects saturated model:
I ~ C x SH x R
where I is the freshwater inter-circuli
spacing (until the first freshwater winter), C
is the circuli pair being considered, SH is
the standardized year class strength at
hatching adjusted for variation in adult
body size and juvenile age structure, and R
is the river identity. All the possible
interactions were included in the model.
Likewise, to model variation in marine
scale growth we considered the following
saturated structure:
I ~ C x R x SM x FW
where I is the marine inter-circuli spacing
(until the first marine winter), C is the circuli
pair being considered, SM is the
standardized year class strength at
smolting (taking into account the smolt age
of the population) adjusted for variation in
adult body size, R is the river identity, and
FW is the scale radius at the end of the first
winter in freshwater. As previously, all
interactions were included in the model.
We used the squared root of the
dependent variable in every model to help
normalize error terms.
Random effects were tested in both
saturated models, and these were
assumed to be independent among
individuals and to follow a normal
distribution with mean zero and variances
σ2a and σ2b, respectively; the observation
error εi,j, was also assumed to be
independent and normally distributed. We
calculated
variance
components
todetermine the relative strengths of within
and among individual differences in intercirculi
spacing,
and
allowed
for
autocorrelation in inter-circuli spacing by
considering an autoregressive (AR) model
of order one in the autocorrelation
structure. This provided a better fit to the
data than a model without correlated serial
errors. We run models incorporating the
effect of year class strength at various time
lags (-4 to +4) to account for the fact that
competition experienced by migrants at
sea may also be affected by earlier and
later year classes. All analyses were
performed on R 2.15.0 language (R
Development Core Team 2012) using the
nlme 3.1-103 package (Pinheiro et al.
2012).
Results
Scale reliability
There was no significant distortion of scale
radius due to the impression process (t29 =
0.547, P = 0.465), indicating that acetate
impressions gave an accurate, unbiased
representation
of
scale
size.
Repeatabilities of scale size were high,
5
Scale radius and fork length were positively
correlated (r = +0.747, P = 0.001), and the
relationship was not different among rivers
(F1,941 = 0.326, P = 0.568) allowing us to
use scale measurements to reconstruct
changes in body size regardless of river
identity.
Figure 2. Temporal changes in scale growth (mm,
mean ± 95 CI) of sea trout during the first year in
freshwater (A-B) and during the first marine growing
season (post-smolt growth, C-D ) in the rivers Ulla
and Lerez.
Ulla
1.0
A
0.8
0.8
●
0.6
●
●
●
●
●
●
●
●
●
●
●
0.4
PSG (mm)
FW winter length (mm)
1.0
Ulla
0.2
●
●
●
●
●
0.6
●
●
●
●
0.4
Determinants of individual variation in
freshwater growth
0.2
0.0
0.0
1996
1998
2000
2002
2004
2006
1998
2000
2002
2004
Year of hatching
Smolt year
Lerez
Lerez
1.0
2006
2008
1.0
B
0.8
0.6
●
●
D
0.8
●
●
●
●
●
●
●
●
●
0.4
●
0.2
PSG (mm)
FW winter length (mm)
C
●
●
●
0.6
●
●
●
●
●
●
●
●
●
●
●
●
0.4
0.2
0.0
0.0
1996
1998
2000
2002
2004
2006
1998
Year of hatching
2000
2002
2004
2006
2008
Smolt year
both for smolt scale length (α-Cronbach =
0.879) and for scale size attained at the
end of the first freshwater growing season
(α-Cronbach = 0.898).
Figure 3. Fitted values of mixed effects model
describing inter-circuli spacing during the first year in
freshwater according to year of hatching.
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
0.18
0.16
0.14
0.12
0.10
Fitted Values
0.18
0.16
0.14
0.12
0.10
0.18
0.16
Inspection of freshwater growth profiles,
obtained by plotting circuli number against
cumulative scale length (Figure 1), reveals
considerable variation in growth slopes
among individuals, as well as between
rivers and years. Annual scale growth
differed significantly among years, both in
freshwater (R. Ulla F9,489 = 2.82, P = 0.005,
Figure 2A; R. Lerez F11,452 = 7.15, P <
0.001, Figure 2B) and in the sea (R. Ulla
F11,489 = 3.43, P < 0.001, Figure 2C; R.
Lerez F11,452 = 1.89, P = 0.04, Figure 2D).
The minimal adequate model of inter-circuli
spacing in freshwater included all three
main effects, i.e. circuli number, year class
strength, and river identity (BIC= -94350).
None of the interactions were significant,
and these were removed from the model
following Zuur et al. (2009). The inclusion
of an autocorrelation structure, as well as
of random intercepts and slopes, was
found necessary to adequately describe
the freshwater growth of sea trout.
Inspection of parameter estimates (Table
1) and fitted values (Figure 3) indicates that
there is considerable variation in the
growth slope of individuals within the same
cohort, and that early scale growth
decreases rapidly from the first summer
until the first freshwater winter. Freshwater
growth differs significantly between the two
neighbouring rivers, with sea trout from the
R. Ulla growing faster (i.e. displaying wider
inter-circuli spacing) than those from the R.
Lerez.
Determinants of individual variation in
marine growth
0.14
0.12
0.10
10 20 30 40 50 10 20 30 40 50 10 20 30 40 50 10 20 30 40 50 10 20 30 40 50
Circuli
As with freshwater growth, the minimal
adequate model of inter-circuli spacing
during the first marine growing season
6
included the effects of circuli number, year
class strength and river identity (BIC= 106635.4). The terms capturing variation in
freshwater size, as well as all the
interactions, were not significant and were
removed. As with freshwater growth, the
inclusion of random effects and a
correlation structure was necessary to
adequately describe marine growth of sea
trout (Table 2). Inspection of fitted values
during the first summer at sea displayed in
all cases a positive slope, reflecting a
period of rapid, accelerated growth (Figure
4). Parameter estimates indicated that sea
trout from the R. Ulla grew faster in the sea
(as they did in freshwater) than those from
the R. Lerez.
Figure 4. Fitted values of mixed effects model
describing inter-circuli spacing during the first marine
growing season (post smolt growth, PSG) according
to year of smolting. For comparisons, circulus
number one represents transition into the marine
environment for all fish.
1997
1998
1999
2000
2002
2003
2004
2005
2001
0.040
0.035
0.030
0.025
contrast, variation in marine growth was
much higher among individuals (R. Lerez
27%, R, Ulla 32%) than within individuals
(R. Lerez 2%, R. Ulla 3%).
Density-dependent growth
The negative, statistically significant sign of
the parameter estimates for year class
strength on inter-circuli spacing indicates
that parental abundance (and thus likely
offspring abundance) had a negative effect
on the subsequent growth of juveniles,
though the density-dependent effect on
growth was c. 2.5 times stronger in
freshwater (Table 1, parameter estimate 6.31 x 10-6, P = 0.020) than at sea (Table
2, parameter estimate -2.55 x 10-6, P =
0.048). The absence of significant
interaction terms suggests that the
negative effect of density on growth was
the same for both rivers. On the other
hand, models of marine scale growth
lagged at 1-4 years were not significant,
with model fitting decreasing with
increasing time-lags (BIC lag1= -96965.25,
BIC lag 2= -82305.8, BIC lag3= -71133.14,
BIC lag4= -63639.08) suggesting that there
was little, if any, evidence for interference
competition at sea among individuals of
different smolt years.
Fitted Values
0.040
Discussion
0.035
0.030
0.025
2006
2007
2008
2009
0.040
0.035
0.030
0.025
0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 0 10 20 30 40
Circuli
Partition of variance components
Analysis of variance components indicated
that variation among individuals in
freshwater inter-circuli spacing (R. Lerez
25%, R. Ulla 29%) was larger than within
individual variation (R. Lerez 23%, R. Ulla
19%), although differences were small. In
Our study suggests that growth of juvenile
sea trout, a species exhibiting partial
migrations (Wysujack et al. 2009; Acolas et
al. 2012) is suppressed at high population
densities, not only in freshwater, butto a
lesser extent also in the marine
environment. Under the assumption that
scale size and inter-circuli spacing are both
positively related to somatic growth –
assumptions that are generally upheld by
empirical evidence in salmonids (MarcoRius et al. 2012) and other fishes (Cheung
et al. 2007), we found that temporal
fluctuations in year class strength had a
marked effect on juvenile growth at two
different life-history stages, and at two
different spatial scales.
Annual fluctuations in salmonid abundance
are usually large (Nicola et al. 2008) and
can be expected to have large effects on
intra- and inter-cohort competition (Einum
7
et al. 2011), which should be manifested in
changes in individual growth (Parra et al.
2011). Yet, density-dependent growth has
been difficult to detect at the individual
level in the wild because growth data
derived from mark and recapture are often
restricted to a few time events and tend to
provide only a snapshot of growth
performance (Vincenzi, Satterthwaite &
Mangel 2008). In contrast, growth circuli
continue to be deposited over the entire
lives of many fishes, and once formed,
remain unchanged (Cheung et al. 2007).
These characteristics afford the fine
resolution necessary to quantify individual
variation in fish growth, and as our study
shows, to test for density-dependent
effects at the individual level.
Selection can act strongly on salmonid
body size (Garcia de Leaniz et al. 2007)
and we found large differences in scale
growth increments of sea trout from two
neighbouring rivers, highlighting the
marked effect that spatial heterogeneity
and local conditions can have on salmonid
growth (Foldvick, Finstad & Einum 2010).
In general, density–dependent effects were
stronger in freshwater than at sea, and also
stronger in the river Lerez (with less
accessible area and poorer juvenile
growth) than in the much larger River Ulla.
These results are consistent with resource
competition being the chief reason for
negative density-dependent effects on
salmonid fitness (Finstad et al. 2009).
Migratory salmonids typically move in small
shoals when they enter the sea in order to
minimize predation (Dutil & Coutu 1988)
and are able to exploit a wider range of
prey resources than in freshwater (Hansen
& Quinn 1998), marked density-dependent
effects are perhaps less likely to occur at
sea.
Density-dependent growth at sea has not
been reported before for anadromous
brown trout, but this may reflect the
difficulty of detecting such a process in a
coastal species with a relatively short
marine phase, as well as the limited power
of simple scale growth analysis (MarcoRpus et al. 2012). Negative densitydependent marine growth appears to be
relatively common among other migratory
salmonids (e.g. Atlantic salmon - Hansen &
Quinn 1998; coho salmon – Emlen et al.
1990;sockeye salmon - Martinson et al.
2008), though it is most readily apparent
during the late marine phase (Ruggerone
et al. 2006). Unlike in freshwater, where
density-dependent growth is well explained
by territorial behaviour and interference
competition,
similar
underlying
mechanisms at sea remain obscure
(Snover, Watters & Mangel 2005).
Seasonal migrations can result in high
concentrations of sea trout close to shore
as the species rarely moves more than 100
km offshore in this area (Caballero, Cobo &
Gonzalez 2006). Productivity in these
coastal waters is regulated by short,
localized upwelling episodes that reappear
with a frequency of 14 ± 4 days (ÁlvarezSalgado et al. 2000) resulting in patchily
distributed and temporally abundant prey
that provide the conditions necessary for
marine density-dependence effects to
develop. On the other hand, we found no
evidence for lagged effects on marine
growth,
suggesting
that
densitydependence at sea results chiefly from the
effect of single smolt cohorts (i.e. smolts
entering the sea on the same year) rather
than from the effects of multiple year
classes.
Although our study did not consider the
potential effect of other marine fish
(including sea trout from other nearby
populations, as well as other marine
fishes), the two study populations occupy
adjacent estuaries and are the largest in
the area (Caballero, Cobo & Gonzalez
2006; Marco-Rius et al. 2012). It could also
be argued that if marine growth of sea trout
depended on the presence of other,
unaccounted fish competitors, failure to
include these would have likely introduced
random noise, making it more difficult (not
less) to detect density-dependent marine
growth.
Our analysis of variance components
indicates that variation in inter-circuli
spacing was larger between individuals
than within-individuals, both in freshwater
and at sea. This supports the contention
that inter-circuli spacing is a good indicator
of individual growth performance (MarcoRius et al. 2012) and can be used to
examine how individuals respond to
density-dependence regulation. Migration
has been viewed as a strategy to ‘escape’
8
from harsh conditions (Hebblewhite &
Merrill 2009) which can protect offspring
against density-dependence (Economou
1991). Salmonid alevins are thought to
benefit from dispersal early in life through
improved growth at the expense of
increased risk of predation (Einum et al.
2011), and our study indicates that
seaward migration may confer similar
benefits as sea trout smolts appear to
benefit from reduced density-dependence
during the first marine growing season.
This is consistent with the existence of
marine compensatory growth, whereby
individuals that grow poorly in freshwater
are able to catch up later during their
marine life (Marco-Rius et al. 2012),
presumably because competition is weaker
during the post-migratory than the premigratory phase (Snover, Watters &
Mangel 2005).
However, our results also indicate that sea
trout do not escape completely from
density-dependence
constraints
by
migrating into the sea, and that their
marine growth is still impacted by the
presence of conspecifics, presumably due
to competition for food (Peterman 1984).
Recent models predict that densitydependence could help maintain partial
migrations if, as our study of sea trout
indicates,
resident
and
migratory
individuals are subjected to densitydependent forces of varying strength
before and aftermigration (Taylor & Norris
2007). The findings of our study have
implications
for
conservation
and
management because despite strong
homing behaviour on this species, the
existence of negative density-dependent
growth at sea suggests that sea trout
should not be managed on a river by river
basis and that populations may be better
conserved taking a wider, coastal
perspective.
Acknowledgements
We thank Pilar Alvariño and Laura Cal for
technical assistance and Sonia Consuegra
for helpful comments on the manuscript.
References
Abramoff, M.D, Magalhães, P.J. & Ram, S.J.
(2004) Image processing with ImageJ.
Biophotonics International, 11, 36–42.
Acolas, M.L., Labonne, J., Bagliniere, J.L. &
Roussel, J.M. (2012) The role of body size
versus growth on the decision to migrate: a
case
study
with
Salmo
trutta.
Naturwissenschaften, 99, 11-21.
Álvarez-Salgado, X.A., Gago, J., Míguez, B.M.,
Gilcoto, M. & Pérez, F.F. (2000) Surface
waters of the NW Iberian margin: upwelling
on the shelf versus outwelling of upwelled
waters from the Rías Baixas. Estuarine,
Coastal and Shelf Science, 51, 821–837.
Begon, M., Mortimer, M. & Thompson, D.J.
(1996) Population Ecology: A Unified Study
of Animals and Plants. Blackwell Science,
Oxford.
Berryman, A.A. (2004) Limiting factors and
population regulation. Oikos, 105, 667–670.
Brook, B.W. & Bradshaw, C.J.A. (2006)
Strength
of
evidence
for
density
dependence in abundance time series of
1198 species. Ecology, 87, 1445–1451.
Caballero, P., Cobo, F. & González, M.A.
(2006) Life history of a sea trout (Salmo
trutta L.) population from the North-West
Iberian Peninsula (River Ulla, Galicia,
Spain). Sea Trout Biology, Conservation
and Management (eds G. Harris & N.
