TESIS DOCTORAL BIOLOGÍA TRÓFICA DEL ATÚN ROJO

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La presente Tesis Doctoral ha sido subvencionada por el Ministerio de Ciencia e
Innovación (MICINN, CTM2007-65178-C02-01/MAR, CTM2011-29525-C04-01 y
CTM2011-27505), Junta de Andalucía (RNM-02469), Comunidad Europea (GA no.
212797), Secretaría General del Mar, Ministerio de Medio Ambiente, Rural y Marino y
Red Eléctrica de España - Fundación Migres.
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1. INTRODUCCIÓN
1.1. Taxonomía, distribución y situación del stock
El atún rojo atlántico, Thunnus thynnus (L., 1758), es el mayor de todos los
túnidos, pudiendo alcanzar más de 3 m de longitud y 600 kg de peso (Mather et al.,
1995). Es considerado como uno de los depredadores por excelencia de los
ecosistemas pelágicos. Posee un cuerpo fusiforme y robusto perfectamente adaptado
para la natación (Fig. 1.1), tratándose de una especie altamente migratoria que puede
llegar a alcanzar una velocidad media de 5.9 km/h y una máxima de entre 13 y 31
km/h (Lutcavage et al., 2000; Wilson et al., 2005). Desde la antigüedad es una especie
muy apreciada, debido a su gran tamaño, excelente calidad y sabor de su carne y, en
los últimos años, ha alcanzado elevados precios, sobre todo en el mercado japonés
(Lioka et al., 2000).
Figura 1.1. Atún rojo.
Hasta hace poco se aplicaba la hipótesis de que el atún rojo atlántico ocupaba
principalmente las aguas superficiales y subsuperficiales de las zonas costeras y alta
mar, pero la telemetría mediante marcas electrónicas de archivo (tanto internas como
ϯ
externas por satélite) y acústicas han revelado que, tanto los juveniles como los
adultos de atún rojo, descienden con frecuencia a profundidades de 500 m a 1000 m
(Lutcavage et al., 2000). Aunque se trata de una especie de aguas abiertas,
estacionalmente puede acercarse a la costa, tolerando una amplia gama de
temperaturas (Collette y Nauen, 1983). Una de las principales características de esta
especie, que comparte con algunos de sus congéneres y con ciertos escualos
(Dickson y Graham, 2004), es su capacidad para elevar y mantener la temperatura
corporal por encima de la temperatura ambiente, en ocasiones más de 20 ºC. Esto lo
consigue mediante un sistema de intercambio de calor contracorriente, la rete mirabile.
Este sistema consiste en una extensa red de vasos sanguíneos formada por capilares
que impiden que el calor metabólico se escape. Esto permite al pez mantener un
metabolismo elevado y constante, parecido al de los homeotermos, así como habitar
zonas del océano con un rango de temperaturas muy amplio (Graham y Dickson,
2004; Dickson y Graham, 2004).
Según la clasificación establecida por Collete et al. (2001), el atún rojo atlántico
pertenece a la sublclase Actinopterygii, orden Perciformes y familia Scombridae, la
cual comprende 15 géneros y alrededor de 55 especies de peces marinos
epipelágicos. Inicialmente, Gibbs y Collete (1967) diferenciaron dos subespecies de
atún rojo del norte, el del Pacífico, Thunnnus thynnus orientalis (Temminck y
Schelegel, 1844), presente en el noroeste del océano Pacífico, y el del Atlántico,
Thunnus thynnus thynnus (L., 1758). Actualmente, son consideradas como dos
especies distintas, Thunnus orientalis y Thunnus thynnus . En la zona oriental, la
especie atlántica se distribuye desde las costas de Noruega hasta las Islas Canarias,
llegando hasta las aguas de Sudáfrica, y entrando también en el Mediterráneo (Lee,
1998). En la zona occidental se distribuye desde la Península de Labrador y Terranova
hasta el Golfo de México y el Mar Caribe y frente a las costas de Venezuela y Brasil
(Fig. 2.1).
ϰ
Figura 2.1. Área de distribución del atún rojo atlántico (Fuente: www.fishbase.org)
Actualmente, se cree que el atún rojo alcanza la madurez sexual con
aproximadamente 25 kg (edad 4) en el Mediterráneo (Corriero et al., 2005) y con
aproximadamente 145 kg (edad 9) en el Golfo de México (Diaz y Turner, 2007). Los
juveniles y adultos de atún rojo se alimentan de forma oportunista, como la mayoría de
los depredadores. Sin embargo, en general, los juveniles se alimentan de crustáceos,
peces y cefalópodos, mientras los adultos se alimentan sobre todo de peces pelágicos
como arenque (Clupea arengus), boquerón (Engraulis encrasicolus), sardina (Sardina
pilchardus), lanzón (Ammodytes spp.) y caballa (Scomber scomburs) (Rooker et al.,
2007). El crecimiento de los juveniles es rápido para un teleósteo (unos 30 cm al año),
pero más lento que otros túnidos y marlines. Los ejemplares nacidos en junio alcanzan
una talla aproximada de 30-40 cm y un peso de 1 kg en octubre (Mather et al., 1995).
El crecimiento en longitud tiende a ser menor en los adultos que en los juveniles, pero
el crecimiento en peso se incrementa (Fromentin y Powers, 2005). El atún rojo es una
especie longeva, con un ciclo vital de 35 años, tal y como han indicado estudios
recientes a partir de depósitos de radiocarbono en otolitos (Neilson y Campana, 2008)
Desde 1982, la Comisión Internacional para la Conservación de los Atunes
Atlánticos (CICAA o ICCAT) considera a las poblaciones oriental y occidental de forma
ϱ
separada, y, en consecuencia, son evaluadas y gestionadas de forma independiente.
El límite regional entre las dos unidades espaciales se encuentra en el meridiano 45º
W (Fig. 1.3). No obstante, actualmente se acepta el hecho de que tiene lugar un cierto
grado de mezcla entre ambos stocks (ICCAT, 2002; Rooker et al., 2007).
Figura 1.3. Delimitación geográfica de los stocks oriental
y occidental en el atún rojo atlántico (ICCAT, 2007).
La separación de los dos stocks se realiza principalmente bajo la premisa de la
existencia de dos zonas de puesta; el stock occidental, cuya única zona de puesta
conocida se sitúa en el Golfo de México y Sur de Florida (Nemerson et al., 2000),
teniendo lugar desde mediados de abril a mediados de junio (Richards, 1976; Baglin,
1982; Block et al., 2001), y la población oriental, cuyos reproductores migran desde el
Atlántico Norte a las áreas de puesta del Mediterráneo (Fig. 1.4) (Mather et al., 1995;
Ravier y Fromentin, 2001) desde mayo a Julio (Susca et al., 2001; Medina et al., 2002;
Corriero et al., 2003).
ϲ
Figura 1.4. Zonas de puesta del atún rojo en el área del Mediterráneo según datos de
distribución larvaria. Los círculos de menor tamaño indican capturas de 1-4 larvas (por neuston
o bongo), los círculos medianos indican una abundancia de 5-10 larvas y los de mayor tamaño
una cantidad de 10 o más larvas (revisado por Rooker et al., 2007).
A pesar de la gestión independiente de ambos sotcks, los datos procedentes
de campañas de marcado ponen de manifiesto la capacidad del atún rojo de realizar
migraciones transatlánticas desde ambas zonas de reproducción (Lutcavage et al.,
1999; Block et al., 2001, 2005; Rooker et al., 2003; Walli et al., 2009) y cuestionan
todas las asunciones anteriores sobre migraciones, zonas de puesta y estructura de
los stocks. A partir de datos de marcas convencionales se han estimado tasas de
mezcla de entre el 1 y 10% dependiendo del tamaño de los atunes marcados y de la
zona de origen (Mather et al., 1995; Rooker et al., 2007). Sin embargo, la información
proporcionada por marcas electrónicas en los últimos años sugiere que los esquemas
de migración y desplazamiento del atún rojo varían considerablemente entre
individuos, años y zonas, y la tasa de mezcla puede ser mayor a la estimada
inicialmente. El marcado electrónico ha mejorado de forma considerable la
comprensión sobre la dinámica espacial del atún rojo. Por ejemplo, Block et al. (2005)
apoyan la existencia de una distribución solapada en las áreas tróficas del Atlántico
norte, una hipótesis ya planteada anteriormente por diversos autores, tales como
Tiews (1963) o Mather et al. (1995). Los estudios de marcado, sin embargo, no
proporcionan información sobre el lugar de nacimiento de los peces, una información
que sin duda es clave para comprender la estructura de la población. El análisis de
ϳ
elementos traza en el otolito, en particular el uso de isótopos estables de oxígeno y
carbono depositados durante los primeros años de vida, ha resultado útil para
distinguir entre las regiones natales de atún rojo en el Golfo de México y el
Mediterráneo (Rooker et al., 2003). Su análisis ha proporcionado datos referentes al
grado de mezcla o conectividad que existe entre ambos stocks (Rooker et al., 2007,
2008a). Los resultados obtenidos demuestran que una importante proporción (~4364%) de atún rojo atlántico capturado en la pesquería del Atlántico occidental (formada
sobre todo por peces de categoría mediana a juveniles) procedía de las zonas de cría
del Este. Igualmente, los atunes rojos de gran tamaño y medianos del Mediterráneo
eran en su mayor parte (~82-86%) de origen oriental. Por tanto, la evidencia inicial
sugiere que la pesquería occidental recibe un flujo considerable de la población del
Mediterráneo. Los últimos análisis realizados señalan una elevada tasa de atunes
adultos que regresan a sus zonas de origen, siendo muy similares en ambas zonas de
puesta; 95.8% para el Mediterráneo y 99.3% para el Golfo de México (Rooker et al.,
2008a). Estos resultados corroboran las hipótesis planteadas a partir de los datos
procedentes de las marcas electrónicas y parecen confirmar que los atunes adultos
muestran una conducta de retorno al lugar de nacimiento y de fidelidad a la zona de
reproducción, tanto en el Mediterráneo (Block et al., 2005) como en el Golfo de México
(Block et al., 2005, Teo et al., 2007).
Al contrario que los estudios de marcado electrónico y de isótopos estables que
muestran que hay una importante contribución de la unidad de población del Atlántico
oriental sobre la del Atlántico occidental, los estudios genéticos muestran una clara
diferenciación entre las zonas de reproducción del Golfo de México (área de
reproducción del stock occidental), del Mediterráneo oriental y, sorprendentemente, del
Mediterráneo occidental (Boustany et al., 2008).
Fromentin y Powers (2005) consideran que el atún rojo atlántico podría
observarse como una metapoblación; es decir, un conjunto de poblaciones locales
separadas que ocuparían hábitats diversos, y que exhiben su propia dinámica
ϴ
(incluyendo la migración), pero con un cierto grado de influencia demográfica de otras
poblaciones locales. Esta hipótesis supone una alternativa menos rígida que el actual
concepto de dos stocks, de manera que probablemente las poblaciones influyen entre
sí mediante interacciones de tipo ecológico más que genético. Los últimos análisis
realizados a partir de las series históricas de las principales pesquerías de atún rojo
del siglo XX (incluyendo capturas totales y composición por tallas de la captura)
plantean la estructura de la población del atún rojo atlántico como una unión de al
menos tres subpoblaciones: una muy migratoria en todo el Atlántico norte (que
desovaría en el Mediterráneo occidental y central), otra más residente en el
Mediterráneo, que desova en el Mediterráneo central y oriental, y una tercera más
residente en el Atlántico occidental, que desova en el Golfo de México (Fromentin,
2009).
Históricamente, el atún rojo atlántico ha sido explotado de forma sostenible en
el Mediterráneo durante miles de años hasta finales del siglo XX (Mather et al., 1995;
Doumengue, 1998). Sin embargo, en las últimas décadas el esfuerzo pesquero se ha
incrementado notablemente, con lo que la biomasa de los stocks occidental y oriental
está descendiendo de forma alarmante, lo que pone en riesgo la supervivencia de un
recurso que desde 1980 está considerado como sobreexplotado, en el caso del stock
occidental, y desde mediados de los años 90 en el oriental (Sissenwine et al., 1998;
ICCAT, 2008). Esta situación de sobreexplotación pesquera llevó a la ICCAT a
establecer sendos Planes de Recuperación para ambos stocks, estableciéndose en
1998 y 2006 para la población occidental y oriental, respectivamente.
Los resultados de la última evaluación realizada por el Comité Científico
Permanente para la Investigación y las Estadísticas (SCRS) en 2010 para el atún rojo
del Atlántico oriental y Mediterráneo indicaron que la biomasa del stock reproductor
había descendiendo rápidamente en los últimos años, mientras que la mortalidad por
pesca había aumentado a un ritmo elevado, especialmente en los atunes grandes, si
bien en los últimos años hay una clara disminución. Las estimaciones del estado del
ϵ
stock respecto del objetivo de lograr el Rendimiento Máximo Sostenible (MSY) son
inciertas, pero se puede concluir que a pesar de que la mortalidad por pesca ha
disminuido recientemente para los grandes adultos, esta permanece demasiado
elevada y la SSB demasiado baja, alrededor del 36 %, del nivel necesario para
obtener el MSY (Fig. 1.5).
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Figura 1.5. Estimaciones de mortalidad por pesca F2-5: para edades de 1 a 5 años y F10+: para
edades 10+, SSB: biomasa del stock reproductor en toneladas y Recruits: reclutamiento del
stock oriental de atún rojo atlántico. Las líneas gris y negra corresponden a los dos escenarios
más plausibles (ICCAT, 2010).
Análisis recientes de capturas por talla y edad muestran importantes cambios
en los patrones de selectividad en los tres últimos años para varias flotas que operan
en el mar Mediterráneo o en el Atlántico este. Esto podría ser en parte el resultado de
la puesta en práctica de las regulaciones sobre talla mínima establecidas en la Rec.
06-05 que ha conducido a una captura declarada de peces más jóvenes mucho menor
y, por consiguiente, a un aumento en el peso medio anual en la captura por talla desde
2007. Además, la mayor abundancia y mayores concentraciones de atún rojo pequeño
en el Mediterráneo noroccidental detectadas mediante prospecciones aéreas podrían
ϭϬ
también ser un reflejo de los resultados positivos de la regulación sobre el incremento
de la talla mínima (ICCAT, 2011).
Estas estimaciones deben ser consideradas con precaución, ya que, a pesar
de las mejoras en la cantidad y calidad de los datos para la evaluación de 2010,
existen ciertas limitaciones, incluyendo una pobre cobertura espacial y temporal para
las estadísticas detalladas de talla y de captura-esfuerzo en muchas pesquerías,
especialmente en el Mediterráneo. También era evidente una infradeclaración en las
capturas totales, especialmente durante los años 1998-2007 (ICCAT, 2011).
1.2. Biología trófica. Antecedentes
Como base para la regulación de los stocks y para habilitar medidas que
permitan una adecuada gestión y conservación del atún rojo, se hace imprescindible
un profundo conocimiento tanto de los movimientos migratorios como de la biología
trófica y reproductora. Los esfuerzos de los científicos dedicados al estudio del atún
rojo se han centrado fundamentalmente en el conocimiento de las migraciones
mediante el uso de marcas electrónicas (ej.: Block et al., 2005; Lutcavage et al., 2000),
en su actividad reproductora (ej.: Medina et al., 2002, 2007; Mylonas et al., 2007). Por
el contrario, los esfuerzos dedicados al estudio de su biología trófica han sido menos
significativos, a pesar de que es sabido que la drástica disminución de este predador
apical puede producir un efecto en cascada muy perjudicial en la estabilidad de los
ecosistemas pelágicos.
La biología trófica es la disciplina encargada de relacionar los aspectos
biológicos y fisiológicos de las especies con su hábitat, régimen alimentario, tipo de
dieta y relaciones bióticas, tales como depredación y competencia. En el caso de los
peces, mediante este tipo de estudios es posible comprender la dinámica de las
relaciones ecológicas que existen entre especies, además de proporcionar algunas
bases para poder establecer métodos adecuados que contribuyan a una correcta
administración de los recursos pesqueros. No sólo es importante generar este tipo de
ϭϭ
conocimiento para especies de valor económico, sino también para todas aquellas con
las que se relacionan ecológicamente, ya que una alteración en su dinámica podría
afectar directamente o indirectamente la supervivencia de cualquier especie asociada.
Los estudios que determinan la biología trófica de las especies y el
conocimiento de los hábitos alimenticios de éstas aportan información básica y
necesaria para comprender el papel biológico y ecológico que desempeña un
organismo dentro del ecosistema, ya que el alimento constituye uno de los factores
intrínsecos más importantes, al influir directamente en el crecimiento y la reproducción,
así como en la forma en la que se desarrolla su ciclo de vida, proceso que se da a
expensas de la energía que el organismo recibe del exterior (Nikolsky, 1963; Wootton,
1999). Asimismo, el conocimiento de los hábitos alimenticios de las especies permite
evaluar su estatus en la comunidad, es decir su nivel trófico, posibles relaciones con
otras especies o grupos, y proporcionar una idea aproximada de su entorno y, por
tanto, el efecto que puede producirse en cualquier tipo de uso y gestión del mismo.
Las fuentes bibliográficas sobre biología trófica de Thunnus thynnus son
escasas. En trabajos precedentes, el atún rojo es definido como un depredador que
presenta un comportamiento oportunista y generalista, se alimenta de una gran
variedad de peces e invertebrados (Chase, 2002; Estrada et al., 2005; Sarà y Sarà,
2007; Karakulak et al., 2009; Logan et al., 2011) y tiene la capacidad de producir un
impacto importante en el funcionamiento de los ecosistemas (Hinke et al., 2004;
Overholtz, 2006;Frank et al., 2005, 2007) (Fig. 1.6).
ϭϮ
Figura 1.6. Diagrama simplificado de la red de alimentación de un ecosistema pelágico
(reproducido de Olson y Waters, 2003).
Estudios basados en análisis de contenidos estomacales revelan que la dieta
del atún rojo atlántico está constituida principalmente por teleósteos, seguida por
cefalópodos y, en menor medida, crustáceos Crane, 1936; Dragovich, 1969;
Eggleston y Bochenek, 1990; Sanz Brau, 1990). En estudios de la dieta mediante el
análisis del contenido estomacal realizados en las otras dos especies de atún rojo,
Thunnus orientalis (Pinkas, 1971), y Thunnus macoyii (Young et al., 1997),
encontramos un patrón similar al observado en T. thynnus.
La reconstrucción de la dieta del atún rojo a partir de análisis de contenido
estomacal tiene, no obstante, una serie de limitaciones, tales como: i) la gran eficacia
del proceso digestivo (Aloncle y Delaporte, 1973), favorecida por el calentamiento
visceral (Carey et al., 1984; Graham y Dickson, 2001; Itoh et al., 2003), ii) la desigual
velocidad en la digestión de los diferentes tipos de alimento, que puede dar lugar a
errores al determinar las proporciones en la que las distintas presas son ingeridas, y iii)
la regurgitación frecuente de comida, inducida por el estrés causado durante la captura
(Chase, 2002). Como consecuencia de ello, en muchas ocasiones los estómagos
aparecen vacíos o contienen sólo partes duras difíciles de digerir, como otolitos de
ϭϯ
peces o picos de cefalópodos. Teniendo en cuenta estos inconvenientes, es de suma
utilidad complementar los estudios de contenidos estomacales con análisis de isótopos
estables de carbono (į13C) y nitrógeno (į15N) en diferentes tejidos.
1.3. Utilización de análisis de isótopos estables
Los análisis de isótopos estables (SIA) están emergiendo como una herramienta
importante para identificar la dieta animal y determinar posiciones tróficas (Peterson y
Fry, 1987; Hobson, 1999). Los isótopos son átomos de un elemento común que tienen
el mismo número de protones y electrones, pero difieren en el número de neutrones
(Thomson et al., 1921). Diferencias en el número de neutrones generan diferencias en
la masa atómica, que pueden ser medidas con un espectrómetro de masas. Los
isótopos estables tienen una combinación de protones y neutrones que no se alteran a
lo largo del tiempo. Los isótopos estables pesados son una pequeña proporción en la
abundancia natural de un elemento siendo muy valiosos para su uso como trazadores
de procesos ecológicos. Los isótopos pesados del carbono y el nitrógeno (13C y
15
N)
suponen un 1,11% y 0,37%, respectivamente, en el total de la abundancia natural de
estos elementos, mientras los porcentajes restantes lo componen los isótopos ligeros
(Sulzman, 2007). Los valores de los isótopos estables se expresan en partes por mil a
partir de un estándar de referencia, según la siguiente ecuación:
į = [(Rsample/Restándar)-1] × 103,
siendo R la razón entre los isótopos pesado y ligero (15N/14N y
13
C/12C). Como
estándares se usan VPDB (Vienna Pee Dee Belemnite) y nitrógeno atmosférico.
En estudios de biología trófica, los isótopos estables proporcionan a menudo una
información más representativa, aunque menos específica, que la aportada por el
análisis del contenido estomacal, ya que reflejan una integración de todas las presas
asimiladas en los tejidos del depredador (Abend y Smith, 1997). La abundancia
relativa de los isótopos estables de carbono y nitrógeno presentes en los tejidos de
organismos depredadores está directamente relacionada con la existente en sus
ϭϰ
presas, transfiriéndose dichos isótopos a lo largo de la cadena trófica mediante
procesos de enriquecimiento (DeNiro y Epstein, 1978, 1981; Fry, 1988). En la práctica,
existe un enriquecimiento de
13
C en torno al 1‰ por nivel trófico (DeNiro y Epstein,
15
1978; Peterson y Fry, 1987); en cambio, el enriquecimiento isotópico para
N es del
3%-4‰ (Minegawa y Wada, 1984; Fry, 1988; Post, 2002). De este modo, los niveles
de
13
C informan sobre la fuente primaria de carbono, mientras que los valores de į15N
permiten establecer la posición trófica (Post, 2002).
