Aquaculture research, tập 41, số 2, 2010

Furthermore, the assump-tion of a ¢xed growth rate c in Eqns 6 and 7 is con-trary to the biology and growth trajectory of ¢sh.Another exponential ¢sh growth model was pro-posed more rece

REVIEW ARTICLE

Modelling growth and body composition in fish

nutrition: where have we been and where are we going?

Andre¤ Dumas, James France & Dominique Bureau

Department of Animal and Poultry Science, Centre for Nutrition Modelling, University of Guelph, Guelph, Ontario, Canada

Correspondence: A Dumas, Department of Animal and Poultry Science, Centre for Nutrition Modelling, University of Guelph, Guelph, Ontario N1G 2W1, Canada E-mail: adumas@uoguelph.ca

Abstract

Mathematical models in ¢sh nutrition have proven

indispensable in estimating growth and feed

require-ments Nowadays, reducing the environmental

foot-print and improving product quality of ¢sh culture

operations are of increasing interest This review

starts by examining simple models applied to

de-scribe/predict ¢sh growth pro¢les and progresses

to-wards more comprehensive concepts based on

bioenergetics and nutrient metabolism Simple

growth models often lack biological interpretation

and overlook fundamental properties of ¢sh (e.g

ec-tothermy, indeterminate growth) In addition, these

models disregard possible variations in growth

trajec-tory across life stages Bioenergetic models have

served to predict not only ¢sh growth but also feed

requirements and waste outputs from ¢sh culture

op-erations However, bioenergetics is a concept based

on energy-yielding equivalence of chemicals and has

signi¢cant limitations Nutrient-based models have

been introduced into the ¢sh nutrition literature over

the last two decades and stand as a more biologically

sound alternative to bioenergetic models More

me-chanistic models are required to expand current

un-derstanding about growth targets and nutrient

utilization for biomass gain Finally, existing models

need to be adapted further to address e¡ectively

con-cerns regarding sustainability, product quality and

body traits

Keywords: modelling, ¢sh, growth, body

composi-tion, nutrition

IntroductionAquaculture has become a multinational industryover the last 30 years and is expected to maintain anaverage annual growth rate of44% over the period2010^2030 (Bruge're & Ridler 2004) Greater demandfor ¢sh, combined with the reduction in capture ¢sh-eries and more a¡ordable retail prices for several spe-cies, has contributed to foster and sustain theaquaculture industry (NRC 1999; FAO 2006) How-ever, intensi¢cation and potential for development ofthe aquaculture sector have created challenges re-garding pro¢tability, environmental sustainabilityand product quality, most of which are related ulti-mately to nutrition (e.g Naylor, Goldburg, Primavera,Kautsky, Beveridge, Clay, Folke, Lubchenco, Mooney

& Troell 2000; Watanabe 2002) These concernsalong with uncertainties surrounding productioncosts stress, among other things, the need to developaccurate tools to manage production and predict sce-narios soundly

Here, mathematical modelling ^ de¢ned as the use

of equations to describe or simulate processes in asystem ^ represents an e¡ective approach to taking

up the challenges that aquaculture is facing matical models in animal nutrition have proven in-dispensable in estimating growth and feedrequirements that have always represented major

Mathe-¢elds of interest in livestock production (Kellner1911; Murray1914; Brody1945; Blaxter1989; Baldwin1995; Dumas, Dijkstra & France 2008) In aquacul-ture, the quality, safety and health bene¢ts of ¢shproducts are now of increasing interest (Hocquette,

Richardson, Prache, Me¤dale, Du¡y & Scollan 2005;

Caswell 2006; Moza¡arian & Rimm 2006)

Composi-tion of ¢sh with reference to carcass yield, fatty acid

composition and levels of lipid and contaminants has

recently received further attention in studies on

nu-trition, genetics and health (Rasmussen 2001;

Blan-chet, Lucas, Julien, Morin, Gingras & Dewailly 2005;

Hamilton, Hites, Schwager, Foran, Knuth & Carpenter

2005; Tobin, Kause, Mntysaari, Martin, Houlihan,

Dobly, Kiessling, Rungruangsak-Torrissen, Ritola &

Ruohonen 2006)

This article begins by summarizing brie£y the

bio-logical properties of ¢sh growth Thereafter, major

current models applied in ¢sh nutrition are reviewed

and challenged Finally, a global perspective is o¡ered

and future directions in modelling are suggested to

address better the concerns in ¢sh production

Biological properties of fish growth

Despite its complexity, growth takes place in a highly

organized scheme in animals Diverse regulatory

strategies exist in organisms to adjust in£ux of

che-micals (amino acids, fatty acids, minerals, etc.) and

excretion of waste products even in a disruptive

en-vironment in order to maintain homeostasis (Nelson

& Cox 2000) As growth processes do not occur in a

chaotic manner, they can be generally described and

predicted using conventional mathematics

Growth, body composition and metabolic

utiliza-tion of nutrients or allocautiliza-tion of resources are related

to each other and change considerably during the

lifespan of animals Growth trajectories of animals ^

de¢ned here as the pattern of weight gain achieved

through time ^ display an almost universally

sigmoi-dal shape with an asymptotic body size at adult stage

(Fig 1a) It is well documented that growth rate

in-creases during the juvenile stage, i.e the so-called

self-accelerating phase of growth, and levels o¡ when

the animal approaches the adult stage or induces

re-productive growth This last portion of the growth

curve is also referred to as the self-inhibiting phase

of growth (Brody 1927; Charnov, Turner & ler 2001; Lester, Shuter & Abrams 2004) In contrastwith birds and mammals, several species of ¢sh, mol-luscs, crustaceans and amphibians are capable ofgrowing well beyond their size at sexual maturity.These organisms display a much less evident self-in-hibiting phase (Fig 1b) This phenomenon, also re-ferred to as indeterminate growth, results in adebatable position of asymptotic weight at the adultstage Indeterminate growth is regulated by environ-ment and genetics (Sebens 1987), which a¡ect thephysiological capacity of an organism to synthesizemuscle ¢bres throughout its life cycle (Biga & Goetz2006) Another peculiarity of ¢sh is their ectother-mic nature Growth rate of ¢sh is thus highly depen-dent on water temperature To date, few attemptshave been made to describe ¢sh growth with an alge-braic expression that accommodates their ectother-mic nature and indeterminate growth

Winemil-Current models in fish nutritionThe complexity of interactions in nutrition, the vastamount of information available nowadays and thesubstantial cost of experiments make the use ofmathematical models appealing Models are helpfultools in that they have the ability to represent com-plex phenomenon (e.g growth) in a relatively simpleway [e.g weight gain as a function of protein deposi-tion (PD)] The following sections review brie£y ex-tant models currently applied in ¢sh nutrition

Simple growth functionsGrowth functions are any models where weight orlength (dependent variable, y) is calculated usingtime, t, as the predictor (independent variable) takingthe form y 5 f(t), where f represents some functionalrelationship Growth functions are usually analyticalsolutions to di¡erential equations that can be ¢tted to

Age (arbitrary units)

(b)(a)

Age (arbitrary units)

Figure 1 Typical growth trajectory of (a) terrestrial animals and (b) ¢sh

the growth data generally by means of non-linear

re-gression analysis (Thornley & France 2007) The

sig-moidal or curvilinear shape of the growth trajectory

indicates that linear regression is not suitable to

de-scribe growth, unless only small portions of the

curve are considered For this reason, growth

func-tions stand presumably as the best means of

estimat-ing animal growth Because a large number of

growth functions had been proposed in the last

cen-tury, only those that have been widely applied in ¢sh

studies or that have considered the e¡ect of

tempera-ture on growth of ectotherms are discussed here For

a broader description of extant growth functions in

animal science and theories associated with them,

the reader is referred to Ricker (1979), Parks (1982),

Ratkowski (1990), Seber and Wild (2003) and

Thorn-ley and France (2007)

von Bertalanffy equation

The equation of von Bertalan¡y (1957) stands as the

most studied and applied growth function to predict

growth of ¢sh and other ectotherms (Ricker 1979;