Milner), pp 234-247. Blackwell Publishing,
Oxford.
Campana, S.E. & Thorrold, S.R. (2001)
Otoliths, increments, and elements: keys to
a comprehensive understanding of fish
populations? Canadian Journal of Fisheries
and Aquatic Sciences, 58, 30–38.
Cheung, C.H.Y., Chaillé PM, Randall D.J., Gray
J.S. & Au D.W.T. (2007) The use of scale
increment as a means of indicating fish
growth and growth impairment. Aquaculture,
266, 102–111.
Dutil, J.D. & Coutou, J.M. (1988) Early marine
life of Atlantic salmon, Salmo salar,
postsmolts in the northern Gulf of St.
Lawrence. Fisheries Bulletin, 86, 197–212.
Economou, A.N. (1991) Is dispersal of fish
eggs, embryos and larvae an insurance
against density dependence? Environmental
Biology of Fishes, 31, 313–321.
Einum, S., Sundt-Hansen, L. & Nislow, K.H.
(2006) The partitioning of density-dependent
dispersal, growth and survival throughout
ontogeny in a highly fecund organism.
Oikos, 113,489–496.
Einum, S, Nislow, K.H., McKelvey, S. &
Armstrong, J.D. (2011) The spatial scale of
competition from recruits on an older cohort
in Atlantic salmon. Oecologia, 167, 1017–
1025.
Elliott, J.M. (1994) Quantitative Ecology and the
9
Brown Trout. Oxford University Press, New
York, USA.
Emlen, J.M., Reisenbichler, R.R., McGie, A.M.
&
Nickelson,
T.E.
(1990)
Densitydependence at sea for coho salmon
(Oncorhynchus kisutch). Canadian Journal
of Fisheries and Aquatic Sciences, 47,
1765–1772.
Finstad, A.G., Einum, S., Ugedal, O. & Forseth,
T. (2009) Spatial distribution of limited
resources and local density regulation in
juvenile Atlantic salmon. Journal of Animal
Ecology, 78, 226–235.
Foldvik, A., Finstad, A.G. & Einum, S. (2010)
Relating juvenile spatial distribution to
breeding patterns in anadromous salmonid
populations. Journal of Animal Ecology, 79,
501–509.
Friedland, K.D., Clarke, L., Dutil, J. & Salminen,
M. (2006) The relationship between smolt
and postsmolt growth for Atlantic salmon
(Salmo salar) in the Gulf of St Lawrence.
Fisheries Bulletin, 104, 149–155.
Garcia de Leaniz, C., Fraser, N. & Huntingford,
F.A. (2000). Variability in performance in
wild Atlantic salmon, Salmo salar L., fry from
a single redd. Fisheries Management and
Ecology, 7, 489–502.
Garcia de Leaniz, C., Fleming, I.A., Einum, S.,
Verspoor, E., Jordan, W.C. et al. (2007) A
critical review of adaptive genetic variation
in Atlantic salmon: implications for
conservation. Biological Reviews, 82, 173–
211.
Grant, J.W.A. & Imre, I. (2005) Patterns of
density-dependent growth in juvenile
stream-dwelling salmonids. Journal of Fish
Biology, 67, 100–110.
Hansen, L.P. & Quinn, T.P. (1998) The marine
phase of the Atlantic salmon (Salmo salar)
life cycle, with comparisons to Pacific
salmon. Canadian Journal of Fisheries and
Aquatic Sciences, 55, 104–118.
Hebblewhite, M. & Merrill, E.H. (2009) Tradeoffs between predation risk and forage differ
between migrant strategies in a migratory
ungulate. Ecology, 90, 3445-3454.
Jenkins Jr, T.M., Diehl, S., Kratz, K.W. &
Cooper, S.D. (1999) Effects of population
density on individual growth of brown trout in
streams. Ecology, 80, 941–956.
Kaspersson, R. & Höjesjö, J. (2009) Densitydependent growth rate in an age-structured
population: a field study on stream-dwelling
brown trout Salmo trutta. Journal of Fish
Biology, 74, 2196-2215.
Krebs, C.J. (2009). Ecology: The Experimental
Analysis of Distribution and Abundance, 6th
edn. Benjamin Cummings, San Francisco,
USA.
Kuparinen, A., Garcia de Leaniz, C.,
Consuegra, S. & Merilä, J. (2009) Growthhistory perspective on the decreasing age
and size at maturation of exploited Atlantic
salmon. Marine Ecology Progress Series,
376, 245–252.
Kvingedal, E. & Einum, S. (2011) Intracohort
and intercohort spatial density dependence
in juvenile brown trout (Salmo trutta).
Canadian Journal of Fisheries and Aquatic
Sciences, 68, 115–121.
Marco-Rius, F., Caballero, P., Morán, P. &
Garcia de Leaniz, C. (2012) And the last
shall be first: heterochrony
and
compensatory marine growth in sea trout
(Salmo trutta). PLoS One, 7, e45528
Martinson, E.C., Helle, J.H., Scarnecchia, D.L.
& Stokes, H.H. (2008) Density-dependent
growth of Alaska sockeye salmon in relation
to climate-oceanic regimes, population
abundance, and body size, 1925 to 1998.
Marine Ecology Progress Series, 370, 1–18.
Minto, C,, Myers, R.A. & Blanchard, W. (2008)
Survival variability and population density in
fish populations. Nature, 452, 344–347.
Mobæk, R., Mysterud, A., Egil Loe, L., Holand,
Ø. & Austrheim, G. (2009) Density
dependent and temporal variability in habitat
selection by a large herbivore; an
experimental approach. Oikos, 118, 209–
218.
Nicola, G.G., Almodóvar, A., Jonsson, B. &
Elvira, B. (2008) Recruitment variability of
resident
brown
trout
in
peripheral
populations
from
southern
Europe.
Parra, I., Almodóvar, A., Ayllón, D., Nicola,
G.G. & Elvira, B. (2011). Ontogenetic
variation in density-dependent growth of
brown trout through habitat competition.
Peterman, R.M. (1984). Density-dependent
growth in early ocean life of sockeye salmon
(Oncorhynchus nerka). Canadian Journal of
Fisheries and Aquatic Sciences, 41, 1825–
1829.
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D.,
& the R Development Core Team .(2012)
nlme: Linear and Nonlinear Mixed Effects
Models. R package v. 3.1-103. (http://cran.rproject.org/web/packages/nlme/index.html).
Poethke, H.J., Pfenning, B., & Hovestadt, T.
(2007) The relative contribution of individual
and kin selection to the evolution of densitydependent dispersal rates. Evolutionary
Ecology Research, 9, 41–50.
R Development Core Team (2012) R: A
Language and Environment for Statistical
Computing. R Foundation for Statistical
Computing, Vienna, Austria. [v. 2.15.0].
10
Ross, S.T. (1986) Resource partitioning in fish
assemblages: a review of field studies.
Copeia, 1986, 352–388.
Ruggerone, G., Nielsen, J., Farlet, E., Ignell, S.,
Hagen, P. et al. (2006) Long-term trends in
annual Bristol Bay sockeye salmon scale
growth at sea in relation to sockeye
abundance and environmental trends,
1955–2000. NPAFC Technical Report, 4,
56–58.
Snover, M.L., Watters, G.M. & Mangel, M.
(2005) Interacting effects of behavior and
oceanography on growth in salmonids with
examples for coho salmon (Oncorhynchus
kisutch). Canadian Journal of Fisheries and
Aquatic Sciences, 62, 1219–1230.
Taylor, C.M. & Norris, D.R. (2007) Predicting
conditions for migration: effects of density
dependence and habitat quality. Biology
Letters, 3, 280–283.
van de Pol, M. (2012) Quantifying individual
variation in reaction norms: how study
design affects the accuracy, precision and
power of random regression models.
Methods in Ecology and Evolution, 3, 268–
280.
Van Zwol, J.A., Neff, B.D. & Wilson, C.C.
(2012) The effect of competition among
three salmonids on dominance and growth
during the juvenile life stage. Ecology of
FreshwaterFish, 21, 533-540.
Vincenzi, S., Satterthwaite, W.H. & Mangel, M.
(2011) Spatial and temporal scale of
density-dependent body growth and its
implications for recruitment, population
dynamics and management of streamdwelling salmonid populations. Reviews in
Fish Biology and Fisheries, 22, 813–825.
Vøllestad, L.A. & Olsen, E.M. (2008) Nonadditive effects of density-dependent and
density-independent factors on brown trout
vital rates. Oikos, 117, 1752-1760.
Ward, D.M., Nislow, K.H., Armstrong, J.D.,
Einum, S. & Folt, C.L. (2007) Is the shape of
the density-growth relationship for stream
salmonids evidence for exploitative rather
than interference competition? Journal of
Animal Ecology, 76, 135–138.
Wilson, D.S. (2004) What is wrong with
absolute individual fitness? Trends in
Ecology and Evolution, 19, 245-248.
Wysujack, K., Greenberg, L.A., Bergman, E. &
Olsson, I.C. (2009) The role of the
environment in partial migration: food
availability affects the adoption of a
migratory tactic in brown trout Salmo trutta.
Ecology of Freshwater Fish, 18, 52-59.
Zuur, A.F., Ieno, E., Walker, N., Saveliev, A. &
Smith, G. (2009) Mixed Effects Models and
Extensions in Ecology with R. Springer, New
York, USA.
11
Insights for planning an effective stocking program in
anadromous brown trout (Salmo trutta)
92!
!
Insights for planning an effective stocking
program in anadromous brown trout (Salmo
trutta)
Francisco Marco-Rius, Graciela Sotelo, Pablo Caballero, Paloma Morán
Abstract: Brown trout is a salmonid species with a high socio-economic value related with recreational
fishing. Because of that, stocking programs have been developed in many populations, although have
focused on resident populations. In order to explore which factors promote that migratory behaviour when
implementing stocking actions, 28 brown trout artificial crosses were carried out in a non-commercial
hatchery, and the returning success of their offspring was further evaluated. Return rate was examined
attending to: male phenotype (anadromous vs. resident), mean egg size, parents’ similarity at major
histocompatibility complex (MHC) class II β-gene, and stocking procedure. At the end of the
experiment, 35 of the captured returning adults (9.4%) belonged to 14 of those crosses. Return success
shows significantly affected by parental MHC similarities, stocking procedure and male phenotype. Our
results indicate that planting fertilized eggs in nursery areas of the river, together with the selection of
anadromous males as brood stock and mate pairs with higher similarity at the MHC locus can be an
appropriate option to increase the migratory part of trout populations. In addition, nursery areas can allow
an important decrease in the cost per stocked individual, being 32 times less expensive than the cost per
hatchery-reared individuals.
Introduction
Conservation of native populations is an
important challenge for managers (Waples and
Hendry 2008). For instance, many salmonid
populations have declined dramatically during
the last century because of overexploitation
(Almodovar and Nicola 2004; Saura et al.
2010), habitat degradation (Miranda et al.
2012), or climate change (Kennedy and Crozier
Received 11 February 2013. Accepted 13 May 2013.
Paper handled by Associate Editor Alistair Coulthard.
F. Marco-Rius1 and P. Morán. Universidad de Vigo, Departamento de Bioquímica, Genética e
Inmunología, Vigo 36310, Spain.
G. Sotelo. CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBio,
Universidade do Porto, 4485-661 Vairão, Portugal.
P.Caballero. Consellería de Medio Rural, Servizo de Conservación da Natureza, Xunta de
Galicia, Pontevedra, Spain.
1
Corresponding author (e-mail: [email protected]).
1
2010). Therefore, different solutions have been
proposed as habitats restoration (Anton et al.
2011) or population enhancing by supportive
breading programs (Saura et al. 2006). In the
planning of latter, the component of anadromy
is never taken into account and a specific
program for anadromous brown trout
enhancement is not available for management.
In this study, we propose the baseline for
develop a program for anadromous populations
based on selecting the brood stock, the use of
different ages for stocking and the relationship
with the returning rate after sea migration and
the economic cost.
Brown trout (Salmo trutta) is often subjected to
stocking due to recreational fishing (Juanes et
al. 2011). For this reason, captivity bred trout
are released into rivers and lakes to enhance
local populations (Jonsson and Jonsson 2011).
Various authors have addressed pros and cons
of this method (Fleming et al. 1997; Jonsson et
al. 2003) and the results appear largely
dependent on the parental stock used, where
offspring
success
is
reduced
when
domesticated trout are used (Moran et al. 1991;
Martinez et al. 1993; Dahl et al. 2006).
Moreover, hatchery environments often favour
completely different traits than natural
selection does in the wild (Rogell et al. 2012).
One of the traits affected by hatchery
environments is the component of anadromy of
brown trout populations (Serrano et al. 2009).
Brown trout is a partly migratory species, i.e.
both migratory and resident individuals cooccur in the same population (Jonsson and
Jonsson 1993). Migratory individuals return to
their natal river (Jonsson and Jonsson 2012) for
reproduction after spending one or more years
in seawater, where they can attain up to ten
times larger size at maturity than resident
individuals do (Jonsson 1985). Furthermore,
females migrate more than males (Milner et al.
2003), probably because female fitness is most
directly related to body size (Jonsson &
Jonsson 2011). However, the mechanisms
controlling trout migrations are not fully
understood, although it is thought that both
genetic and environmental factors play
important roles (Ferguson 2006; Giger et al.
2006). It has been reported that hatchery-reared
trout are less migratory than wild conspecifics
(Serrano et al. 2009). Thus, stocking may
reduce the migratory predisposition of
populations. However, supportive breeding and
stocking are still important long-term
conservation tools for sustaining harvestable
populations (Saura et al. 2006).
Stocking of hatchery fish is economically
expensive. For example, in Atlantic salmon
(Salmo salar) the cost varies from 0.022€ per
eyed ova to 1.063€ per one-year-old smolt or
2.127€ per two-year-old smolt in UK
(Aprahamian et al. 2003). Hatchery conditions
for brown trout are approximately the same and
the rearing cost will be equivalent. But still,
supportive breeding is assumed necessary in
rivers where Atlantic salmon are exterminated.
In such cases, the brood stock should be taken
from a neighbouring river with similar
environmental conditions (Hansen et al. 2006).
Iberian Peninsula represents the southern
distribution limit of anadromous brown trout.
There, many populations have declined due to
dams’ construction (Ayllon et al. 2006; Juanes
et al. 2011) with negative effects for sport
angling while resident trout is still fairly
common and exhibit high genetic variability
(Campos et al. 2007). In such cases, supportive
breading could be the only way to maintain or
recover the populations present. Indeed,
Atlantic salmon stocks have been restored in
rivers where they became extinct in the 1990s
(Hervella and Caballero 1999; Saura et al.
2
2006), reflecting the increased damage of
anadromous populations.
In order to provide a logical framework to
develop a successful supportive breeding
program for sea trout populations, different
brown trout artificial crosses were performed
in a non-commercial hatchery in northwest
Spain. Only large migratory sea trout females
were used , mainly because the high number
of eggs that they produce compared to resident
females (Jonsson and Jonsson1993). Although
age and size at maturity or residency are partly
inherited traits, some additional benefits
(related with maternal effects) may be expected
when using large-sized females. For example,
eggs produced by larger females are larger than
those produced by small-sized females, and fry
from large eggs usually experience lower
mortality (Einum and Fleming 1999). On the
other hand, both migratory and resident males
were included in the crosses. Under natural
conditions, males compete for access to
females during spawning and as a
consequence, large males tend to breed with
large females (Jacob et al. 2007).