Los isótopos estables se transfieren a lo largo de la cadena trófica con
incrementos constantes entre los diferentes niveles tróficos. La “separación” isotópica
entre en el tejido de consumidor y su dieta se conoce como factor de discriminación
(Martínez del Río y Wolf, 2004) o factor de enriquecimiento (Fry, 1988). Los isótopos
estables nos proporcionan información de la dieta de un depredador a una escala de
tiempo determinada en función de la actividad metabólica de tejido y del organismo
analizado (Peterson y Fry, 1987). Concretamente, los SIA proporcionan información
sobre la dieta y la posición trófica a una escala de tiempo mayor que los análisis de
contenido estomacal (SCA), que, aunque permiten identificaciones más detalladas,
están limitados por la velocidad de digestión de la especie sometida a estudio. En
ocasiones, el SCA no es un método viable, puesto que requiere la identificación de las
presas encontradas en los estómagos, lo que en muchas ocasiones no es posible
debido al avanzado estado de digestión en el que se encuentran. La combinación de
SCA y SIA genera datos de la dieta en dos escalas de tiempo diferentes. Por otra
parte, si se llevan a cabo SIA en diferentes tejidos, con tasas de renovación distintas,
se puede obtener la integración de los isótopos en tejido a diferentes escalas de
tiempo.
Para el ratio isotópico del carbono (į13C), el incremento isotópico por nivel
trófico es muy pequeño (0-1‰) y se debe a la perdida preferencial de
12
CO2 en la
respiración (DeNiro y Epstein, 1978; Checkley y Entzeroth, 1985). El isótopo del
carbono es usado para determinar fuentes de producción primaria (DeNiro y Epstein,
ϭϱ
1978; Fry y Sherr, 1984) y muestra variaciones entre regiones bentónicas y pelágicas,
entre zonas de costa y en mar abierto (Hobson et al., 1994; France, 1995), y a través
de gradientes latitudinales (Rau et al., 1989; Graham et al., 2010). El enriquecimiento
trófico del isótopo del nitrógeno (į15N) es debido, probablemente, a la eliminación
preferente de isótopos ligeros (14N) en los productos de excreción nitrogenados (Steele
y Daniel, 1978). De manera general, el factor de discriminación para el į15N (ǻ15N) es
más elevado que para el į13C (ǻ13C), por lo que el isótopo del nitrógeno es una
medida más robusta de la posición trófica, con un enriquecimiento trófico comprendido
entre el 2 y el 4‰ (DeNiro y Epstein, 1981; Minewaga y Wada, 1984; Gannes et al.,
1998; Post, 2002). La posición trófica (TP) puede calcularse en función de
į15N,
según la siguiente ecuación:
TP = Ȝ + (į15Nconsumidor secundario - į15Nbase)/ ǻn,
donde į15Nbase es el valor de į15N del organismo del nivel trófico más bajo usado en la
comparación, Ȝ es el TP del organismo de base, y ǻn es el factor de discriminación
para el consumidor secundario (Post 2002).
Los isótopos estables, junto con otros trazadores químicos, se han utilizado
para llevar a cabo diversos estudios en atún rojo, relacionados con la fisiología
(Radtke et al., 1987), estructura del stock (Secor y Zdanowicz, 1988; Rooker et al.,
2008a, 2008b; Dickhut et al., 2009) y ecología trófica (Estrada et al., 2005; Sarà y
Sarà, 2007; Logan et al., 2011). Los primeros análisis isotópicos (į13C y į18O)
realizados en otolitos de atunes demostraron que los valores de į18O se veían
afectados por la temperatura del cerebro y se podían utilizar para reconstruir su
historia fisiológica (Radtke et al., 1987). No obstante, estimar parámetros fisiológicos a
partir de los valores isotópicos puede ser erróneo, puesto que la temperatura del agua
puede alterar estos valores (Campana, 1999; Rooker et al., 2008a). Más adelante, los
valores isotópicos del carbono y oxígeno se utilizaron para identificar las zonas de
nacimiento del atún rojo, obteniéndose diferencias entre los individuos nacidos en el
Golfo de México y aquéllos que nacían en el Mar Mediterráneo. Así, se comprobó que
ϭϲ
para determinar estructuras en el stock del atún rojo, la variabilidad geográfica que
muestran los valores isotópicos analizados en otolitos es una herramienta más
determinante que otros trazadores usados con anterioridad (Secor y Zdanowicz, 1988;
Rooker et al., 2001). Posteriormente, se llevaron a cabo análisis isotópicos en tejidos
blandos de atún rojo con el objetivo de definir patrones tróficos (Estrada et al., 2005;
Sarà y Sarà, 2007; Logan et al., 2010). Los incrementos en los valores de į15N en los
tejidos de organismos consumidores en función de la dieta se utilizaron para estimar la
posición trófica del atún rojo atlántico en zonas de alimentación del Atlántico NW y el
Mar Mediterráneo (Estrada et al., 2005; Sarà y Sarà, 2007).
Los trazadores químicos muestran, claramente, un gran potencial para el
estudio de diversos aspectos de la ecología del atún rojo, aunque también presentan
dificultades. Estimar el movimiento y la estructura del stock a partir de trazadores
químicos conlleva una serie de dificultades debido al alto comportamiento migratorio
que muestra el atún rojo atlántico. Los valores isotópicos analizados en otolitos
pueden mostrar sesgos debido a las diferencias fisiológicas causadas por la edad de
los individuos y la región habitada (Radtake et al., 1987). En los tejidos blandos, que
presentan tasas de renovación metabólica generalmente elevadas, las señales
químicas que reflejan las zonas habitadas a lo largo de la vida del organismo se
pierden y se equilibran hacia los valores de los últimos lugares ocupados.
Para aplicar los análisis isotópicos a los estudios de biología trófica, deben
tenerse en cuenta ciertas consideraciones: i) la escala temporal de la información
incorporada en los tejidos de los consumidores, ii) las diferencias isotópicas existentes
entre el organismo consumidor y la dieta, iii) la variabilidad de los valores isotópicos
entre los principales componentes de los tejidos (Ej.: lípidos y proteínas) (Gannes et
al., 1997). Una vez considerados estos factores, la combinación de análisis de
contenidos estomacales y de isótopos estables en distintos tejidos de atún rojo,
permite obtener una aproximación de la dieta de esta especie en diferentes escalas de
tiempo.
ϭϳ
Si se desconoce la tasa de renovación de un determinando tejido al que se le
han realizado análisis de isótopos, los resultados pueden confundir debido a los
cambios temporales en la dieta o en el hábitat. Estos problemas son particularmente
relevantes en peces pelágicos de vida larga como el atún rojo, que puede ocupar una
amplia distribución geográfica durante el período de tiempo en el que un determinado
tejido incorpora un valor isotópico. Se asume que el factor de discriminación es en
torno al 0,4‰ para el į13C y del 3,4‰ para el į15N (DeNiro y Epstein, 1978; DeNiro y
Epstein, 1981; Minegawa y Wada, 1984; Peterson y Fry, 1987), aunque varía entre
especies y tejidos (Macko et al, 1982; Pinnegar y Polunin, 1999, Vanderklift y Ponsard,
2003). En el caso de partir de factores de discriminación incorrectos, se pueden
obtener errores sustanciales en la estimación de la posición trófica (Post, 2002) y de la
dieta (Philips y Gregg, 2003), por lo que es recomendable la realización de estudios
experimentales que permitan determinarlos con la mayor exactitud posible.
En muchos tejidos de grandes pelágicos, es necesario realizar correcciones en
los valores de į13C debido a los errores introducidos por los lípidos contenidos en ellos
(Logan et al., 2011; Varela et al., 2011, 2012). Tales errores pueden ser corregidos
mediante extracción lipídica, siendo el método modificado de Bligh y Dyer (1959) el
más eficaz para tejidos de teleósteos (Logan et al., 2008) Sin embargo, las
extracciones lipídicas pueden alterar los valores de į15N (Pinnegar y Polunnin, 1999;
Sotiropoulus et al., 2004; Sweeting et al., 2006). Existen correcciones de tipo
matemático, pero su aplicabilidad a un diverso rango de especies y de tejidos ha sido
puesto en duda (Kiljunen et al., 2006; Post et al., 2007; Preum, 2011).
1.4. Aplicación de modelos de mezcla isotópicos
Los modelos de mezcla isotópicos permiten estimar la contribución de
diferentes fuentes (presas) a una mezcla (consumidor). Estos modelos sólo pueden
generar soluciones únicas cuando el número de fuentes es ” al número de isótopos
analizados +1. Por ejemplo, supongamos que la dieta está compuesta por tan sólo 3
ϭϴ
fuentes de alimento (x, y, z) y hemos medido la razón isotópica de 2 elementos (į13C y
į15N), tanto en el consumidor como en las fuentes de alimento; así, podemos definir
las siguientes ecuaciones:
1. į13Cconsumidor = fXį13CX + fYį13CY + fZį13CZ + ǻį13C
2. į15Nconsumidor = fXį15NX + fYį15NY + fZį15NZ + ǻį15N
3. fX + fY + fz = 1;
se trata de un sistema de 3 ecuaciones con 3 incógnitas, donde fi (fx,fy,fz) es la
contribución de la fuente i (x,y,z) a la mezcla, į13C es la firma isotópica del carbono,
į15N es la firma isotópica del nitrógeno, ǻį13C es el factor de discriminación presadepredador para el isótopo del carbono y ǻį15N es el factor de discriminación presadepredador para el isótopo del nitrógeno (normalmente se asume que los factores de
discriminación son constantes para todas las presas). No obstante, si aumentamos el
número de fuentes, la incertidumbre en la contribución de las fuentes también
aumenta, ya que al aumentar el número de incógnitas existirían múltiples soluciones
posibles para el sistema (Phillips y Gregg, 2003; Moore y Semmens, 2008), de modo
que la solución aportada por estos modelos se basaría en cálculos de probabilidades.
Los modelos de mezcla bayesianos, como el aquí utilizado (SiaR; Parnell et al.,
2010), reducen las incertidumbres asociadas al uso de modelos, ya que permiten
incorporar las variaciones en las firmas isotópicas de presas, consumidor y de factores
de discriminación isotópicos. Sin embargo, estos modelos son muy sensibles a
pequeñas variaciones en los factores de discriminación (Bond y Diamond, 2011), por
lo que es de gran importancia realizar ensayos experimentales que permitan
determinarlos con la mayor exactitud posible.
1.5. Objetivos de la Tesis
Para lograr una gestión y explotación sostenible de las poblaciones del atún rojo
atlántico es de suma importancia obtener un amplio conocimiento sobre la biología de
esta especie. A pesar de los progresos realizados recientemente, aún es escasa la
ϭϵ
información disponible sobre el comportamiento trófico del atún rojo. Por ello, con
objeto de ampliar nuestro conocimiento en esta materia en las poblaciones orientales,
se plantean los siguientes objetivos
1.
Caracterización de la dieta mediante análisis de contenidos estomacales. Se
procederá a la identificación (hasta el nivel taxonómico mas bajo posible) y
cuantificación de las presas a partir de la observación de tejidos blandos
reconocibles. Estos análisis serán completados mediante el análisis de las
partes duras que pueden permanecer en el estómago una vez que el
material blando ha sido digerido por completo (otolitos de peces, picos de
cefalópodos o pinzas de cangrejos).
2.
Reconstrucción de la dieta mediante análisis de isótopos estables de
carbono y nitrógeno (į13C y į15N).
- Obtención de las correspondientes firmas isotópicas en tejido muscular y
hepático de los individuos muestreados.
- Estimación de los factores de discriminación isotópicos entre la presa y los
tejidos analizados (músculo e hígado). Este objetivo requerirá mantener
atunes en cautividad alimentados con un solo tipo de presa.
- Reconstrucción de dietas mediante la aplicación de modelos de mezcla
(“mixing models”). Un paso previo para la aplicación de los modelos será la
caracterización de las presas más frecuentes en cada clase de edad
considerada, así como la obtención de sus correspondientes firmas
isotópicas específicas. Utilizando estos datos junto con los factores de
discriminación estimados, se realizará el análisis mediante “SiaR mixing
model”. 3.
Con el fin de observar variaciones anuales, espaciales y ontogénicas, estos
análisis se llevarán a cabo en muestras obtenidas durante varios años
consecutivos en: i) individuos de edad 0+ capturados en el Mar Mediterráneo
ϮϬ
mediante curricán y ii) individuos adultos capturados por almadrabas
situadas en el Estrecho de Gibraltar.
La estimación de los factores de discriminación isotópicos entre la presa y
los tejidos del atún (músculo e hígado) se realizarán tanto en individuos
juveniles como en adultos. De esta manera podremos observar si existen
variaciones en los factores de discriminación en función de la edad.
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275-291
ϯϭ
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&+$37(5Prey-muscle carbon and nitrogen stable-isotope
discrimination factors in Atlantic bluefin tuna (Thunnus thynnus)
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2. CHAPTER 1. Prey-muscle carbon and nitrogen stable-isotope
discrimination factors in Atlantic bluefin tuna (Thunnus thynnus)
Abstract
Analyses of carbon and nitrogen stable isotopes naturally occurring in animal
tissues prove useful in trophic ecology investigations. The correct interpretation of field
stable-isotope data should be supported by previous experimental work, but the
development of isotopic models is frequently based on untested assumptions. This
study was conducted to provide reference isotopic data that may be of application in
further studies designed to reconstruct diets and elucidate trophic relationships of
Atlantic bluefin tuna, Thunnus thynnus. The influence of the fat content on carbon and
nitrogen isotope ratios (į13C and į15N), as well as prey-predator discrimination factors
(ǻ13C and ǻ15N), were assessed in white-muscle samples of wild-caught bluefin tuna
reared on chub mackerel for approximately five months. Lipid extraction resulted in
significantly lower values of C:N ratio, and higher values of į13C and į15N. Four
significantly different ǻ13C and ǻ15N estimates were obtained using all possible
combinations of bulk and lipid-extracted muscle data from predator and prey. Removal
of lipids from bluefin tuna muscle resulted in significantly different ǻ13C and ǻ15N
estimates, whereas lipid extraction from prey samples did not significantly change the
results. We suggest that, where specific experimental data are not available, the values
proposed here for ǻ13C (-0.16‰) and ǻ15N (1.64‰) be used in diet reconstruction and
food-web investigations on bluefin tuna and other closely related species.
Key-words: diet-tissue discrimination, lipid extraction, stable isotopes, Thunnus thynnus,
trophic markers
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2.1. Introduction
The Atlantic bluefin tuna, Thunnus thynnus (Linnaeus, 1758), is a large
migratory fish that occurs throughout the North Atlantic and the Mediterranean Sea,
and exhibits physiological and morphological adaptations that enable it to exploit a
wide range of pelagic environments (Mather et al., 1995; Collette et al., 2001;
Fromentin and Powers, 2005). Bluefin tunas, in general, and the Atlantic bluefin tuna,
in particular, are currently the focus of intense scientific (and popular) debate due to the
concerning stock depletion that has occurred in the last two decades (Mather et al.,
1995; Sissenwine et al., 1998; Fromentin, 2001; Fromentin and Powers, 2005;
MacKenzie et al., 2009). In spite of recent significant advances in the understanding of
tuna biology and ecology, there still exists a lack of knowledge on key processes of the
population dynamics that are essential for an effective stock assessment and the
implementation of ecosystem-based management strategies (Fromentin and Powers,
2005).
Thunnus thynnus is a key apex predator that feeds on a variety of fish and
invertebrates, and so has been regarded as an opportunistic and generalist feeder
(Chase, 2002; Estrada et al., 2005; Sarà and Sarà, 2007; Karakulak et al., 2009; Logan
et al., 2011). Consequently, T. thynnus, like other tunas, play a key role in pelagic
ecosystems, since they may exert an important influence on the food-web dynamics
through top-down predatory control and can cause shifts in the trophic interactions
between species (Hinke et al., 2004; Overholtz, 2006; Frank et al., 2005; Frank et al.,
2007). Reports on foraging habits based on stomach content analysis have revealed
that T. thynnus feed mainly on teleosts, though also squid and, to a lesser extent,
crustaceans (Chase, 2002; Sarà and Sarà, 2007; Karakulak et al., 2009; Logan et al.,
2011). A similar feeding pattern has been found in the two co-generic bluefin tuna
species, T. orientalis (Pinkas, 1971) and T. maccoyii (Young et al., 1997).
The accurate determination of diet composition in Thunnus from direct stomach
content analyses is hampered by a number of constraints: i) the efficient and rapid
ϯϲ
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digestion process (Aloncle and Delaporte, 1970), favoured by visceral warming (Carey
et al., 1984; Graham and Dickson, 2001; Itoh et al., 2003), which often results in a high
percentage of empty, or nearly empty, stomachs (Chase, 2002); ii) the uneven
digestion rates of different prey, which may bias final conclusions, and iii) the stressinduced regurgitation that sometimes happens during capture (Chase, 2002). For these
reasons, the analysis of carbon and nitrogen stable-isotope ratios (į13C and į15N) as
dietary markers is a powerful complementary tool to the study of bluefin tuna feeding
ecology. Furthermore, isotopic analyses allow a long-term integrated measure of diet
compared to the extremely short timescale provided by direct prey identification from
gut contents (McKechnie, 2004; Logan et al., 2011).
The natural abundance of carbon and nitrogen stable isotopes present in the
tissues of animals allow researchers to investigate the origin and pathways of organic
matter from primary producers to intermediate and top-level consumers, since isotopes
are transferred throughout the food web following a stepwise pattern of enrichment
(DeNiro and Epstein, 1978, 1981; Minagawa and Wada, 1984; Fry, 1988), usually
referred to as discrimination factor (Martínez del Río et al., 2009). In practice, the
overall relative increase in nitrogen stable isotope values between consumers and their
diet (ǻ15N) is usually assumed to be 3-4‰ per trophic level (DeNiro and Epstein, 1981;
Minagawa and Wada, 1984; Fry, 1988; Sweeting et al., 2007a), while the į13C value
changes much less as carbon moves through food webs, the average ǻ13C being
estimated as < 1‰ (DeNiro and Epstein, 1978; Peterson and Fry, 1987; Post, 2002).
Hence, į13C signatures are typically used to infer primary sources of carbon, whereas
į15N provides a more robust measure of the relative trophic position of an organism
(Fry, 1988; Vander Zanden and Rasmussen, 1999; Post, 2002). Nevertheless, the
isotopic signatures should be referred to appropriate isotopic baselines to reliably
identify dietary carbon sources or positions in food chains (Post, 2002). Reliable
estimations of discrimination factors are also required to calculate trophic position in
the food web (Caut et al., 2009). The stable-isotope methodology may help resolve
ϯϳ
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complex questions in animal trophic ecology, but its application is often based on
assumptions that are not tested previously; therefore, the proper interpretation of
stable-isotope patterns in the field demands comparative laboratory experiments to
assess their validity (Gannes et al., 1997; Martínez del Río et al., 2009).
In earlier research on T. thynnus feeding ecology, based on estimates
published for other species, Estrada et al. (2005) and Sarà and Sarà (2007) assumed a
į15N discrimination factor of 3.4‰ per trophic level for white muscle, whereas Logan et
al. (2011) used isotopic discrimination factors of 1.4‰ (ǻ13C) and 1.3‰ (ǻ15N) for liver
tissue. Vizzini et al. (2010) measured stable-isotope composition (į13C and į15N) with
the purpose of investigating the transfer of pollutants from the diet to muscle and liver
tissues of wild and farmed bluefin tuna. However, no experimental research has been
conducted specifically to determine carbon and nitrogen stable-isotope discrimination
factors between bluefin tuna and their prey, though such reference values could be
useful in future trophic ecology studies.
As į13C values are generally lower in samples that are rich in fat tissue, lipid
extraction or mathematical corrections are commonly, but not always, performed to
reduce variability due to differences in the lipid content of the samples (Sotiropoulos et
al., 2004; Trueman et al.; 2005; Murry et al., 2006; Sweeting et al., 2006; Ingram et al.,
2007; Post et al., 2007; Logan et al., 2008; Logan, 2009; Hoffman and Sutton, 2010).
Pretreatment of samples to remove lipids seems to be particularly relevant in the case
of migratory fishes such as the bluefin tuna, because their fat reserves are known to
vary markedly depending on seasonality, migratory patterns, foraging success and life
cycle stages (Medina et al., 2002; Mourente et al., 2002; Abascal et al., 2004;
Goldstein et al., 2007; Golet et al., 2007; Goñi and Arrizabalaga, 2010), so that
fluctuations of the lipid content can significantly affect the results of stable-isotope
analyses (Logan, 2009).
The present study was undertaken to determine the discrimination factors for
carbon and nitrogen stable-isotope ratios (į13C and į15N) in Atlantic bluefin tuna (T.
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thynnus) relative to a common prey in the east Atlantic and Mediterranean Sea, the
Atlantic chub mackerel (Scomber colias Gmelin, 1789). We analyzed samples of white
muscle, which is the most commonly used (Vanderklift and Ponsard, 2003;
Sotiropoulos et al., 2004; Post et al., 2007), and probably the most suitable (Pinnegar
and Polunin, 1999), tissue for ecological studies based on stable-isotope analysis in
fishes. In addition, we assessed the impact of the fat content on į13C and į15N values.