Hernandez-Llamas & Ratkowsky 2004; De Graaf &

Prein 2005; Katsanevakis 2006) The equation was

¢rst proposed by Pˇtter (1920), a German ¢sh

biolo-gist, who conceptualized growth as anabolism

pre-vailing over catabolism The di¡erential and integral

forms of his equation, currently referred to as the von

Bertalan¡y equation, are

whereZ and k are rate parameters for anabolism and

catabolism, respectively, k is a rate constant equal to

k(1 b), and u equals 1  b The allometric exponent

for anabolism b is allowed to vary between 0 and 1

The equation has an asymptote, a £exible point of

in-£exion, and adheres to the law of allometry

(0obo1) Various rearrangements of the von

Berta-lan¡y equation exist in the literature (Ricker 1979;

Katsanevakis 2006)

The assumption regarding an asymptotic ¢nal size

led to unrealistic values for indeterminate growers

and, for this reason, was regarded as a mathematical

artefact rather than a fact of nature (Knight 1968;

Ro¡ 1980) Parker and Larkin (1959) removed thecatabolic part of Eq (1) in order to relax the con-straint on the ¢nal asymptote and suggested estimat-ingm and b by ¢tting to particular life history groupsand growth stanzas:

dW

The assumption that growth is determined by thedi¡erence between anabolism and catabolism hasbeen proven inaccurate because it overlooks the role

of timing of maturation on the shape of the growthcurve (Day & Taylor 1997; Lester et al 2004) Evidencesuggests that the change in growth rate of indetermi-nate growers results from the decision to allocatemore resources towards gonad development ratherthan movement towards equilibrium between ana-bolism and catabolism (Day & Taylor1997; Czarnol˛es-

ki & Kozlowski 1998; Charnov et al 2001) However,the e¡ect of reproduction is not always perceptible inectotherms with indeterminate growth, especially in

an environment with £uctuating water tures (Dumas & France 2008)

tempera-(Correction added on 9 September 2009, after ¢rstonline publication: In the sentence containing Equa-tion (1),‘u equals 1b’ was corrected to ‘u equals 1 b’.)

Thermal-unit growth coefficient (TGC)The French botanist Re¤aumur laid the basis of thethermal-unit concept in1735 in an attempt to explainthe time required from sowing to harvesting of crops

by summing the degre¤-chaleur over that period (Allen1976; Bonhomme 2000) The concept was introduced

in ichthyology at the turn of the 20th century raŁdek 1930) Although Norris (1868) noted that thedevelopment rate of trout eggs varies with tempera-ture, Wallich (1901) apparently ¢rst applied the con-cept of the thermal unit to record the development

(Be›leh-of ¢sh eggs Wallich (1901) de¢ned one thermal unit

as 11F above 321F during 1 day, meaning that themean daily water temperature of 361F is equivalent

to four thermal units Krogh (1914) showed that therelationship between developmental rate (usually in

% day 1) and temperature ( 1C) exhibited a straightline (slope has degree-day as denominator) over a cer-tain range of temperature The time and thermalsummation (degree-day) needed for hatching ¢sheggs can thus be estimated using a simple regressionequation (Krogh 1914; Embody 1934; Hayes 1949).The thermal-unit concept was also applied to esti-mate growth of hatched ¢sh Iwama and Tautz (1981),

who did not use the concept explicitly, started from

Eq (2) and related the rate parameter for anabolism

to mean daily water temperature averaged over the

rearing period (T):

dW

wherem (40) has units of g1  b( 1C day) 1, T (a

con-stant) is water temperature ( 1C) and the allometric

exponent b (40) is dimensionless Integrating Eq (3)

W1b¼ W01bþ mTð1  bÞt

ð4Þ

where W0is the initial (time 0) value of W Starting

from Iwama and Tautz (1981), Cho (1992) explicitly

introduced the degree-day concept into their model

and proposed, without formal mathematical

deriva-tion, a modi¢cation to Eq (4):

Wn1=3¼ W01=3þ c

1000

Xn i¼1

Ti

where c [g1/3( 1C day) 1)] is TGC and Ti( 1C) is mean

daily temperature

From an inspection of Eq (1) and Eq (3), it is

evi-dent that the TGC model is a special case of von

Ber-talan¡y’s equation with

m¼3 TGC  T

1000 ; b¼ 2=3; l ¼ 0

The TGC model has since been widely used in the

aquaculture literature (e.g Einen, Holmefjord,

—s-grd & Talbot 1995; Kaushik 1998; Willoughby 1999;

Stead & Laird 2002; Hardy & Barrows 2002) This

simple model has been adapted recently to the

di¡er-ent growth stanzas of rainbow trout (Oncorhynchus

mykiss, Walbaum) across life stages (Dumas, France

& Bureau 2007) Despite its convenience, the

ther-mal-unit approach can entail systematic errors in

si-tuations where the temperature moves too far away

from the optimum for growth (Krogh 1914; Hayes

1949; Ricker 1979; Jobling 2003)

Exponential equation or specific growth

rate (SGR)

The origin of SGR goes back in 1798 and was

devel-oped to address demographic concerns Reverend

Thomas Malthus, a mathematician, published an

es-say in 1798 in which he stated that the human

popu-lation increased according to a geometric progression(Gilbert 1993) His model, known as Malthus’ Law orthe Malthusian Model, corresponds to the exponentialgrowth equation:

W¼ W0emtwhere W is body weight, W0is body weight at time

t 5 0,m is a growth coe⁄cient (in units of per unit oftime) and time t is measured as age

The growth coe⁄cientm is better known as SGR,which is used ubiquitously in ¢sh studies The equa-tion for SGR (m) is

The SGR model is based on the incorrect tion that ¢sh growth is continually exponential Thishas proven not to be the case and, therefore, growthpredictions have to be re-calculated every time thepredicted growth curve moves too far away from theobserved trajectory (Brett 1979) Unlike Brett (1974),Elliott (1975) plotted the relationships between SGR,body weight and temperature and derived the follow-ing equation to predict the growth of brown troutSalmo trutta (Linne¤):

assump-dW

dt ¼ ða þ b2TÞW1b 1 ð5Þwith the integral form (provided T is assumed con-stant):

W¼ b1ða þ b2TÞt þ Wb 1

0

where b1, b2and a are weight exponent

(dimension-less), slope [% (day 1C) 1] and intercept (% day 1)

of the relationship between SGR (% day 1) and T

( 1C) respectively

The Elliott model is often used to investigate ¢sh

growth, especially in the ecology literature (Craig

1982; Allen 1985; Jensen 1990) From an inspection of

Eqns (1) and (5), it is evident that the Elliott model is

also a special case of von Bertalan¡y’s equation with

m¼ a þ b2T; b¼ 1  b1;l¼ 0

Moreover, Eq (3) of Iwama and Tautz (1981) has

many similarities to Eq (5) Therefore, Elliott (1975)

in-troduced the e¡ect of temperature into Eq (2) of Parker

and Larkin (1959) before Iwama and Tautz (1981)

Equation (5) needs to be solved repeatedly over the

growing period because slope and intercept change

with water temperature and body weight (Fig 2) This

drawback limits application of the Elliott (1975) model

because predictions can be applicable only to very

short intervals and preclude comparison between

stu-dies, especially under £uctuating water temperatures

Elliott, Hurley and Fryer (1995) revised the Elliott

model and included considerations for optimum (Topt)

and limiting (Tlim) temperatures for growth (Fig 2)

The resulting equation takes the form

b

0þ bcðT  TlimÞt100ðTopt TlimÞ

ð6Þ

where c is the SGR of a 1g ¢sh at Topt,Tlim5 lower (TL)

or upper (TU) temperature at which SGR is 0:Tlim5TL

if T Toptor Tlim5TUif T4Topt

Equation (6) is valid as long as the water

tempera-ture does not change Under £uctuating temperatempera-ture

conditions, the equation needs to be extended

and body weight at the end of a growing

period (t1, t2, , tk), Wk, is now predicted using the

following:

Wbk¼ Wb

0þ bc100

in days

The authors reported that Eq (7) yields signi¢cantdiscrepancies when the growing period exceeded 3months (Elliott et al.1995) Furthermore, the assump-tion of a ¢xed growth rate c in Eqns (6) and (7) is con-trary to the biology and growth trajectory of ¢sh.Another exponential ¢sh growth model was pro-posed more recently by Lupatsch and Kissil (1998):