Secondly, in addition to these parental effects,
it
has
been
suggested
that
major
histocompatibility complex (MHC) is subject
to selection, and ultimately related to local
adaptation, through different processes related
with class I and II subfamilies. The role of
MHC polymorphism in pathogen resistance
and reproductive success has been tested in
several vertebrates. For example, in rhesus
macaques (Macaca mulatta), heterozygous
sires in MCH class II showed better
reproductive success than homozygous sires
(Sauermann et al. 2001). Also, the survival of
Chinook salmon (Oncorhynchus tshawytscha)
was increased by heterozygosity of the MHC
class IIB genes when infected by the
haematopoietic necrosis virus (IHNV) (Arkush
Fig. 1. Rivers/streams used in the study: The
brood stocks were captured in Tea and Deza, the
eyed ova were stocked at Cabanelas and the
hatchery-reared one-year old brown trout were
stocked at Lerez.
Galicia
Spain
Deza
´
Lerez
Cabanelas
Tea
kilometers
0
5
10
20
et al. 2002). Moreover, associations between
certain MHC class II alleles and the bacterial
infection (forunculosis) were found in Atlantic
salmon, suggesting that different alleles are
related with high resistance and low resistance
to infection (Langefors et al. 2001). Balancing
selection has been proposed as the most likely
mechanism maintaining genetic variation
within and among populations (reviewed in
Bernatchez and Landry 2003) and we expect
that the same holds for brown trout.
In the present experiment we measured the
return-rate of adult sea trout either (1) planted
3
Table 1. Number of returned adults captured by the trap located in river Lérez, between 2010 and 2011. It
is reported the cross identity, the stocking type performed, the amino acid distances and the male
phenotype used (‘A’, anadromous, and ‘R’, resident).
Number of
returned adults
1
2
1
1
6
3
2
1
2
1
1
3
1
1
1
5
1
Cross
number
1
3
4
5
6
7
8
10
12
19
20
24
27
28
Stocking type
Eyed ova
Eyed ova
1-year-old
1-year-old
Eyed ova
1-year-old
1-year-old
Eyed ova
Eyed ova
Eyed ova
Eyed ova
Eyed ova
1-year-old
Eyed ova
1-year-old
Eyed ova
Eyed ova
as eggs in a small nursery tributary stream, or
(2) released the fish as hatchery-reared oneyear-old juveniles in the main river. We
assessed three parameters: (a) male phenotype
(as paternal effect), (b) eggs size (as maternal
effect) and (c) MHC similarities.
AA distance
0.010
0.169
0.161
0.161
0.005
0.030
0.198
0.165
0.222
0.156
0.067
Male
phenotype
A
R
R
R
A
A
R
R
R
A
A
0.202
0.061
0.156
A
A
A
total of 18 anadromous females, nine
anadromous males and 17 resident males were
selected for the experiment (Table S1). Each
trout was individually identified with a passive
integrated transponder (PIT) tag and a small
piece of a pelvic fin was clipped and stored in
ethanol for subsequent genetic analysis. A total
of 28 artificial pair-crosses were done at the
hatchery resulting in a total of 8 full-sib
families and 20 half-sib families. Eleven
crosses involved migratory males, and 17
crosses involved resident males. Egg size was
estimated by taking 30 random eggs from each
cross and a calliper (0.1mm of precision) was
used for the measurement. Then, the average
size of the eggs was calculated. Finally, eggs
were reared during 30 days in the hatchery
using a recirculation system between 5° and 9°
C.
Materials and methods
Experimental design
In December 2007, adult trout were caught
before the spawning season in an upstream trap
located in the low part of Tea and Deza streams
(northwest Spain; Fig.1). These streams
support two neighbour populations where the
anadromous populations abundance have been
maintained stable during the last years
(Hervella and Caballero 1999). Both streams
have an upstream trap to provide samples. A
4
Table 2. GLM parameter estimates, using a Poisson distribution for number of returned adults between
2010 and 2011. The reference group of the factor is in parenthesis.In February 2008, 35,000 eyed eggs
were transported and planted intra-gravel with the help of a plastic tube as described in Palm et
al. (2009), at Cabanelas stream, a small tributary of the river Lérez (main river; Northwest Spain,
Fig. 1).
Estimate
Std. Error
z value
Intercept
-6.2515
5.8832
-1.06
0.2880
MHC
-8.3593
2.6422
-3.16
0.0016
Stock Type (stock 1+)
-0.9808
0.3909
-2.51
0.0121
Phenotype (Resident)
-1.1221
0.5119
-2.19
0.0284
Egg size
0.1429
0.1099
1.30
0.1936
The remaining 78,000 eggs were reared in the
hatchery until spring 2009, resulting in 23,109
one-year-old individuals. Due to space
problems in the hatchery, fry from different
crosses were mixed in five tanks of 5,000 litres
subjected to the same water temperature and
daily food quantity. However, this procedure
did not allow monitoring the survival achieved
by each single cross. These fish, after adipose
fin clipping (performed to further distinguish
them from planted individuals), were released
in March 2009 in the middle course of the main
river, very close to the mouth of Cabanelas
stream (Fig. 1). We released one-year-old
hatchery trout. According to Jokikokko and
Jutila (2004), this maximizes smolt production.
All sea trout returning between January 2010
and December 2011 were monitored in a trap
located 4 km upstream of the river mouth.
Each returning adult was measured, weighted
and marked with a PIT tag in order to identify
possible recaptures of the same individual in
subsequent years. The presence/absence of
adipose fin was recorded; and a small piece of
Pr(> |z|)
a pelvic fin was clipped for the genetic
analysis.
MHC sequencing and MHC diversity
analysis
To estimate the MHC diversity of the
experimental crosses, a 298-301 (253-256 bp
excluding primers) bp fragment of the exon 2
of a MHC class II B gene was amplified for
parental fish using the primers CL007 (5´GATCTGTATTATGTTTTCCTTCCAG-3´)
and AL100 (5´-CACCTGTCTTGTCCAGTA
TG-3´) (Olsén et al. 1998). PCR reaction was
carried out in a total volume of 20 µl,
containing 1 µl of genomic DNA, 2 µl of
reaction buffer (10× buffer), 0.5 mM dNTP,
2.5 mM MgCl2, 0.35 µM of each primer and 1
U of Taq DNA polymerase (Bioline). Cycling
conditions consisted of 35 cycles of 30 s
denaturation at 95 ºC, 30 s annealing at 57 ºC
and 45 s extension at 72 ºC, followed by a final
7 min extension at 72 ºC.
PCR products were cloned using pGEM®-T
Vector System (Promega). Twenty white
colonies per cloned product were amplified by
PCR using primers T7 and M13.
5
Fig. 2. Relationship between returning success (adults captured between 2010 and 2011) and MHC
parental similarity (based on amino acid sequences, see main text). The solid line represents the trend line
fitted by GLM and the dash line is a loess line.
0.25
MHC parents' similarity
0.20
0.15
0.10
0.05
0.00
0
2
4
6
8
Number of returned adults
Resulting PCR fragments were run in SSCP
gels as described in Campos et al. (2006) to
identify homozygous/heterozygous individuals.
For each identified allelic variant, different
PCR products were cleaned and sequenced
using a dRhodamine terminator cycle
sequencing kit and an automated sequencer
ABI PRISM 3100.
The MHC diversity of each cross was
calculated, following Landry et al. (2001), as
the mean pairwise distance between the four
parental alleles considering the four allelic
combinations (i.e., genotypes) that could be
carried by the offspring, in both nucleotide and
amino acid sequences. The distances were
computed as uncorrected p-distances using
Mega 5.05 (Tamura et al., 2011). However,
only one of the sequences is presented in the
results (the amino acid sequences) due to the
high correlation between the sequences
(cor=0.993, t12=27.89, P<0.0001).
6
Microsatellites genotyping and parentage
assignment
To assign returning fish to parental/
experimental crosses both, parents and
returning fish, were genotyped using eight
microsatellites loci: Str591INRA, Str85 (Presa
and Guyomard, 1996), Str60 (Estoup et al.
1993), Satr-UBA (Grimholt et al. 2002),
SSsp2201 (Patterson et al. 2004), Ssa85,
Ssa197 (O’Reilly et al. 1996) and SsaD157
(King et al. 2005). Microsatellites were
amplified by PCR (polymerase chain reaction)
in three multiplex reactions: Str60, Str591INRA
and Str85 in multiplex I, Ssa85, Ssa197 and
Satr-UBA in multiplex II, and SSsp2201 and
SsaD157 in multiplex III. PCR reactions were
carried out in a final volume of 20 μL,
containing 2 μL of extracted genomic DNA,
0.2 μl of Taq polymerase (Green Taq
polymerase 5U/μl), 1 μl of MgCl2 50 mM, 1 μl
of dNTPs Mix 10mM, and 10mM primer
volumes of 0.35 μl (SSsp2210, SsaD157), 0.5
μl (Str60), 0.6 μl (Str85) and 1 μl
(Str591INRA, Ssa85, Ssa197, Satr-UBA). Each
reaction was carried out for 35 cycles, with the
following conditions: for 20 s at 95°C; 20 s at
55°C, and 20 s at 72°C, for multiplex I; 20 s at
95°C; 20 s at 58°C, and 20 s at 72°C, for
multiplex II; and 50 s at 95°C; 50 s at 58°C,
and 80 s at 72°C, for multiplex III. PCR
products were prepared into a final volume of
20 μL. The mix, together with Rox 400, was
denatured at 95 °C for 5 min and immediately
put on ice. Allele size of fluorescently labelled
fragments was measured in a 3100 ABI
PRISM (Applied Biosystems, Foster City, CA,
U.S.A.) automated sequencer followed by
analysis with GeneMapper 3.7 Software
(Applied Biosystems).
Parentage assignment was done using the
software PASOS (Duchesne et al. 2005). This
program combines an approach based on
parental pair likelihoods with a filtering
procedure. The allocation of a descendant in
PASOS is based on the search for the most
likely pair among all potential pairs of known
parents. Putative parent–offspring pairs sharing
alleles at the eight loci, or having as maximum
one mismatch in one locus, were assigned to
parent pairs.
To evaluate cross diversity at neutral markers,
microsatellite similarity of each cross was
calculated as an average correlation coefficient
(Rij) between allele sizes for homologous gene
copies from two individuals (Streiff et al.
1998), given that, for microsatellite loci
evolving under a stepwise mutation model,
differences in allele size contain more
evolutionary information than just allele
identity. This index was computed using
SPAGeDi v. 1.4 (Hardy and Vekemans 2002).
Statistical analysis
A generalized linear model (GLM) with a
Poisson distribution was used in order to
explore the differences in the number of
returning adults (R) captured in the upstream
trap:
R = M+E+D+T; where M was the male
phenotype used in the cross (resident or
migratory/anadromous), E was the mean egg
size of artificial crosses, D was the MHC
diversity of the parental cross estimated from
amino acid MHC sequences, and T was the
stocking type (planted eggs or released as
juveniles) used in the recaptured individual. All
interactions were included in the saturated
model and model selection was done according
Akaike information criteria (AIC). We used the
residual deviance to perform a goodness of fit
test for the overall model.
The Pearson correlation coefficient was used to
evaluate the association between cross
similarities at MHC class IIB locus and cross
7
diversity at neutral markers to evaluate whether
or not values of similarities at MHC locus
between parents are caused by similarities in
genetic background and hence, biasing the
MHC similarities values.
Finally, length and weight of the returning
trout were compared attending to the stocking
procedure used to explore benefits or costs
over the final body size in hatchery and wild
recaptured individuals. Data were analyzed
using the software R 2.15.1 (R Development
Core Team 2012).
numbers of alleles did not differ between both
neighbouring streams used as brood stock.
The 28 experimental crosses worked out, and
the eggs produced by each female varied
approximately between 3,000 and 13,000.
Mortality during the first 30 days of rearing
conditions varied between 5% and 7%, and it
occurred mostly during the first ten days.
Between 2 and 4 years after the
commencement of the experiment, a total of
351 sea trout adults were recaptured in the
upstream trap of the main river. All the fish
were successfully genotyped for the eight
microsatellites
loci
and
assigned
unambiguously to parental crosses. The
percentage of analysed individuals that were
allocated to experimental crosses was 9.4%
(n=33); corresponding to 14 out of 28 crosses
(see table 1). Of these 33 individuals, 24 (72.7
%) had been stocked as planted eyed ova,
while only 9 had been released as one-yearolds, meaning that hatchery-reared fish
contributed only up to the 27.3% of returned
adults. Moreover, parental analysis showed that
25 individuals belonged to crosses involving
anadromous males, and eight to crosses
involving resident males.
As well, crosses with more similar MHC
alleles showed the highest number of returned
individuals, for both stocking procedures
(Figure 2). Crosses number 6, 20 and 27 turn to
be the most successful in the return rate: they
accounted for 15 out of the 33 returning adults
(45.5%). Cross 6 was the pair with more
similar male-female MHC alleles (p-distance
of 0.05), cross 27 was the fourth (p-distance of
0.061) and cross 20 was the fifth in this
ranking (p-distance of 0.067) among all
performed at the hatchery. The three crosses
were performed by use of a single, although
different, sea-run male..
Results
The final alignment of the exon 2 of the MHC
class II β-gene for the 44 individuals involved
in the 28 artificial crosses consisted of 256 bp,
and resulted in 63 alleles. They were defined
by 99 polymorphic sites, including a 3 bp
deletion (positions 189-191) present in 33 out
of the 63 alleles. Only 12 of the isolated alleles
were observed in more than one individual
while the remaining 51 were singletons.
Among the analysed parents, 12 were
homozygous and 32 were heterozygous.
When translated into protein sequences, the
alignment rendered 59 alleles, 32 comprising
84 amino acids and 27, 85 (due to the
mentioned deletion), with a total of 51
polymorphic positions observed. Sixteen
alleles were present in more than one
individual and 43 were singletons, and among
the 44 parents, 14 were homozygous and 30,
heterozygous.
All the parental trout were successfully
genotyped for the eight microsatellites loci. No
genotyping errors due to null alleles, large
allele dropout or stutter bands were observed.
Microsatellite loci were highly polymorphic,
and the number of alleles per locus varied from
6 at Str591INRA to 45 at SSsp2201 and the
8
Fig.3. Box plots of fork length and weight of returned adults (at capture), according to the type of
stocking used: planted as eyed ova versus released as one-year-old individuals. No significant
differences were observed between stocking types for any trait/measure.