Our main goal was to provide reference isotopic data that may be of use in further
studies designed to reconstruct diets and elucidate trophic relationships of bluefin tuna.
2.2. Material and methods
2.2.1. Animals and sampling
Wild-caught Atlantic bluefin tuna (Thunnus thynnus) were maintained in a sea
cage of a commercial farm located off the coast of Almería, Southeast of Spain. The
tuna were captured by purse seine around the Balearic Islands, Spain, in late Mayearly June, 2009, and transferred into towing cages where they were transported over
11-25 days to the farm facility. The fish were not fed during the transportation. Upon
arrival, approximately 1000 individuals were herded into one of the grow-out cages of
the farm, which measured about 90 m in diameter and 25 m in depth. Over a period of
3 to 5 months, the fish were fed baitfish to satiation once a day, six days a week. The
only baitfish species used throughout the rearing was Atlantic chub mackerel, Scomber
colias. The chub mackerel (averaging 22 cm in straight fork length and 130 g in body
mass) were caught from nearby fishing grounds in the Alborán Sea and Gulf of Cádiz
(southern coast of Spain).
Samples of white muscle from tuna and prey were frozen and stored at -20 ºC
until isotopic analysis. Two samples of mackerel were collected from each of the 12
different bait batches supplied throughout the experiment. Tuna muscle samples were
removed from the caudal peduncle of nine fish (average body mass, 117 kg) that died
in the purse seine during capture on May 25 and June 11, 2009 (hereafter referred to
ϯϵ
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as June samples). Afterwards, ten samples were taken from farm-reared fish
(averaging 280 kg in body mass) on September 29, 2009, and another 14 samples
from farmed fish (average body mass 200 kg) were collected on November 23, 2009.
The sampling dates were subjected to commercial slaughters scheduled according to
market demands.
2.2.2. Sample treatment
The white muscle samples were thawed, rinsed with distilled water, transferred
to glass vials and freeze-dried for at least 48 h. Each lyophilized sample was ground to
powder and then split into two subsamples, one of which (bulk muscle sample) was
prepared directly for į13C and į15N analysis as indicated below. The other subsample
(lipid-extracted muscle sample) was subjected to total lipid extraction prior to the
isotopic analysis, according to a modification of the method of Bligh and Dyer (1959).
The subsample was placed in 5 ml of a chloroform-methanol solution (2:1 v/v) for 10
min, and then centrifuged at 1500 g for 5 min. Afterwards, the supernatant was
removed with a glass Pasteur pipette and placed in glass vials. After evaporation of the
solvent at 40 ºC for 24-48 h, the lipid fraction was weighed to the nearest 0.0001 g. The
lipid content (%) was then calculated on a dry weight basis (lipid mass × 100 / total
lyophilized muscle mass). Finally, the lipid-extracted fraction of each sample was dried
for 24 h at 40ºC for further į13C and į15N analysis.
2.2.3. Stable isotope analysis
Aliquots (0.9-1.1 mg) of bulk and lipid-extracted samples were placed into tin
capsules. The content of
13
C and
15
N was measured by continuous gas flow system
using a Thermo Finnigan Elementary Analyzer Flash EA 1112 coupled to a Finnigan
MAT Delta Plus mass spectrometer. The relative abundances of
13
C and
15
N were
reported as isotopic ratios (parts per thousands, ‰) relative to standards:
įX = [(Rsample / Rstandard) – 1] × 1000
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where X is
13
C or
15
N, R is the ratio
13
C/12C or
15
N/14N, and standard refers to Vienna
Pee Dee Belemnite carbonate for į13C and atmospheric nitrogen for į15N (Peterson
and Fry, 1987). Values for C:N are reported as the percent weight ratio corrected for
element atomic mass (14 %C / 12 %N).
The carbon and nitrogen stable isotopic ratio discrimination for each bluefin
tuna muscle sample in relation to the prey (ǻX i) was calculated (eq. 1) as:
ǻX i = įXi Tt – (ȈįXi Sc / 24)
where įXi
Tt
(1)
represents the stable isotopic ratio (carbon or nitrogen) of the tuna (T.
thynnus) muscle samples and ȈįXi Sc / 24 corresponds to the mean isotopic ratio of the
prey (S. colias). The average discrimination factor for each stable isotope was then
Q
determined as
¦ ǻX .
i
L=
2.2.4. Statistical analysis
Values of į13C, į15N and C:N were compared between bulk and lipid-extracted
samples using paired-sample t-tests. The strength of relationships between two
variables was measured by Pearson’s correlation coefficient, r, and simple regression
analyses were used to evaluate these relationships. Comparisons of means of the
different variables throughout the duration of the experiment were carried out by oneway analysis of variance (ANOVA) followed, where relevant, by Gabriel's post-hoc test.
When data did not meet the assumption of variance homogeneity (Levene’s test), the
Kruskal-Wallis test was applied to analyse differences among samplings; MannWhitney U-test was used to locate differences between pairs of sample groups.
Differences in ǻ13C and ǻ15N between the samples of September and November were
analysed by Student’s t-test. A repeated measures ANOVA was used to test for
potential differences among the four discrimination factors that were obtained for each
experimental subject depending on whether consumer and prey muscle tissues were
treated for lipid removal; it was then followed by paired t-tests with Bonferroni
ϰϭ
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corrections. A significance level of Į = 0.05 was considered in all statistical tests.
Statistical analyses were performed using SPSS version 15.0 (SPSS. Inc, Chicago, IL).
2. 3. Results
In all bluefin tuna sample groups, extraction of lipids from muscle resulted in
significantly increased values of į13C and į15N (Table 2.1) (paired-sample t-test, P <
0.01). In the wild-caught tuna, the mean (±SD) increments were 0.98‰ (±0.63‰) for
į13C and 0.38‰ (±0.12‰) for į15N, whereas in the farmed tuna (pooled data of
September and November) the differences were higher: 2.33‰ (±0.67‰) and 0.63‰
(±0.18‰), respectively. C:N ratio values were significantly lower in the lipid-extracted
samples (Table 2.1) (paired-sample t-test, P < 0.01), with mean differences of 0.73
(±0.59) in wild fish and 2.64 (±1.10) in farmed fish.
Table 2.1. Carbon and nitrogen isotopic and elemental ratio analysis of bluefin tuna white
muscle samples.
Lipid content (%)
13
į C
13
į CB
13
(‰)
15
į N
į CLE
13
13
į CLE - į CB
15
į NB
C:N
į NLE
15
September (n =
November (n =
Statistical
9)
10)
14)
tests
12.39
32.57 (±8.65)
b
AN, GT
a
K-W, M-W
-20.89 (±0.71)
-20.52 (±0.57)
-18.25
-18.23 (±0.29)
-18.42 (±0.64)
0.98
2.66 (±0.50)
b
11.11 (±0.30)
a,b
11.83 (±0.28)
b
b
a,b
į NLE - į NB
C:NB
0.38
4.57
0.72 (±0.16)
b
7.15 (±1.37)
C:NLE
3.84
4.13 (±0.14)
C:NB - C:NLE
27.38 (±4.32)
-19.23
10.89
15
b
a
10.51
15
(‰)
June (n =
0.73
3.03 (±1.26)
b
2.10 (±0.70)
b
AN
AN, GT
11.27 (±0.20)
b
K-W, M-W
11.83 (±0.23)
b
K-W, M-W
b
0.57 (±0.18)
b
6.81 (±0.90)
AN, GT
AN, GT
4.45 (±0.52)
b
AN, GT
2.36 (±0.92)
b
AN, GT
Carbon and nitrogen stable-isotope and elemental ratio analysis (mean (±SD)) of bulk (subscript
B) and lipid-extracted (subscript LE) muscle samples from wild tuna (June) and captive tuna
reared on chub mackerel for ~3 months (September) and ~5 months (November). The only
variable that remained unchanged was į13CLE (ANOVA, P=0.67). Mean values within the
same row bearing different superscript letters (abb) are significantly different (Pb0.05). AN,
analysis of variance (ANOVA); GT, Gabriel's post-hoc test; K–W, Kruskal–Wallis test; M–W,
Mann–Whitney U-test.
ϰϮ
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Lipid contents increased significantly between June and September, but did not
change significantly from September to November (even though a decreasing trend
was observed between these months). Significant increases were also observed
between June and September in į15NLE, į13CLE- į13CB and į15NLE- į15NB, whereas
į13CLE was the only isotopic variable that remained unchanged in the course of the
experiment. Likewise, the isotopic data were found to be statistically indistinguishable
between the samplings of September and November (Table 2.1).
C:N and į13C were strongly correlated in bulk (r = 0.96, P < 0.001) and lipidextracted muscle samples (r = 0.93, P < 0.001). Thus, C:N became a good predictor of
į13C through linear functions (P < 0.001) in both bulk (į13CB = -0.56 C:NB – 16.77, r2 =
0.92, P < 0.001) and lipid-extracted (į13CLE = -1.13 C:NLE – 13.48, r2 = 0.86, P < 0.001)
samples (Fig. 2.1A,B). No significant correlation was found, however, between C:N and
į15N either in untreated or lipid-extracted samples (P > 0.1) (Fig. 2.1C,D). A significant
linear relationship (r2 = 0.41, P < 0.001) was also observed between C:NB and the
increase in į13C following lipid extraction (į13CLE – į13CB = 0.39 C:NB – 0.39) (Fig.
2.1E). Nevertheless, the relationship between data of C:NB and į15NLE – į15NB showed
a poor fit to the linear model (r2 = 0.17, P = 0.04) (Fig. 2.1F). The regression between
non-extracted and lipid-extracted muscle isotopic ratios (Fig. 2.1G,H) was not
significant for į13C (P = 0.09) but was significant for į15N (į15NLE = 0.71 į15NB + 3.83,
r2 = 0.53, P < 0.001). The lipid content did not correlate with į15NB (P = 0.06) (Fig.
2.2C), but was correlated positively with C:NB and negatively with į13CB (P < 0.01),
though the regression analyses produced poor fits (Fig. 2.2A,B).
The į13C and į15N discrimination factors (ǻ13C and ǻ15N) from prey to bluefin
tuna muscle were estimated using data of isotope ratios of both lipid-extracted and bulk
tuna samples collected in September and November (Table 2.2). Similarly, untreated
as well as lipid-extracted muscle samples of Atlantic chub mackerel were used in the
calculations. The mean (± SD) lipid content of the prey samples was 6.88% (± 3.91%),
and the mean (± SD) values obtained for į13CB, į15NB and C:NB were, respectively,
ϰϯ
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-18.55‰ (±0.59‰), 9.63‰ (±0.36‰) and 3.84 (±0.24) (data not shown on tables).
Significant differences were observed in these variables after chloroform-methanol
treatment (paired-sample t-test, P < 0.01), with measurements of -18.26‰ (±0.46‰),
9.77‰ (±0.37‰) and 3.71 (±0.13) for į13CLE, į15NLE and C:NLE, respectively. For the
calculation of discrimination factors, the expression “ȈįXi
/ 24” in eq. 1 was
Sj
substituted by the relevant į13C and į15N above values.
A
B
-17
į CLE (‰)
y = -0,56x - 16,77
R2 = 0,92
-20
-21
-18
-19
13
13
į C B (‰)
-19
-22
-23
5
6
7
9
-21
10
3,5
4
4,5
C:NB
C
12
8
y = -1,13x - 13,48
R2 = 0,86
-20
5
5,5
6
5
5,5
6
C:NLE
D
12,4
į NLE (‰)
į NB (‰)
11,6
12
15
15
11,2
10,8
11,6
11,2
10,4
5
6
7
8
9
3,5
10
4
4,5
C:NLE
C:NB
į NLE- į NB (‰)
15
3
2
y = 0,39x - 0,39
R2 = 0,41
1
15
13
13
į C LE- į C B (‰)
F
1
E
4
0,8
0,6
y = 0,07x + 0,15
R2 = 0,17
0,4
0,2
0
5
6
7
8
9
5
10
7
8
9
10
C:NB
C:NB
F
12,8
-17
G
y = 0,71x + 3,83
R2 = 0,53
12,4
į NLE (‰)
-18
12
15
-19
13
į C LE (‰)
6
11,6
-20
-21
-22,5
-22
-21,5
-21
13
į C B (‰)
-20,5
-20
-19,5
11,2
10,6
10,8
11
11,2
11,4
11,6
11,8
15
į NB (‰)
Figure. 2.1. Relationships between different isotopic variables, and between isotopic variables
and C:N ratios. Subscripts B and LE refer to bulk and lipid-extracted samples, respectively.
Non-significant regressions are not shown in the plots.
ϰϰ
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A
B
-19
11,0
y = -5,27x - 19,12
R2 = 0,31
-20
-21
13
C:NB
į CB (‰)
9,0
7,0
y = 8,67x + 4,39
R2 = 0,29
-22
-23
5,0
0,1
0,2
0,3
0,4
0,5
0,1
0,2
0,3
Lipid fraction
0,4
0,5
Lipid fraction
C
12
11,2
15
į NB (‰)
11,6
10,8
10,4
0,1
0,2
0,3
0,4
0,5
Lipid fraction
13
15
Fig. 2.2. Relationships of C:N ratio, į C and į N of bulk samples with lipid fraction. Only the
significant regressions are shown.
Four distinct discrimination factor estimates for carbon (ǻ13C) and nitrogen (ǻ15N)
stable isotopes were calculated using all possible combinations of bulk and lipidextracted muscle data from predator and prey (Table 2.2). None of the mean ǻ13C and
ǻ15N estimates were found to be significantly different between the samples of
September and November (Student’s t-test, P > 0.1). Considering the pooled data of
both months, repeated-measures ANOVA revealed significant differences among the
different estimates of the discrimination factor for both į13C and į15N (P < 0.001).
Subsequent pairwise comparisons showed that the lipid-extracted bluefin tuna samples
yielded significantly higher ǻ13C and ǻ15N values, but the pretreatment of prey samples
for lipid removal did not significantly affect the resulting discrimination factors of either
į13C or į15N (Table 2.2). The mean ǻ13C value obtained from lipid-extracted samples in
November was -0.16‰ and the ǻ15N estimated from untreated samples was 1.64‰.
These values could be used as estimates of bluefin tuna muscle discrimination factors.
ϰϱ
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Table 2.2. Prey-muscle 13C and 15N discrimination factor estimates.
13
15
ǻ C (‰)
13
13
ǻ N (‰)
13
13
15
15
15
15
Sampling
ǻ CB-b
ǻ CB-le
ǻ CLE-b
ǻ CLE-le
ǻ NB-b
ǻ NB-le
ǻ NLE-b
ǻ NLE-le
September (n = 10)
-2.34 (±0.71)
-2.63 (±0.71)
0.33 (±0.29)
0.03 (±0.29)
1.48 (±0.30)
1.34 (±0.30)
2.20 (±0.28)
2.06 (±0.28)
November (n = 14)
-1.96 (±0.57)
-2.26 (±0.57)
0.13 (±0.64)
-0.16 (±0.64)
1.64 (±0.20)
1.49 (±0.20)
2.20 (±0.23)
2.06 (±0.23)
Pooled data (n = 24)
-2.12 (±0.65)
a
-2.42 (±0.65)
a
0.21 (±0.52)
b
-0.08 (±0.52)
b
1.57 (±0.25)
a
1.43 (±0.25)
a
2.20 (±0.25)
b
2.06 (±0.25)
b
Distinct discrimination factor values (ǻ13C and ǻ15N), expressed as means (±SD), were calculated using the four possible combinations of bulk (subscripts B
and b) and lipid-extracted (subscripts LE and le) muscle samples from tuna (capital subscripts) and chub mackerel (lower-case subscripts). Bold values are
considered the most suitable estimations for each isotope discrimination factor. Repeated measures ANOVA performed on the pooled data (lower row)
revealed significant differences among the discrimination factors obtained for each experimental subject (P < 0.001); significant differences between means
(values bearing different superscript letters, a < b) were subsequently located by paired t-test procedure applying Bonferroni correction of the significance
level.
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2.4. Discussion
In recent years there has been a growing use of stable-isotope analysis in
animal diet reconstructions, but the accurate interpretation of trophic ecology based on
isotopic models requires laboratory experiments to validate the technique and
determine key parameters such as diet-tissue discrimination factors (Gannes et al.,
1997; Caut et al., 2008, 2009; Martínez del Río et al., 2009). Although laboratory-based
feeding experiments with Atlantic bluefin tuna (Thunnus thynnus) are impractical, the
farming industry allows specimens to be maintained in sea cages under semicontrolled
conditions,
thus
offering
the
opportunity
to
conduct
comparative
experimental research. Rearing tuna specimens on a single prey enabled us to assess
the impact of the fat content on the measured levels of carbon and nitrogen stable
isotopes and estimate their diet-consumer muscle discrimination factors.
2.4.1. Lipid extraction
Because lipids are naturally depleted in
13
C compared to proteins and
carbohydrates, significant variations in lipid content result in biased carbon stableisotope analyses (DeNiro and Epstein, 1978). To counteract the effect of lipid content
variability, either direct lipid extraction or mathematical normalization are frequently
applied in į13C analyses (Pinnegar and Polunin, 1999; Sweeting et al., 2006; Post et al.,
2007; Logan et al., 2008; Logan, 2009; Martínez del Río et al., 2009; Hoffman and
Sutton, 2010). In some instances, however, the relevance of applying mathematical
corrections has been questioned (Kiljunen et al., 2006; Post et al., 2007). In fishes,
removal of lipids from white muscle samples does frequently cause significant
increases in į13C (Pinnegar and Polunin, 1999; Sotiropoulos et al., 2004; Murry et al.,
2006; Logan et al., 2008; Logan, 2009; Hoffman and Sutton, 2010). The mean
differences (ranging from 0.3 to 2.0‰) between lipid-extracted and bulk muscle
samples reported for a variety of fish species (Pinnegar and Polunin, 1999;
ϰϳ
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Sotiropoulos et al., 2004; Logan et al., 2008; Elsdon et al., 2010) are consistent with
the į13C shift we observed in wild bluefin tuna (0.98‰). Nevertheless, this range is
lower than the mean į13C change obtained following lipid extraction in samples from
cage-reared tuna (2.33‰). This finding is not surprising because the lipid content
measured in the samples of September and November (i.e., farmed fish fed to satiation
for 3-5 months) was much higher than that recorded in samples from wild-caught
specimens. The efficacy of lipid extraction in reducing the į13C variability associated
with varying fat content is reflected by the fact that į13CLE remained consistent
throughout the three samplings, while į13CB decreased markedly in association with the
lipid content between the first sampling (wild fish) and the two subsequent ones (cagereared fish).
Similar variations in muscle lipid content were reported by other authors in
cage-reared bluefin tuna between June-July and September (Giménez-Casalduero and
Sánchez-Jerez, 2006; YerlȚkaya et al., 2009). It is well known that bluefin tuna lipid
stores are depleted during spring-summer as a result of high energy expenditure linked
to long-distance reproductive migration and gametogenesis (Medina et al., 2002;
Mourente et al., 2002; Abascal et al., 2004; Goldstein et al. 2007). After the breeding
season, bluefin tuna move to energy-rich foraging grounds to restore lipid reserves
(Rodríguez-Roda, 1964; Cort, 1990; Mather et al., 1995; Block et al., 2001), part of
which are accumulated in fat tissue between muscle myomeres (Mourente and Tocher,
2009).
C:N was a good predictor of į13C in both bulk (r2 = 0.92) and lipid-extracted (r2 =
0.86) samples. However, although C:N is generally highly correlated with the į13C
positive shift due to lipid extraction in aquatic animals (Post et al., 2007; Logan et al.,
2008; Hoffman and Sutton, 2010), in the present study the regression analysis showed
that C:NB accounted for only 41% of the interindividual variance of į13CLE - į13CB. In
addition, while C:N is thought to be highly dependent on the lipid content of muscle
samples, the muscle lipid fraction of our cage-reared Atlantic bluefin tuna explained
ϰϴ
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only 29% of the C:NB variation. In contrast, Logan (2009) observed a strong linear
relationship between C:NB and % lipid (r2 = 0.966) in wild Atlantic bluefin tuna. In view
of these results, the application of regression models in our experimental design does
not appear to be as reliable for predictions of lipid extraction-dependent į13C
differences as it has been found in previous interspecific studies (Sweeting et al., 2006;
Pinnegar and Polunin, 1999; Post et al., 2007; Logan et al., 2008; Logan, 2009;
Hoffman and Sutton, 2010). Although Fagan et al. (2011) report results similar to ours
for lake whitefish, the unexpected poor correlation between tissue C:NB and lipid
content could also indicate incomplete lipid removal. Iverson et al. (2001) showed that
the effectiveness of different chloroform-methanol extraction methods may significantly
differ in samples containing >2% lipid. Incomplete lipid removal would also explain the
clear correlation found between C:NLE and į13CLE values (Fig. 2.1B) as opposite to the
expected slope close to zero in the regression C:NLE vs. į13CLE; such a bias would
influence the carbon discrimination factor values, thus causing artificially low estimates
in extracted samples.
Lipid-extraction treatments commonly used in analyses of į13C in fish muscle
samples may also cause significant (though generally weak: 0.36‰-1‰) shifts in į15N
(Pinnegar and Polunin, 1999; Sotiropoulos et al., 2004; Murry et al., 2006; Sweeting et
al., 2006; Ingram et al., 2007; Elsdon et al., 2010). Logan (2009) calculated the
alteration of į15N linked to chemical lipid extraction to be 0.7‰ in Atlantic bluefin tuna
muscle. Our results are consistent with all the above observations, as we measured
mean į15N increments of 0.38‰ in the June samples (wild fish) and 0.63 ‰ in the
pooled samples of September and November (cage-reared fish). C:N showed no
significant linear relationship with į15N in bulk or lipid-extracted samples, and the lipid
content did not correlate with į15NB. These results seem to indicate that the
measurements of į15N ratios are less dependent on the amount of lipid than those of
į13C.