Y¼ aXbecTwhere Y and X are weight gain (g ¢sh 1day 1) andbody weight (g ¢sh 1), respectively, a and c are con-stants, b is weight exponent (dimensionless) and T iswater temperature ( 1C) This equation is also a spe-cial case of the von Bertalan¡y with Z 5 aecTand

Y¼ aXbecTThis model has been used successfully to describethe growth trajectory of warmwater ¢sh species such

as gilthead seabream (Lupatsch & Kissil 1998), opean sea bass (Lupatsch, Kissil & Sklan 2001), whitegrouper (Lupatsch & Kissil 2005) and barramundi(Glencross 2006) within a relatively narrow range oftemperature ( 20^27 1C) It assumes an exponentialrelationship between water temperature and growthrate, which can be true only for a certain range of op-timal temperature, and appears in disagreementwith the thermal-unit concept and reaction kineticmodels for ectotherms The latter showed that growthrate is inhibited at high temperature, and relationshipbetween growth rate and temperature displays anasymmetric bell-shaped curve (Sharpe & DeMichele1977; School¢eld, Sharpe & Magnuson 1981).(Correction added on 9 September 2009, after ¢rstonline publication: In the sentence ‘This equation isalso a special case of the von Bertalan¡y with

Figure 2 E¡ects of body weight (BW) and temperature

on speci¢c growth rate (SGR) Lower (TL) and upper (TU)

temperatures indicate where SGR is zero (adapted from

Elliott 1975)

Based on visual appraisal of typical growth curves

(e.g Fig.1), animals do not grow geometrically, i.e

ex-ponentially, across life stages The exponential

growth function is therefore not suitable for

accu-rately predicting or describing the growth trajectory

of ¢sh and other animals Furthermore, this function

yields unavoidably systematic deviations (Fig 3)

Growth data on Arctic charr Salvelinus alpinus

(Linne¤) obtained from Simmons (1997) are used here

to compare the TGC and SGR models (constant water

temperature: 12 1C; duration: 112 days) Using the

lat-ter equation, growth is underestimated from 11.5 g

(W0) to 174.2 g (Wf) whereas body weight increases

steeply from 174.2 to 678 g over a 56-day period,

which is unrealistic This is in agreement with Brett

(1974,1979) and Cho (1992) who pointed out that SGR

leads to underestimation of growth between values of

W0andWkused to compute SGR and to serious

over-estimations of weight gain beyond Wk

In spite of its limitations, SGR remains widely

accepted by editors and recommended

ubiqui-tously in the ¢sh literature likely because of its ease

of use (Barton 1996; Willoughby 1999; Alanr et al

2001; Stead & Laird 2002) At best, SGR can serve in

comparing di¡erent performances, although

com-parisons using SGR are valid only if ¢sh have similar

W0and Wkand are reared at the same water

tem-perature because, as stated earlier, growth rate of

¢sh varies with size and temperature For all the

reasons mentioned above, SGR ¢nds very little

biolo-gical support and is therefore largely unsuitable as a

¢sh growth model and tool to compare short-term

growth performance

Simple models of feed conversion

to biomassGoals in animal nutrition are arguably to maximizethe conversion of inputs (e.g feed, investments) intohigh-quality outputs over a short period of time Im-proving the conversion of dietary inputs to leanrather than adipose tissue growth is of bene¢t toproducers and consumers It can also contribute toreduced waste outputs and provide room for man-oeuvre given the volatility of pro¢t margins As a con-sequence, several studies have turned their attentiontowards feed e⁄ciency, protein utilization and lipiddistribution as a function of ¢sh size, feeding leveland alternative ingredients for example (Aursand,Bleivik, Rainuzzo, Jrgensen & Mohr 1994; Azevedo,Cho, Leeson & Bureau 1998; Lupatsch, Kissil, Sklan &Pfe¡er 2001; Cheng, Hardy & Usry 2003) These stu-dies have generated a large amount of information(e.g on body composition) that still needs to be ex-plored and synthesized

Most of these studies were designed to describe imal responses (e.g weight gain) within speci¢c ex-perimental conditions Unfortunately, their ability todescribe a wide array of animal responses in varyingsituations is limited because their experimental de-signs prevent representation of the mechanisms inthe internal structure of the organism that are re-sponsible for the observed responses For this reason,several mathematical modellers have insisted on theneed to move from a requirement-based (input^out-put) to a rate:state approach where the major vari-ables in play can be described and relateddynamically, similar to a metabolic pathway (AFRC1991;Thornley & France 2007; Lo¤pez 2008) The rate:-state formalism consists of representing the rate ofchange of pools, referred to as state variables, usingdi¡erential equations (Dijkstra, Mills & France2002) Such formalism considers the state of a pool

an-as the result of dynamic exchanges, i.e in£ux (e.g.protein synthesis) and e¥ux (e.g protein degrada-tion) of substances Di¡erential equations are a valu-able tool and have been proven essential in dynamicmodelling in describing the behaviour of a systemconcisely and e⁄ciently (Kleiber 1961; France & Keb-reab 2006) The rate:state formalism is discussedfurther in Nutrient-based models

Bioenergetic modelsAnimal energetics refers to the quantitative study ofenergy exchanges induced by metabolic processes in

Figure 3 Comparison between observed and predicted

body weight of Arctic charr (Salvelinus alpinus Linne¤)

using the thermal-unit growth coe⁄cient (TGC) and

spe-ci¢c growth rate (SGR) Growth data are from Simmons

(1997)

living organisms to stay alive, grow and reproduce

(Nelson & Cox 2000) Energy exists in materials of

dietary and body origin and is released in the form of

heat to support work (Blaxter 1989)

Models constructed on the basis of bioenergetic

principles utilize mathematical equations describing

the heat transactions and adhere generally to a

fac-torial scheme, also referred to as an energy budget

The factorial approach follows from the

metaboliz-able energy concept (Armsby 1903; HMSO 1975),

where energy expenditures or heat production are

al-located to di¡erent metabolic processes according to

an order of priority (NRC 1993; Bureau, Kaushik &

Cho 2002) Inspired by Ivlev (1939) and Winberg

(1956), Warren and Davis (1967, 1968) adhered to the

factorial approach and proposed a simple additive

equation to describe the energy budget of ¢sh:

where C is intake of energy and F and U are energy

losses in faeces, and urine and gills respectively (all

variables in units of MJ day 1) VariableDB

repre-sents growth (energy gain) of the ¢sh and R is energy

loss through metabolic processes associated with

maintenance and heat increment of feeding Each

component of the equation is described using

mathe-matical relationships derived mostly using statistical

analyses

Equation (8) gained acceptance in ¢sheries andwas adopted by Ricker (1968), Elliott (1976a, b) andKitchell, Stewart and Weininger (1977) A systematicterminology for the description of energy budget andmetabolic processes in animal nutrition was devel-oped later by NRC (1981), and heat losses were cate-gorized as shown in Fig 4

Fish growth has usually been predicted using twodi¡erent approaches in bioenergetic models One way

of forecasting ¢sh growth assumes that energy take drives weight gain This assumption is encoun-tered mostly in ¢sheries and ecology studies becauseavailability of food in natural ecosystems often limits

in-¢sh growth (Elliott 1976a, b; Kitchell et al 1977; From

& Rasmussen 1989) An alternative approach ers genetic or desired growth rate rather than nutri-tion as the factor limiting animal growth (Hubbell1971; Calow 1973; Oldham, Emmans & Kyriazakis1997) Here, intake of energy is a function of the re-quirements of the individual to achieve a givengrowth capability or growth target This approachwas suggested by Winberg (1956, p.174) and is mostlyused in aquaculture where ¢sh are generally fed tosatiation with nutritionally complete diets (Cho1990; Lupatsch, Kissil & Sklan 2001; Zhou, Xie, Lei,Zhu & Yang 2005) Genetically determined growthcapability of ¢sh is assessed using simple growthfunctions, especially the TGC model and the

Heat Increment

Waste formation andexcretionProduct formationDigestion andabsorption

require-exponential model of Lupatsch and Kissil (1998) (Cho

& Bureau 1998; Lupatsch & Kissil 1998; Lupatsch,

Kissil & Sklan 2001; Glencross 2006)