500
●
Fork length (mm)
450
400
350
300
250
●
Planted
Eyed
ova
Stocked
One$year$old+individuals+
●
1200
Weight (gr)
1000
800
600
400
200
Planted
Stocked
One$year$old+individuals+
Eyed ova
The GLM model that best fitted the data was:
R= M+E+D+T (AIC= 105.83) with no
significant interaction present (Table 2). The
goodness of fit test showed that this model
fitted reasonably well because the chi-squared
test was not statistically significant (Res.
deviance= 54.86, df=51, P= 0.33). No
significant effect of the average egg size was
observed on adult return; while MHC parental
dissimilarities, stocking type and male
phenotype showed significant influences on the
returning rate (Table 2). Among parents
distance at the MHC locus, evaluated in terms
of amino acid sequences, was the most
important factor, followed by stocking
procedure and male phenotype (Table 2).
Correlation coefficient between similarities at
MHC locus and microsatellites was not
significant (cor=0.147, t13=0.538, P=0.599).
This result means that differences associated to
MHC dissimilarities are independent from
similarities in genetic background and hence,
similarities in parental MHC locus reflect that
they can be used as specific factor to explore
differences in returning rate.
Finally, differences in length or mass among
the individuals belonging to the experiment
were not significantly different regarding the
stocking method used (F1,30=1.07, P=0.31 and
9
F1,30=0.77, P=0.39 respectively), which
suggests that adult length and mass was not
improved by rearing conditions at the hatchery
(Fig. 3).
Discussion
We have evaluated several factors that can be
manipulated at the initial stages of a brown
trout enhancing program in order to maximise
the migratory success of released brown trout
individuals. The factors showing an influence
on returning success were in order of
importance: similarities at MHC class B locus
between parents, stocking procedure and male
phenotype. On the other hand, no relationship
was observed regarding maternal effects (i.e.,
egg size).
Genetic background is an important factor
determining a restoration achievement in
brown trout populations, as has been
previously tested in resident populations
(Lepori et al. 2005; Araguas et al. 2009).
However, due to difficulties in seaward
migration and return rate monitoring, it is
difficult to evaluate the genetic background of
anadromous populations. In this study we have
observed that male selection can determine the
achievement or failure of a restoration program
together with MHC dissimilarities and stocking
procedure. Beyond genetic background, these
factors are specific characteristics of the
individuals that can be easily manipulated at
the hatchery in order to produce more suitable
crosses to enhance anadromous populations.
However, allele introgression has to be taken
into account, trying to conserve the genetic
structure as far as possible. Response to
climate warming can disrupt the adaptation of
natural populations, especially at the southern
limit (Horreo et al. 2011) and the use on nonnative brood stock in conservation programs
cannot be used over existent native populations
(Almodovar et al. 2001). Here we have used
two neighbouring with no differences in allelic
number. Indeed, spatial scale of local
adaptation in brown trout have been tested
using microsatellites markers and outliers test
for selection, suggesting that differences in
adaptation cannot be understood at the level of
individual population (Meier et al. 2011). In
addition, studies over five microsatellites in
Atlantic salmon in the north west of Spain
indicated that allelic number, allelic richness
and observed and expected heterozygosities did
not differ significantly between rivers in
recently analysed populations (Saura et al.
2006), suggesting that introgression effects
may be small and if existent, an obstacle for
the offspring derived from the experiment due
to the lower adaptation to the local
environment (reviewed in Garcia de Leaniz et
al. 2007).
Maternal effect
First, size at migration represents a threshold
that individuals have to reach in order to
complete smoltification and survive to marine
conditions (Økland et al. 1993). Anadromous
brown trout smolt typically at age-2 in Spain,
and possible maternal effects may be diluted by
this time or in the subsequent sea-sojourn
(Heath and Blouw 1998).. Thus, benefits
related with egg size (Einum and Fleming
1999) could not be detected in relation to the
number of returned adults, despite that
maternal effects are crucial in the survival in
early stages of life (i.e. emerged fry or alevins;
Heath and Blouw 1998; Kamler 2005).
Male phenotype
When anadromous males are used in artificial
crosses performed at the hatchery (together
with anadromous females), the possibility that
offspring return to the river to spawn increases,
10
representing in this study the 68.8% of the total
returned individuals. As pointed out by
previous rearing experiments in brown trout,
proportion of migratory offspring was higher
with anadromous parents than with resident
parents (Skrochowska 1969), suggesting that
there are some genetic factors in offspring born
from both anadromous male and female which
may produce (a) more susceptible individuals
to migrate and/or (b) individuals with better
adaptations to smoltification and sea life
conditions.
More specifically, many experiments have
shown that there are genetic factors inherent to
stocks that have a primary influence on
migration or on the spatial marine distribution
in salmonids (Kallio-Nyberg et al. 1999;
Kallio-Nyberg et al. 2002), suggesting that
differences in migration success among crosses
could be related with differences in tendencies
to migrate of different stocks. In our study, due
to the lack of a downstream trap for sampling
the individuals that started the migration, we
cannot distinguish if differences in migratory
success among families are because the
corresponding offspring remain in the river (do
not migrate) or because they experience higher
mortality rates at sea. In both cases, the final
result is a lower proportion of migrants that
come back to the river to spawn.
Also Kalio-Nyberg et al. (2010) used resident
trout with success when restoring a population
of sea trout.. Thus, although anadromous
individuals are the most appropriate to be used
in order to increase the migratory population of
brown trout, also resident individuals can be
used when anadromous individuals are
exterminated. This suggests that life history
variation is influenced by complex interactions
between genetic and environmental factors in
brown trout (Ferguson 2006), and not only the
genetic factors will determine the migratory
behaviour (Jonsson and Jonsson 1993; Forseth
et al. 1999). This becomes essential in hatchery
management because the potential anadromous
brood stock from wild populations can become
extinct or near extinction due to different
processes as contaminations (Kallio-Nyberg et
al. 2010) or dams construction (Ayllon et al.
2006; Juanes et al. 2011). In these cases,
resident individuals can be used as a first step
in the recovery of anadromous populations. In
addition, sea trout males represent a lower
proportion of migrants, male:female sex ratio is
1:2.2 in the Atlantic Spanish coast (Caballero
et al. 2006; Caballero et al. 2012) and 2:3 in
the North Sea area (Jonsson and Jonsson
1993), representing an additional difficulty to
find the anadromous males for the crosses. In
these cases, resident males represent a feasible
option.
MHC parental similarities
Performing artificial crosses taking into
account parental similarities at MHC class II B
can help to enhance sea trout populations by
increasing the offspring’s survivorship at both
freshwater and seawater stages. Differences at
MCH class II B showed that offspring born
from the most similar parents exhibited the best
return rate to the river. Although no studies
have reported positive relation between MHC
class II B similarities and benefits in freshwater
or seawater, MHC class I studies performed in
Atlantic salmon showed that (i) divergence in
MHC class I was positively related with
uninfected fish after sea migration (Consuegra
and Garcia de Leaniz 2008) or (ii) MHC class I
polymorphism was positive associated with
resistance to bacterial and viral diseases
(Grimholt et al. 2003).
However, a recent study carried out in brown
trout has observed that balancing selection acts
on variation at MHC class I in wild populations
11
suggesting that not only the most dissimilar
crosses are favoured by selection but also the
most similar (O’Farrell et al. 2012). According
to our results and the balancing selection acting
over brown trout MHC locus, there is
reasonable evidence that return rate of sea trout
is mediated in part by similarities at parental
MHC locus. In the future, although difficult, it
would be desirable to test dissimilarities at
MHC loci in resident and migratory full-sibs in
order to explore if individuals carrying the
most similar allele combinations tend to
migrate to the sea more than those with most
dissimilar ones.
Stocking procedure
Stocking procedure had a significant effect
over the number of returned adults: 24
returning individuals (6.9 % of total return
captures) were recovered from a stocking effort
of 35,000 eyed ova. If we take into account the
area used for the experiment (0.61 km2), it
represents a density of 0.163 one-year-old ind
m-2 in the tributary. Monitoring programs
implemented by the Spanish government in
river Lérez have estimated that mean brown
trout density is around 0.285 ind m-2 in a total
watershed of 449 km2 (Hervella and Caballero
1999). Thus, our stocking effort of 35000 eggs
performed in the 0.13% of the total watershed
obtained the 6.9% of total returning adults,
while juveniles’ density was lower than
registered in the main river. These results
suggest that the problem imposed by
domestication on anadromy of hatchery-reared
individuals can be partially overcome when
eyed ova are planted in the river instead of
individuals being maintained in the hatchery
for later release. Therefore, selecting brood
stock from the native river and using tributaries
or small channels as nurseries is suggested here
as one of the best solutions due to several
reasons: (i) because a low stocking effort can
result in a relatively high number of sea trout
returning adults as observed in this experiment
(75% of the total returning adults that belonged
to the experiment), (ii) we avoided genetic
impacts of hatcheries in wild populations that
affects the fitness of hatchery-reared
individuals (iii) we avoid direct and indirect
effects in wild and hatchery populations when
they interact (reviewed in Naish et al. 2008;
Seamons et al. 2012) and (iv) we avoid the sea
mortality associated to distance from releasing
site of brown trout smolts (Jonsson and
Jonsson 2012). In addition, as shown in
Aprahamian et al. (2003), the difference in
economic cost between individuals reared until
the first year and the stoked eggs is 32 times
more expensive per unit, representing a total
saving of 23,800€ at the same time that both
returning rates and diversity in the crosses
experimented by planted individuals are
higher. Thus, the expected advantage of
“helping” the fish at the hatchery to increase
size and promote survival in the wild is not a
prudent measure for brown trout conservation
or for increasing the number of migratory
individuals and in addition, it results in a noneconomically viable management plan
Here we have reported that by manipulating
parental MHC dissimilarities, stocking
procedure and male phenotype we can obtain
significant results in the context of returning
rate and they are easily manipulated at the
hatchery. Moreover, stocking programs have to
be combined with sustainable management
according to the FAO-EIFAC Code of Practice
for Recreational Fisheries (EIFAC 2008).
Water quality (Griffiths et al. 2011), habitat
conditions (Anton et al. 2011) combined with a
regulated fishing effort is critical for
maintaining the brown trout populations.
12
Acknowledgments
We wish to thank two anonymous referees and
the associate editor for useful comments on
previous versions of this manuscript. Funding
for this work was provided by grants from
Ministerio de Ciencia y Tecnología
(CGL2007-60572 and CGL2010- 14964), and
Fondos FEDER (PGIDIT03PXIC30102PN;
Grupos de Referencia Competitiva, 2010/80).
Francisco Marco-Rius was supported by a
doctoral scholarship from the Spanish
Government (BES-2008-001973).
References
Almodóvar, A., and Nicola, G. G. 2004.
Angling impact on conservation of Spanish
stream-dwelling brown trout Salmo trutta.
Fisheries Manag. Ecol. 11: 173–182.
Almodóvar, A., Suarez, J., Nicola, G. G., and
Nuevo, M. 2001. Genetic introgression
between wild and stocked brown trout in the
Douro River basin, Spain. J Fish Biol. 59:
68–74.
Antón, A., Elosegi, A., García-Arberas, L.,
Díez, J., and Rallo, A. 2011. Restoration of
dead wood in Basque stream channels:
effects on brown trout population. Ecol.
Freshw. Fish 20: 461–471.
Aprahamian, M. W., Martin Smith, K.,
McGinnity, P., Mckelvey, S., and Taylor, J.
2003.
Restocking
of
salmonids—
opportunities and limitations. Fish. Res. 62:
211–227.
Araguas, R. M., Sanz, N., Fernández, R., Utter,
F. M., Pla, C., and García-Marín, J-L. 2009.
Role of genetic refuges in the restoration of
native gene pools of brown trout. Conser.
Biol. 23: 871–878.
Arkush, K. D., Giese, A. R., Mendonca, H. L.,
McBride, A. M., Marty, G. D., and Hedrick
P. W. 2002. Resistance to three pathogens in
the endangered winter-run chinook salmon
(Oncorhynchus tshawytscha): effects of
inbreeding and major histocompatibility
complex genotypes. Can. J. Fish. Aquat. Sci.
59: 966–975.
Ayllon, F., Morán, P., and Garcia-Vazquez, E.
2006. Maintenance of a small anadromous
subpopulation of brown trout (Salmo trutta
L.) by straying. Freshw. Biol. 51: 351–358.
Bernatchez, L., and Landry, C. 2003. MHC
studies in nonmodel vertebrates: what have
we learned about natural selection in 15
years? J. Evol. Biol. 16: 363–377.
Caballero, P., Cobo, F., and González, M. A.
2006. Life history of a sea trout (Salmo
trutta L.) population from the North-West
Iberian Peninsula (River Ulla, Galicia,
Spain). In Sea Trout: Biology, Conservation
and Management. Edited by G. Harris and
N.
Milner.
Blackwell
Scientific
Publications, Oxford. pp. 234–247.
Caballero, P., Morán, P., and Marco-Rius, F. A
review of the genetic and ecological basis of
phenotypic plasticity in brown trout. In
Trout: from physiology to conservation.
Edited by S. Polakof and T. W. Moon. Nova
Science,Hauppauge, NY.
Campos, J. L., Posada, D., and Moran, P. 2006.
Genetic variation at MHC, mitochondrial
and microsatellite loci in isolated
populations of Brown trout (Salmo trutta).
Cons. Gen. 7: 515–530.
Campos, J. L., Posada, D., Caballero, P., and
Moran, P. 2007. Spatio-temporal genetic
variability in sea trout (Salmo trutta)
populations from north-western Spain.
Freshw Biol. 52: 510–524.
Consuegra, S., and Garcia de Leaniz, C. 2008.
MHC-mediated mate choice increases
parasite resistance in salmon. Proc. R. Soc.
B, 275: 1397–1403.
13
Dahl, J., Pettersson, E., Dannewitz, J., Järvi, T.,
and Löf, A-C. 2006. No difference in
survival, growth and morphology between
offspring of wild-born, hatchery and hybrid
brown trout (Salmo trutta). Ecol. Freshw.
Fish, 15: 388–397.
Duchesne, P., Castric, T., and Bernatchez, L.
2005. PASOS (parental allocation of singles
in open systems): a computer program for
individual parental allocation with missing
parents. Mol. Ecol. Notes, 5: 701–704.
EIFAC [European Inland Fisheries Advisory
Commission]. 2008. EIFAC Code of
Practice for Recreational Fisheries. Rome:
EIFAC. Occasional Paper No. 42, 45 pp.
Einum, S., and Fleming, I. A. 1999. Maternal
effects of egg size in brown trout (Salmo
trutta): norms of reaction to environmental
quality. Proc. R. Soc. B, 266: 2095–2100.
Estoup, A., Presa, P., Krieg, F., Vaiman, D.,
and Guyomard, R. 1993. (CT)n and (GT)n
microsatellites: a new class of genetic
markers for Salmo trutta L. (brown trout).
Heredity, 71: 488–496.
Ferguson, A. 2006. Genetics of sea trout, with
particular reference to Britain and Ireland. In
Sea Trout: Biology, Conservation and
Management. Edited by G. Harris and N.
Milner. Blackwell Scientific Publications,
Oxford. pp. 155–182.
Fleming, I. A., Lamberg, A., and Jonsson, B
1997. Effects of early experience on the
reproductive performance of Atlantic
salmon. Behav. Ecol. 8: 470-480.
Forseth, T., Nesje, T. F., Jonsson, B., and
Harsaker, K. 1999. Juvenile migration in
brown trout: a consequence of energetic
state. J. Anim. Ecol. 68: 783–793.