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Unlike the processes responsible for increased į13C following lipid extraction,
the mechanisms involved in the positive shifts of į15N values resulting from lipid
extraction treatments are not yet fully understood. Solvents that are commonly used to
extract polar lipids, such as methanol, can also remove proteins attached to structural
fats, thus leading to altered į15N measurements (Sotiropoulos et al., 2004, Sweeting et
al., 2006; Ingram et al., 2007). As argued by Ingram et al. (2007), extraction of
structural polar proteins along with membrane lipids would lead to increased į15N only
if such proteins are significantly depleted in
15
N in relation to other nitrogenous
compounds, in which case į15NLE - į15NB and C:NB would be positively correlated. In
support of this, and unlike Ingram et al. (2007), we found a weak but significant positive
correlation (P = 0.02, r = 0.42) between those variables. Another plausible cause for
increases in į15N with lipid extraction is that nitrogenous waste products of protein
metabolism (namely, ammonia and ammonium), which are soluble in lipids, can be
leached by organic solvents (Murry et al., 2006; Ingram et al., 2007). Given the
unpredictable effects of chemical lipid extraction on į15N as well as the uncertainties
about their underlying biological causes, we take on here the recommendation of many
authors (Sotiropoulos et al., 2004; Murry et al., 2006; Sweeting et al., 2006; Logan et
al., 2008; Elsdon et al., 2010) and believe that lipid-extracted samples should be used
for the estimation of the į13C discrimination factor from prey to bluefin tuna muscle,
whereas bulk samples are more suitable for the analysis of į15N fractionation.
2.4.2. Discrimination factors
A turnover of ~94% in 5 months has been estimated for white muscle of juvenile
yellowfin tuna, Thunnus albacares (Olson et al., 2010). Therefore, though we were not
able to design programmed samplings to make accurate calculations of isotopic
turnover rates for bluefin tuna muscle, a rearing time of approximately 5 months
appears to be long enough to allow reliable estimations of stable-isotope discrimination
factors. Our study did reveal significant differences in the data of isotopic composition
ϱϬ
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between June (wild tuna) and September (cage-reared tuna fed chub mackerel), but
these values were statistically indistinguishable between the tuna sampled in
September and November. This appears to indicate that the isotopic data recorded
three months after the diet switch were close to the isotopic equilibrium with the prey.
Removal of lipids from bluefin tuna muscle resulted in significantly different ǻ13C
and ǻ15N estimates in relation to untreated samples. However, the extraction of lipids
from prey samples did not significantly change the results obtained with bulk samples.
Therefore, in the present case where prey showed low lipid contents compared to tuna,
lipid extraction from prey samples was apparently not as critical as lipid extraction from
consumer samples for į13C discrimination factor calculations, but this result should not
be generalized across taxa. Our best estimate of the į13C discrimination factor
between prey and bluefin tuna muscle was -0.16‰, which was calculated from lipidextracted samples 5 months after the diet switch. The discrimination factor calculated
for į
13
N was 1.64‰, which was obtained using bulk samples. A į13C trophic
fractionation per trophic level near to zero is widespread in animals (DeNiro and
Epstein, 1978; Peterson and Fry, 1987; Post, 2002), though recent studies have
suggested higher į13C discrimination factor for fishes (Sweeting et al., 2007b). In
contrast, the discrimination factors reported for į15N are more variable (DeNiro and
Epstein, 1981; Minagawa and Wada, 1984; Vanderklift and Ponsard, 2003).
As experimental measurements of discrimination factors are difficult to perform
in many species like tunas, estimates made in other species are often taken from
published reviews. This can lead to strongly biased results in isotopic studies (Caut et
al., 2009). In a trophic study on bluefin tuna, Estrada et al. (2005) applied the widely
assumed 3.4‰ discrimination factor for į15N reported by Minagawa and Wada (1984)
in white muscle. Olson et al. (2010) assumed a diet-muscle į15N discrimination factor
of 2.4‰ for yellowfin tuna, based on the mean value of marine fishes as reviewed by
Vanderklift and Ponsard (2003). Logan et al. (2011), based on data from other fish
species (Sweeting et al., 2007a), used a discrimination factor of 1.3‰ for young bluefin
ϱϭ
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tuna liver, which is closer to our estimated ǻ15N value of 1.64‰. In farmed bluefin tuna
reared on round sardinella, Sardinella aurita, Vizzini et al. (2010) found a remarkably
similar į15N discrimination factor (1.7‰) of tuna muscle in relation to the diet. The dietmuscle į15N discrimination factor (1.6‰) obtained by Bode et al. (2007) in tank-reared
sardine (Sardina pilchardus) was also similar to our estimates. These estimations are
far lower than the 3-4‰ value that is commonly used when experimental species- and
tissue-specific assessments of ǻ15N are unavailable.
The discrimination factors derived from this study likely do not reflect the total
variability occurring in natural populations. Rapid growth rates in captive fish can
potentially result in decreased ǻ15N (Trueman et al., 2005), while other factors like
temperature (Barnes et al., 2007), diet (Gaye-Siessegger et al., 2003) and metabolic
rate (Gaye-Siessegger et al., 2004) have been found to affect į13C and į15N values.
Nevertheless, we believe that, unless experimental assessments suited to the specific
field data are available, the diet-white muscle discrimination factor values obtained
here (ǻ13C = -0.16‰, ǻ15N = 1.64‰) should be applied in diet and food-web
investigations
involving
stable-isotope
analysis
of
bluefin
tuna
and
other
phylogenetically related tuna species. Studies using other tissue types (e.g., liver,
scales, blood, etc.) would require further experimental research to determine tissuespecific discrimination factors.
Acknowledgments
The authors are deeply indebted to Antonio Bode (Spanish Institute of Oceanography,
A Coruña, Spain) and John Logan (Massachusetts Division of Marine Fisheries,
U.S.A.) who helped us assimilate isotopic techniques and principles. We also wish to
thank Ángel Luis Fernández (Nature Pesca, S.L., Garrucha, Spain) for graciously
providing farm facilities and valuable assistance in samplings, Grup Balfegó for wild
tuna samples, and María Lema (Unidad de Técnicas Instrumentales de Análisis,
Universade da Coruña) for isotopic analysis. This work was granted by the Spanish
ϱϮ
&KDSWHUĵ&DQGĵ1GLVFULPLQDWLRQIDFWRUVLQEOXHILQWXQD
Ministry of Science and Innovation (MICINN, CTM2007-65178-C02-01/MAR), Junta de
Andalucía (RNM-02469) and Red Eléctrica de España - Fundación Migres.
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3. CHAPTER 2.
13
C and
15
N analysis in muscle and liver of wild and
reared young-of-the-year (YOY) Atlantic bluefin tuna
Abstract
Stable isotope analysis (SIA) has become a useful tool in trophic ecology
research, but experimental studies are essential for their correct application in field
studies. Young-of-the-year (YOY) Atlantic bluefin tuna (ABFT) caught in the
Mediterranean Spanish coast were reared on a common natural prey to determine
prey-muscle and prey-liver discrimination factors. Significantly increased isotopic
values were found in lipid-extracted tuna samples of muscle and liver versus bulk
samples, while C:N values decreased following lipid extraction. Such isotopic
differences were paralleled by differences in the discrimination factors estimated for
į13C (ǻ13C) in muscle and liver, though ǻ15N was apparently unaffected by lipid
removal. Isotopic differences were observed between muscle and liver for wild ABFT,
which was probably due to different turnover rates. The ǻ13C estimates obtained from
lipid-extracted samples of muscle and liver were 0.32±0.04 and 0.39±0.23,
respectively. ǻ15N values estimated from bulk samples were 1.46±0.06 for muscle and
1.62±0.22 for liver.
Key words: stable isotope analysis, juvenile Thunnus thynnus, trophic markers, diet-tissue
discrimination factors, lipid extraction
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3.1. Introduction
In the last decades stable isotope analysis (SIA) is emerging as an important
tool in ecology studies. In particular, stable isotope ratios of carbon (13C/12C, į13C) and
nitrogen (15N/14N, į15N) are often used to solve questions related to trophic ecology.
SIA can provide trophic information over several weeks or months, depending on the
tissue turnover rate (Gannes et al., 1998). In fishes, slow turnover tissues like muscle
(Hesslein et al., 1993; MacAvoy et al., 2001) may provide information on feeding at
mid-time scale (months), whereas SIA performed in liver, which has a faster metabolic
rate (Suzuki et al., 2005; Guelinckx et al., 2007), may give information at a shorter time
scale (Logan et al., 2006; MacNeil et al., 2006). Therefore, knowing the isotopic
signatures of juvenile tuna tissues and their most common preys, dietary proportions
can be estimated using mixing models (Phillips and Gregg, 2001; Moore and
Semmens, 2008). Variations in feeding patterns can thus be traced between the
Balearic breeding ground and the Spanish Mediterranean coast. For the calculation of
dietary proportions from mixing models, prey-consumer tissue discrimination factors
should be accurately estimated a priori. A į15N increase of 3-4‰ is generally assumed
per trophic level (DeNiro and Epstein, 1981; Minagawa and Wada, 1984; Fry, 1988;
Sweeting et al., 2007a), while į13C is assumed to rise around 1‰ (DeNiro and Epstein,
1978; Peterson and Fry, 1987; Post, 2002). In earlier research on Atlantic bluefin tuna
(ABFT) feeding ecology, Estrada et al. (2005) assumed a į15N enrichment rate of 3.4‰
per trophic level in muscle tissue, whereas Logan et al. (2011) used isotopic
discrimination factors of 1.4‰ (ǻ13C) and 1.3‰ (ǻ15N) in ABFT liver based on
estimates made for other fish species by Sweeting et al. (2007a,b). Varela et al. (2011)
reported diet-muscle discrimination factors of 1.64‰ for 15N and -0.16‰ for 13C in adult
ABFT, but no experimental research has been conducted thus far to determine stableisotope discrimination factors between juvenile ABFT and their prey. The main aim of
this study was to provide reference data of į13C and į15N discrimination factors in
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muscle and liver of young-of-the-year (YOY) ABFT, which can be useful in trophic
ecology studies at early stages of the species’ life span.
3.2. Material and methods
3.2.1. Animals and sampling
YOY ABFT (n = 350), averaging 35 cm in fork length and 830 g in body mass,
were caught by trolling using barbless hooks off the coast of Cartagena (Murcia, SE
Spain) in September and October, 2009 (Fig. 3.1). Live fish (n = 254) were placed into
1200-L cylindrical plastic tanks and transported onboard to El Gorguel (Cartagena, SE
Spain), where they were transferred to a circular off-shore cage measuring 25 m in
diameter and 20 m in depth. 50% of the fish died during capture and transport
operations (Fig 4.1)
Figure 3.1. Young-of-the-year (YOY) Atlantic bluefin tuna (ABFT) were collected
and reared off Cartagena, SE Spain (Ɣ) in September and October, 2009
Over a period of 3 months, the fish were fed baitfish to satiation once a day, six
days a week. The bait supplied consisted of only round sardinella, Sardinella sp.
(averaging 8.87 cm in fork length and 5.98 g in body mass), which were collected from
ϲϱ
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the same area as the YOY ABFT were captured (Fig. 1). A previous study showed that
Sardinella accounted for over 50% by mass of the stomach contents from YOY ABFT
sampled in September-October, 2009 (Varela et al., 2010).
Samples of white muscle removed from the caudal peduncle and liver were
taken from ten tuna that died on capture. Sardinella sp. muscle samples were collected
from the different bait batches supplied throughout the experiment. Upon completion of
the experiment (January 23, 2010) samples were taken from five cage-reared juvenile
tuna (averaging 45.76 cm in fork length and 1891.60 g in body mass). All samples were
frozen and stored at -20 ºC until analysis.
Figure 3.2. Capture and transport of Young-of-the-year (YOY) Atlantic bluefin tuna (ABFT)
collected and reared off Cartagena.
3.2.2. Sample treatment
Tissue samples were thawed, rinsed thoroughly with distilled water, transferred
to glass vials and lyophilized for 48 h. Each frozen-dried sample was ground to powder
and then separated into two subsamples, one of which (bulk sample) was used as such
ϲϲ
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for C:N analysis (C:NB) and SIA (į13CB and į15NB). The other subsample (lipidextracted sample) was subjected to three cycles of lipid extraction with chloroformmethanol prior to the analyses (C:NLE, į13CLE and į15NLE) and the lipid content was
determined by gravimetric methods as described in Varela et al. (2011).
3.2.3. Stable isotope analysis (SIA)
Aliquots (~1 mg) of bulk and lipid-extracted samples were placed into tin
13
capsules. The relative abundances of
C and
15
N were measured as į13C and į 15N
(Peterson and Fry, 1987) by continuous gas flow system using a Thermo Finnigan
Elementary Analyzer Flash EA 1112 coupled to a Finnigan MAT Delta Plus mass
spectrometer. The relative abundances of
13
C and
15
N were reported as isotopic ratios
(parts per thousands, ‰) relative to standards:
įX = [(Rsample / Rstandard)-1] x 1000
where X is
13
C or
15
N, R is the ratio
13
C/12C or
15
N/14N, and standard refers to Vienna
Pee Dee Belemnite carbonate for į13C and atmospheric nitrogen for į15N (Peterson
and Fry, 1987). Precision of either C or N isotopic determinations was better than 0.03
‰.
C:N values are reported as the percent weight ratio corrected for element atomic mass
(14% for C and 12 % for N). The prey-tissue carbon and nitrogen stable isotopic ratio
discrimination was calculated as the difference of į13C and į 15N values between the
tuna tissues and the bait muscle.
3.2.4. Statistical analysis
Potential significant differences in the values of į13C, į15N and C:N between
bulk and lipid-extracted samples were tested by paired-sample t-tests. Student’s t-test
was used to assess whether the differences between white muscle and liver data
means were statistically different from each other. Repeated measures analysis of
variance (ANOVA) was performed to test the occurrence of differences among the four
ϲϳ
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discrimination factors calculated per individual combining the lipid-extracted and bulk
samples from consumer and diet; where relevant, significant differences between
means were identified by paired-t tests with Bonferroni corrections. A significance level
of Į = 0.05 was chosen for all statistical tests. Statistical analyses were performed
using SPSS v. 15.0 (SPSS Inc, Chicago). Data are expressed as mean ± 1SD.
3.3. Results
After rearing for 90 days on Sardinella sp., YOY ABFT experienced weight and
length increases of 125% and 30%, respectively. Concomitantly, the total lipid content
in muscle and liver increased from 8.48±0.02% to 13.34±0.02% and from 14.36±1.97%
to 41.27±6.27%, respectively.
The mean lipid content of round sardinella muscle was 5.67±0.85%. No
significant differences between lipid-extracted and bulk bait muscle (Table 3.1) were
observed in į13C and į15N values (paired-sample t-test, P > 0.05). However, a
significant decrease was observed in the C:N ratio following lipid extraction (pairedsample t-test, P = 0.017).
Table 3.1. Comparison of stable isotopic analysis (SIA) and elemental (C:N) analysis
(corrected for element atomic mass) between bulk and lipid-extracted bait (Sardinella
sp.) samples.
Paired-sample t-test,
Bulk muscle
Lipid-extracted muscle
P-value
(n = 5)
(n = 5)
13
-17.85±0.13
-17.59±0.04
=0.066
15
į N (‰)
8.67±0.41
8.89±0.50
=0.117
C:N
3.64±0.01
3.56±0.05
=0.017
į C (‰)
Significantly increased į13C and į15N values were found in lipid-extracted
muscle tuna samples as compared to bulk samples (Table 3.2), with the only exception
of į15N in reared tuna (paired-sample t-test, P = 0.815). C:N values significantly
decreased after lipid extraction (paired-sample t-test, P < 0.05).
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Table 3.2. Variations of the isotopic signatures and C:N values (means ± SD) in bulk (subscript
B) and lipid-extracted (subscript LE) muscle samples of wild-caught and 3-month reared YOY
ABFT.
13
13
į C (‰)
į CB
13
į CLE
Paired-sample t-test, P-value
15
į NB
15
į NLE
Paired-sample t-test, P-value
C:NB
C:N
C:NLE
Paired-sample t-test, P-value
15
į N (‰)
Wild fish
(n = 10)
-17.71±0.18
-17.51±0.23
Reared fish
(n = 5)
-18.70±0.65
-17.27±0.09
=0.011
=0.007
9.01±0.50
9.65±0.46
<0.001
3.57±0.02
3.52±0.03
<0.001
10.13±0.14
10.16±0.21
=0.815
4.83±0.54
3.88±0.07
=0.013
Student’s t-test, Pvalue
=0.026
=0.043
<0.001
=0.057
=0.006
<0.001
The values of į13CLE, į15NB, CNB and CNLE measured in muscle samples of
reared fish were significantly higher than in wild fish (t-test, P < 0.05), whereas į13CB
was higher in wild fish (t-test, P = 0.026). į15NLE was the only isotopic variable that
remained unchanged (t-test, P > 0.057) (Table 3.2).
Lipid extraction also caused significant increases in į13C and į15N values in all
liver samples, except į15N in reared fish (paired-sample t-test, P = 0.628). However,
C:N significantly decreased following lipid extraction (paired-sample t-test, P < 0.001),
the difference between C:NB and C:NLE being much greater in reared tuna than in wild
individuals (Table 3.3). Significantly higher values were found in all the variables
measured in liver of reared fish compared to wild fish (t-test, P < 0.01), with the
exception of į13CB, which was higher in wild individuals (t-test, P <0.001).
Table 3.3. Variations of the isotopic signatures and C:N values (means ± SD) in bulk
(subscript B) and lipid-extracted (subscript LE) liver samples of wild-caught and 3-month
reared YOY ABFT.
Student’s t-test,
Wild fish
Reared fish
P-value
(n = 10)
(n = 5)
13
į CB
-18.88±0.17
-21.28±0.37
<0.001
13
į C (‰)
13
į CLE
-17.92±0.12
-17.20±0.23
<0.001
<0.001
Paired-sample t-test, P-value
<0.001
15
į NB
8.65±0.32
10.29±0.22
<0.001
15
į N (‰)
15
į NLE
8.71±0.31
10.26±0.16
<0.001
Paired sample t-test, P-value
=0.003
0.628
C:NB
4.64±0.23
10.90±1.23
<0.001
C:N
3.93±0.07
4.82±0.29
=0.002
C:NLE
<0.001
Paired sample t-test, P-value
<0.001
ϲϵ
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Statistical analyses detected significant differences in į13CB, į13CLE and į15NLE
between muscle and liver tissues from wild YOY ABFT (compare tables 3.2 and 3.3;
results of these statistical analyses are not shown on tables), with lower values
corresponding to liver (t-test, P < 0.001); however, į15NB values did not differ between
both tissues (t-test, P > 0.142). In the reared fish samples, significant differences
between tissues were observed only for į13CB (t-test, P < 0.001). The C:N ratios
measured in liver were lower than those obtained in muscle (t-test, P < 0.01) in all
tissue samples (both untreated and lipid-extracted) from wild and reared individuals.
Discrimination factors for carbon (ǻ13C) and nitrogen (ǻ15N) stable isotopes
were estimated using combinations of bulk and lipid-extracted tissue data from
consumer and prey (Table 3.4). In both muscle and liver tissues, significant differences
were observed among the different discrimination factor values estimated for į13C
(repeated measures ANOVA, P < 0.01), but ǻ15N was not apparently affected by the
sample treatment (P > 0.05). Subsequent pairwise comparisons (Table 3.4) indicated
that lipid extraction from the tuna samples led to significantly higher values of ǻ13C
(paired t-test with Bonferroni correction, P < 0.05).
13
15
Table 3.4. Prey-tissue C and N discrimination factor estimates (means ± SD) obtained for
YOY ABFT muscle and liver from combinations of bulk (subscripts B and b) and lipid-extracted
(subscripts LE and le) samples from tuna (capital subscripts) and round sardinella (lower-case
subscripts). Bold values are considered the most suitable estimations for each isotope
discrimination factor.
0XVFOH
ǻ&%E
“DE
ǻ&Å
ǻ&%OH
ǻ&/(E
E
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“F
ǻ&/(OH
“DF
ǻ1%E
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ǻ1Å
ǻ1%OH
ǻ1/(E
“ “
ǻ1/(OH
“
/LYHU
ǻ&Å
ǻ1Å
ǻ&%E
ǻ&%OH
ǻ&/(E
ǻ1%OH
ǻ1/(E
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ǻ&/(OH ǻ1%E
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Repeated measures ANOVA detected significant differences among the discrimination factors
13
calculated for į C in both tissues; significant differences between means (values bearing
different superscript letters) were identified by paired t-test procedure applying Bonferroni
corrections of the significance level (P < 0.05).
ϳϬ
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3.4. Discussion
YOY ABFT reared on round sardinella underwent weight and length increases
comparable to those reported by Kataviü et al. (2003) and Tiþina et al. (2007) for
farmed juvenile ABFT averaging ~10 kg in body mass. Their fat content in muscle was
relatively low in comparison with wild-caught (Mourente et al., 2002) and reared adults
(Varela et al., 2011). Previous studies have correlated the fat content of the samples
with differences in į13C values, showing that lipid extraction often causes a rise in į13C
values as a result of the naturally lower content of
13
C in lipids compared to proteins
and carbohydrates (Murry et al., 2006; Logan and Lutcavage, 2008; Hoffman and
Sutton, 2010; Varela et al., 2011). In this study, significant į13C increases were
observed in all tuna samples following lipid extraction. The effect of lipid removal on
į13C was higher in liver than in white muscle, which was probably due to the higher
lipid content of that organ. Similarly, in reared Atlantic salmon (Salmo salar) Trueman
et al. (2005) found that į13C increases following lipid extraction were higher in liver than
in muscle.