Probably the most adapted bioenergetic model for

farmed ¢sh is theFISH-PRFEQprogram (Cho & Bureau

1998) The model follows a factorial scheme and

esti-mates feed requirements and waste outputs from

ex-pected growth performance, digestible energy of the

diet and body energy deposition TheFISH-PRFEQmodel

has been used or adapted to di¡erent ¢sh species and

for various purposes (Kaushik 1998; Papatryphon,

Petit, van der Werf, Kaushik & Claver 2005; Zhou

et al 2005)

Bioenergetic models predict energy gain, but they

provide little information on the chemical

composi-tion (moisture, protein, lipid and ash) of biomass

gain This characteristic has two signi¢cant

draw-backs Firstly, the bioenergetic models can entail

sys-tematic errors because the relationship between

recovered energy and weight gain changes across life

stages (Bureau et al 2002) More energy is contained

per unit of biomass gain for a large ¢sh (e.g

10 kJ g 1BW) than for a small ¢sh (e.g 5 kJ g 1BW)

under typical rearing conditions Studies have

shown that the composition of biomass gain includes

more lipid and less water in a large ¢sh than in a

small ¢sh (Shul’man 1974; Dumas, de Lange, France

& Bureau 2007) Protein and lipid deposition (LD)

are two distinct biological processes driven by

di¡er-ent factors or determinants that are overlooked in

bioenergetic models Secondly, the recovered energy

can serve to determine the energy retention

e⁄-ciency, but it is of no utility in assessing the e⁄ciency

of nutrient utilization or rates of deposition unless

re-liable equations are developed to describe body

com-position across life stages

It has been shown that feed evaluation systems

and animal growth models based on bioenergetics

have major limitations (Birkett & de Lange 2001a;

Ba-jer, Whitledge & Hayward 2004; Dijkstra, Kebreab,

Mills, Pellikaan, Lo¤pez, Bannink & France 2007)

Feed evaluation systems cannot rely on bioenergetics

exclusively and have to consider dietary proteins and

other nutrients, especially with ¢sh that rely heavily

on proteins to meet their metabolic needs Moreover,

digestible proteins, along with dietary amino acids,

a¡ect feed e⁄ciency and nitrogen retention e⁄ciency

signi¢cantly (Azevedo, Leeson, Cho & Bureau 2004a;

Encarnacao, de Lange, Rodehutscord, Hoehler,

Bu-reau & BuBu-reau 2004; Booth, Allan & Anderson

2007) The e¡ect of protein intake, and not only

en-ergy, on ¢sh growth performance was soon

acknowl-edged and included in models to estimate feedrequirements, weight gain, and e⁄ciency of energyand protein retention of African cat¢sh (Machiels &Henken 1986), tilapia (van Dam & De Vries 1995), carp(Schwarz & Kirchgessner 1995), European sea bass(Lupatsch, Kissil & Sklan 2001, Lupatsch et al 2003),gilthead sea bream and white grouper (Lupatsch et al.2003; Lupatsch & Kissil 2005)

Although the factorial approach assumes that ergetic costs of metabolic processes are additive, evi-dence suggests that energy is allocated in acompensatory fashion, i.e according to the meta-bolic scope of the animal at a particular life stage(Wieser 1989; Rombough 1994) This particularitymay explain why the concept of energy requirementfor maintenance remains debatable and is a¡ected bybody composition and other factors such as ambienttemperature and breed (e.g Close, Mount & Brown1978; ARC 1981; Thompson, Meiske, Goodrich, Rust

en-& Byers 1983; Campbell, Crim, Young en-& Evans 1994;Knap 2000) For instance, models based on bio-energetic principles assume that growth and feede⁄ciency will be nil when animals are fed a mainte-nance ration (recovered energy 5 0) This assump-tion has been proven inaccurate in ¢sh, as well as inother animals, where positive weight gain was stillobserved even though animals were fed at or below amaintenance ration and the whole-body energy bal-ance was negative (Huisman 1976; Le Dividich, Ver-morel, Noblet, Bouvier & Aumaitre 1980; Meyer-Burgdor¡, Osman & Gˇnther 1989; Lupatsch, Kissil

& Sklan 2001; Bureau, Hua & Cho 2006)

Bioenergetic models have also been used to mate feed requirements of ¢sh and waste outputsfrom ¢sh culture operations (Winberg 1956; NRC1993; Cho & Bureau 1998; Lupatsch & Kissil 1998,2005) Assessing waste outputs requires good esti-mates of body composition in order to compute, forexample, nitrogen and phosphorus discharge intothe environment

esti-Nutrient-based modelsHistorically, animal nutritionists ¢rst considered nu-trients (i.e chemicals and macromolecules that pro-vide essential nourishment for maintenance, growthand reproduction) rather than energy to study theconversion of feed to biomass (for a review, see Du-mas et al 2008) Chemical (water, nitrogen, fat,minerals and carbon) and physical (bone, muscle,adipose tissue, blood, skin, hair and o¡al) composi-

tions of carcass and chemical composition of

feed-stu¡s were estimated for farm animals before the

20th century (Wol¡ 1895) Wol¡ (1895) appears to be

the ¢rst to adopt a factorial approach to describe

rela-tively and in detail the fate of dietary nitrogen,

car-bon and fat with consideration of intake, losses

through faeces and urine, and recovery as body fat

and body £esh in the carcass

In view of the limitations of bioenergetics, animal

nutritionists and growth modellers have returned to

more nutrient- or biochemical-oriented approaches

(e.g Machiels & Henken 1986; Gerrits, Dijkstra &

France 1997; Birkett & de Lange 2001b) These

nutri-ent-based models may be de¢ned as mechanistic

sys-tems designed to simulate the fate of dietary

nutrients, with consideration of utilization of amino

acids, fatty acids and their precursors Similar to

bioenergetics, nutrient-based models serve to predict

growth, nutrient requirements and waste outputs

However, these models further explain the sing of nutrients by considering intermediary meta-bolism and are therefore more mechanistic.Bioenergetic models are mostly descriptive, rely on arather simple framework of energy transaction, re-present energy using units of joules or calories andoverlook the stoichiometry of energy-yielding nutri-ents Nutrient-based models are more explanatory,rely on metabolic pathways of nutrients, representenergy in terms of ATP (e.g mol ATP per moleculesubstrate), and consider the stoichiometry of chemi-cal reactions These nutrient-based models have beenshown to be e¡ective for mammals and ¢sh (e.g Gill,Thornley, Black, Oldham & Beever 1984; van Dam &

proces-De Vries 1995)

Partitioning of nutrients can follow either a ial or a compartmental scheme Figures 5 and 6 illus-trate and contrast the factorial and compartmentalapproaches respectively The former approach is con-sistent with conventional bioenergetic models andadheres to the same assumptions (e.g energy is allo-cated according to a hierarchy, metabolic processesare additive) The latter was introduced in the 1950sinto animal nutrition by Blaxter, Graham and Wain-man (1956)-these authors did not nominate it as com-partmental or mechanistic modelling, though-andconsists of subdividing a given level of organization(e.g whole animal, tissue, cell) into di¡erent pools(e.g amino acids in the blood, intracellular glucose)(Thornley & France 2007)

factor-Pools are referred to as state variables (i.e a tity that de¢nes the size of the pool at a given point intime) and can be in steady state (e.g blood glucose in

quan-a fquan-asting quan-animquan-al) or non-stequan-ady stquan-ate (e.g muscle tein content in a growing animal) Flows of substrates(e.g lysine and other metabolites) between pools andinto and out of the system are represented as termswithin di¡erential equations, which are usually

pro-Intake

Anabolism andCatabolism

Figure 5 Example of a factorial framework of nutrient

partitioning (adapted from Blaxter & Mitchell 1948;

Bir-kett & de Lange 2001a) Flow of nutrients through each

metabolic process (intake, faecal and urinary excretion,

anabolism and catabolism, basal metabolism and

produc-tion) is determined mostly using regression and mass

balance equations

Blood

Fatty acidsAmino acids

Protein inviscera

Lipid invisceraProtein in

dressedcarcass

Lipid indressedcarcassCatabolism

Figure 6 Example of a simple compartmental framework of nutrient partitioning (adapted from Gill et al 1989) Flow ofnutrients between each pool (amino acids, fatty acids, protein and lipid in the viscera and dressed carcass) is determinedusing di¡erential and stoichiometric equations

based on rules of stoichiometry and saturation

ki-netics Unlike equations based on regression analysis,

di¡erential equations suit the mathematical

descrip-tion of dynamic systems better because they can

ex-hibit a wide array of behaviour (May 1976; Dijkstra &

France 1995)