Garcia de Leániz, C., Fleming, I. A., Einum,
S., Verspoor, E., Jordan, W. C., Consuegra,
S., et al. 2007. A critical review of adaptive
genetic variation in Atlantic salmon:
implications for conservation. Biol. Rev.
Camb. Philos. Soc. 82: 173–211.
Giger, T., Excoffier, L., Day, P. J. R.,
Champigneulle, A., Hansen, M. M., Powell,
R., and Largiadèr, C. R. 2006. Life history
shapes gene expression in salmonids. Curr.
Biol. 16: 281–282.
Griffiths, A. M., Ellis, J. S., Clifton-Dey, D.,
Machado-Schiaffino, G., Bright, D., GarciaVazquez, E., and Stevens, J. R. 2011.
Restoration versus recolonisation: The
origin of Atlantic salmon (Salmo salar L.)
currently in the River Thames. Biol. Cons.
144: 2733–2738.
Grimholt, U., Drabløs, F., Jørgensen, S. M.,
Høyheim, B., and Stet, R. J. M. (2002) The
major histocompatibility class I locus in
Atlantic salmon (Salmo salar L.):
polymorphism, linkage analysis and protein
modelling. Immunogenetics, 54: 570–581.
Grimholt, U., Larsen, S., Nordmo, R.,
Midtlyng, P., Kjoeglum, S., Storset, A., et
al. 2003. MHC polymorphism and disease
resistance in Atlantic salmon (Salmo salar);
facing pathogens with single expressed
major histocompatibility class I and class II
loci. Immunogenetics, 55: 210–219.
Hansen, M. M., Bekkevold, D., Jensen, L. F.,
Mensberg, K.-L. D., and Nielsen, E. E.
2006. Genetic restoration of a stocked
brown trout Salmo trutta population using
microsatellite DNA analysis of historical
and contemporary samples. J. Appl. Ecol.
43: 669–679.
Hardy, O.J., and Vekemans, X. 2002.
SPAGeDi: a versatile computer program to
analyse spatial genetic structure at the
individual or population levels. Mol. Ecol.
Notes, 2: 618-620.
Heath, D.D., and Blouw, D.M. 1998. Are
maternal effects in fish adaptive or merely
physiological side effects? In: Maternal
14
effects as adaptations. Edited by: T.A.
Mousseau and W. Fox. Oxford University
Press, Oxford. pp. 178-201.
Hervella, F., and Caballero, P. (1999)
Inventario Piscícola dos Ríos Galegos.
Consellería de Medio Ambiente. Xunta de
Galicia, Santiago de Compostela.
Horreo, J. L., Machado-Schiaffino, G., Ayllon,
F., Griffiths, A. M., Bright, D., Stevens, J.
R., and Garcia-Vazquez, E. 2011. Impact of
climate change and human-mediated
introgression on southern European Atlantic
salmon populations. Glob. Change Biol. 17:
1778–1787.
Jacob, A., Nusslé, S., Britschgi, A., Evanno,
G., Müller, R., and Wedekind, C. 2007.
Male dominance linked to size and age, but
not to “good genes” in brown trout (Salmo
trutta). BMC Evol. Biol. 7: 207.
Jokikokko, E., and Jutila, E. 2004. Divergence
in smolt production from the stocking of 1summer-old and 1-year-old Atlantic salmon
parr in a northern Baltic river. J. Appl.
Ichthyol. 20: 511–516.
Jonsson, B., and Jonsson, N. 1993. Partial
migration: niche shift versus sexual
maturation in fishes. Rev. Fish Biol. Fish. 3:
348–365.
Jonsson, B. 1985. Life History Patterns of
Freshwater Resident and Sea-Run Migrant
Brown Trout in Norway. T. Am. Fish. Soc.
114: 182–194.
Jonsson, N., Jonsson, B., and Hansen, L. 2003.
The marine survival and growth of wild and
hatchery-reared Atlantic salmon. J. Appl.
Ecol. 40: 900–911.
Jonsson, B., and Jonsson, N. 2011. Ecology of
Atlantic salmon and brown trout: habitat as
a template for life histories. SpringerVerlag, Dordrecht, the Netherlands.
Jonsson, B., and Jonsson, N. 2012. Naturally
and hatchery produced European trout
Salmo trutta: do their marine survival and
dispersal differ? J Coast Conservat. 1-9.
doi:10.1007/s11852-012-0224-1.
Juanes, F., Gephard, S., De La Hoz, J., Morán,
P., Dopico, E., Horreo, J. L., and GarciaVazquez, E. 2011. Restoration of native
Atlantic salmon runs in northern Spain: do
costs outweigh benefits? Knowl. Manag.
Aquat. Ec. 402: 22-53.
Kallio-Nyberg, I., Peltonen, H., and Rita, H.
1999. Effects of stock-specific and
environmental factors on the feeding
migration of Atlantic salmon (Salmo salar)
in the Baltic Sea. Can. J. Fish. Aquat. Sci.
56: 853–861.
Kallio-Nyberg, I., Saura, A., and Ahlfors, P.
2002. Sea migration pattern of two sea trout
(Salmo trutta) stocks released into the Gulf
of Finland. Ann. Zool. Fenn. 39: 221–235.
Kallio-Nyberg, I., Jutila, E., Koljonen, M-L.,
Koskiniemi, J., and Saloniemi, I. 2010. Can
the lost migratory Salmo trutta stocks be
compensated with resident trout stocks in
coastal rivers? Fish. Res. 102: 69–79.
Kamler, E. 2005. Parent–egg–progeny
relationships in teleost fishes: an energetics
perspective. Rev. Fish Biol. Fish. 15: 399–
421.
Kennedy, R. J., and Crozier, W. W. 2010.
Evidence of changing migratory patterns of
wild Atlantic salmon Salmo salar smolts in
the River Bush, Northern Ireland, and
possible associations with climate change. J.
Fish Biol. 76: 1786–1805.
King, T. L., Eackles, M. S., and Letcher, B. H.
2005. Microsatellite DNA markers for the
study of Atlantic salmon (Salmo salar)
kinship, population structure, and mixedfishery analyses. Mol. Ecol. Notes, 5: 130–
132.
Landry, C., Garant, D., Duchesne, P., and
Bernatchez, L. 2001. “Good genes as
15
heterozygosity”:
the
major
histocompatibility complex and mate choice
in Atlantic salmon (Salmo salar). Proc. Roy.
Soc. B, 268: 1279–1285.
Langefors, A., Lohm, J., Grahn, M., Andersen,
O., and von Schantz, T. 2001. Association
between major histocompatibility complex
class IIB alleles and resistance to
Aeromonas salmonicida in Atlantic salmon.
Proc. Roy. Soc. B, 268: 479–485.
Lepori, F., Palm, D., Brännäs, E., and
Malmqvist, B. 2005. Does restoration of
structural heterogeneity in streams enhance
fish and macroinvertebrate diversity? Ecol.
Appl. 15: 2060–2071.
Martinez ,P., Arias, J., Castro, J., and Sanchez,
L. 1993. Differential stocking incidence in
brown trout (Salmo trutta) populations from
Northwestern Spain. Aquaculture, 114: 203–
216.
Meier, K., Hansen, M. M., Bekkevold, D.,
Skaala, O., and Mensberg, K. L. D. 2011.
An assessment of the spatial scale of local
adaptation in brown trout (Salmo trutta L.):
footprints of selection at microsatellite DNA
loci. Heredity, 106: 488–499.
Milner, N. J, Elliott, J. M., Armstrong, J. D.,
Gardiner, R., Welton, J. S., and Ladle, M.
2003. The natural control of salmon and
trout populations in streams. Fish. Res. 62:
111–125.
Miranda, R., Martínez-Lage, J., Molina, J.,
Oscoz, J., Tobes, I., and Vilches, A. 2012.
Effects of stress controlled loading of a
reservoir on downstream fish populations in
a pyrenean river. Environ. Eng. Manag. J.
11: 1125–1131.
Morán, P., Pendás, A. M., Garcia-Vazquez, E.,
and Izquierdo, J. 1991. Failure of a stocking
policy, of hatchery reared brown trout,
Salmo trutta L., in Asturias, Spain, detected
using LDH-5* as a genetic marker. J. Fish
Biol. 39: 117–121.
Naish, K. A., Taylor, J. E., Levin, P. S., Quinn,
T. P., Winton, J. R., Huppert, D., and
Hilborn, R. 2008. An evaluation of the
effects of conservation and fishery
enhancement hatcheries on wild populations
of salmon. Adv. Mar. Biol. 53: 61–194.
O’Farrell, B., Dennis, C., Benzie, J. A.,
McGinnity, P., Carlsson, J., de Eyto, E., et
al. 2012. Balancing selection on MHC class
I in wild brown trout Salmo trutta. J. Fish
Biol. 81: 1357–1374.
Økland, F., Jonsson, B., Jensen, A. J., and
Hansen, L. P. 1993 Is there a threshold size
regulating seaward migration of brown trout
and Atlantic salmon? J. Fish Biol. 42: 541–
550.
Olsén, K. H., Grahn, M., Lohm, J., and
Langefors, Å. 1998. MHC and kin
discrimination in juvenile Arctic charr,
Salvelinus alpinus(L.). Anim. Behav. 56:
319–327.
O'Reilly, P. T., Hamilton, L. C., McConnell, S.
K., and Wright, J. M. 1996. Rapid analysis
of genetic variation in Atlantic salmon
(Salmo salar) by PCR multiplexing of
dinucleotide
and
tetranucleotide
microsatellites. Can. J. Fish. Aquat. Sci. 53:
2292–2298.
Palm, D., Lindberg, M., Brännäs, E.,
Lundqvist, H., Östergren, J., and Carlsson,
U. 2009. Influence of European sculpin,
Cottus gobio, on Atlantic salmon Salmo
salar, recruitment and the effect of gravel
size on egg predation — implications for
spawning habitat restoration. Fish. Manag.
Ecol. 16: 501–507.
Presa, P., and Guyomard, R. 1996.
Conservation of microsatellites in three
species of salmonids. J. Fish Biol. 49: 1326–
1329.
16
R Development Core Team. 2012. R: A
Language and Environment for Statistical
Computing. R Foundation for Statistical
Computing, Vienna.
Rogell, B., Dannewitz, J., Palm, S., Petersson,
E., Dahl, J., Prestegaard, T., et al. 2012.
Strong divergence in trait means but not in
plasticity across hatchery and wild
populations of sea-run brown trout Salmo
trutta. Mol. Ecol. 21: 2963–2976.
Sauermann, U., Nürnberg, P., Bercovitch, F.,
Berard, J., Trefilov, A., Widdig, A., et al.
2001. Increased reproductive success of
MHC class II heterozygous males among
free-ranging rhesus macaques. Hum. Genet.
108: 249–254.
Saura, M., Caballero, P., Caballero, A., and
Morán, P. 2006. Genetic variation in
restored Atlantic salmon (Salmo salar L.)
populations in the Ulla and Lérez rivers,
Galicia, Spain. ICES J. Mar. Sci. 63: 1290–
1296.
Saura, M., Morán, P., Brotherstone, S.,
Caballero, A., Álvarez, J., and Villanueba,
B. 2010. Predictions of response to selection
caused by angling in a wild population of
Atlantic salmon (Salmo salar). Freshw.
Biol. 55: 923–930.
Seamons, T. R., Hauser, L., Naish, K. A., and
Quinn, T. P. 2012. Can interbreeding of wild
and artificially propagated animals be
prevented by using broodstock selected for a
divergent life history? Evol. Appl. 5: 705–
719.
Serrano, I., Larsson, S., and Eriksson, L-O.
2009. Migration performance of wild and
hatchery sea trout (Salmo trutta L.) smolts
— implications for compensatory hatchery
programs. Fish. Res. 99: 210–215.
Skrochowska, S. 1969. Migrations of the seatrout (Salmo trutta L.) brown trout (Salmo
trutta M. Fario L.) and their crosses. Pol.
Arch. Hydrobiol. 16: 149–180.
Streiff, R., Labbe, T., Bacilieri, R.,
Steinkellner, H., Glössl, J., and Kremer, A.
1998. Within-population genetic structure in
Quercus robur L., and Quercus petraea
(Matt.) Liebl. assessed with isozymes and
microsatellites. Mol. Ecol. 7: 317-328.
Tamura, K., Peterson, D., Peterson, N.,
Stecher, G., Nei, M., and Kumar, S. 2011.
MEGA5: molecular evolutionary genetics
analysis using maximum likelihood,
evolutionary distance, and maximum
parsimony methods. Mol. Biol. Evol. 28:
2731–2739.
Waples, R. S., and Hendry, A. P. 2008. Special
issue: evolutionary perspectives on salmonid
conservation and management. Evol. Appl.
1: 183–188.
17
Environmental induced methylation changes associated with
seawater adaptation in brown trout
110!
!
Aquaculture 392-395 (2013) 77–83
Contents lists available at SciVerse ScienceDirect
Aquaculture
journal homepage: www.elsevier.com/locate/aqua-online
Environmental induced methylation changes associated with seawater
adaptation in brown trout
Paloma Morán a,⁎, Francisco Marco-Rius a, Manuel Megías b, Lara Covelo-Soto a, Andrés Pérez-Figueroa a
a
b
Dpto. Bioquímica, Xenética e Inmunoloxía, Facultade de Bioloxía, Universidade de Vigo, Vigo 36310, Spain
Dpto. Bioloxía Funcional, Facultade de Bioloxía, Universidade de Vigo, Vigo 36310, Spain
a r t i c l e
i n f o
Article history:
Received 21 September 2012
Received in revised form 30 January 2013
Accepted 3 February 2013
Available online 12 February 2013
Keywords:
Salmo trutta
Epigenetic
Diet
MSAP
Smoltification
Salt
a b s t r a c t
Migratory trout (sea trout) and sedentary trout (freshwater trout) coexist and interbreed. However, the
mechanisms underlying the development of the migrant morphotype are still unclear. At one point parr–
smolt transformation is initiated and, as a result, juvenile trout are ready to leave the river and migrate to
the sea. The smoltification process has been linked to various factors such as body size, growth rate and
the physiological state of the fish. In addition, the process is also strongly influenced by environmental factors
such as year, seasonality, water temperature and flow rate. For example, hatchery environment can depress
the natural parr–smolt transformation and consequently, the success of the seawater migration of reared
trout from these hatchery programmes might be adversely affected. We have investigated whether changes
in DNA methylation, by means of MSAP (methylation-sensitive amplified polymorphism), could be involved
in anadromy. We identified dramatic differences in genome-wide methylation patterns between hatchery
reared and seawater brown trout. Furthermore, we demonstrated that salt enriched diets can trigger
short-term genome-wide methylation changes in hatchery reared trout. However, these changes only lasted
for a short period of time. Determining the duration of this effect could result in increased survival of
hatchery-reared trout in seawater when fed on salt-enriched diets. Altogether, these results suggest that
salt-induced alterations in DNA methylation patterns could play an important role in enabling fish acclimation to seawater conditions, potentially with important economic consequences for fish farming.