Unlike tuna samples, lipid removal did not result in significantly different į13C
values in samples of the baitfish supplied to reared ABFT, e. g., round sardinella
(present study) or chub mackerel (Varela et al., 2011). The lower lipid content of these
samples (< 7%) is the most likely reason for these discrepant results (Pinnegar and
Polounin, 1999).
It is known that chloroform-metanol lipid extraction can alter į15N values of
bluefin tuna samples (Logan and Lutcavage, 2008, Logan, 2009, Varela et al., 2011).
Therefore, bulk samples may be more suitable for the calculation of ǻ15N, whereas
lipid-extracted samples should be used for ǻ13C estimations. Several authors have
suggested that the increase in į15N values following lipid extraction with chloroformmethanol can be caused by removal of some proteins or amino acids bound to polar
lipids (Sotiropoulos et al., 2004, Sweeting et al., 2006, Elsdon et al., 2010). In contrast,
Murry et al. (2006) proposed that į15N shifts after lipid extraction may be due to
ϳϭ
&KDSWHU&DQG1RIZLOGDQGUHDUHGDJHEOXHILQWXQD
leaching of nitrogenous metabolites (ammonia and ammonium) remaining in the tissue.
As į15N shifts induced by lipid extractions are generally weak (0.3‰-1‰), some
authors have questioned the need to analyze į15N and į13C from untreated and lipidextracted samples separately (Ingram et al., 2007).
A good comprehension of the feeding ecology of fish larvae and juveniles is
essential for the understanding of the early life-history and recruitment variability in
fishes (Young and Davis, 1990), since the ability of a fish to find food resources at early
life-history stages can be a major determinant of its recruitment success (Hjort, 1914;
May, 1974; Lasker, 1975). In recent years, the use of SIA in animal diet reconstructions
has experienced a remarkable increase due to the improvement of mixing models
(Phillips and Gregg, 2003; Semmens and Moore, 2008; Erhardt, 2009). Stable isotope
mixing models are based on the potential contribution of different isotopic sources (i.e.,
diet) to an isotopic mixture (i.e., consumer), and require previous knowledge of the
species-, tissue- and diet-specific discrimination factors (Hussey et al., 2010). Some
SIA-based studies on tuna trophic ecology have used discrimination factors from
published data of other species (Estrada et al., 2005; Olson et al., 2010; Vizzini et al.,
2010; Logan et al., 2011). This may cause significant biases in diet estimations,
whereby laboratory experiments have been encouraged in order to accurately
determine specific diet-tissue discrimination factors (Gannes et al, 1997; Caut et al.,
2008, 2009; Wolf et al., 2009). The discrimination factor values estimated in the
present paper for į13C (from lipid-extracted samples) and į15N (from bulk samples) in
muscle (ǻ13C = 0.32±0.04 and ǻ15N = 1.46±0.06) and liver (ǻ13C = 0.39±0.23 and ǻ15N
= 1.62±0.22) are lower than others previously used in adult tunas (Estrada et al., 2005;
Sarà and Sarà 2007; Olson et al., 2010; Vizzini et al., 2010; Logan et al., 2011). A very
similar nitrogen discrimination factor (ǻ15N = 1.48±0.30) was estimated in muscle of
adult ABFT reared on chub mackerel (Scomber colias) for three months (Varela et al.,
2011).
ϳϮ
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The differences observed in most isotopic measures between muscle and liver
in wild YOY ABFT may be due to the occurrence of different turnover rates in these
organs (Suzuki et al., 2005; Guelinckx et al., 2007). In this regard, liver and muscle
could provide information about feeding patterns at two different time-scales (Gannes
et al., 1998; Logan, 2009).
The rearing conditions of experimental YOY ABFT in the present study implied
feeding to satiation, which led to fast growth of the fish. These and other unnatural
circumstances could have affected the resulting isotopic values of the analysed tissues
with respect to wild individuals (Gaye-Siessegger et al., 2003, 2004; Trueman et al.,
2005 ), introducing artificial bias in the discrimination factors obtained. In spite of this
fact, our data provide the first estimate of ǻ15N and ǻ13C values determined under
semi-controlled conditions for white muscle and liver tissues of juvenile ABFT. This
data may be useful in further investigations on the foraging ecology of juvenile bluefin
tuna during their transition from breeding ground to foraging coastal waters.
Acknowledgements
We thank María Lema (Unidad de Técnicas Instrumentales de Análisis, Universade da
Coruña) for isotopic analysis. This work was granted by the Spanish Ministry of
Science and Innovation (CTM2007-65178-C02-01/MAR), Junta de Andalucía (RNM02469) and the European Community (GA no. 212797).
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4. CHAPTER 3. Estimating diets of pre-spawning Atlantic bluefin
tuna from stomach content and stable isotope analyses
Abstract
Stomach content analysis (SCA) and stable isotope analysis (SIA) coupled with
isotopic mixing model analysis were used to estimate diet composition of pre-spawning
Atlantic bluefin tuna (ABFT), Thunnus thynnus, caught by trap in the Strait of Gibraltar
area. SCA provided poor information on diet as most of the stomachs appeared empty
or contained only hard remains. Mixing model diet compositions estimated from muscle
tissue SIA data did not show clear inter-annual variations and suggested that ABFT fed
on prey that occupy high and intermediate level positions of the food web. Otherwise,
diet compositions estimated from liver tissue SIA showed greater inter-annual
variations and appeared to indicate that ABFT fed on prey located at lower trophic
levels. The different dietary compositions inferred from muscle and liver samples were
most probably due to the different turnover rates of these organs, which would provide
trophic information at two distinct time scales. Our findings suggest that a combination
of SCA and SIA is more suitable than using SCA alone to determine temporal and
regional variations in ABFT diet composition.
Key words: Thunnus thynnus, bluefin tuna, diet, stomach contents, stable isotopes, mixing
models
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4.1. Introduction
The Atlantic bluefin tuna (ABFT), Thunnus thynnus (Linnaeus, 1758), is a large
migratory fish that occurs throughout the North Atlantic Ocean and the Mediterranean
Sea and exhibits physiological and morphological adaptations that enable it to exploit a
wide range of pelagic environments (Mather et al., 1995; Collette et al., 2001;
Fromentin and Powers, 2005). Since the 1990s ABFT stocks have become heavily
depleted because of overfishing. ABFT stock assessment is currently hampered by an
incomplete knowledge of the species’ life history and ecology, including foraging habits.
Based on stomach content analysis (SCA), studies of diet composition have been
carried out in ABFT from foraging areas of the Atlantic Ocean and Mediterranean Sea
(Crane, 1936; Bigelow and Schroeder, 1953; Krumholz, 1959; Dragovich, 1970; Ortiz
de Zárate and Cort, 1986; Eggleston and Bochenek, 1990; Chase, 2002; Karakulak, et
al., 2009; Butler et al., 2010; Logan et al., 2011). These studies describe the ABFT as a
key apex predator that feeds on a great variety of fish and invertebrates, and so has
been regarded as an opportunistic and generalist feeder.
Spanish traps (Fig. 4.1) are set in the Atlantic coast at the entrance of the Strait
of Gibraltar and have been used since ancient times to catch tuna that swim close to
the coastline in their “arrival” and “return” seasonal migrations in and out of the
Mediterranean Sea. An eastward or "arrival" run of pre-spawning fish takes place in
April and May, and a westward or "return" run of spent fish occurs in July and August
(Rodríguez-Roda, 1964; Mather et al., 1995). A prior SCA of specimens captured on
eastward migration in the Strait of Gibraltar area indicated that the stomachs are most
often empty (Rodríguez-Roda, 1964), which has led to the conclusion that ABFT do not
feed during their migration to Mediterranean spawning grounds. However, no further
foraging studies have been made that support this assumption.
ϴϮ
&KDSWHU'LHWRISUHVSDZQLQJEOXHILQWXQD
Fig 4.1. Scheme of the ABFT trap off Barbate (Cadiz, South of Spain).
SCA provides useful but incomplete information of ABFT diets due to fast
digestion, uneven digestion rates of different food items, and probable food
regurgitation during fishing operations, which may result in a high percentage of empty,
or nearly empty, stomachs (Carey, 1984; Chase, 2002). Furthermore, as SCA reveals
only the composition of recently ingested food, tracking diet throughout a broad
geopraphic and temporal scale requires a large number of samples across space and
time. Stable isotope analysis (SIA) has been used to reconstruct ABFT diets as a
suitable complement to SCA (Estrada et al., 2005; Sarà and Sarà, 2006; Logan 2009;
Logan et al., 2011). The carbon stable isotope composition (į13C) is regarded as a
dietary source indicator (Pinnegar and Polunin, 2000), while nitrogen isotopic ratios
(į15N) serve as appropriate indicators of consumer trophic position (Minagawa and
Wada, 1984; McCutchan et al., 2003; Jennings et al., 2008). Both į13C and į15N can
provide trophic information over weeks or months, depending on the tissue turnover
rate (Gannes et al., 1998). In fishes, slow turnover tissues like muscle (Hesslein et al.,
1993; MacAvoy et al., 2001) produce information on feeding at mid-time scale
(months), whereas tissues with faster metabolic rates such as liver (Suzuki et al., 2005;
Guelinckx et al., 2007) give information at a shorter time scale (Logan et al., 2006;
MacNeil et al., 2006). Knowing the isotopic signatures of ABFT tissues and their most
common prey, dietary proportions can be estimated applying mixing models (Phillips
ϴϯ
&KDSWHU'LHWRISUHVSDZQLQJEOXHILQWXQD
and Gregg, 2001, 2003; Semmens and Moore, 2008; Erhardt, 2009; Parnell et al.,
2010). Stable isotope mixing models are based on the potential contribution of different
isotopic sources (i.e., diet components) to a mixture (i.e., consumer), and require
previous knowledge of diet-tissue discrimination factors (Hussey et al., 2009).
In this study, SCA, SIA and stable isotope mixing model analysis were performed to
estimate the diet composition of ABFT prior to spawning in the Mediterranean Sea,
using muscle and liver tissues as distinct time-scale diet markers.
4.2. Material and methods
4.2.1. SCA
ABFT (n = 189), ranging between 143 and 262 cm in straight fork length (SFL),
were sampled by trap off Barbate (Gulf of Cádiz, southern Spain) in 2009 (May, 11-21),
2010 (May, 4-13) and 2011 (June, 15) as they moved to the Mediterranean Sea to
spawn (Fig. 4.2). The stomachs were removed and stored at -20ºC until analysis. In the
laboratory, every stomach was thawed and cut open, and all the contents washed
through a 1-mm mesh size sieve. Identification of taxa was carried out to the lowest
possible taxonomic level. The wet weight of prey items was recorded to the nearest
0.1g, and the fish size (SFL) measured to the nearest 0.1cm. Hard part remains (fish
otoliths, cephalopod beaks and crab claws) were used for identification of fully digested
prey using specific taxonomic keys (Clarke, 1986; Harkönen, 1986; Campana, 2004;
Tuset et al., 2008).
ϴϰ
&KDSWHU'LHWRISUHVSDZQLQJEOXHILQWXQD
20º
15º
10º
5º
0º
5º
10º
15º
45º
50º
Atlantic
Ocean
40º
45º
Iberian
Peninsula
35º
30º
40º
Mediterranean
Sea
Gulf of
Cadiz
Africa
10º
5º
0º
35º
5º
10º
15º
Fig. 4.2. Atlantic bluefin tuna (ABFT) were collected by trap off
Barbate (Gulf of Cadiz, southern Spain) (Ɣ)
4.2.2. SIA
White muscle and liver tissue samples were collected from 116 ABFT. Muscle
samples (or the whole organism when the prey size was small) from 2-9 specimens of
each of 13 potential prey species were also taken to predict ABFT diet composition
using SIA followed by stable isotope mixing model analysis. The list of prey chosen for
SIA was primarily based on the identification of hard remains in the stomach contents,
and was completed with known common ABFT prey in the Iberian Atlantic (Ortiz de
Zárate and Cort, 1986; Logan et al. 2011). It consisted of 9 fish species (Trachurus
trachurus, T. mediterraneus, T. picturatus, Micromesistius poutassou, Sardina
pilchardus, Engraulis encrasicolus, Myctophum punctatum, Scomber scombrus and S.
colias), 3 crustaceans (Polybius henslowii, Meganyctiphanes norvegica and Pasiphaea
sivado) and 1 cephalopod (Illex coindetii). Prey were collected from the Gulf of Cádiz
(Fig. 4.2.) during a research cruise carried out in March, 2009, and stored at -20ºC until
use.
ϴϱ
&KDSWHU'LHWRISUHVSDZQLQJEOXHILQWXQD
In the laboratory, ABFT and prey samples were thawed, rinsed thoroughly with
distilled water, transferred into glass vials and freeze-dried for 48 h. Then, they were
ground to powder and then separated into two subsamples, one of which was used for
į15N analysis, while the other was treated three times with chloroform-methanol for lipid
extraction prior to į13C analysis. Total lipid contents were calculated as described by
Varela et al. (2011, 2012). Aliquots (~1 mg) of bulk and lipid-extracted samples were
placed into tin capsules. The relative abundances of
13
C and
15
N (respectively, į13C
and į15N) were measured by continuous gas flow system using a Thermo Finnigan
Elementary Analyzer Flash EA 1112 coupled to a Finnigan MAT Delta Plus mass
spectrometer and expressed as parts per thousand (‰) relative to standards (Peterson
and Fry, 1987).
4.2.3. Mixing model
A Bayesian mixing model (multiple sources, dual-isotope linear mixing-model
SIAR; Parnell et al., 2010) was used to estimate the relative proportion of multiple prey.
Mixing model analyses require accurate estimates of isotopic discrimination factors
between sources (prey) and mixture (consumer) tissues to reliably ascertain
quantitative diet estimates (Bond and Diamond, 2011). Here we applied the preymuscle discrimination factors previously estimated for ABFT (ǻ13C (‰) = -0.16 ± 0.64,
ǻ15N (‰) = 1.64 ± 0.20; Varela et al., 2011). The prey-liver discrimination factors used
were ǻ13C (‰) = 0.42 ± 0.34 and ǻ15N (‰) = 0.68 ± 0.42, based on an experimental
trial carried out on farmed ABFT (unpublished data).
4.2.4. Statistical analysis
To test statistically significant differences among years, mean values of į13C,
į15N and % lipid content were analyzed using one-way analysis of variance (ANOVA)
followed, where relevant, by Tukey’s post hoc test. When data did not meet the
assumption of variance homogeneity (Levene's test), a Kruskal–Wallis test was
ϴϲ
&KDSWHU'LHWRISUHVSDZQLQJEOXHILQWXQD
performed to assess differences among years; differences between pairs of sample
groups were then assessed by a Mann–Whitney U-test. Significant differences in į13C,
į15N and lipid content between liver and muscle tissues were analyzed by a Student’s
t-test or a Mann-Whitney U-test.
Simple regression analyses were used to evaluate the relationship between
į15N and % lipid content of muscle and liver tissues. A significance level of Į = 0.05
was considered in all statistical tests. Statistical analyses were performed using
Statgraphic Plus version 5.1.
4.3. Results
4.3.1. SCA
Stomach contents provided poor information on ABFT diet as most of the
stomachs appeared empty or contained only hard remains. In 2009, only 3 (3.70%) of
the 81 stomachs analyzed showed recognizable prey in their soft contents (Sarda
sarda, Auxis rochei and Polybius henslowii). In 9 of the 91 stomachs collected in 2010
(9.89%) the analysis of soft contents allowed the identification of 6 fish species
(Sardina pilchardus, Sardinella sp., Trachinatus ovatus, Pagellus acarne, Exocoetus
volitans and Scomber japonicus). Of these prey, S. sarda, A. rochei, T. ovatus, P.
acarne and E. volitans are typical bycatches of southern Spanish traps, but are
uncommon natural ABFT prey; hence, they were not considered in mixing model
analyses. No soft stomach contents were observed in the 2011 sample.
Some of the stomachs sampled in 2009 (n = 18, 22.22%) showed identifiable
hard parts (otoliths of Trachurus trachurus, beaks of Illex coindetii and claws of
Polybius henslowii) (Table 4.1). In the sample of 2010, 8 stomachs (8.79%) contained
the same types of hard parts as those found in 2009, while only a single otolith of
Micromesistius poutassou was found among the 21 stomachs from 2011 (Table 4.1).
ϴϳ
&KDSWHU'LHWRISUHVSDZQLQJEOXHILQWXQD
Table 4.1. Hard remains identified from stomach contents of Atlantic bluefin tuna
captured in traps off Barbate (Gulf of Cadiz, Southern Spain), 2009-2011.
Years
2009
(N = 81)
2010
(N = 91)
2011
(N = 21)
Prey groups
Hard remains
Preys
Fishes
Otoliths
Cephalopods
Crustaceans
Beaks
Claws
Fishes
Otoliths
Cephalopods
Beaks
Crustaceans
Claws
Trachurus sp.
Scomber colias
Micomesistius poutassou
Phycidae
Labridae
Illex coindetii
Polyboius henslowii
Trachurus sp.
Scomber colias
Micomesistius poutassou
Illex coindetii
Octopoidae
Polybius henslowii
Fishes
Otoliths
Micomesistius poutassou
No. of
stomachs
4
2
2
1
2
8
3
1
1
1
6
1
1
1
4.4.2. SIA
Stable isotope signatures were determined in muscle samples or whole
specimens of 13 common prey items of ABFT, which were collected from the Gulf of
Cádiz, near the Strait of Gibraltar, in 2009. The three species of Trachurus and the two
species of Scomber were grouped into single generic prey items (Table 4.2, Fig.4.3).
13
15
Table 4.2. Mean ± SD values of į C, į N, and weight of Atlantic bluefin tuna preys
collected from the Gulf of Cadiz in 2009. Isotopic values bearing asterisk were obtained by
gathering several individuals into a single sample.
Preys
Trachurus spp. (n=9)
Scomber spp. (n=6)
Micromesistius poutassou (n=3)
Sardina pilchardus (n=3)
Engraulis encrasicolus (n=3)
Myctophum punctatum (n=3)
Illex coindetii (n=3)
Polybius henslowii (n=1)
Meganyctiphanes norvegica* (n=4)
Pasiphaea sivado* (n=2)
13
ǻ C (к)
-17.77±1.47
-18.77±0.73
-17.99±0.51
-18.05±0.87
-18.24±0.81
-19.70±0.36
-17.480.75
-19.20±0.00
-18.60±0.00
-20.18±0.00
15
į N (к)
9.69±1.47
9.89±0.94
10.38±0.26
10.09±0.79
9.35±1.42
8.28±0.11
9.64±1.21
9.00±0.00
5.57±0.00
6.17±0.00
Weight (g)
129.27±136.03
109.33±66.10
165.67±12.34
40.33±19.73
14.00±12.17
3.23±0.68
187.67±148.08
0.30±0.06
0.76±0.18
Source
Present study
Present study
Present study
Present study
Present study
Present study
Present study
Logan 2009
Present study
Present study
ϴϴ
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Isotopic values of ABFT were measured in white muscle and liver tissues
(Table 4.3). While ABFT muscle į15N values did not significantly differ among years,
significant differences in į13C values were found to occur between 2010 and 2011
(Kruskal-Wallis followed by Mann-Whitney U-test, p < 0.001). Liver į13C and į15N
values showed significant differences among years (ANOVA followed by Tukey’s test,
p < 0.005), except for į13C between 2010 and 2011 (p > 0.05). Significant inter-annual
differences were also identified in muscle-liver į15N separation (Table 4.3). In all years,
the į13C and į15N values obtained from muscle tissue were significantly higher than
those observed in liver (Student’s t-test or Mann-Whitney U-test, p < 0.005, statistical
analysis not shown on table). į13C and į15N values of prey and ABFT are plotted in
Fig. 4.3. While ABFT muscle į15N values are highest, the liver isotope values are
intermediate among prey sources.
Table 4.3. . Atlantic bluefin tuna fork length (cm), body mass (kg), lipid content (%),
13
15
į C and į N values (mean ± SD) by tissue type in specimens collected in 2009-2011.
Values within the same row bearing different superscript letters are significantly
different (p < 0.05). AN, analysis of variance (ANOVA); K-W, Kruskal-Wallis test; M-W,
Mann-Whitney U-test, TK, Tukey’s test.