The compartmental approach overcomes, to a

cer-tain extent, the lack of £exibility and theoretical basis

associated with the underlying assumptions of the

factorial approach (AFRC 1991; Beever, France &

Al-derman 2000), but may require comprehensive

data-sets Compartmental models for animals have been

designed for a wide range of purposes (e.g to predict

feed intake, digestion rate, growth) since 1980

(Ma-chiels & Henken 1986; Imamidoost & Cant 2005; Bar,

Sigholt, Shearer & Krogdahl 2007)

In nutrient-based models, growth results from

ac-cretion of chemicals (mostly water, protein, lipid and

ash), not energy as assumed fundamentally in

bioe-nergetic models Therefore, the accuracy of these

models depends on consistent mathematical

descrip-tion of the reladescrip-tionships between nutrient deposidescrip-tion

and weight gain

Modelling body composition and rates of

nutrient deposition in farm animals

The reliability of bioenergetic and nutrient-based

models depends to a considerable extent on valid

esti-mates of nutrient deposition rates Moreover, the

search for optimal nutrient conversion into biomass

and maximum pro¢ts, as well as concerns regarding

product quality (e.g fatness, fatty acid composition

and bio-accumulation of various constituents) and

environmental sustainability are strong motives for

modelling body composition and nutrient deposition

in farm animals

Numerous data exist on body composition of

var-ious ¢sh species, especially rainbow trout, European

sea bass and white grouper (e.g Reinitz 1983;

Lu-patsch, Kissil & Sklan 2001; Lupatsch & Kissil 2005)

From these studies, it can be concluded that

whole-body protein contents are comparable between

spe-cies and constant across the grow-out phase, and

the contents of moisture, lipid, ash and energy vary

in a similar pattern among species as ¢sh size

in-creases (cf Lupatsch, Kissil & Sklan 2001; Bureau

et al 2002)

In the past, boundaries or limits to contents of

body water (BH2O), body protein (BP), body lipid

(BL) and body ash (BA) in ¢sh have not been

deter-mined from large datasets (cf Shearer 1994; Jobling2001; Lupatsch, Kissil & Sklan 2001; Bureau et al.2002) Recently, Dumas, de Lange et al (2007) devel-oped equations to predict body composition in rain-bow trout using data from 66 studies Theseequations account for the variation in body composi-tion, represent possible benchmarks for future com-parison and provide reliable foundations forassessment of the e¡ects of di¡erent factors on thecomposition of growth in ¢sh

Estimating body composition andrates of nutrient deposition usingregression analysis

Mathematical description of body composition in imal nutrition started 460 years ago McMeekan(1941) stressed the importance of assessing meatquality in animal production and addressing require-ments of speci¢c markets The author recognized thetechnical di⁄culty, high cost and time requirementassociated with chemical analysis and insisted onthe need to develop indices of composition, i.e math-ematical equations McMeekan (1941) proposed line-

an-ar regression equations to predict contents of notonly body fat but also muscle and bones in baconpigs Equations were of the form yi¼ b0þ b1xiwhere yiis the ith ¢tted value of the outcome (i.e ske-leton, muscle or fat) in units of g,b0is the intercept,b1

is the slope and xiis the ith value of a given predictor(e.g length of carcass) McMeekan (1941) overlookedthe e¡ect of body weight on carcass composition.Moreover, he did not describe body composition withequations of allometric formðyi¼ 10b 0 xb1

i Þ eventhough, in his days, the concept of allometry wascommonly applied in biology to designate rate ofchange between di¡erent anatomical characteristics

of an organism (for a review, see Gayon 2000).Furthermore, the allometric equation had alreadybeen used in animal production to examine the rate

of fat deposition in di¡erent body parts of poultry(Lerner 1939) Almost 30 years later, KotarbinŁska(1969) related body protein to fat-free lean mass andbody water to body protein using linear regressions

of allometric form She also related body ash to bodyprotein assuming an isometric rather than an allo-metric relationship These isometric and allometricrelationships based on regression analysis still pre-vail in estimating body composition of farmed ani-mals, including ¢sh (Parker & Vanstone 1966; Groves1970; ARC 1981; Weatherley & Gill 1983; de Lange,Morel & Birkett 2003; Dumas, de Lange et al 2007)

Rates of nutrient deposition have been a topic of

in-terest and have found several applications in animal

nutrition for the last 40 years (Oslage & Fliegel 1965;

Thorbek1969) Assessing rates of nutrient deposition,

mostly protein deposition (PD) and lipid deposition

(LD) represents a comprehensive way of examining

e⁄ciency of utilization of feed components for

growth, and e¡ects of genetics, nutrition and

envir-onment on composition of the growth response and

dietary requirements (Black, Davies, Bray, Giles &

Chapple 1995; Schinckel & de Lange 1996) Because

growth and PD are associated, the amino acid pro¢le

in PD can serve in approximating amino acid

re-quirements of growing animals (e.g M˛hn & de

Lange 1998)

In ¢sh, description of the nutrient deposition rate

has received little attention to date, despite its

simpli-city, relevance and acceptance in modelling growth

of livestock species over the past three decades (ARC

1981; Black et al.1995; NRC 1996,1998) To our

knowl-edge, the concept of nutrient deposition rate was

in-troduced recently into the ¢sh literature and

consisted of describing rates of PD, LD and ash

de-position on a degree-day basis in rainbow trout

across life stages (Dumas, de Lange et al 2007) Such

quantitative description still needs to be extended to

other strains and ¢sh species

Estimating nutrient deposition using

explicit partitioning rules

Partitioning rules attempt to represent the utilization

of dietary nutrients or energy for protein relative to

LD and have often served as a means to adjust for

body composition, especially in pig nutrition

Parti-tioning rules and their associated partiParti-tioning

fac-tors ¢gure among the debatable parameters in

nutrition modelling, and the reader is referred to

re-views by de Lange, Morel and Birkett (2008),

Sand-berg, Emmans and Kyriazakis (2005a, b) and

Emmans and Kyriazakis (1997) for more details The

present section describes the partitioning rules

en-countered in ¢sh nutrition models

Rule 1: Body composition regulates dietary

nutrient partitioning

The ¢rst rule assumes that growing animals regulate

the breakdown of protein and lipid according to their

current and/or target body lipid to body protein ratio

(BL:BP) Machiels and Henken (1986) introduced this

rule into ¢sh nutrition Preferential body lipid to bodyprotein ratio (prefBL:BP), minimum value for this ra-tio (minBL:BP) and mature lipid weight to matureprotein weight (mBL:mBP) are variations of theBL:BP ratio that have been proposed over the last 15years (Whittemore1995; Emmans & Kyriazakis1999).Whittemore (1995) estimated the minBL:BP forpigs to be 0.5:1 In a study conducted by Reinitz(1983), the BL:BP ratio for juvenile rainbow trout sta-bilized at 0.1:1 during 84 to 140 days of starvation(¢sh were still alive) This value may be considered asthe minBL:BP ratio for that species at that size(o10 g)

The BL:BP ratio has been arbitrarily set as a meter in order to avoid unrealistic prediction of bodycomposition (Machiels & Henken 1986) Althoughthese authors did not further justify their concept,the BL:BP ratio ¢nds biological support Indeed, thecorrection in body composition that occurs duringcompensatory growth lends credence to the concept.For instance, Kyriazakis and Emmans (1992) showedthat farm animals following a period of nutritionallimitation seek to correct their body composition inorder to return to the normal or preferential bodycomposition needed to achieve their growth target.The fraction of energy requirements that is sup-plied by oxidation of body fat increases non-linearly

para-as the BL:BP ratio becomes higher (Machiels & ken 1986) Neither Machiels and Henken (1986) norKyriazakis and Emmans (1992) proposed an equation