© 2013 Elsevier B.V. All rights reserved.
1. Introduction
Two different morphotypes of brown trout (Salmo trutta L.) can be
found in rivers from the European Atlantic coast and Baltic Sea: the
migratory trout (sea trout) and the resident trout (freshwater
trout). Although both life-history forms present morphological, demographic and ecological differences (Bagliniere et al., 2000), they
coexist and interbreed (Ruzzante et al., 2001). Juveniles from both
morphotypes are indistinguishable. To date, genetic differences between sea trout and sedentary brown trout inhabiting the same
river have not been reported (Charles et al., 2005; Cross et al., 1992;
Hindar et al., 1991; Pettersson et al., 2001). Brown trout has little importance in commercial fisheries but an extraordinary value for recreational fisheries and hereby for the tourist industry. Consequently,
many hatcheries are now focusing on raising stocks from wild parent
fish. Under hatchery conditions some stocks of brown trout undergo a
domestication process and can be successfully reared in captivity.
However, domestication can result in lower fitness when individuals
are returned back to the wild. The contribution of hatchery reared
⁎ Corresponding author. Tel.: +34 986813899; fax: +34 986812556.
E-mail address: [email protected] (P. Morán).
0044-8486/$ – see front matter © 2013 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.aquaculture.2013.02.006
trout to natural populations has been repeatedly shown to be rather
small (reviewed by Hansen, 2002). Moreover, the artificial environment in which the anadromous brown trout has been reared can
also depress the natural parr–smolt transformation and adversely affect the success of seawater migration and the survival of the fish in
the long-term (Sundell et al., 1998). This could represent an important failure of these expensive practices, especially when breeding
migratory sea trout populations represent the main goal of the
enhancement programme. As an illustrative example, the cost of the
Atlantic salmon breeding expenditure relative to the number of
captured salmons in a Spanish river has been estimated to be 4000
Euros per captured individual (Horreo et al., 2012).
In natural conditions, the two trout morphotypes (i.e. migrant and
resident) become well established during the second year of their lives
thanks to the ability of some juveniles to undergo smoltification
(Økland et al., 1993). This process involves important physiological
and morphological adaptations to seawater, thus preparing the trout
for the ocean life prior to its downstream migration. Changes in their external morphology include variations in body shape and coloration patterns (Björnsson et al., 2011), those referring to physiological features
involve increased salinity tolerance, olfactory sensitivity and changes
in its metabolic and growth rates, as well as alterations in haemoglobin
and visual pigment concentrations (McCormick et al., 1998).
78
P. Morán et al. / Aquaculture 392-395 (2013) 77–83
What natural selection mediated mechanism does underline the
migration decision? Trout smoltification has been linked to various
interrelated threshold factors such as body size, growth rate and
physiological conditions that could activate migratory pathways
(Acolas et al., 2012; Jonsson and Jonsson, 1993). In addition, the
smoltification process is also strongly influenced by environmental
factors, such as year, season, water temperature and flow rate
(Jensen et al., 2012). The smoltification process involves high individual costs but also high benefits in reproductive performance (Jonsson
and Jonsson, 1997, 1998).
Increasing salinity tolerance requires differentiation and activation
of the epithelial transport and the synthesis of new transport proteins
(McCormick, 2001). Changes in Na+/K +-ATPase, Na+/K +/2Cl− cotransporter and in the distribution of the mitochondrial-rich cells
(MRCs) have been recorded in the gills of the freshwater salmon
(Salmo salar) when compared to the seawater morphotype (Hiroi and
McCormick, 2007). This is the result of MRCs being distributed on the
primary filaments and secondary lamellae of the freshwater salmon
but completely absent in those of the seawater salmon (Hiroi and
McCormick, 2007). In concordance with physiological changes, differences in gene expression of several genes such as transaldolase 1, constitutive heat-shock protein HSC70-1 and endozepine have also been
described between migratory and sedentary brown trout belonging to
different populations (Giger et al., 2008). Similar findings have also
been reported in populations of Atlantic salmon (Seear et al., 2010).
Different experiments have demonstrated the genetic basis of
some components of anadromy. In rainbow trout (Le Bras et al.,
2011) and Arctic charr (Norman et al., 2011), several QTLs (quantitative trait loci) on three linkage groups have been associated with fish
performance in the seawater environment. However, the seawater
morphotype can be externally induced and various features of the
seawater gill phenotype could be induced by feeding freshwater rainbow trout with a salt enriched diet (Perry et al., 2006). In addition, it is
well known that hatchery conditions substantially reduce smoltification
(Aarestrup et al., 2000) and seawater migration compared to natural
conditions (Marco-Ríus et al., unpublished results). Thus, genetic and
environmental factors underlie life-history strategies, which have led
to the consideration of anadromy in brown trout as a threshold quantitative trait (Ferguson, 2007).
Epigenetics can help in understanding the smoltification process in
the brown trout. Smoltification is a reversible process that can be stimulated by external conditions, for example, full-sib individuals might show
different behaviours. Thus, we hypothesised that methylation might be
part of the acclimation mechanisms of the brown trout to the seawater
conditions, since the methylated state is usually associated with gene expression inactivation and conversely, gene activation is associated with
demethylation (reviewed by Mazzio and Soliman, 2012).
Environmental stimuli are known to alter cytosine methylation
throughout the genome at specific loci. For example, Navarro-Martín
et al. (2011) have shown that methylation of the gonadal aromatase
promoter is involved in temperature-dependent sex ratios in sea bass.
In addition, emerging evidences strongly suggest that certain dietary
bioactive food components can change gene expression via alterations
in DNA methylation and histone modifications (reviewed by Tammen
et al., in press). In humans, it has been suggested that the bioactive nutritional component (also called epigenetic diet) could be incorporated
into our regular dietary regimen and therapeutically used for medical
purposes (Hardy and Tollefsbol, 2011).
In this study, we investigated whether changes in DNA methylation might be involved in anadromy and whether the level of DNA
methylation might change in response to osmotic stresses induced
by environmental stimuli such as a salt enriched diet. Understanding
external stimuli effects will allow for better fish management, and in
turn, increasing or decreasing the smoltification rates.
To achieve this, we designed a hatchery experiment where
one-year-old trout were fed a salt enriched diet for a period from 2
to 28 days. Since gill is one of the most sensitive tissues responding to
the transition between freshwater and the marine environment, we
employed the methylation-sensitive amplified polymorphism (MSAP)
methodology that allows detecting the methylation state of a particular
recognition sequence. Thus, any methylation changes in the gill tissues
in response to different diets and/or salinity concentrations could be
measured. Physiological changes promoted by a salt enriched diet
were monitored in the gill tissue by inmunocytochemistry analysis
using the Na +/K +-ATPase antibody. This was done at different critical
phases of the experiment. In order to test the seawater proficiency of
the hatchery trout fed with a salt enriched diet, these fish together
with a control group were transferred to seawater and the survival
rates compared.
The final goal of the study was to gain further knowledge about
the time from the hatchery until the onset of the migration to the
sea, and to try to reduce mortality at this critical period by rearing
trout under the most optimal conditions.
2. Materials and methods
2.1. Experimental design
The experimental design included two stages. The first one aimed
to explore the differences in methylation induced by a salt enriched
diet during one month. One-year-old hatchery reared trout were
used in this experiment. One hundred full-sibling individuals were
bred in a circular fibreglass tank at the Carballedo hatchery facilities
(Northwest Spain) and fed daily with commercial trout pellets. Two
weeks later six specimens were separated representing the freshwater control group (FW-control). The remaining trout were fed using
a diet supplemented with 11% NaCl as described in Perry et al.
(2006). Thereafter, six individuals were sampled 2, 4, 6, 8, 10, 14, 21
and 28 days after the beginning of the treatment. Samples were labelled as follows: FW-2, FW-4 FW-6, FW-8, FW-10, FW-14, FW-21,
and FW-28. No mortality was recorded during the whole experimental period. All fish involved in the experiment were euthanized using
MS-222 (Sigma) and the gill tissue removed. Those individuals which
were not euthanized during the experiment were returned to their
normal diet and no mortalities were recorded after one week of
daily checks. Gills were fixed in ethanol for DNA extraction. For
immunocytochemistry, gills were fixed in 4% paraformaldehyde in
phosphate buffer 0.1 M pH 7.4 (PB) for two days at 4 °C and then
stored in PB.
The second part of the experiment intended to explore the differences in seawater survival in relation to the diet. Following the same
procedures described above, 100 fish of the same stock and age were
used. Fish were separated in two tanks (50 individuals per tank) and
reared under the same environmental conditions. In one tank, trout
were fed with commercial trout pellets whereas in the other they
were fed using a diet supplemented with 11% NaCl as previously described. After 4 days, the adipose fin of those trout fed with commercial trout pellets was removed for identification purposes. Then, the
two groups of trout were transported to the O Grove aquarium to
be transferred to the same seawater environment. Fish were acclimated to salty water in two phases, starting from 0 ppt to 15 ppt
during 24 h followed by a second increase from 15 ppt to 37 ppt by
filling the tank with natural sea water. During 24 days, the tank was
checked in the morning and mortality was recorded. Survival differences were calculated using a mixed effect linear model with a Poisson
distribution (Zuur et al., 2009) in the lme4 package (Pinheiro and Bates,
2009) implemented in R 2.15.1 (R Development Core Team, 2012)
where treatment (fed with salt or not) was a fixed factor and time
(days of survival) a random effect.
Forty individuals (20 of each group), reared in seawater for a period
of 10 to 12 days, were analysed further using MSAP (group labelled as
GROVE).
2.2. DNA isolation and MSAP genotyping
DNA was extracted from gill tissue using the NucleoSpin® Tissue
Kit (Macherey-Nagel). DNA quality was verified by electrophoresis
on 1% agarose gels. After DNA quantification using a Nanodrop 1000
spectrophotometer (Thermo Fisher Scientific), samples were normalized to 100 ng μl −1.
Genomic cytosine methylation in CCGG sites of different samples
were analysed using MSAP techniques as described in Reyna-López
et al. (1997) and Xu et al. (2000). This methodology is based on the
use of the isoschizomers HpaII and MspI which recognise the same sequence (5′-CCGG) but differ in their sensitivity to DNA methylation.
For each individual, 50 ng of DNA were independently digested
and ligated with both EcoRI/MspI and EcoRI/HpaII enzyme combinations (New England Biolabs). The reaction was performed in a final
volume of 11 μl containing 5 U EcoRI, 1 U MspI (or HpaII), 5 pmol
EcoRI adaptor, 50 pmol HpaI adaptor and 0.4 U of T4 DNA ligase
(Roche). The mixture was incubated at 37 °C for a period of 2 h.
Preselective PCR reactions were then performed using 4 μl of 1:10
ligation dilution in 20 μl volumes containing 2.5 mM of MgCl2,
187.5 μM of each dNTP, 20 pmol of EcoRI-A and HpaII/MspI-T
preselective primers and 1 U of Taq polymerase (Bioline) in 1 × PCR
buffer (Bioline). PCR conditions for preselective PCR were as follows:
72 °C for 2 min, 20 cycles of 94 °C for 20 s, 56 °C for 30 s, 72 °C for
2 min, and a final step of 60 °C for 30 min. Selective PCR reactions
were performed in 20 μl using 1 μl of 1:200 preselective PCR dilution.
Two primer combinations (EcoRI-AAG and HpaII/MspI-TC; EcoRI-ACT
and HpaII/MspI-TC) were used. Primer sequences are available in
Morán and Pérez-Figueroa (2011). PCR contains 2.5 mM of MgCl2,
187.5 μM of each dNTP, 8.3 pmol of each selective primer and 1 U
of Taq polymerase in 1 × PCR buffer. Cycling conditions for selective
PCR were 94 °C for 2 min, 10 cycles of 94 °C for 20 s, 66 °C for 30 s,
and 72 °C for 2 min, followed by 20 cycles of 94 °C for 20 s, 56 °C
for 30 s and 72 °C for 2 min, ending with 60 °C for 30 min.
Individual PCR products were analysed along with 500 ROX size
standards (Applied Biosystems) in an ABI Prism 3130 Genetic Analyzer (Applied Biosystems). Fragment analysis was performed using
GeneMapper v.3.7 software (Applied Biosystems). DNA fragments
less than 100 bp in length, longer than 500 bp or less than 70 RFU
(Relative Fluorescent Units) were excluded from the analysis due
to their low levels of reproducibility.
2.3. Data analyses
MSAP profiles, pooled from both primer combinations, were
analysed using the R package msap (Pérez-Figueroa, in press. http://
msap.r-forge.r-project.org). Every fragment was scored as follows:
present in both EcoRI-HpaII and EcoRI-MspI products (1/1), denoting
a non-methylated state; present only in either EcoRI-HpaII (1/0) or
EcoRI-MspI (0/1) products, corresponding to a methylated state; or
absent from both EcoRI-HpaII and EcoRI-MspI products (0/0), which
we considered as an hyper-methylation of the target. Because the investigated hatchery pool shows high methylation rates (patterns 1/0
and 0/1) and low genetic diversity, it seems reasonable to assume that
the 0/0 pattern would be the result of hyper-methylation of the restricted targets and not due to genetic differences (i.e. mutation). Individual
fragments (loci) were, therefore, classified into “methylation-susceptible
loci” (MSL) if the observed proportion of methylated scores (1/0, 0/1 and
0/0) exceeded a 5%, and “methylation-susceptible fragments” if the
methylated state was the dominant marker (1 for the methylated state
and 0 for the non-methylated state). Only those fragments showing
polymorphism, with at least two occurrences of each state, were used
for subsequent analyses (Herrera and Bazaga, 2010).
Analysis in msap followed a band-based strategy (Bonin et al.,
2007). The amount of overall epigenetic variation was estimated
using the Shannon diversity index (S). Epigenetic differentiation
79
among groups was assessed by means of principal coordinates analysis (PCoA) followed by analyses of molecular variance (AMOVA;
Excoffier et al., 1992).
2.4. Inmunocytochemistry
Paraformaldehyde fixed gills were cryoprotected in 30% sucrose
dissolved in PB for 24 h, embedded in OCT compound (Tissue-Tek),
and rapidly frozen at − 80 °C. Thereafter 20 μm thin sections were
obtained using a cryostat (Microm HM505 E) at − 20 °C and collected
on superfrost slides (Menzel GmbH & Co.). This was followed by a
standard immunocytochemistry. Briefly, sections were rinsed in a
PB saline solution (PBS), then immersed in a blocking solution
containing 1% bovine serum albumin for 1 h and finally, incubated
overnight with a monoclonal anti-Na +/K +-ATPase alpha-1 subunit
antibody raised against chicken Na, K-ATPase (1:100; alpha6F, Developmental Studies Hybridoma Bank; Perry et al., 2006) at room
temperature. Then, sections were washed with PBS and incubated
in a biotinylated goat anti-mouse antibody (1:100; Vector Laboratories) for 1 h. Sections were rinsed again in PBS and incubated in the
avidin–biotin complex (ABC; 1:100; Vector Laboratories) for 1 h.
Peroxidase activity was developed with 0.5% 3–3′-diaminobenzidine
tetrahydrochloride (SIGMA) and 0.01% H202. The reaction was stopped
in PBS and sections were subsequently dehydrated and cover slipped.