Sampling
2009 (n=48)
2010 (n=47)
2011 (n=21)
Statistical
tests
Fork length (cm)
204.87±21.76 205.02±15.75 202.81±25.06
KW
Body mass (kg)
170.73±56.30 180.58±44.13 179.16±61.30
KW
a
Muscle
22.93±5.80
Liver
41.73±11.48
Muscle
-18.01±0.44
18.11±7.20
b
17.35±6.09
b
KW+MW
ab
30.78±9.21
b
AN+TK
b
-18.11±0.19
a
KW+MW
b
AN+TK
Lipid content (%)
13
į C (‰)
15
į N (‰)
a
37.19±11.94
a
-18.52±0.41
a
b
Liver
-18.26±0.37
Muscle-liver
0.24±0.53
0.32±0.49
0.71±0.25
KW+MW
Muscle
11.14±0.54
10.94±0.54
11.02±0.48
KW
Liver
8.57±0.76
b
10.12±0.92
Muscle-liver
2.60±0.66
b
0.89±1.10
a
-18.85±0.37
a
a
9.09±0.79
a
1.81±0.87
-18.82±0.46
b
c
c
AN+TK
AN+TK
ϴϵ
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12
TTM09
TTM10
11
TTM11
MP
TTL11
10
SP
Tspp
Sspp
EE
IC
PH
9
į15N
TTL10
MyP
TTL09
8
7
PS
6
MN
5
-20,5
-19,5
-18,5
-17,5
-16,5
13
į C
13
15
Fig. 4.3. Mean į C and į N values of prey and Atlantic bluefin tuna (Thunnus thynnus, ABFT)
liver and muscle. EE, Engraulis encrasicolus; IC, Illex coindetii; MN, Meganyctiphanes
norvegica; MP, Micromesistius poutassou; MyP, Myctophum punctatum; PH, Polybius
henslowii; PS, Pasiphaea sivado; SP, Sardina pilchardus; Sspp, Scomber spp.; Tspp,
Trachurus spp.; TTL09, TTL10, TTL11, Thunnus thynnus liver (years 2009, 2010, 2011);
TTM09, TTM10, TTM11, Thunnus thynnus muscle (years 2009, 2010, 2011). Error bars
represent SD.
The highest lipid contents in muscle and liver were observed in 2009 (Table 4.3).
No significant relationship was found between nitrogen stable isotope ratios and % lipid
content for either muscle or liver samples (p > 0.05) (Fig. 4.4).
ϵϬ
&KDSWHU'LHWRISUHVSDZQLQJEOXHILQWXQD
13,0
10,5
12,0
10,0
11,5
9,5
11,0
9,0
į15N
į 15 N
11,0
A
12,5
10,5
8,5
10,0
8,0
9,5
7,5
9,0
7,0
8,5
6,5
6,0
15,0
8,0
7,0
12,0
17,0
22,0
27,0
32,0
D
20,0
25,0
30,0
35,0
40,0
45,0
50,0
55,0
60,0
65,0
Lipid content (%)
Lipid content (%)
B
E
13,0
12,0
12,5
11,0
12,0
11,5
10,0
į 15N
į15N
11,0
10,5
9,0
10,0
8,0
9,5
9,0
7,0
8,5
6,0
15,0
8,0
5,0
10,0
15,0
20,0
25,0
30,0
35,0
40,0
20,0
25,0
30,0
40,0
45,0
50,0
55,0
60,0
65,0
F
C
13,0
35,0
Lipid content (%)
Lipid content (%)
12,0
12,5
11,0
12,0
11,5
10,0
į 15 N
į15N
11,0
10,5
9,0
10,0
8,0
9,5
9,0
7,0
8,5
8,0
9,0
14,0
19,0
24,0
Lipid content (%)
29,0
34,0
6,0
10,0
15,0
20,0
25,0
30,0
35,0
40,0
45,0
50,0
Lipid content (%)
15
Figure 4.4. Relationships between į N and lipid content (%) for muscle (A, B and C; years
2009, 2010 and 2011, respectively) and liver (E, F and G; years 2009, 2010 and 2011,
respectively) samples.
4.4.3. Mixing model
Mixing-model estimations of dietary contributions of the 13 prey species were
made for the three years of the study using data from ABFT muscle and liver isotopic
data (Table 4.4). The diet compositions estimated from muscle tissue showed slight
ϵϭ
55,0
&KDSWHU'LHWRISUHVSDZQLQJEOXHILQWXQD
differences among years. Fishes represented the most abundant prey group in all
years, 2009, 2010 and 2011 (55.56%, 67.79% and 62.47%, respectively), whereas the
squid Illex coindetii appeared to be the predominant prey species (30.83%, 13.75%
and 23.18%, respectively). The diets reconstructed from liver tissue varied more
markedly among years. Fishes proved the dominant prey group in 2010 and 2011
(48.17% and 56.00%, respectively), while crustaceans predominated in 2009 (68.44%).
4.4. Discussion
4.4.1. SIA
The į15N values measured in muscle tissue of ABFT spawners sampled in the
Strait of Gibraltar are higher than the į15N values obtained from ABFT captured in the
Mediterranean Sea (Sarà and Sarà, 2007; Vizzini et al., 2010; Varela et al., 2011,
2012). In contrast, they are lower than those measured from western ABFT muscle
tissue (Estrada et al., 2005; Logan et al., 2011) and similar to those reported for
juvenile ABFT captured in the Cantabrian Sea (Logan et al., 2011). Since muscle and
liver turnover rates have not been thus far determined for ABFT, the present results of
SIA should be interpreted with caution. In a close species, the yellowfin tuna, Graham
(2008) estimated half-life turnover rates of 63 and 12 days for muscle and liver,
respectively. Therefore, as muscle tissue shows a rather slow turnover rate, it seems
unlikely that our muscle tissue į15N values reflect only local sources, but they also
incorporate nitrogen isotope values from more distant prey from offshore feeding
grounds, which are expected to show lower values than prey inhabiting feeding
grounds (Logan, 2009).
ϵϮ
&KDSWHU'LHWRISUHVSDZQLQJEOXHILQWXQD
Table 4.4. Mixing model estimates of prey contributions for ABFT muscle and liver. Values are presented as mean proportion estimates with upper and lower
95% confidence intervals.
Muscle
Preys
Trachurus spp.
Scomber spp.
Micromesistius poutassou
Fishes
Sardina pilchardus
Engraulis encrasicolus
Myctophum punctatum
Cephalopods Illex coindetii
Polybius henslowii
Crustaceans Meganyctiphanes norvegica
Pasiphaea sivado
2009
Low95%
0,98
0,00
1,32
0,00
0,00
0,00
15,36
0,00
0,06
0,00
Liver
Preys
Trachurus spp.
Scomber spp.
Micromesistius poutassou
Fishes
Sardina pilchardus
Engraulis encrasicolus
Myctophum punctatum
Cephalopods Illex coindetii
Polybius henslowii
Crustaceans Meganyctiphanes norvegica
Pasiphaea sivado
ϵϯ
High95%
26,89
11,92
29,46
26,43
21,28
6,42
45,19
8,96
8,94
4,81
2010
Mean
14,58
4,65
15,54
12,94
9,45
2,40
30,83
3,22
4,60
1,79
Low95%
0,37
0,00
0,28
0,00
0,00
0,00
0,38
0,00
0,00
0,00
2009
Low95%
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
17,10
1,48
High95%
19,95
15,62
15,36
16,77
21,88
18,17
19,04
18,59
37,58
25,24
High95%
21,65
21,99
25,23
24,61
24,91
16,44
25,85
20,44
9,81
10,59
2011
mean
11,54
11,11
13,43
12,51
12,10
7,09
13,75
9,25
4,70
4,51
Low95%
0,64
0,00
2,30
0,29
0,00
0,00
9,75
0,00
0,82
0,00
2010
Mean
8,79
6,09
5,81
6,57
8,44
7,72
8,41
7,08
27,27
13,82
Low95%
0,00
0,00
0,00
0,00
0,00
3,44
0,00
0,00
0,00
9,65
High95%
10,83
25,43
12,93
13,89
13,64
47,47
8,10
31,47
8,52
36,55
High95%
21,60
15,98
31,66
24,96
22,11
8,75
36,73
12,85
12,61
6,48
mean
11,08
6,97
17,39
12,87
10,92
3,24
23,18
5,02
6,87
2,47
2011
mean
4,04
11,07
4,91
5,30
5,20
25,48
3,05
14,79
3,14
23,01
Low95%
0,00
0,00
0,00
0,00
0,00
1,93
0,00
0,17
0,00
0,00
High95%
14,00
34,72
19,47
18,93
18,01
39,07
10,98
34,72
7,23
21,67
mean
5,10
17,44
7,99
7,90
7,06
20,95
3,86
17,05
2,56
10,08
&KDSWHU'LHWRISUHVSDZQLQJEOXHILQWXQD
In liver tissue, similar į15N values were reported for ABFT from Mediterranean
(Vizzini et al., 2010; Varela et al., 2012) and eastern Atlantic (Logan et al., 2011)
locations, while the
15
N ratios were higher in ABFT captured close to the Virginia coast
(USA) (Logan et al., 2011). Such disparities between western and eastern ABFT
populations can be explained by geographical gradients in stable isotope values across
the Atlantic Ocean (Graham et al., 2010). Spatial and temporal variations in į15N data
may be caused by two factors (Graham et al., 2007), the trophic position of prey and
the isotopic values of nitrogenous compounds that serve as nutrients to organisms
located at low trophic levels. The similar (or even slightly lower) į15N values of large
bluefin from the studied area compared to juveniles from the Cantabrian Sea could
reflect a lower position prey base for the Strait of Gibraltar. Basal isotope levels in
these regions can be compared using the isotopic signatures of the krill,
Meganyctiphanes norvegica, which is an important zooplankton component of the food
web in both ecosystems. While the mean į15N of M. norvegica in the Cantabrian Sea
was 8.3‰ (Logan, 2009; Logan et al., 2011), we obtained a mean value of 5.6‰ in krill
collected west of the Strait of Gibraltar area. Considering average liver į15N values of
10.41‰ for Cantabrian juveniles (estimated from Logan et al., 2011) and 9.06‰ for
breeders sampled in the Strait of Gibraltar (present data), and a discrimination factor
ǻ15N = 0.68‰, such a difference in the isotopic baselines would result in a separation
of approximately two trophic positions (1.99) between young and adult bluefin tuna
from the respective areas (calculated from the equation of Post, 2002).
Unlike N isotope ratios, our muscle and liver į13C estimates are close to those
published previously for both western and eastern ABFT (Estrada et al., 2005; Sarà
and Sarà, 2007; Vizzini et al., 2010; Logan et al., 2011; Varela et al., 2011), though in
several of these studies į13C values were not corrected for lipid content, which limits
the accuracy of comparisons. The small differences between the present values and
those reported by Varela et al. (2011) from bluefin captured in the Balearic Islands can
be explained by the fact that these migrating fish reach the spawning ground shortly
ϵϰ
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after crossing the Strait of Gibraltar, whereby the isotopic signature is not expected to
change substantially.
Rodríguez-Roda (1964) found that most of the stomachs from ABFT captured
by trap in the Strait of Gibraltar were empty. This observation has led to the assumption
that bluefin tuna do not feed significantly during the pre-spawning migration. It is known
that food deprivation may cause increased į15N values (Hobson et al., 1993; Gannes
et al., 1997) and decreased somatic condition (Morais et al., 2011; Ortega and
Mourente, 2011). If pre-spawning ABFT were undernourished, a negative relationship
between į15N and % lipid content would be expected similar to what Doucett et al.
(1999) reported for Atlantic salmon (Salmo salar), which cease feeding during the
reproductive migration. However, no significant relationship between į15N and % lipid
content was found in ABFT muscle and liver, suggesting that either foraging does
occur or the stored lipid reserves can cope with energy costs associated with the
reproductive migration. In connection with this, conventional tagging has shown that
ABFT concentrates on the region west of Gibraltar, and electronic tag data indicate
they may spend weeks west of the Strait of Gibraltar before moving into the
Mediterranean Sea, suggesting that this area is a foraging ground (Block et al., 2005;
Rooker et al., 2007; Stockesbury et al., 2007).
4.4.2. SCA and multiple-source SIAR model
As in previous work (Rodríguez-Roda, 1964), the poor information withdrawn
from SCA in trap-caught ABFT evidences limitations of this method in diet
assessments when used alone. The fact that ABFT often spend a long time (>24 h)
swimming in trap facilities before being slaughtered, together with their high digestive
efficiency (Aloncle and Delaporte, 1970) as well as likely regurgitations induced by
stressful fishing operations (Chase, 2002), result in stomachs that are empty or retain
only hard remains.
ϵϱ
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When prior information on prey species composition is limited, employing
representative prey sources in isotope mixing models can provide useful information on
diets (Polito et al., 2011). For the parameterization of the multi-source SIAR model,
pertinent prey sources were identified mainly from the analysis of hard remains
removed from the stomach contents, as well as previous SCAs carried out in Spanish
Atlantic waters (Ortiz de Zárate and Cort, 1986; Logan et al., 2011). The analysis of
stable isotope data across years 2009 to 2011 allowed us to investigate inter-annual
trophic variability. In 2009 and 2011, mixing model results obtained from muscle tissue
suggested that ABFT preyed mainly on species with high į15N values, thus located in
high trophic levels of the food web. In contrast, prey items located in intermediate
trophic levels appeared to contribute more significantly to ABFT diet in 2010. The
mixing-model performed from liver SIA data estimated that ABFT fed primarily on prey
located in lower trophic levels (crustaceans and myctophids). These results agree with
the observations of Logan et al. (2011) on ABFT sampled in the eastern Atlantic
Ocean. The different dietary compositions inferred from muscle and liver samples are
probably due to the different turnover rates of these organs (Tieszen et al., 1983;
MacNeil et al., 2005), which, therefore, would provide information at two distinct time
scales (Gannes et al., 1998). Since the turnover rate of ABFT liver appears to be faster
than that of muscle, the results of the mixing model analysis using isotopic data of this
organ are thought to be more representative of the prey consumed near the Strait of
Gibraltar. Significant muscle-liver į15N separations are believed to reflect recent
dispersal from offshore feeding areas (Logan et al., 2011). According to this, the lowest
difference between muscle and liver į15N found in ABFT sampled in 2011 might
indicate a lengthier residency near the Strait of Gibraltar, which is consistent with the
later date of sampling in this year (June, 15).
In conclusion, our data might indicate dietary variations during the reproductive
migration and would identify an opportunistic feeding behaviour. Although a possible
bias in these results is to be considered with regard to potential among-year variations
ϵϲ
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in prey isotopic values (Kurle et al., 2011; Chouvelon et al., 2012), earlier studies
performed in the Atlantic Spanish coast (Bode et al., 2006; Logan et al., 2011) have
reported similar isotopic values for most of the ABFT prey species dealt with in this
paper. The present observations suggest SIA coupled with mixing models is more
suitable than SCA to track inter-annual and spatial variations in diet composition of
ABFT. By employing tissues that integrate diets over long and medium periods of time,
isotopic mixing models can avoid biases inherent to SCA. Thus, when the identified
prey items are isotopically distinct or can be combined into biologically meaningful
groups, SIA may help resolve trophic relationships in combination with SCA.
Acknowledgements
We thank María Lema (Unidad de Técnicas Instrumentales de Análisis, Universade da
Coruña) for isotopic analysis and David Macías and his team (Instituto Español de
Oceanografía at Fuengirola, Málaga) for assisting in ABFT samplings. This study has
been granted by research projects RNM-02469 (Junta de Andalucía), CTM201127505, CTM2011-29525-C04 (Spanish Ministry of Science and Innovation, MICINN),
and Secretaría General del Mar, Spanish Ministry of Environment, Rural and Marine
Affairs.
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&+$37(5Study of the trophic ecology of young-of-the-year
bluefin tuna (Thunnus thynnus) captured in the western
Mediterranean Sea
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5. Chapter 4. Study of the trophic ecology of young-of-the-year
bluefin
tuna
(Thunnus
thynnus)
captured
in
the
western
Mediterranean Sea
Abstract
Diet composition of young-of-the-year (YOY) Atlantic bluefin tuna (ABFT) foraging in
nearshore waters of the Iberian Mediterranean coast was estimated by integrating data
from stomach content analysis (SCA) and stable isotope analysis (SIA) coupled to
mixing models. SCA revealed that YOY ABFT diets comprise at least 28 taxa, including
13 fishes, 9 crustaceans and 6 cephalopods. The composition of stomach contents
was highly variable among years. Overall, stable isotope (į13C and į15N) data also
showed significant differences between years 2009 and 2010. While mixing-model
analysis from muscle tissue yielded differences between years, the dietary contribution
of the prey groups estimated from liver SIA data was similar in both years. In all cases,
the mixing model estimated that Notolepis rissoi was the most abundant prey species.
Key-words: juvenile Thunnus thynnus, diet, stomach content analysis, stable isotope analysis,
isotopic mixing models
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5.1. Introduction
Atlantic bluefin tuna (ABFT), Thunnus thynnus (Linnaeus, 1758), perform longdistance reproductive migrations from the Atlantic Ocean to Mediterranean spawning
grounds. Roule (1917) first assumed that the waters around the Balearic archipelago
represented an important spawning ground for the species, based on the finding of
ABFT larvae. This was later confirmed from ichthyoplankton surveys carried out in the
1970’s (Duclerc et al., 1973; Dicenta et al., 1975; Rodríguez-Roda, 1975; Dicenta,
1977). More recent studies (García et al., 2002; Alemany et al., 2010) have shown, in
fact, that all scombrid species occurring in the Mediterranean Sea spawn in Balearic
waters. The maximum standard length of ABFT larvae ever sampled in the Balearic
spawning ground in June-July is approximately 9 mm (García et al., 2002, 2007;
Catalán et al., 2011; Reglero et al., 2011), which suggests that once this critical size is
reached, ABFT larvae move away and become inaccessible to planktonic surveys over
the spawning area. A few months after hatching (August-November), ABFT juveniles
become apparent nearshore along the Spanish Mediterranean coast (Sanz-Brau,
1990).
A good comprehension of the juvenile feeding ecology is essential for the
understanding of the early life-history and recruitment variability in fishes (Young and
Davis, 1990). While the available information on feeding habits of young and larval
ABFT in Spanish Mediterranean waters is scarce, nothing is yet known about the
foraging activity of juvenile ABFT during their short migration from the Balearic Sea to
the Iberian Peninsula coastline. Stomach content analysis (SCA) of specimens
captured off the coast of Valencia (E Spain) showed that YOY ABFT mainly prey on
sardine (Sardina pilchardus), anchovy (Engraulis encrasicolus) and southern shortfin
squid (Illex coindetii) (Sanz-Brau, 1990). Catalan et al. (2007, 2011) found that ABFT
and albacore larvae (Thunnus alalunga) primarily feed on copepod larvae and
cladocerans in the Balearic breeding ground, though a diet composed of only
zooplankton does not appear enough to support larval growth (Reglero et al., 2011).
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The natural abundance of carbon (13C/12C, į13C) and nitrogen (15N/14N, į15N)
stable isotopes are often used to elucidate trophic interactions. Thus, į13C values are a
reliable indicator of a consumer’s primary carbon source (DeNiro and Epstein, 1978;
Fry and Sherr, 1984), whereas į15N values are indicative of its relative trophic position
(Minagawa and Wada, 1984; McCutchan et al., 2003; Jennings et al., 2008). Both į13C
and į15N are transferred throughout the food webs in stepwise processes. It is known
that stable isotope analysis (SIA) can provide trophic information at different time
scales depending on the turnover rate of the tissue analyzed (Tieszen et al., 1983;
Macneil et al., 2005). In tuna, muscle provides information on feeding over several
months, while liver, which shows a faster incorporation rate, reflects more recent
foraging (Graham, 2008).
The objective of this study was to combine SCA with muscle and liver SIA data
from YOY ABFT to gain insight into the feeding ecology of bluefin tuna at early life
stages. By integrating SCA data with SIA coupled to mixing models, we aim to a)
determine the diet composition of YOY ABFT foraging in nearshore waters of the
Iberian Mediterranean coast, and b) tentatively reconstruct past diets encompassing
the journey of juvenile ABFT from breeding grounds to the continental coastline.
5.2. Material and methods
5.2.1. SCA
YOY ABFT (n = 210) were caught by trolling off Cartagena (Murcia, SE Spain)
in September-October, 2008-2010 (Fig. 5.1). The fish size ranged from 21 to 43 cm
straight fork length, and the weight varied between 180.35 and 1330.00 g.
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Figure 5.1. Young-of-the-year (YOY) Atlantic bluefin tuna (ABFT) were captured
off Cartagena SE Spain (Ɣ). In a previous study (Sanz-Brau, 1990) YOY ABFT
were sampled for SCA in a close area (Ƈ).
Stomachs were removed and stored at -20ºC. In the laboratory, they were
thawed, cut open and the contents washed through a sieve of 1000 µm mesh size.
Identification of taxa was carried out to the lowest possible taxonomic level. The size
and colour of the gall bladder and texture of the stomach inner lining were visually
checked to ascertain whether the stomach contents had been ingested during fishing
operations (Talbot and Higgins, 1982). The wet weight of prey items was recorded to
the nearest 0.01g, and the fish size (straight fork length, SFL) measured to the nearest
0.01cm. Hard part remains (fish otoliths, cephalopod beaks and crab claws) were used
for identification of fully digested prey using specific taxonomic keys (Clarke, 1986;
Harkönen, 1986; Campana, 2004; Tuset et al., 2008).
The dietary importance of each food item was assessed by the following indices:
– percentage of frequency of occurrence %Oi (= number of stomachs containing prey
item i × 100/total number of non-empty stomachs);
– percentage of abundance %Ni (= number of individuals of prey item i × 100/total
number of all prey items);
– percentage of weight %W i (= weight of prey item i × 100/total weight of all prey
items).
ϭϭϬ
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Although these three dietary indices are often used in feeding studies in fishes (Hyslop,
1980), they present disadvantages when used alone as indicators of dietary
importance. %O ignores the relative importance of preys in number and weight, while
%N confers a higher significance on the number of preys, which may be, however,
small and little representative in terms of mass. Otherwise, %W may overestimate the
importance of larger, but poorly represented preys. To compensate for these
limitations, we used the index of relative importance (IRI), which incorporates
percentages of number, weight and frequency of occurrence, and is defined by the
equation:
IRIi = (%Ni + %W i) x %Oi (Pinkas, 1971).