Hen-to describe this non-linear relationship explicitly.Despite some biological support, the concept re-mains highly empirical and its purpose appears more

to accommodate the prediction of body compositionthan to describe the real metabolic processes (Sand-berg et al 2005a) Intake of dietary energy and diges-tible amino acids, and not only target BL:BP ratio at agiven body weight, a¡ects the partitioning of energybetween PD and LD in growing animals (Emmans &Kyriazakis 1997; Encarnacao et al 2004;Weis, Birkett,Morel & de Lange 2004) Finally, variations in photo-period and temperature are other factors likely to af-fect the BL:BP ratio in ¢sh (e.g Brown 1957; Jobling2001; Hemre & Sandnes 2008)

Rule 2: Protein intake regulates dietarynutrient partitioning

Starting with Machiels and Henken (1986), van Damand De Vries (1995) moved away from the constraint

on BL:BP ratio They related the proportion of energy

obtained from oxidation of body fat (or body protein)

to protein-feeding level rather than the BL:BP ratio

They did not measure oxidation of body constituents

per se, but rather estimated it using a calibration

pro-cedure Their results indicated a positive relationship

between body protein oxidation and both dietary

protein intake and the protein to gross energy (P:GE)

ratio of the diet These observations are in agreement

with other studies on ¢sh (Halver & Hardy 2002;

Aze-vedo et al 2004a) Their concept, namely AALIRAT

(i.e auxiliary variable determining the proportion of

ATP requirement provided by oxidation of body fat),

has the advantage of representing the e⁄ciency

of protein use and the protein-sparing e¡ect of extra

energy

The models proposed by Machiels and Henken

(1986) and van Dam and De Vries (1995) estimated

fat deposition poorly, indicating that nutrient

parti-tioning is not just a matter of BL:BP ratio or level of

dietary protein intake These discrepancies result to

some extent from inaccurate assumptions regarding

the energetic costs of metabolic transactions Both

models set a ¢xed value for ATP requirement for

pro-tein synthesis [0.06 and 0.075 mol ATP g 1protein

synthesized in Machiels and Henken (1986) and van

Dam and De Vries (1995) respectively], but this

ener-getic cost seems to be rather variable across studies

(Jobling 1985; Rombough 1994)

Machiels and Henken (1986), along with van Dam

and De Vries (1995), assumed that no dietary

nutri-ents were oxidized to support energy requiremnutri-ents

for maintenance and growth, an assumption that

has been proven inaccurate in mammals and ¢sh

(Lyndsay 1976; Kim, Grimshaw, Kayes & Amundson

1992; Stoll, Burrin, Jahoor, Henry, Yu & Reeds 1998;

Halver & Hardy 2002) The models of Machiels and

Henken (1986) and van Dam and De Vries (1995)

re-cognized the existence of amino acid and glucose

blood pools (cf their £ow diagrams) However, no

at-tempt was made to describe these pools

mathemati-cally because they were not considered a source of

ATP

Rule 3: Biochemical saturation kinetics

regulates dietary nutrient partitioning

The saturation kinetic approach is used in

compart-mental modelling and utilizes enzyme-kinetic

equa-tions such as the Michaelis^Menton and the Hill (e.g

Gill et al 1984; Pettigrew, Gill, France & Close 1992;

Bar et al 2007) Flows of biochemical entities (e.g

amino acids, fatty acids, glucose, volatile fatty acidsand acetyl-CoA) are regulated by their respectiveconcentration in pools and by various constants (e.g.maximum velocity and substrate a⁄nity) The con-straints on nutrient partitioning no longer refer di-rectly to explicit ¢xed ratios (e.g minBL:BP) Certainbiochemical transactions can exert control overothers and kinetic constants such as maximum velo-city (Vmax) can serve to achieve a realistic body com-position (Halas, Dijkstra, Babinszky, Verstegen &Gerrits 2004) Nevertheless, this approach can fail torepresent accurately observed empirical relation-ships For instance, the predicted relationships be-tween energy intake and PD displayed a curvilinearshape, whereas observed data followed a linear pat-tern in evaluating a model designed to partition diet-ary nutrients in growing pigs (Halas et al 2004).Assumptions are sometimes made in models based

on saturation kinetics that basically mask a lack ofprecise knowledge and may lead to discrepancies

A global and fresh perspective ondescribing composition of fish growthDescribing body composition and e¡ects of nutrientdeposition on weight gain is crucial in animal nutri-tion modelling (NRC 1998; Kyriazakis 1999; de Lange

et al 2003) Partitioning of nutrients in models based

on energy and nutrient metabolism needs to rely onequations that re£ect accurately the limits or bound-aries to body nutrient contents in ¢sh (From & Ras-mussen 1984; Lupatsch, Kissil & Sklan 2001; Bureau

et al 2002) Assuming the allocation of dietary ents responds to an organized biological scheme, var-ious deterministic equations can therefore bedeveloped to describe body composition and deposi-tion of body constituents (e.g protein, lipid, water,ash and, eventually, amino acids and fatty acids).The dependence of bioenergetic growth models onaccurate estimation of body composition has beenstressed in several ¢sh studies (Cui & Wootton 1988;From & Rasmussen 1989; Jobling 1994; Cui & Xie2000) To date, several bioenergetic models have as-sumed a constant energy content per unit of ¢sh bio-mass or energy retention per unit of time (Winberg1956; Solomon & Bra¢eld 1972; Elliott 1976a; Kitchell

nutri-et al 1977; Brnutri-ett 1995) However, body lipid has beenshown to vary with plane of nutrition and age or lifestage in ¢sh and unavoidably a¡ects the energy con-tent of the body (Weatherley & Gill 1987; Azevedo

et al 2004a; Azevedo, Leeson, Cho & Bureau 2004b)

Cui and Wootton (1989) observed that energy content

of ¢sh biomass was a major parameter a¡ecting the

accuracy of bioenergetic models, especially at low

and high feeding levels Quantitative description of

body composition in growing ¢sh and of feed

utiliza-tion thus becomes essential if systematic errors in

bioenergetic models are to be avoided

The equations developed in Dumas, de Lange et al

(2007) addressed, to a certain extent, the need to

con-sider the e¡ect of body composition in bioenergetic

models by indicating that body energy content as

protein can be easily predicted whereas prediction of

body energy as lipid requires further modelling

stu-dies Moreover, it is easier and probably more

accu-rate to predict BW from BP than from recovered

energy The results of Dumas, de Lange et al (2007)

provide realistic bounds for di¡erent body

constitu-ents that can help setting correct assumptions and

accurate relationships in nutrient-based models

In models based on nutrient partitioning,

see-mingly arbitrary rules founded on untested

assump-tions (Cui & Xie 2000) were set to avoid unrealistic

estimates of body composition (see,‘‘Estimating

nutri-ent deposition using explicit partitioning rules’’)

Most nutrient-based models fail to de¢ne

quantita-tively what the realistic bounds are for di¡erent body

constituents (e.g BP) This has led to assumptions

that can be misleading For instance, van der Meer

and van Dam (1998) applied a minimum BL content

of 1% relative to BW, a value that is unlikely to occur

in fed ¢sh (restricted or not) weighing43 g (Phillips

Jr, Livingston & Dumas 1960; Reinitz 1983)

Body protein in ¢sh remains at a stringent

con-stant fraction of BW across life stages (Groves 1970;

Lupatsch et al 2003; Dumas, de Lange et al 2007)

Re-gression analysis is therefore an appropriate tool to

describe the relationship between BW and BP

Although BL and BA are highly correlated with BW,

large variability across life stages suggests the need

for more comprehensive models Rates of PD and LD

varied across life stages and higher values of PD and

LD were observed in ¢sh with faster growth rates

(Dumas, de Lange et al 2007) Dietary lipid intake

promoted LD, but not PD, in certain studies on

rain-bow trout (Rasmussen, Ostenfeld & McLean 2000;

Ge¤lineau, Bolliet, Corraze & Boujard 2002;