Imnunoreactive cells on the primary filaments and secondary lamellas were individually counted from sagittal sections of the trailing
edge of gill filament. Twenty paired secondary lamellas and their corresponding primary filaments were also counted in three different
areas of the gills of three fishes from each group: Control, FW-4,
FW-8, FW-21, FW-28 and GROVE.
A mixed effect linear model (Zuur et al., 2009) was used in order
to explore the differences in density of immunoreactive cells. The location (primary filaments and secondary lamellas) and fish group
were treated as fixed factors while effects of fish and gill areas were
treated as random effects.
3. Results
3.1. Seawater survival
Trout mortality was monitored daily after the first phase of acclimation was completed. Mortality during the first 24 h (from 0 ppt
to 15 ppt) was significantly higher in the control group (9 out of 50
trout) than in the FW-4 group (3 out of 50 trout) (Z14 = 15.25,
p b 0.001). Mortality progressively decreased over the first 15 days
but remained higher in the control group than in the treatment
group(Fig. 1). Due to an unusual and unexpected increase in seawater
temperature, all fish died between 17 and 24 days.
3.2. Methylation analysis
The two primer combinations used in the MSAP analysis produced
a total of 879 loci, with 875 of them being classified as MSL by the
msap package. The frequency of polymorphic MSL was 47% and the
Shannon's diversity index was 0.425 ± 0.180 (mean ± SE).
The frequency of the different methylation states (expressed as a
percentage) in the target sequence (hemimethylation, complete methylation of internal cytosine, full methylation and non-methylated) observed in the different experimental groups is shown in Table 1.
Important divergences (up to 10%) were observed in the case of the
hemimethylation of the target sequence whereas subtle differences
(around 5%) were observed in the other categories. This pattern suggests that during the first days of feeding on a salt enriched diet
(FW-2, FW-4 and FW-6) there is an increase in both hemimethylation
and internal methylation when compared to the full methylation
80
group and GROVE were remarkable (Fig. 3A,B). In hatchery trout, immunoreactive cells were scattered throughout the primary filaments
and secondary lamellas, whereas in GROVE trout most immunoreactive cells were located in the primary filaments, with the differences
being statistically significant (Z54 = 2.70, p b 0.01). The number of
positive cells found in the primary filaments of the GROVE group
was concentrated in the base of the lamellas, whereas in the hatchery
trout they were more dispersed. In addition, immunoreactive intensity appeared to be higher in the GROVE trout, although this was not
properly quantified. Cell counting revealed temporal changes in the
immunoreactive cell distribution (Fig. 3C), with differences becoming
significant in seawater after seven days (Z6 = − 3.27, p = 0.016 and
Z6 = − 4.37, p = 0.005 respectively). It was also observed that the
number of immunoreactive cells decreased during the first week of
treatment, in the primary filament and in the lamellas of both FW-4
and FW-8 groups. However, the number of positive cells increased
progressively in FW-14 and FW-28 groups until they nearly reached
those of the control group.
4. Discussion
Fig. 1. Survival rate of the control (normal diet) and treatment groups (salt-enriched
diet) in seawater. Individuals were acclimatised during the first day by mixing freshwater and seawater to obtain between 0 ppt and 15 ppt seawater. After this, freshwater was gradually replaced to obtain 37 ppt at the end of the second day.
(partial demethylation) or unmethylated states (de novo methylation).
In a later stage (FW-21, Fw-28), the normal pattern (control) is
restored.
Differences in the genome-wide methylation rates were statically
significant between all tested groups (AMOVA; ΦST = 0.380, p b 0.001).
Pairwise analyses (Table 2) revealed significant differences between
hatchery trout groups, including control, and all the groups of trout
fed with a salt enriched diet, the most different group those trout transferred to seawater conditions (GROVE). Results also reflected a gradual
change in methylation patterns, with differences between groups becoming less apparent when samples were close in time. Fig. 2 shows
the first and second principal coordinates resulting from PCoA which
indicates the epigenetic variation and the degree of differentiation
among groups. The first coordinate, accounting for 27.8% of the total
variance, identified three distinct clusters: (i) control, FW-14, FW-21
and FW-28, (ii) FW-2, FW-4 FW-6, FW-8, FW-10 and (iii) GROVE
group. Furthermore, the most differentiated group is FW-4 and the closest is FW-10, suggesting a progressive and reversible methylation
change over time. This is also supported by the slight differences observed between the control group and FW-14, FW-21 and FW-28.
3.3. Immunocytochemistry
The existence of Na +/K +-ATPase imnunoreactivity in large cuboidal cells located in the primary filaments and secondary lamellas of
the gills had been previously identified (Perry et al., 2006). Differences in the number of positive cells distribution between the control
A growing number of evidences demonstrate the importance of epigenetic factors in the modulation of phenotypic plasticity (reviewed by
Johnson and Tricker, 2010). Most of the published literature focuses on
DNA methylation and many studies use genome-wide methods such as
MSAP in order to detect methylation changes after exposure to different
environmental conditions (Cao et al., 2011, 2012; Da et al., 2012; Tan,
2010; Wang et al., 2011). These experiments reveal important gaps in
our understanding of the effects of diet contaminants, drought, etc. on
the individual performance.
During the seawater transition, trout undergo physiological transformations involved in osmoregulation, lipid metabolism, oxygen transport
and body colour and shape, with many genes involved in these processes
(McCormick, 2001). Accordingly, we have observed dramatic differences
in genome-wide methylation patterns between hatchery-reared and
seawater transferred trout, suggesting that gene activation/deactivation
during smoltification may be controlled, at least partially, by epigenetic
mechanisms. Because the MSAP analysis only detects a subset of the epigenetic variation caused by C-methylation in the CCGG motif, our results
might have underestimated the magnitude of the epigenetic changes;
however, they strongly suggest an important role of methylation in the
smoltification process.
Environmental conditions during early development have proved
to alter the epigenetic patterns of fish embryos (reviewed by Faulk
and Dolinoy, 2011), and many epigenetic changes with potential evolutionary implications can be inherited (Bossdorf et al., 2008). However, methylation could affect the individual entire lifetime or be
limited to certain stages of its life cycle because this process can respond very rapidly to environmental changes, allowing for increasing
fish resistance to environmental stress (Angers et al., 2010). Therefore, knowing the time interval in which epigenetic variations might
take place, would permit to design new breeding strategies in
which environmental stimuli for inducing or repressing gene expression can be applied.
By manipulating the fish food intake we have shown that a salt
enriched diet can trigger genome-wide methylation changes, suggesting
that there could be a link between an external stimulus, smoltification,
Table 1
Frequency (%) of the different states of methylation at the target sequence.
Target state (band pattern)
Control
FW-2
FW-4
FW-6
FW-8
FW-10
FW-14
FW-21
FW-28
GROVE
Unmethylated (HPA+/MSP+)
Hemimethylated (HPA+/MSP−)
Internal C methylation (HPA−/MSP+)
Full methylation (HPA−/MSP−)
14.3
8.6
15.2
61.8
10.7
13.2
18.7
57.5
8.0
12.9
20.5
58.6
10.1
15.5
15.5
58.9
11.1
10.8
17.9
60.2
10.7
11.0
18.2
60.1
15.7
8.7
15.0
60.5
13.1
9.1
14.2
63.5
13.2
6.9
16.4
63.6
9.5
12.9
14.7
62.8
81
Table 2
Pairwise AMOVAs between all pairs of the experimental groups.
Control
Control
FW-2
FW-4
FW-6
FW-8
FW-10
FW-14
FW-21
FW-28
GROVE
0.37
0.39
0.36
0.34
0.26
0.08
0.08
0.10
0.54
FW-2
FW-4
FW-6
FW-8
FW-10
FW-14
FW-21
FW-28
GROVE
0.0022
0.0019
0.0331
0.0019
0.0189
0.0415
0.0022
0.0071
0.0124
0.3400
0.0020
0.0020
0.0077
0.0173
0.0153
0.0022
0.0024
0.0025
0.0017
0.0025
0.0021
0.0043
0.0020
0.0020
0.0020
0.0017
0.0025
0.0023
0.0023
0.0023
0.0020
0.0020
0.0021
0.0022
0.0021
0.4429
b0.0001
b0.0001
b0.0001
b0.0001
b0.0001
b0.000
b0.0001
b0.0001
b0.0001
0.09
0.06
0.10
0.15
0.39
0.32
0.39
0.41
0.06
0.14
0.19
0.45
0.35
0.44
0.31
0.01
0.07
0.41
0.34
0.40
0.35
0.06
0.37
0.32
0.38
0.42
0.32
0.27
0.32
0.43
0.09
0.08
0.59
0.00
0.50
0.54
ΦST below the diagonal. p-values above the diagonal. Bolded values p b 0.01.
and epigenetics. In this study, epigenetic divergence between the control group and the salt-fed trout increased sharply after two days. The
largest differences were observed after 4 days of exposure to a salty
diet and progressively decreased during the following days. Our results
(see Fig. 2) provide evidence that reversibility was an important characteristic of the genomic regions that experienced salt-induced epigenetic
changes. Surprisingly, after two weeks on the salt-enriched diet
appeared to have no effect on the global methylation pattern, suggesting
that the epigenetic change induced by the salt was reversible. This finding is also supported by the observed changes in the distribution and
number of Na+/K+-ATPase immunoreactive cells in the gill tissues. Importantly, greater methylation changes were preceded by larger differences (FW-8) in Na +/K +-ATPase immunoreactive cells.
Although methylation changes were induced by a salt-enriched
diet, the methylation patterns in the freshwater reared trout are not
identical to those experienced by the seawater reared ones (see
Fig. 2). However, our study provides clear evidence for salt intake
leading to physiological and morphological changes in freshwater
fish that could make them to resemble to the seawater morphotype,
although only for a short period of time.
The seawater-transfer experiment performed at the O Grove
aquarium showed that the survival rate of the salt-fed trout was
significantly higher than that of the control group during the first
days. Although rearing conditions were not optimal, our results support the idea of salt intake being able to supply the fish with the physiological tools required for a better adaptation to seawater. Overall,
our experiment could also provide a more plausible explanation for
the hatchery practice of feeding salmonids with a salt enriched diet
before releasing them into the river or the sea (Zaugg et al., 1983).
Li and Leatherland (2012) reviewed the reasons why epigenomic
programming could have implications in aquaculture. Among several
factors, they suggested that artificial fertilization and handling practices produce changes in the endocrine system that could induce
changes in the epigenome, leading to a reduction in fitness. This
idea is supported by the fact that salmonid fish raised in hatcheries
do not often perform well in the wild (Araki et al., 2007). However,
differences in the genome methylation between wild and hatchery
of certain species such as steelhead have not been found (Blouin et
al., 2010). We, therefore, suggest that the tissue selected for the analysis was probably not the most informative one, given that there are
great differences in the methylation patterns between tissues (Morán
and Pérez-Figueroa, 2011).
Our results also suggest that salt-induced alterations could play an
important role in enabling fish acclimation to seawater conditions.
This process could facilitate the seawater migration of the hatcheryreared trout when released into the river as part of enhancing
population programmes, and potentially enhance the aquaculture
production of the diadromous fish. Our results open new research
avenues to explore how changes in other external factors such as
rearing temperature, day–night cycles or different diets, could also affect the methylation patterns and increase both fish harvesting and
welfare. However, more detailed studies are required since methylation changes might only be temporary affected by environmental
changes and potentially misinterpreted.
5. Conclusions
Different patterns of methylation have been observed for trout living in seawater when compared to those living in freshwater,
suggesting that methylation plays a key role in the smoltification process. Salt intakes in the hatchery reared trout promote temporary
genome-wide methylation changes that make them more similar to
seawater reared ones. This result is supported by the distribution of
the Na +/K +-ATPase immunoreactivity cells in the gill tissue. Feed
hatchery-reared trout with a salt-enriched diet increases survival in
seawater. Our study reveals how induced epigenetic changes can be
successfully applied in fish harvesting.
Fig. 2. Results from Principal Coordinates Analysis (PCoA) for the epigenetic differentiation between the experimental groups. The first two coordinates (C1 and C2) are
displayed with the indication of the percentage of variance explained in brackets.
Scores represent individual samples. Labels indicate the centroids of each group. Ellipses represent the dispersion associated to each value. The long axis of the ellipse shows
the direction of maximum dispersion and the short axis, the direction of minimum
dispersion.
Acknowledgments
We wish to thank Pilar Alvariño, Nieves Santamaría and Ma. Jesús
Iglesias-Briones for their technical assistance. The hatchery experiment was carried out at the Carballedo hatchery (Xunta de Galicia).
82
Fig. 3. Na+/K+-ATPase alpha-1 subunit immunoreactive cells. A. Stained cells in the control trout are located in the secondary lamellas, less frequent in their more distal tip and in
the primary filaments. B. Immunoreactive cells in the GROVE group. Note the absence of positive cells in the secondary lamellas and the cell grouping near the base of the secondary
lamellas of the primary filaments. C. Number of cells in the secondary lamella corresponding to 10 paired secondary lamellas and those in the primary filaments correspond to the
length of 10 consecutive primary lamellas. Scale bar in A and B: 10 μm.
Seawater experiments were done at the O Grove aquarium. The authors are also very grateful to Bluedisplay S.A. for allowing us the
use of the aquarium for our experiment. In particular, we would like
to thank the individual contributions of Ester Alonso for assisting
us with trout handling and that of Pablo Caballero for obtaining the
necessary licences to carry out this experiment.
This work has been funded by grants from the Ministerio de
Ciencia y Tecnología (CGL2010-14964), and Fondos FEDER
(PGIDIT03PXIC30102PN; Grupos de Referencia Competitiva, 2010/80,
Xunta de Galicia).
References
Aarestrup, K., Nielsen, C., Madsen, S.S., 2000. Relationship between gill Na+, K+-ATPase
activity and downstream movement in domesticated and first-generation offspring
of wild anadromous brown trout (Salmo trutta). Canadian Journal of Fisheries and
Aquatic Sciences 57, 2086–2095.
Acolas, M.L., Labonne, J., Baglinière, J.L., Roussel, J.M., 2012. The role of body size
versus growth on the decision to migrate: a case study with Salmo trutta.
Naturwissenschaften 99, 11–21.
Angers, B., Castonguay, E., Massicotte, R., 2010. Environmentally induced phenotypes
and DNA methylation: how to deal with unpredictable conditions until the next
generation and after. Molecular Ecology 19, 1283–1295.
Araki, H., Cooper, B., Blouin, M.S., 2007. Genetic effects of captive breeding cause a
rapid, cumulative fitness decline in the wild. Science 318, 100–103.
Bagliniere, J.L., Ombredane, D., Marchand, F., 2000. Critères morphologiques pour
l'identification des deux formes (rivière et mer) de truite (Salmo trutta) présentes sur
un mêm bassin. Bulletin Français de la Pêche et de la Pisciculture 357 (358), 375–383.
Björnsson, B., Stefansson, S., McCormick, S., 2011. Environmental endocrinology of
salmon smoltification. General and Comparative Endocrinology 170, 290–298.