To determine the relative importance of the different preys observed, IRI was
expressed as percent:
Q
%IRIi =ҏIRIi /
¦IRI ) × 100.
i
L=
5.2.2. SIA
White muscle and liver samples (n = 23 and n = 16, respectively) were collected
from YOY ABFT in 2009 and 2010. To reconstruct ABFT diet composition from SIA
coupled with a stable isotope mixing model, samples (whole organisms) of major prey
species, according to the present SCA results and previous studies, were also taken.
Preys of similar small sizes of those found in ABFT stomachs were collected in
summer, 2011 off the coast of Murcia (SE Spain) during a research cruise. The
selected list of representative prey species consisted of 4 fishes (Trachurus picturatus,
Ceratoscopelus maderensis, Sardinella aurita and Notolepis rissoi), 3 crustaceans
(Pasiphaea sivado, hyperiid amphipods and Meganyctiphanes norvegica) and 1
cephalopod (Illex coindetii), which represent the most common prey species. The prey
guts were removed prior to SIA to avoid possible bias due to contamination with
stomach contents. All samples were stored at -20ºC until use.
ϭϭϭ
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In the laboratory, samples were thawed, rinsed with distilled water, introduced
into glass vials and freeze-dried for at least 48h. Subsequently, the tissues were
ground to fine powder using pestle and mortar. Part of the resulting sample was used
as such for į15N analysis, while lipids were removed from the remaining sample
following a modified protocol after Bligh and Dyer (1959) to avoid the effects of lipid
contents on į13C measurements. Aliquots of ~1 mg were placed into tin cups for SIA
using a Thermo Finnigan Elementary Analyzer Flash EA 1112 coupled to a Finnigan
MAT Delta Plus mass spectrometer. The carbon and nitrogen isotopic ratios are given
as į13C and į15N (Peterson and Fry, 1987), expressed in parts per thousand (‰),
relative to standards:
įX = [(Rsample / Rstandard)-1] × 1000,
where X is
13
C or
15
N, R is the ratio
13
C/12C or
15
N/14N, and standard refers to Vienna
Pee Dee Belemnite carbonate for į13C and atmospheric nitrogen for į15N (Peterson
and Fry, 1987).
5.2.3. Mixing model
To estimate the dietary contribution of prey items, a Bayesian mixing model
(multiple sources, dual-isotope linear mixing-model siaR, Parnell et al., 2010) was
applied. Mixing models require appropriate isotopic prey-tissue discrimination factors to
obtain accurate estimations. The prey-muscle discrimination factors applied to estimate
diet compositions were those obtained from an experimental trial carried out on farmed
YOY ABFT (Varela et al., 2012): ǻ13C (‰) = 0.32±0.09 and ǻ15N (‰) = “ for
muscle, and ǻ13C (‰) = 0.39±0.23 and ǻ15N (‰) = 1.62±0.22 for liver.
5.2.4. Statistical analysis
Significant differences in į13C and į15N values, either between tissues (white
muscle and liver) or between years (2009 and 2010) were tested by Student’s t-test
with Į = 0.05. Statistical analyses were performed using Statgraphic Plus version 5.1.
ϭϭϮ
&KDSWHU'LHWRI<2<EOXHILQWXQD
5.3. Results
5.3.1 SCA
Stomach contents showed remarkable differences between years. Identification
to the species level was not always possible due to the advanced stage of digestion of
many specimens. Thus, a high percentage of the soft contents consisted of
unidentifiable and/or unquantifiable remains that could not be used for the calculation
of feeding indices (Fig. 5.2). The diet composition inferred from SCA comprised at least
28 taxa, including 13 fish, 9 crustaceans and 6 cephalopods (Table 5.1). In 2008,
cephalopods appeared to be the most abundant prey group (%IRI=81.79%), Illex
coindetii being the most representative species (%IRI = 90.51%). Fishes were the most
abundant prey group in 2009 and 2010 (%IRI = 91.80% and %IRI = 48.45%,
respectively). However, while Sardinella sp. was the predominant prey species in 2009
(%IRI = 83.77%), Illex coindetii (%IRI = 64.57%) was the most abundant prey item in
2010.
Figure 5.2. Percent prey weight composition in stomach contents of YOY bluefin tuna.
Fish, cephalopod, stomatopod and anomuran individuals found in the stomachs
were all at an early life stage (either juvenile or larval), whereas the other crustaceans
retrieved from the stomach contents were adult forms.
ϭϭϯ
&KDSWHU'LHWRI<2<EOXHILQWXQD
Table 5.1. Stomach contents of YOY bluefin tuna captured off Cartagena (Murcia, SE Spain) in 2008, 2009 and 2010.
YEAR 2008
YEAR 2009
YEAR 2010
Preys
Fishes
C. aper
M. punctatum
Callyonimus sp.
Unidentifiable fishes
Cephalopods
I. coindeti
Sepietta sp.
Unident. cephalopods
Crustaceans
Hyperiid amphipod
Caridea
Pasiphaea sp.
Gammarid amphipod
%W
%N
%O
5,77 18,56 38,10
3,08 9,28 23,81
1,97 2,06 9,52
0,02 1,03 4,76
0,70 6,19 19,05
91,31 52,58 71,43
89,24 48,45 61,90
0,98 2,06 4,76
1,10 2,06 4,76
IRI
926,70
294,32
38,39
4,99
131,14
10277.80
8523,84
14,47
15,04
%IRI
7,37
3,13
0,41
0,05
1,39
81,79
90,51
0,15
0,16
Preys
Fishes
Sardinella sp.
N. rissoi
Myctophidae
C. maderensis
Trachurus sp.
M. puncatum
N. elongatus
C. aper
%W
%N
%O
76,23 44,23 46,99
49,01 16,35 16,87
16,09 6,25 0,10
2,80 5,77 9,64
1,58 1,44 2,41
1,27 1,44 3,61
1,13 0,48 1,20
0,81 1,44 1,20
0,27 0,96 2,41
2,92
2,37
0,10
0,28
0,03
28,87 42,86
15,46 14,29
7,22 9,52
1,03 4,76
4,12 14,29
1362,22
254,84
69,72
6,23
59,40
10,84
2,71
0,74
0,07
0,63
Sparidae
Callyonimus sp. larvae
G. argenteus
Stomiidae
Scorpaenidae
0,22
0,00
0,24
0,12
0,05
Unident. crustaceans
0,13
1,03
5,53
0,06
Sparidae larvae
Unident. larvae
Unident. fishes
Cephalopods
Illex coindetii
T. sagittatus
Bathypolypus sp.
Sepiolidae
T. eblanae
Unident. cephalopods
Crustaceans
Pasiphaea sp.
Hyperiid amphipod
Sergestidae
Caridea
Gammarid amphipod
Anomura
Euphasiacea
Stomatopodo larvae
0,01 0,48 1,20
0,03 2,40 7,23
2,61 3,85 8,43
17,36 11,06 20,48
8,84 3,85 8,43
2,38 0,96 2,41
1,31 0,48 1,20
0,37 2,88 4,82
0,35 1,44 3,61
4,10 1,44 3,45
6,40 44,71 38,55
4,47 2,88 4,82
1,11 18,75 21,69
0,52 5,77 3,61
0,20 8,17 13,25
0,04 1,92 2,41
0,03 1,92 2,41
0,01 0,96 2,41
0,02 4,33 3,61
ϭϭϰ
4,76
1,44
0,48
0,48
0,48
0,48
3,61
1,20
1,20
1,20
1,20
IRI
9183.51
3202.77
359,63
23,97
4,77
3,45
1,81
1,83
0,33
%IRI
91,80
83,77
9,41
0,63
0,12
0,09
0,05
0,05
0,01
Preys
Fishes
P. auriga
C. aper
C. maderensis
Trachurus sp.
N. rissoi
M. punctatum
Callyonimus sp.
Unident. larvae
%W
%N
%O
IRI
%IRI
32,81 63,47 31,13 2997,44 48,45
6,31 31,05 15,09 563,92 17,06
3,54 15,07 16,98 316,05 9,56
5,40 9,13 4,72
68,54
2,07
3,52 1,83 3,77
20,18
0,61
3,06 1,37 1,89
8,36
0,25
1,64 2,28 1,89
7,39
0,22
0,09 1,37 1,89
2,76
0,08
0,08 0,46 0,94
0,50
0,02
0,37
0,00
0,18
0,07
0,03
0,01
0,00
0,00
0,00
0,00
9,17 0,91 1,89
19,03
0,58
65,27 23,74 33,02 2939,24 47,51
61,17 19,63 26,42 2134,54 64,57
3,62 1,83 2,83
15,40
0,47
0,48 2,28 2,83
7,83
0,24
0,01
0,07
16,83
493,44
112,15
7,97
2,36
1,20
0,64
22,74
327,32
32,91
22,12
3,27
1,63
0,07
0,06
0,01
0,10
0,00
0,00
0,44
4,93
2,93
0,21
0,06
0,03
0,02
0,59
3,27
0,86
0,58
0,09
0,04
0,00
0,00
0,00
0,00
Unident. fishes
Cephalopods
I. coindetii
Ommastrephidae
Sepiloidae
Crustáceans
Hyperiid amphipod
Stomatopoda larvae
Pasiphaea sp.
Palaemonidae
1,92
1,22
0,09
0,59
0,01
12,79 16,98
10,96 11,32
0,91 1,89
0,46 0,94
0,46 0,94
249,65
137,89
1,89
0,99
0,44
4,04
4,17
0,06
0,03
0,01
&KDSWHU'LHWRI<2<EOXHILQWXQD
5.3.2 SIA
Carbon (į13C) and nitrogen (į15N) isotopic values of YOY ABFT were measured
in white muscle and liver tissues (Table 5.2). į13C and į15N data showed significant
differences between years (Student’s t-test, p <0.05), with the only exception of the
į15N values obtained in liver (Student t-test, p = 0.281). In both 2009 and 2010,
significant differences in į13C values were found between muscle and liver tissues
(Student’s t-test, p < 0.05), whereas differences were not observed for į15N values
(Student’s t-test, p > 0.05). The mean values in muscle were į13C2009 (-17.51 ±0.23) >
į13C2010 (-18.03±0.12), and į15N2009 (9.01±0.50) > į15N2010 (8.29±0.69). The mean
values for liver were į13C2009 (-17.92 ±0.12) > į13C2010 (-18.45 ±0.23), and į15N2009
(8.65±0.32) > į15N2010 (8.39±0.49) (Table 5.2).
13
15
Table 5.2. į C, į N, size (cm) and weight (g) values (mean±SD) of YOY bluefin
tuna captured off Cartagena (Murcia, SE Spain) in 2009 and 2010.
Sampling
Fork lenght (cm)
Body mass (g)
Muscle
Liver
Student's t-test, P-value
Muscle
15
į N
Liver
Student's t-test, P-value
13
į C
Student's t-test,
P-value
-7
6.447·10
2009
2010
35.20±1.69
28.50±3.12
835.05±91.85
445.89±155.53
4.583·10
-17.51±0.23
-17.92±0.12
0.017
9.01±0.50
8.65±0.32
0.142
-18.03±0.12
-18.45±0.23
-5
1.487·10
8.29±0.69
8.39±0.49
0.692
2.955·10
-4
1.426·10
-6
-5
0.012
0.281
Values on the right column show P-values resulting from the comparisons
between 2009 and 2010 (Student’s t-test). The P-values obtained from
comparisons between muscle and liver samples by Student’s t-test are shown in
rows beneath the relevant compared data.
Isotopic values of most common prey species are shown in table 5.3.
13
15
Table 5.3. Values (mean±SD) of į C and į N of ABFT preys
collected off Murcia coast (SE, Spain)
Preys
Notolepis rissoi (n=1)
Cerastocopelus maderensis (n=3)
Trachurus picturatus (n=6)
Sardinella aurita (n=3)
Illex coindetii (n=3)
Pasiphaea sivado (n=3)
Meganyctiphanes norvegica (n=1)
Hyperiid amphipod (n=1)
į C
13
į N
15
-19.30±0.00
-18.68±0.74
-19.13±0.19
-19.16±0.09
-18.19±0.10
-18.43±0.08
-20.04±0.00
-19.09±0.00
9,02±0.00
7.94±0.53
7.67±0.48
5.18±0.23
9.44±0.48
8.32±0.26
5,78±0,00
5.46±0.00
ϭϭϱ
&KDSWHU'LHWRI<2<EOXHILQWXQD
5.3.3 Mixing model
Mixing-model analysis of dietary contributions of the 8 selected prey species
was made for muscle and liver isotopic data (Table 5.4). The diet compositions
estimated from muscle tissue showed differences between years. Fishes contributed
similarly to the YOY diet in both years (58.53% and 54.85% respectively), while
substantial differences were observed in the relative contribution of crustaceans and
cephalopods to the diet in 2009 as well as 2010.
Table 5.4. Mixing model estimates of prey contributions for YOY ABFT muscle and liver. Values
are presented as mean proportion estimates with upper and lower 95% confidence intervals.
Muscle
Year 2009
Low 95% Up 95% mean%
Notolepis rissoi
10,29
52,74
31,82
Cerastocopelus maderensis
0,00
26,93
11,73
Fishes
Trachurus picturatus
0,00
26,21
11,18
Sardinella sp.
0,00
11,09
3,80
CCephalopods Illex coindetii
2,81
37,16
20,04
Pasiphaea sivado
0,00
25,84
11,13
Crustaceans Meganyctiphanes norvegica
0,00
15,48
6,12
Hyperiid amphipod
0,00
11,66
4,18
Preys
Year 2010
Low 95% Up 95% mean%
15,58
59,35
38,58
0,00
12,21
4,47
0,00
22,17
8,24
0,00
10,42
3,56
0,00
9,43
3,46
0,00
9,28
3,40
22,40
47,80
34,83
0,00
9,88
3,46
Liver
Year 2009
Low 95% Up 95% mean%
Notolepis rissoi
3,70
56,30
28,24
Cerastocopelus maderensis
0,00
26,69
10,84
Fishes
Trachurus picturatus
0,00
29,09
13,08
Sardinella aurita
0,00
13,59
5,05
Cephalopods Illex coindetii
0,00
31,53
14,10
Pasiphaea sivado
0,00
25,83
10,68
Crustaceans Meganyctiphanes norvegica
0,00
33,74
12,58
Hyperiid amphipod
0,00
15,44
5,44
Preys
Year 2010
Low 95% Up 95% mean%
3,31
44,48
22,65
0,00
26,01
12,10
0,00
29,02
13,78
0,00
14,94
5,97
0,05
27,79
14,67
0,00
25,00
12,05
0,00
25,80
12,19
0,00
16,76
6,60
Mixing-model estimations from liver tissue indicated that the dietary contribution
of the prey groups was similar in both years. While fishes appeared to be the most
abundant prey group in both 2009 and 2010 (57.20% and 54.49%, respectively),
cephalopods proved the least represented prey category (14.10% and 14.67%,
ϭϭϲ
&KDSWHU'LHWRI<2<EOXHILQWXQD
respectively). In all cases, the mixing model estimated that Notolepis rissoi was the
most abundant prey species (Table 5.4).
5.4. Discussion
In the present study, SCA and SIA were performed in YOY ABFT captured in
the western Mediterranean Sea with the aim to improve our knowledge of their feeding
habits during the first life stages. Determination of diet compositions from SCA requires
thorough examination of a large number of individuals of different sizes collected over
the distribution range of the species and encompassing as much as possible the
seasonal variability (Bode et al., 2006). Our SCA study was performed on a limited
number of samples (n = 210), which were, in addition, collected in the same season.
Furthermore, the fast digestion rates of tuna (Aloncle and Delaporte, 1970; Chase,
2002) results in stomachs that are frequently empty, which may reduce the robustness
of SCA data. SIA, which provides an integrated measure of energy flow through trophic
pathways, coupled with isotopic mixing models, can solve some of the biases inherent
to SCA (Logan et al., 2011; Mèndez-Fernandez et al., 2012; Varela et al., submitted).
5.4.1. SCA
As in juvenile ABFT from the Tyrrhenian Sea (Piccinetti and Piccinetti-Manfrin,
1970; Sinopoli et al., 2004), our SCA shows that cephalopods represent an important
constituent of YOY ABFT diet in the western Mediterranean Sea (years 2008 and
2010). The high contribution of juvenile Illex coindetii may be related to the
reproductive season of ommastrephids, which occurs in the second half of the year
(Gonzalez and Guerra, 1996; Quetglas et al., 1998). In comparison with other
cephalopod species, Illex coindetii has high lipid and protein contents (Rosa et al.,
2005) and represents a primary food source for large pelagic predators (Romeo et al.,
in press). The variability among years observed in the amount of cephalopods
ϭϭϳ
&KDSWHU'LHWRI<2<EOXHILQWXQD
consumed by juvenile tuna is not unexpected, since their abundance can be highly
affected by environmental changes, which cause large inter-annual variations (Vila et
al., 2010; Silva et al., 2011). Otherwise, Sanz-Brau (1990) found that YOY ABFT
caught off Valencia, nearby our sampling area, preyed mainly on pelagic fish species,
similar to what we observed in the specimens sampled in 2009. The above
observations indicate that the diet composition of juvenile ABFT is highly variable and
depends on the abundance of potential preys in the exploited habitat (Salman and
Karakulak, 2009).
Some of fishes and crustaceans found in YOY ABFT stomachs are known to
perform vertical migrations to shallow layers at dusk (see Onsrud and kaartvedt, 1998;
Kaartvedt et al., 2002; Olivar et al., 2012). Sinopoli et al. (2004) suggested that the
presence of mesopelagic fishes, such as myctophids or paralepidids, in the stomachs
of YOY ABFT was related to vertical movements of tuna to deep waters. Although
juvenile ABFT are capable of diving as down as 800 m deep, they spend the majority of
their time swimming in shallow waters above 20 m (Galuardi and Lutcavage, 2012).
We, therefore, concur with Mostarda et al. (2007) and Battaglia et al. (2012) that a
most plausible hypothesis is that these mesopelagic fishes perform nocturnal
migrations to the epipelagic zone, then becoming vulnerable to predators that occur in
shallower depths from dusk to dawn. Our results may indicate that foraging occurs
during the nighttime, as was suggested for adult ABFT captured in the Mediterranean
Sea (Karakulak et al., 2009, Battaglia et al., 2012). However, this hypothesis should be
explored by tracking vertical movement patterns. It is interesting that electronic tagging
data suggest that immature Pacific bluefin tuna (50-70 cm in fork length) are less active
foragers at night (Kitagama et al., 2004, 2007).
5.4.2 SIA
Thus far, there is only one paper reporting natural abundances of
15
N and 13C in
YOY ABFT (Sarà and Sarà, 2007). These authors found low į15N values in muscle and
ϭϭϴ
&KDSWHU'LHWRI<2<EOXHILQWXQD
liver of young tuna captured in the Tyrrhenian Sea compared to the results of our
isotopic analyses. In contrast, their į13C values were similar, though they did not
perform lipid extraction prior to į13C analysis nor did they apply mathematical
corrections to avoid the effects of lipids on į13C. Our SIA results can also be compared
with those obtained from individuals of smaller tuna species captured in the western
Mediterranean having similar sizes to our YOY ABFT, and hence presumably
occupying the same trophic niche. Cardona et al. (2012) reported similar į13C values in
muscle of little tunny (Euthynnus alletteratus) and Atlantic bonito (Sarda Sarda), but
found higher 15N ratios. Yet, these analyses were performed on lipid-extracted samples
and the impact of lipid removal on į15N values, which was estimated in an increase of
0.7‰ in ABFT tuna muscle (Logan, 2009), was not taken into account.
In comparison with juvenile ABFT captured in the Cantabrian Sea (Logan et al.,
2011), our muscle and liver samples showed lower į15N values and higher į13C values,
which may reflect trophic shifts. Nevertheless, these differences can be also due to
regional baseline differences.
5.4.3. Mixing model
Mixing model analyses from muscle and liver stable isotope data estimated that
the analyzed YOY ABFT had fed mainly on mesopelagic fish and crustaceans,
corroborating SCA findings. Mesopelagic fish constitute an important component of the
food web in these ecosystems, linking zooplankton (their main prey) to top predators
(Olivar et al., 2012) andrepresents an important energy source for tunas as they show
high lipid levels compared with other teleosteans (Sabatés et al., 2003).
The differences in dietary compositions assessed from SCA and SIA are
probably related to the fact that SCA provides detailed information of what was
ingested in the previous hours, whereas SIA gives an indication of diet over a longer
time period. Thus, the low proportion of krill inferred from SCA compared with SIA can
ϭϭϵ
&KDSWHU'LHWRI<2<EOXHILQWXQD
be explained by the capture time, since the YOY ABFT were caught during the daytime
when krill would have been digested.
In conclusion, both SCA and SIA indicate that YOY ABFT show a low degree of
feeding specialisation and qualify them as opportunistic and generalist feeders that can
subsist on a wide range of preys.
Acknowledgments
The authors wish to thank María Lema (Unidad de Técnicas Instrumentales de
Análisis, Universade da Coruña) for isotopic analysis. We also thank Maria José
Meléndez (IEO Fuengirola), who collected prey samples. This work was funded by the
European Community (contract no. 212797), Spanish government (contract nos.
CTM2011-29525-C04-01 and CTM2011-27505) and Andalusian government (contract
no. RNM-02469).