Chaiyape-chara, Casten, Hardy & Dong 2003) Therefore,

geno-type, life stage and life history and feeding regime

(diet composition and ration) stand out as

explana-tory variables to be included in future mechanistic

models to predict better the composition of biomass

gain

Relationships between body constituents and BWacross life stages of ¢sh di¡er from that of other farmanimals In contrast to beef cattle and pigs, BP ishighly and linearly associated with BW even beyondmarket size (cf Dumas, de Lange et al 2007 withBlack, Cambel, Williams, James & Davies 1986 andNRC 1996) Similarly, BL and BH2O contents appearmore linearly related to BW in ¢sh than in other live-stock species (Black et al.1986; NRC 1996) Percentage

of dressed carcass of ¢sh is, similar to ducks andsmall game birds, relatively constant, whereas it in-creases with BW up to market size in other animalssuch as pigs, broiler chickens and turkeys (Black

et al 1986; Swatland 1994; Landgraf, Susanbeth,Knap, Looft, Plastow, Kalm & Roehe 2006; Dumas,

de Lange et al 2007) Unlike other livestock species(e.g NRC 1998), the relationship between daily PDand BW in ¢sh displays a pattern with no negativeslope in older animals (Dumas, de Lange et al 2007)

In contrast to contemporary perceptions (Elliott1976b; Shearer 1994), evidence suggests that BP var-ies isometrically with BW (i.e assumes the same rate

of change between absolute BP content and BW) in

¢sh (Dumas, de Lange et al 2007)

Concluding remarks: towardsmechanistic modelling of fish growthVariations in growth trajectory, body compositionand rates of nutrient deposition are, to date, better de-scribed than explained, although certain hypotheseshave been suggested Explanatory studies are there-fore required to further improve our understanding

of the underlying mechanisms responsible for thesevariations

Sexual maturation stands out as a mechanismlikely governing growth trajectory and thus rates ofnutrient deposition Triggering the maturation pro-cess in salmonids involves a reduction or cessation

of feed intake, deterioration of £esh quality, ment of large gonads and secondary sexual charac-ters, and greater catabolism of body protein andbody lipid stores to supply nutrients for new tissuesynthesis (Love 1980; Sargent, Tocher & Bell 2002;Roth, Dorenfeld Jenssen, Magne Jonassen, Foss & Ims-land 2007) Ageing entails a decrease in the e⁄ciency

develop-of nutrient utilization in mammals and ¢sh (Brody1945; Weatherley & Gill 1987)

It is not yet clear as to what the determinants ofsexual maturation in ¢sh are It appears that environ-mental conditions at embryonic and larval stages,

along with genetics, interact to program the growth

trajectory and the age at sexual maturity throughout

the ontogeny of rainbow trout The e¡ect of

incuba-tion temperature on growth rate could a¡ect the

tim-ing of sexual maturity ultimately Evidence suggests

that temperature during egg incubation could be

re-sponsible for muscle growth dynamics before the ¢rst

spawning in ¢sh (Johnston, Manthri, Alderson,

Smart, Campbell, Nickell, Robertson, Paxton & Burt

2003; Albokhadaim, Hammond, Ashton, Simbi,

Bayol, Farrington & Stickland 2007; Martell & Kie¡er

2007) Intermediate temperature during incubation

promotes hyperplasia and thus growth rate at

the juvenile stage (Fauconneau & Paboeuf 2001;

Rowlerson & Veggetti 2001; Steinbacher, Haslett,

Obermayer, Marschallinger, Bauer, Snger & Stoiber

2007)

Body lipid stores are possibly another determinant

of the onset of sexual maturation Feeding level and

energy intake, which are known to govern the energy

stores (i.e body lipid content) in ¢sh (Azevedo et al

1998; Rasmussen & Ostenfeld 2000; Yamamoto,

Shi-ma, Furuita & Suzuki 2002), may induce or delay the

timing of sexual maturation because lipid reserves

have been shown to in£uence maturation in ¢sh

(Rowe, Thorpe & Shanks 1991; Silverstein, Shearer,

Dickho¡ & Plisetskaya 1998) In contrast, other

stu-dies concluded that growth rate has a greater role in

triggering the maturation process than body lipid

stores (Shearer, Parkins, Gadberry, Beckman &

Swan-son 2006; Beckman, Gadberry, Parkins, Cooper &

Ar-kush 2007) Here, the e¡ect of growth rate and

nutrition may have been confounded and new

stu-dies are thus required to elucidate the answer to this

question

To sum up, current understanding of causality and

relationships between environmental conditions

during egg incubation, muscle growth dynamics,

plane of nutrition, growth rate and timing of sexual

maturation is fragmentary and has not yet been well

described quantitatively These factors a¡ect growth

trajectory, body composition and nutrient deposition

in ¢sh and will have to be described better in order to

develop meaningful mathematical models

To conclude, there is a need to synthesize

avail-able information and develop £exible explanatory

models Mechanistic models designed to simulate

di¡erent scenarios are crucial to progress towards

optimization of feed e⁄ciency and growth,

reduc-tion of waste outputs, prevenreduc-tion of sexual

matura-tion and, therefore, deterioramatura-tion of growth rate

and £esh quality before ¢sh reach market size in

or-der to avoid a decline in pro¢tability of ¢sh cultureoperations Moreover, future models should be de-veloped to accommodate changes in outcomes ofinterest to the private sector More than 60 yearsago, animal growth stood as the main concern (Ro-bertson 1923; Wright 1926; Brody 1945) Modelswere thus developed or adapted to predict weightgain with respect to time Nowadays, the composi-tion of weight gain, yield of particular anatomicalparts and food safety represent new topics of inter-est to the animal production industry because ofthe continually evolving eating habits of consu-mers and increasing public awareness of healthi-ness and environmental sustainability (Young,Northcutt, Buhr, Lyon & Ware 2001; Hocquette

et al 2005; Torstensen, Bell, Roselund, Hendersen,Gra¡, Tocher, Lie & Sargent 2005; Caswell 2006).Here, mathematical modelling serves as a usefultool to meet current and prospective challenges,extract further information and help orient futureresearch programmes in ¢sh nutrition

ReferencesAFRC (1991) Theory of response to nutrients by farm ani- mals Nutrition Abstracts and Reviews, Series B 6,683^722 Alanr A., Kabri S & Paspatis M (2001) Feeding manage- ment In: Food Intake in Fish (ed by D Houlihan,T Boujard

& M Jobling), pp 332^353 Blackwell Science, Oxford, UK Albokhadaim I., Hammond C.L., Ashton C., Simbi B.H., Bayol S., Farrington S & Stickland N (2007) Larval pro- gramming of post-hatch muscle growth and activity in Atlantic salmon (Salmo salar) Journal of Experimental Biology 210, 1735^1741.

Allen J.C (1976) A modi¢ed sine wave method for ing degree days Environmental Entomology 5, 388^396 Allen K.R (1985) Comparison of the growth rate of brown trout (Salmo trutta) in a New Zealand stream with experi- mental ¢sh in Britain Journal of Animal Ecology 54, 487^ 495.

calculat-ARC (1981) The Nutrient Requirements of Pigs CAB, Slough, UK.

Armsby H.P (1903) The Principles of Animal Nutrition with Special Reference to the Nutrition of Farm Animals JohnWi- ley and Sons, New York, NY, USA.

Aursand M., Bleivik B., Rainuzzo J.R., Jrgensen L & MohrV (1994) Lipid distribution and composition of commer- cially farmed Atlantic salmon (Salmo salar) Journal of the Science of Food and Agriculture 64, 239^248.

Azevedo P.A., Cho C.Y., Leeson S & Bureau D.P (1998) E¡ects

of feeding level and water temperature on growth, ent and energy utilization and waste outputs of rainbow

nutri-trout (Oncorhynchus mykiss) Aquatic Living Resources 11,

227^238.

Azevedo P.A., Leeson S., Cho C.Y & Bureau D.P (2004a)

Growth, nitrogen and energy utilization of juveniles from

four salmonid species: diet, species and size e¡ects

Aqua-culture 234, 393^414.

Azevedo P.A., Leeson S., Cho C.Y & Bureau D.P (2004b)

Growth and feed utilization of large size rainbow trout

(Oncorhynchus mykiss) and Atlantic salmon (Salmo salar)

reared in freshwater: diet and species e¡ects, and

re-sponses over time Aquaculture Nutrition 10, 401^411.

Bajer P.G., Whitledge G.W & Hayward R.S (2004)

Wide-spread consumption-dependent systematic error in ¢sh

bioenergetics models and its implications Canadian

Jour-nal of Fisheries and Aquatic Sciences 61, 2158^2167.

Baldwin R.L (1995) Modeling Ruminant Digestion and

Meta-bolism Chapman & Hall, London, UK.