Blouin, M.S.T., Thuillier, V., Cooper, B., Amarasinghe, V., Cluzel, L., Araki, H., Grunaub, C.,
2010. No evidence for large differences in genomic methylation between wild and
hatchery steelhead (Oncorhynchus mykiss). Canadian Journal of Fisheries and
Aquatic Sciences 67, 217–224.
Bonin, A., Ehrich, D., Manel, S., 2007. Statistical analysis of amplified fragment length
polymorphism data: a toolbox for molecular ecologists and evolutionists. Molecular Ecology 16, 3737–3758.
Bossdorf, O., Richards, C.L., Pigliucci, M., 2008. Epigenetics for ecologists. Ecology
Letters 11, 106–115.
Cao, D., Gao, X., Liu, J., Kimatu, J.N., Geng, S., Wang, X., Zhao, J., Shi, D., 2011. Methylation
sensitive amplified polymorphism (MSAP) reveals that alkali stress triggers more
DNA hypomethylation levels in cotton (Gossypium hirsutum L.) roots than salt
stress. African Journal of Biotechnology 10, 18971–18980.
Cao, D., Gao, X., Liu, J., Wang, X., Geng, S., Yang, C., Liu, B., Shi, D., 2012. Root-specific
DNA methylation in Chloris virgata, a natural alkaline-resistant halophyte, in
response to salt and alkaline stresses. Plant Molecular Biology Reporter 30,
1102–1109.
Charles, K., Guyomard, R., Hoyheim, B., Ombredane, D., Baglinière, J.L., 2005. Lack of genetic differentiation between anadromous and resident sympatric brown trout
(Salmo trutta) in a Normandy population. Aquatic Living Resources 18, 65–69.
Cross, T.F., Mills, C.P.R., de Courcy Williams, M., 1992. An intensive study of allozyme
variation in freshwater resident and anadromous trout, Salmo trutta L., in western
Ireland. Journal of Fish Biology 40, 25–32.
Da, K., Nowak, J., Flinn, B., 2012. Potato cytosine methylation and gene expression
changes induced by a beneficial bacterial endophyte, Burkholderia phytofirmans
strain PsJN. Plant Physiology and Biochemistry 50, 24–34.
Excoffier, L., Smouse, P.E., Quattro, J.M., 1992. Analysis of molecular variance inferred
from metric distances among DNA haplotypes: application to human mitochondrial
DNA restriction data. Genetics 131, 479–491.
Faulk, C., Dolinoy, D.C., 2011. Timing is everything: the when and how of environmentally induced changes in the epigenome of animals. Epigenetics 6, 791–797.
Ferguson, A., 2007. Genetics of sea trout, with particular reference to Britain and
Ireland. In: Harris, G., Milner, N. (Eds.), Sea Trout: Biology, Conservation and
Management. Blackwell Publishing Ltd., Oxford, UK, pp. 157–183.
Giger, T., Excoffier, L., Amstutz, U., Day, P.J.R., Champigneulle, A., Hansen, M.M., Kelso, J.,
Largiadèr, C.R., 2008. Population transcriptomics of life-history variation in the
genus Salmo. Molecular Ecology 17, 3095–3108.
Hansen, M.M., 2002. Estimating the long-term effects of stocking domesticated trout
into wild brown trout (Salmo trutta) populations: an approach using microsatellite
DNA analysis of historical and contemporary samples. Molecular Ecology 11,
1003–1015.
Hardy, T.M., Tollefsbol, T.O., 2011. Epigenetic diet: impact on the epigenome and
cancer. Epigenomics 3, 503–518.
Herrera, C.M., Bazaga, P., 2010. Epigenetic differentiation and relationship to adaptive
genetic divergence in discrete populations of the violet Viola cazorlensis. The New
Phytologist 187, 867–876.
Hindar, K., Jonsson, B., Ryman, N., Ståhl, G., 1991. Genetic relationships among landlocked, resident, and anadromous brown trout, Salmo trutta L. Heredity 66, 83–91.
Hiroi, J., McCormick, S.D., 2007. Variation in salinity tolerance, gill Na+/K+-ATPase,
Na+/K+/2Cl− cotransporter and mitochondria-rich cell distribution in three salmonids Salvelinus namaycush, Salvelinus fontinalis and Salmo salar. The Journal of
Experimental Biology 210, 1015–1024.
Horreo, J.L., de la Hoz, J., Pola, I.G., Machado-Schiaffino, G., Garcia-Vazquez, E., 2012. Ecological and economic costs of supportive breeding: Atlantic salmon (Salmo salar) as a
case study. Aquaculture 356–357, 1–6.
Jensen, A.J., Finstad, B., Fiske, P., Hvidsten, N.A., Rikardsen, A.H., Saksgård, L., 2012. Timing
of smolt migration in sympatric populations of Atlantic salmon (Salmo salar), brown
trout (Salmo trutta), and Arctic char (Salvelinus alpinus). Canadian Journal of Fisheries
and Aquatic Sciences 69, 711–723.
Johnson, L.J., Tricker, P.J., 2010. Epigenomic plasticity within populations: its evolutionary significance and potential. Heredity 105, 113–121.
Jonsson, B., Jonsson, N., 1993. Partial migration: niche shift versus sexual maturation in
fishes. Reviews in Fish Biology and Fisheries 3, 348–365.
Jonsson, N., Jonsson, B., 1997. Energy allocation in polymorphic brown trout. Functional
Ecology 11, 310–317.
Jonsson, N., Jonsson, B., 1998. Body composition and energy allocation in life-history
stages of brown trout. Journal of Fish Biology 53, 1306–1316.
Le Bras, Y., Dechamp, N., Krieg, F., Filangi, O., Guyomard, R., Boussaha, M., Bovenhuis, H.,
Pottinger, T.G., Prunet, P., Le Roy, P., Quillet, E., 2011. Detection of QTL with effects
on osmoregulation capacities in the rainbow trout (Oncorhynchus mykiss). BMC
Genetics 12, 46.
Li, M., Leatherland, J.F., 2012. The implications for aquaculture practice of epigenomic
programming of components of the endocrine system of teleostean embryos:
lessons learned from mammalian studies. Fish and Fisheries. http://dx.doi.org/
10.1111/j.1467-2979.2012.00486.x.
Mazzio, E.A., Soliman, K.F.A., 2012. Basic concepts of epigenetics: impact of environmental signals on gene expression. Epigenetics 7, 119–130.
McCormick, S.D., 2001. Endocrine control of osmoregulation in teleost fish. Integrative
and Comparative Biology 41, 781–794.
McCormick, S.D., Hansen, L.P., Quinn, T.P., Saunders, R.L., 1998. Movement, migration,
and smolting of Atlantic salmon (Salmo salar). Canadian Journal of Fisheries and
Aquatic Sciences 55 (Suppl. 1), 77–92.
Morán, P., Pérez-Figueroa, A., 2011. Methylation changes associated with early maturation stages in the Atlantic salmon. BMC Genetics 12, 86.
Navarro-Martín, L., Viñas, J., Ribas, L., Díaz, N., Gutiérrez, A., Di Croce, L., Piferrer, F.,
2011. DNA methylation of the gonadal aromatase (cyp19a) promoter is involved
in temperature-dependent sex ratio shifts in the European sea bass. PLoS Genetics
7, e1002447.
83
Norman, J.D., Danzmann, R.G., Glebe, B., Ferguson, M.M., 2011. The genetic basis of salinity tolerance traits in Arctic charr (Salvelinus alpinus). BMC Genetics 12, 81.
Økland, F., Jonsson, B., Jensen, A.J., Hansen, L.P., 1993. Is there a threshold size regulating seaward migration of brown trout and Atlantic salmon? Journal of Fish Biology
42, 541–550.
Pérez-Figueroa, in press. A. msap: a tool for the statistical analysis of MethylationSensitive Amplified Polymorphism data. Molecular Ecology Resources. http://dx.
doi.org/10.1111/1755-0998.12064.
Perry, S.F., Rivero-Lopez, L., McNeill, B., Wilson, J., 2006. Fooling a freshwater fish: how
dietary salt transforms the rainbow trout gill into a seawater gill phenotype. The
Journal of Experimental Biology 209, 4591–4596.
Pettersson, J.C.E., Hansen, M.M., Bohlin, T., 2001. Does dispersal from landlocked trout
explain the coexistence of resident and migratory trout females in a small stream?
Journal of Fish Biology 58, 487–495.
Pinheiro, J.C., Bates, D.M., 2009. Mixed-Effects Models in S and S-Plus. Springer-Verlag,
Berlin.
R Development Core Team, 2012. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria [v. 2.15.1].
Reyna-López, G.E., Simpson, J., Ruiz-Herrera, J., 1997. Differences in DNA methylation
patterns are detectable during the dimorphic transition of fungi by amplification
of restriction polymorphisms. Molecular and General Genetics 253, 703–710.
Ruzzante, D.E., Hansen, M.M., Meldrup, D., 2001. Distribution of individual inbreeding
coefficients, relatedness and influence of stocking on native anadromous brown
trout (Salmo trutta) population structure. Molecular Ecology 10, 2107–2128.
Seear, P.J., Carmichael, S.N., Talbot, R., Taggart, J.B., Bron, J.E., Sweeney, G.E., 2010. Differential gene expression during smoltification of Atlantic salmon (Salmo salar L.): a first
large-scale microarray study. Marine Biotechnology 12, 126–140.
Sundell, K., Dellefors, C., Björnsson, B.T., 1998. Wild and hatchery-reared brown trout,
Salmo trutta, differ in smolt related characteristics during parr–smolt transformation. Aquaculture 167, 53–65.
Tammen, S.A., Friso, S., Choi, S., in press. Epigenetics: the link between nature and nurture. Molecular Aspects of Medicine.
Tan, M., 2010. Analysis of DNA methylation of maize in response to osmotic and salt
stress based on methylation-sensitive amplified polymorphism. Plant Physiology
and Biochemistry 48, 21–26.
Wang, W., Pan, Y., Zhao, X., Dwivedi, D., Zhu, L., Ali, J., Fu, B., Li, Z., 2011. Drought-induced site-specific DNA methylation and its association with drought tolerance
in rice (Oryza sativa L.). Journal of Experimental Botany 62, 1951–1960.
Xu, M., Li, X., Korban, S.S., 2000. AFLP-Based detection of DNA methylation. Plant Molecular Biology Reporter 18, 361–368.
Zaugg, W.S.R., Roley, D.D., Prentice, E.F., Gores, K.X., Waknitz, F.W., 1983. Increased seawater survival and contribution to the fishery of Chinook salmon (Oncorhynchus
tshawytscha) by supplemental dietary salt. Aquaculture 32, 183–188.
Zuur, A.F., Ieno, E.N., Walker, N.J., Saveliev, A.A., Smith, G.M., 2009. Mixed Effects
Models and Extensions in Ecology with R. Springer, New York.
FACTOR DE IMPACTO Y CALIDAD DE LAS
PUBLICACIONES
118!
!
FACTOR DE IMPACTO Y CALIDAD DE LAS PUBLICACIONES
El factor de impacto de una revista académica es una medida que refleja el
número medio de citas de artículos durante un año en particular o durante un
periodo concreto. Frecuentemente es usado como una aproximación de la
importancia relativa de la revista en su campo. Revistas con factor de impacto
superior se considerarán más importantes que las que lo tienen más bajo.
El cálculo para el año 2012 de una revista X se realiza de la siguiente forma:
A = Citas totales de la revista X en 2012
B = Citas en 2012 de artículos de la revista X publicados en el período 2010-11
(subconjunto de A)
C = Número de artículos publicados en la revista X durante el período 2010-11
D = B/C = Factor de impacto de 2012
Los trabajos incluidos en esta tesis doctoral se han publicado o están
aceptados en las siguientes revistas:
• PLoS ONE
• Aquaculture
• Canadian Journal of Fisheries and Aquatic Sciences
• Ecology and Evolution
119!
!
Los datos más relevantes de las revistas se detallan a continuación:
• PLoS ONE:
eISSN: 1932-6203
Revista electrónica de acceso abierto que funciona como una asociación entre
su personal interno, asesores internacionales y consejos de redacción. La
revista es editada mediante “Public Library Service” desde el año 2006 y su
país de publicación es Estados Unidos. La revista está incluida en el SCI
dentro de la categoría: Biología, en la que se sitúa, atendiendo al factor de
impacto, la 12ª de un total de 85 revistas. Se sitúa en el primer cuartil. Su
índice de impacto en 2011 fue 4.092 y su factor de impacto en los últimos cinco
años fue 4.537. En esta revista se han publicado dos artículos.
• Aquaculture:
ISSN: 0044-8486
Revista internacional para la investigación en agua dulce y marina. Interesada
en la explotación, mejora y manejo de los recursos vivos acuáticos. La revista
es editada por “Elsevier Science BV” desde el año 1972 y su país de
publicación es Holanda. La revista está incluida en el SCI dentro de las
categorías: Pesquerías, en la que se sitúa, atendiendo al factor de impacto, la
11ª de un total de 50 revistas y Biología Marina y Agua dulce, en la que se sitúa
la 30ª de 97 revistas. Se sitúa en el primer y segundo cuartil respectivamente.
Su índice de impacto en 2011 fue 2.041 y su factor de impacto en los últimos
cinco años fue 2.696. En esta revista se ha publicado un artículo.
120!
!
• Canadian Journal of Fisheries and Aquatic Sciences:
ISSN: 0706-652X
Revista internacional que publica perspectivas, discusiones, artículos y
comunicaciones rápidas relacionadas con investigaciones actuales en células,
organismos, poblaciones, ecosistemas y otros procesos que afecten a los
sistemas acuáticos. La revista es editada por “National Research Council of
Canada” desde el año 1901 y su país de publicación es Canada. La revista
está incluida en el SCI dentro de las categorías: Pesquerías, en la que se sitúa,
atendiendo al factor de impacto, la 6ª de un total de 50 revistas y Biología
Marina y Agua dulce, en la que se sitúa la 21ª de 97 revistas. Se sitúa en el
primer cuartil en ambos casos. Su índice de impacto en 2011 fue 2.213 y su
factor de impacto en los últimos cinco años fue 2.632. En esta revista se ha
publicado un artículo.
• Ecology and Evolution:
ISSN 2045-7758
Revista internacional publicada por primera vez en 2011. EL ISI la ha
catalogado dentro de Contenidos Actuales / Agricultura y Biología y Ciencias
Ambientales. Ecology and Evolution recibirá su primer índice de impacto en
2013. La revista es editada por la “British Ecological Society” y publica bajo
licencia Creative Commons. En esta revista hay un articulo aceptado.
121!
!
122!
!

Identificación de factores de la anadromía en trucha

Transcription

Similar documents

4% - Real Estate Forums

Segundo Domingo de Pascua

Memoria Científica del Grupo - Centro de Investigaciones Biológicas

Domingo de Pentecostés

Domingo de la Resurrección del Señor

Primer domingo de cuaresma - Holy Name of Jesus Parish

ri-champion® N

our new cling rubber stamps

Efecto del fotoperiodo en peces de consumo cultivados en distintos

Super Easy