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6. Discusión general y conclusiones
6.1. Discusión general
Los análisis de contenido estomacal (SCA) llevados a cabo tanto en individuos
adultos como juveniles indican que el atún rojo es un depredador que ocupa los
niveles tróficos superiores dentro de los sistemas pelágicos oceánicos, con capacidad
para consumir una gran variedad de especies de peces e invertebrados. Además, el
atún rojo muestra una alta variabilidad espacial y temporal en su dieta, posiblemente
asociada a la disponibilidad de las presas potenciales (Salman y Karakulak, 2009). A
lo largo del desarrollo ontogénico, se observa un aumento de la talla máxima de la
presa. Así, para los individuos juveniles de edad 0+ (21 cm < FL < 43 cm) y de edad 12 (65 cm < FL < 111 cm) se observaron tallas máximas de presa de 15,1 cm y 25,5 cm
(observación personal), respectivamente. Por otra parte, en los especímenes adultos
(143 cm < FL < 262 cm) la talla máxima de presa registrada fue 46 cm. A diferencia de
los datos publicados en anteriores trabajos (Young et al., 1997; Chase, 2002),
nuestros resultados indicarían que existe una clara relación entre la talla del atún rojo y
la talla máxima de la presa ingerida, tal y como fue sugerido por Graham (2008). Este
hecho podría relacionarse no sólo con el aumento de la apertura mandibular, sino
también con el aumento de la capacidad natatoria a lo largo del desarrollo, lo cual le
permitiría acceder a presas cada vez de mayor tamaño. La discrepancia entre
nuestros resultados y los obtenidos por Young et al. (1997) y Chase (2002) podría
deberse a que aquellos autores no realizaron SCAs en diferentes fases del ciclo vital.
Young et al. (1997) sólo analizaron individuos inmaduros, mientras que Chase (2002)
analizó
únicamente individuos adultos. Su capacidad de alimentación en densas
agregaciones de presas pequeñas puede explicar que un amplio rango de tallas de
ABFT seleccionen el mismo tamaño de presas (Chase, 2002; Logan et al., 2011). El
establecimiento de la relación talla predador-presa puede solventarse seleccionando
grupos taxonómicos concretos como los peces o crustáceos decápodos.
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En cuanto al tipo de presa encontrada, el SCA aportó poca información en los
individuos adultos, ya que la mayoría de las presas encontradas fueron probablemente
ingeridas en las instalaciones de las almadrabas,si bien a partir del análisis de las
piezas duras se determinó que el atún rojo adulto preda principalmente especies
pelágicas, tal y como fue determinado en trabajos anteriores (Chase et al., 2002;
Eggleston y Bochenek, 1990; Butler et al., 2010). Por otro lado, los individuos juveniles
capturados en Cartagena (Murcia) mostraron una alta variabilidad interanual en la
dieta. Así, en 2008 y 2010, los SCA revelaron que la presa principal fue Illex coindetii,
mientras que en 2009 la presa más abundante fue Sardinella sp. La alta contribución
de Illex coindetii a la dieta de los juveniles de atún rojo podría estar asociada a la
época reproductora de los ommastréfidos, la cual se concentra en la segunda mitad
del año (Gonzalez y Guerra, 1996; Quetglas et al., 1998). Además, en comparación
con otras especies de cefalópodos, Illex coindetii muestra un alto contenido en lípidos
y proteínas (Rosa et al., 2005), representado una importante fuente de energía para
los atunes.
El SCA aporta información muy detallada sobre la dieta, pero no ofrece más
que una instantánea de la composición de la misma, pues sólo permite determinar el
alimento ingerido en las 24 horas previas al análisis. Además, existen una serie de
factores que pueden conducir a la obtención de conclusiones sesgadas al utilizar este
método. Entre ellos se encuentran: a) la gran eficacia del proceso digestivo (Aloncle y
Delaporte, 1973), favorecida por el calentamiento visceral proporcionado por la rete
mirabile (Carey et al., 1984; Graham y Dickson, 2001; Itoh et al., 2003), b) la desigual
velocidad en la digestión de los diferentes tipos de alimento, que puede dar lugar a
errores al determinar las proporciones en la que las distintas presas son ingeridas, y c)
la regurgitación frecuente de comida, inducida por el estrés causado durante la captura
(Chase, 2002), hecho que podría alterar los resultados obtenidos. Además de estos
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inconvenientes, el método de captura también puede provocar imprecisiones en lo
resultados. Así, la flota del cebo vivo del Cantábrico emplea de forma mayoritaria jurel
(Trachurus trchurus) y boga (Boops boops) (Rodríguez-Marín et al., 2003) para la
pesca de túnidos juveniles, lo que dificulta la separación entre presa natural y cebo
(Logan et al., 2011). Por otro lado, los individuos adultos capturados en las
almadrabas de Cádiz son mantenidos en las instalaciones durante largos periodos de
tiempo antes de ser sacrificados, haciendo que los estómagos examinados aparezcan
vacíos o contengan sólo partes duras difíciles de digerir (ej.: otolitos de peces o picos
de cefalópodos). Teniendo en cuenta estos inconvenientes, los análisis de isótopos
estables (SIA) de carbono (į13C) y nitrógeno (į15N) en diferentes tejidos pueden ser
utilizados como una herramienta complementaria a los SCAs de suma utilidad
(Estrada et al. 2005; Cherel et al., 2007; Logan et al., 2011; Goñi et al., 2011).
Debido al bajo contenido en
13
C de los lípidos en comparación con el de las
proteínas e hidratos de carbono, es de gran importancia llevar a cabo extracciones
lipídicas que eviten la subestimación de į13C en los tejidos analizados como
consecuencia de su contenido lipídico (DeNiro y Epstein, 1978). En nuestro caso, se
realizaron extracciones lipídicas usando el método modificado de Bligh y Dyer (1959).
Alternativamente, en la literatura pertinente se pueden encontrar distintas ecuaciones
matemáticas que estiman į13C a partir de la relación carbono-nitrógeno (C:N) (ver
McConnaughey y McRoy, 1979; Fry, 2002; Kiljunen et al., 2006; Post et al., 2007;
Logan et al., 2008). Kiljunen et al. (2006) mostraron que en músculo de vertebrados la
normalización lipídica mediante aproximaciones matemáticas es robusta, aunque no
es del todo válida para cualquier especie y tipo de tejido. Estudios más recientes
(Preum, 2011; Abrantes et al., 2012) concluyen que los valores de į13C varían
ostensiblemente en función de la ecuación usada. Además, la composición de ácidos
grasos del atún rojo puede variar considerablemente incluso entre individuos
capturados en la misma zona (Mourente et al., 2002), dificultando así la aplicación
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insesgada de correcciones matemáticas (Fagan et al., 2011). Por tanto, creemos que
la solución más adecuada es realizar extracciones lipídicas, siendo el método de Bligh
y Dyer (1959) el más utilizado y el más efectivo para tejidos de peces (Logan et al.,
2008). No obstante, los procedimientos aplicados para las extracciones lipídicas
pueden producir incrementos en los valores de į15N. Los disolventes comúnmente
usados para las extracciones de lípidos polares (metanol) pueden también eliminar
proteínas ligadas a grasas estructurales, causando alteraciones en los valores de į15N
(Sotiropoulos et al., 2004, Sweeting et al., 2006). Otra posible explicación para el
incremento de į15N tras las extracciones lipídicas es que los productos de excreción
del metabolismo proteico (amoniaco y amonio), que son solubles en lípidos y con bajo
contenido en
15
N, pueden ser extraídos por solventes orgánicos (cloroformo) (Ingram
et al., 2007). Dado que los efectos de las extracciones lipídcas sobre į15N producen un
elevado nivel de incertidumbre, hemos seguido las recomendaciones de diversos
autores (Sotiropoulos et al., 2004; Murry et al., 2006; Sweeting et al., 2006; Logan et
al., 2008; Elsdon et al., 2010) y los valores de į15N fueron obtenidos a partir de
muestras no tratadas. Por tanto, cada muestra fue dividida en dos submuestras para el
análisis de į13C y į15N por separado (ver Harrod et al., 2005), lo que implicó la
duplicación de los costes analíticos y del tiempo de procesado de las muestras. La
optimización de recursos, por consiguiente, parece una razón poderosa para fomentar
la búsqueda de soluciones alternativas fiables, como podría ser el desarrollo de
modelos matemáticos específicos que corrijan los efectos del contenido en grasa de
diferentes órganos sobre los valores de į13C para diferentes especies procedentes de
diversos hábitats (Post et al., 2007; Logan et al., 2008; Fagan et al., 2011).
En el presente estudio, los datos de į15N obtenidos tanto en músculo como en
hígado no mostraron una relación clara con la talla, tal como había sido publicado en
anteriores trabajos (Estrada et al., 2005: Sarà y Sarà 2007). Una relación positiva entre
ambas variables podría indicar que los individuos adultos consumieron presas situadas
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en niveles tróficos más altos con respecto a los juveniles. Sin embargo, hay que tener
en cuenta las variaciones temporales y espaciales de los valores de į15N en
organismos situados en los niveles tróficos más bajos (baseline) (Olson et al., 2010).
Por tanto, podríamos concluir que la variabilidad espacial y temporal observada en los
valores de į15N de músculo e hígado pueden estar asociados a dos factores: la
posición trófica de la presa y los valores isotópicos de los compuestos nitrogenados
que sirven como nutrientes de organismos situados en las posiciones tróficas
inferiores y que pueden variar de manera temporal y espacial (entre áreas oceánicas y
costeras) (Graham et al., 2007)
Los valores de į13C en músculo e hígado también mostraron una alta
variabilidad temporal y espacial. Estos resultados no son inesperados, ya que los
valores de į13C de los consumidores dependen de los cambios en los valores de de
į13C de los productores primarios (Graham 2009), que a su vez están afectados por la
concentración de
13
CO2 disuelto en el agua (Popp et al., 1998) y la composición de la
comunidad fitoplanctónica (Popp et al., 1999).
El SIA aporta información sobre el comportamiento trófico del atún rojo en
función de la tasa de renovación de los tejidos del órgano estudiado (Gannes et al.,
1998). Tejidos animales con tasas de renovación baja, como el músculo (Hesslein et
al., 1993; MacAvoy et al., 2001), aportan información sobre la alimentación en un
periodo de tiempo de varios meses anterior a la captura (Graham, 2007), mientras que
el hígado, que es un órgano con una tasa de renovación más alta (Suzuki et al., 2005;
Guelinckx et al., 2007), ofrece información en una escala de tiempo menor (semanas)
(Graham, 2007). Así pues, conociendo la firma isotópica del atún rojo y de sus presas
potenciales, podemos estimar la composición de la dieta mediante el uso de modelos
de mezcla isotópicos (Parnell et al., 2010), los cuales requieren el conocimiento previo
de factores de enriquecimiento (o discriminación) isotópicos presa-depredador, ya que
ϭϯϯ
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el uso de factores de discriminación inadecuados pueden generar errores importantes
en la estimación de la dieta (Bond y Diamond, 2011).
En la presente memoria, calculamos los factores de enriquecimiento para los
isótopos estables del carbono (ǻ13C) y nitrógeno (ǻ15N) en músculo e hígado de
individuos juveniles y adultos mantenidos en condiciones semicontroladas de
alimentación. Nuestros resultados indican una cierta variabilidad entre los distintos
factores de discriminación calculados que puede ser debida a diversos factores: a)
diferencia de edad (Sweeting et al., 2007a,b), b) diferencias en la dinámica metabólica
entre los tejidos analizados, y c) diferencias entre las dietas (Caut et al., 2008, 2009).
En cualquier caso, las desviaciones típicas obtenidas en músculo mostraron valores
más bajos, lo cual podría
indicar que este tejido es más adecuado para la
reconstrucción de dietas a partir del uso de modelo de mezcla isotópicos, tal y como
ha sido sugerido en estudios anteriores (Pinnegar y Polunin 1999; Suzuki et al., 2005).
Además, con objeto de evaluar el efecto de la composición lipídica sobre ǻ13C y ǻ15N,
los factores de discriminación fueron calculados usando todas las posibles
combinaciones entre muestras con y sin lípidos de depredador y presa. Los resultados
obtenidos indicaron que la eliminación de los lípidos produjo alteraciones en los datos
de ǻ13C y ǻ15N, siendo sus efectos más evidentes sobre los valores del isótopo del
carbono. A tenor de estos resultados, creemos que en futuros trabajos sobre ecología
trófica del atún rojo basados en SIA deberían utilizarse los valores de ǻ13C calculados
tras las extracciones lipídicas (Sotiropoulos et al., 2004; Murry et al., 2006; Logan et
al., 2008; Hoffman y Sutton, 2010). Por otro lado, deberían utilizarse valores de ǻ15N
de muestras sin tratar, puesto que las extracciones lipídicas pueden generar errores
en los valores de į15N, como ya se ha discutido anteriormente.
A partir de factores de discriminación específicos, los modelos de mezcla
isotópicos como SIAR (Parnell et al., 2010), el utilizado en la presente memoria,
ϭϯϰ
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pueden aportar información útil sobre la dieta cuando los resultados obtenidos
mediante SCA son limitados (Polito et al., 2011). Además, mediante el uso de SIAR es
posible evitar muchos de los errores fisiológicos y temporales inherentes al SCA. Las
presas utilizadas para la estimación de la dieta mediante SIAR fueron identificadas a
partir del SCA, así como de los datos publicados en trabajos previos de alimentación
llevados a cabo en atunes capturados alrededor de la península Ibérica (Ortiz de
Zárate y Cort 1986; Sanz-Brau, 1990; Logan et al., 2011). La estimación de la dieta
mediante SIAR indicó que tanto los adultos capturados en el Estrecho de Gibraltar
(presente estudio) como los juveniles de edad 1-2 capturados en el Mar Cantábrico
(observación personal) se alimentan de presas situadas en niveles bajos, medios y
altos de las cadenas tróficas. Este hecho explicaría que, a pesar de haber sido
definido como un “top predator” (Logan et al., 2011), el atún rojo muestra valores de
į15N muy inferiores a los publicados en tiburones como Isurus oxyrinchus, Alopias
vulpinus o Prionace glauca, situados en los niveles tróficos superiores de los sistemas
pelágicos (Estrada et al., 2003; MacNeil et al., 2005). Por otro lado, el SIAR estimó que
los juveniles de edad 0+ capturados en la costa de Cartagena (Murcia) se alimentan
principalmente de especies que realizan movimientos verticales a la superficie durante
la noche (peces mesopelágicos y crustáceos), lo que sugiere que los juveniles
muestran un cierto comportamiento alimenticio nocturno, tal y como fue sugerido por
Karakulak et al. (2009) y por Battaglia et al. (2012) en atunes adultos capturados en el
mediterráneo oriental. Cabe destacar que los peces mesopelágicos tienen una alta
importancia en las redes tróficas, ya que son el enlace entre zooplankton y predadores
superiores de ecosistemas pelágicos (Olivar et al., 2012). Además, en muchas casos
el contenido lipídico de estos peces es superior al de peces pelágicos, representando
una importante fuente de energía para los atunes (Sabatés et al., 2003).
Por otro lado, para aplicar los modelos de mezcla isotópicos es de suma
importancia que exista un equilibrio entre los tejidos estudiados y la dieta. En nuestro
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estudio, la proximidad entre las firmas isotópicas de músculo e hígado indicaría que el
atún se encuentra cerca del equilibrio con las presas de nuestras zonas de estudio
(MacNeil et al., 2005). Sin embargo, para los individuos adultos existe una alta
variabilidad entre las firmas isotópicas de ambos tejidos, lo que indicaría que el atún
habría llegado recientemente al Golfo de Cádiz y por tanto habría que tomar con
cautela los resultados obtenidos a partir de los análisis en músculo, ya que
probablemente este tejido no se encuentra en equilibrio con las presas. Por otro lado,
estudios de marcado electrónico (Block et al., 2005; Rooker et al., 2007; Stockesbury
et al., 2007) indican que el atún rojo permanece varias semanas en el Golfo de Cádiz
antes de cruzar el Estrecho de Gibraltar, lo que implicaría que el hígado sí estaría en
equilibro con las presas de la zona. Para los individuos juveniles, las diferencias
isotópicas músculo-hígado son mucho menores, lo que podría indicar que tanto
músculo como hígado podría estar en equilibrio con las presas locales. No obstante, al
tratarse de una zona costera, el isótopo de carbono puede verse influenciado por
aportes de nutrientes de origen continental (Vinagre et al., 2012), lo que explicaría las
diferencias significativas observados entre músculo e hígado.
El presente trabajo sugiere que SIA y SCA son técnicas complementarias que
son aplicables en estudios sobre el comportamiento trófico del atún rojo. Así, a pesar
de que los resultados obtenidos a partir del SCA pueden estar limitados por varios
factores, la información que aporta es de suma importancia para la ejecución de los
modelos de mezcla isotópicos, ya que permite determinar las presas apropiadas. Por
otro lado, aunque el SIAR aporta información sobre la dieta del atún rojo a una escala
temporal mayor, desde el punto de vista cuantitativo no es tan exacta como la
aportada por el SCA. Ambos métodos indican que el atún rojo muestra una alta
variabilidad espacial, temporal y ontogénica en la dieta y definen a esta especie como
un predador con un comportamiento trófico generalista y oportunista.
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6.2. Conclusiones
1. Los análisis de contenidos estomacales (SCA) revelaron que el atún rojo es un
depredador que ocupa niveles tróficos superiores dentro de los sistemas pelágicos
oceánicos, con capacidad para consumir una amplia variedad de presas.
2. La dieta del atún rojo muestra una alta variabilidad espacial, temporal y
ontogénica, posiblemente asociada a la disponibilidad y abundancia de las presas
potenciales y a sus amplios movimientos migratorios horizontales y verticales en la
columna de agua.
3. Con el fin de evitar los efectos del contenido lipídico sobre į13C en los tejidos
analizados, es de suma importancia llevar a cabo extracciones lipídicas o aplicar
modelos matemáticos correctores fiables. No obstante, la eliminación total de los
lípidos provoca alteraciones en los valores de į15N, por lo que el análisis de į15N
ha de realizarse en muestras no tratadas.
4. En futuros trabajos sobre ecología trófica del atún rojo basados en SIA deberían
utilizarse los valores de ǻ13C calculados tras las extracciones lipídicas, mientras
que deberían utilizarse valores de ǻ15N obtenidos de muestras sin tratar.
5. Los análisis de isótopos estables (SIA) de carbono (į13C) y nitrógeno (į15N) en
diferentes tejidos acoplados a modelos de mezclas isotópicos pueden ser
utilizados como una herramienta complementaria a los SCA evitando conclusiones
sesgadas que se pueden producir si sólo se contemplan esto último análisis. Este
sesgo puede ser de gran importancia, especialmente si los SCA están basados en
un escaso número de muestras de estómagos o si se realizan en reducidos
muestreos espaciotemporales.
6. Los valores de į13C y į15N obtenidos en músculo e hígado de atún rojo mostraron
una alta variabilidad espacial y temporal en concordancia con su complejo ciclo
vital.
ϭϯϳ
'LVFXVLyQ\FRQFOXVLRQHV
7. Para
reconstruir la dieta del atún rojo mediante la aplicación de modelos de
mezcla (“mixing models”) es recomendable utilizar factores de discriminación
específicos como los estimados en la presente memoria.
8. Los factores de discriminación para los isótopos estables de carbono (ǻ13C) y
nitrógeno (ǻ15N) en músculo e hígado de individuos juveniles y adultos muestran
variaciones que podrían estar asociadas a diferencias de edad de los individuos
estudiados, diferencias en la dinámica metabólica entre los tejidos analizados, y
diferencias entre las dietas.
6.3. Futuras investigaciones
1. En la literatura podemos encontrar diversos estudios que sugieren que los factores
de discriminación isotópicos pueden variar en función de la dieta (Caut et al., 2008,
2009). Por ello, y con el fin de identificar estas posibles variaciones, estamos
desarrollando ensayos experimentales donde se analizarán (ǻ13C y
ǻ15N) en
bonito atlántico (Sarda Sarda) alimentados con tres carnadas distintas: Sardina
pilchardus, Loligo sp. y Parapanaeus longirostris. Además, el cálculo de estos
factores de enriquecimiento nos permitirá realizar estimaciones más exactas de la
dieta de pequeños túnidos mediante el uso de modelos de mezcla.
2. En el tercer capítulo de la presente tesis se ha sugerido que la inanición puede
provocar un aumento en el isótopo de nitrógeno (į15N) y una disminución del
contenido lipídico en músculo e hígado del atún rojo, tal y como fue observado por
Doucett et al. (1999) en salmón atlántico (Salmo salar). Sin embargo, este hecho
nunca ha sido corroborado en túnidos. Con este propósito, estamos realizando un
ensayo experimental con bonitos atlánticos mantenidos en inanición durante un
periodo de 60 días, en el cual se analizarán los efectos de una inanición
prolongada sobre el contenido lipídico en músculo e hígado y se estudiará la
evolución de la firma isotópica de ambos tejidos.
ϭϯϴ
'LVFXVLyQ\FRQFOXVLRQHV
3. En el marco del proyecto ACEITUNA (Life cycle and spatial dynamics of bluefin
tuna around the Iberian Peninsula) estamos realizando análisis isotópicos (į15N y
į13C) en tejidos con tasas de renovación distintas (músculo e hígado) de atunes
juveniles capturados en el Mar Cantábrico. En función de los resultados podremos
dilucidar si todos los atunes rojos que realizan la migración trófica a este área
provienen de un mismo hábitat o si, por el contrario, antes de realizar las
respectivas migraciones, distintos atunes pueden seguir estrategias diferentes,
utilizando diferentes hábitats y explotando nichos ecológicos diversos.
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