Bar N.S., Sigholt T., Shearer K.D & Krogdahl — (2007) A

dy-namic model of nutrient pathways, growth, and body

composition in ¢sh Canadian Journal of Fisheries and

Aquatic Sciences 64, 1669^1682.

Barton B.A (1996) General biology of salmonids In:

Princi-ples of Salmonid Culture (ed by W Pennell & B.A.

Barton), pp 29^95 Elsevier Science B.V., Amsterdam, the

Netherlands.

Beckman B.R., Gadberry B., Parkins P., Cooper K.A &

Ar-kush K.D (2007) State-dependent life history plasticity

in Sacramento River winter-run chinook salmon

(Oncor-hynchus tshawytscha): interactions among photoperiod

and growth modulate smolting and early male

matura-tion Canadian Journal of Fisheries and Aquatic Sciences 64,

256^271.

Beever D.E., France J & Alderman G (2000) Prediction of

re-sponse to nutrients by ruminants through mathematical

modelling and improved feed characterization In: Feeding

Systems and Feed Evaluation Models (ed by M.K

Theodor-ou & J France), pp 275^297 CAB International, Oxon, UK.

Be›lehraŁdek J (1930) Temperature coe⁄cients in biology

Bio-logical Reviews 5, 30^58.

Biga P.R & Goetz F.W (2006) Zebra¢sh and giant danio as

models for muscle growth: determinate vs indeterminate

growth as determined by morphometric analysis

Ameri-can Journal of Physiology: Regulatory, Integrative and

Com-parative Physiology 291, R1327^R1337.

Birkett S & de Lange K (2001a) Limitations of conventional

models and a conceptual framework for a nutrient £ow

representation of energy utilization by animals British

Journal of Nutrition 86, 647^659.

Birkett S & de Lange K (2001b) A computational framework

for a nutrient £ow representation of energy utilization by

growing monogastric animals British Journal of Nutrition

86, 661^674.

Black J.L., Cambel R.G.,Williams I.H., James K.J & Davies G.T.

(1986) Simulation of energy and amino acid utilization in

the pig Research and Development in Agriculture 3, 121^

145.

Black J.L., Davies G.T., Bray H.J., Giles L.R & Chapple R.P (1995) Modelling the e¡ects of genotype, environment and health on nutrient utilisation In: Proceeding of the

IV th International Workshop on Modelling Nutrient tion in Farm Animals (ed by A Danfaer & P Lescoat), pp 85^105 Foulum, Denmark.

Utilisa-Blanchet C., Lucas M., Julien P., Morin R., Gingras S & wailly EŁ (2005) Fatty acid composition of wild and farmed Atlantic salmon (Salmo salar) and rainbow trout (Oncor- hynchus mykiss) Lipids 40, 529^531.

De-Blaxter K.L (1989) Energy Metabolism in Animals and Man Cambridge University Press, Cambridge, UK Blaxter K.L., Graham McC.N & Wainman F.W (1956) Some observations on the digestibility of food by sheep, and on related problems British Journal of Nutrition 10, 69^91.

Blaxter K.L & Mitchell H.H (1948) The factorization of the protein requirements of ruminants and of the protein va- lues of feeds, with particular reference to the signi¢cance

of the metabolic fecal nitrogen Journal of Animal Science

7, 351^372.

Bonhomme R (2000) Bases and limits to using ‘degree.day’ units European Journal of Agronomy 13, 1^10.

Booth M.A., Allan G.L & Anderson A.J (2007) Investigation

on the nutritional requirements of Australian snapper grus auratus (Bloch & Schneider,1801): e¡ects of digestible energy content on utilization of digestible protein Aqua- culture Research 38, 429^440.

Pa-Brett J.R (1974) Tank experiments on the culture of pan-size sockeye (Oncorhynchus nerka) and pink salmon (O gor- buscha) using environmental control Aquaculture 4, 341^352.

Brett J.R (1979) Environmental factors and growth Fish Physiology 8, 599^675.

Brett J.R (1995) Energetics In: Physiological Ecology of Paci¢c Salmon (ed by C Groot, L Margolis & W.C Clarke), pp 3^

68 UBC Press,Vancouver, BC, Canada.

Brody S (1927) Growth and development with special reference

to domestic animals III Growth rates, their evaluation and signi¢cance University of Missouri College of AgricultureAgri- cultural Research Bulletin 97 Columbia, USA.

Brody S (1945) Bioenergetics and Growth With Special ence to the E⁄ciency Complex in Domestic Animals Hafner Publishing Company, NewYork, NY, USA.

Refer-Brown M.E (1957) The Physiology of Fishes,Volume 1: lism Academic Press, New York, NY, USA.

Metabo-Bruge're C & Ridler N (2004) Global Aquaculture Outlook in the Next Decades: An Analysis of National Aquaculture Pro- duction Forecasts to 2030 FAO Fisheries Circular No 1001 Food and Agriculture Organization of the United Nations, Rome, Italy.

Bureau D.P., Kaushik S.J & Cho C.Y (2002) Bioenergetics In: Fish Nutrition, 3rd edn., ed by J.E Halver & R.W Hardy),

pp 1^59 Academic Press, San Diego, CA, USA.

Bureau D.P, Hua K & Cho C.Y (2006) E¡ect of feeding level

on growth and nutrient deposition in rainbow trout

(Oncorhynchus mykiss Walbaum) growing from 150 to

600 g Aquaculture Research 37, 1090^1098.

Calow P (1973) The relationship between fecundity,

phenol-ogy, and longevity: a systems approach American

Natur-alist 107, 559^574.

Campbell W.W., Crim M.C.,Young V.R & Evans W.J (1994)

In-creased energy requirements and changes in body

com-position with resistance training in older adults.

American Journal of Clinical Nutrition 60, 167^175.

Caswell J.A (2006) Quality assurance, information tracking,

and consumer labeling Marine Pollution Bulletin 53,650^

656.

Chaiyapechara S., Casten M.T., Hardy R.W & Dong F.M.

(2003) Fish performance, ¢llet characteristics, and health

assessment index of rainbow trout (Oncorhynchus mykiss)

fed diets containing adequate and high concentrations of

lipid and vitamin E Aquaculture 219,715^738.

Charnov E.L., Turner T.F & Winemiller K.O (2001)

Repro-ductive constraints and the evolution of life histories with

indeterminate growth Proceedings of the National

Acad-emy USA 98, 9460^9464.

Cheng Z.J., Hardy R.W & Usry J.L (2003) Plant protein

ingre-dients with lysine supplementation reduce dietary

pro-tein level in rainbow trout (Oncorhynchus mykiss) diets,

and reduce ammonia nitrogen and soluble phosphorus

excretion Aquaculture 218, 553^565.

Cho C.Y (1990) Fish nutrition, feeds, and feeding: with

spe-cial emphasis on salmonid aquaculture Food Reviews

In-ternational 6, 333^357.

Cho C.Y (1992) Feeding systems for rainbow trout and

other salmonids with reference to current estimates of

energy and protein requirements Aquaculture 100, 107^

123.

Cho C.Y & Bureau D.P (1998) Development of bioenergetics

models and the Fish-PrFEQ software to estimate

produc-tion, feeding ration and waste output in aquaculture.

Aquatic Living Resources 11, 199^210.

Close W.H., Mount L.E & Brown D (1978) The e¡ects of

plane of nutrition and environmental temperature on

the energy metabolism of the growing pig British Journal

of Nutrition 40, 423^431.

Craig J.F (1982) A note on growth and mortality of trout,

Sal-mo trutta L., in a¡erent streams of Windermere Journal of

Fish Biology 20, 423^429.

Cui Y & Wootton R.J (1988) E¡ects of ration, temperature

and body size on the body composition, energy content

and condition of the minnow, Phoxinus phoxinus (L.)

Jour-nal of Fish Biology 32,749^764.

Cui Y & Wootton R.J (1989) Bioenergetics of growth of a

cy-prinid, Phoxinus phoxinus (L.): development and testing of

a growth model Journal of Fish Biology 34, 47^64.

Cui Y & Xie S (2000) Modelling growth in ¢sh In: Feeding

Systems and Feed Evaluation Models (ed by M.K

Theodor-ou & J France), pp 413^434 CAB International,

Walling-ford, Oxfordshire, UK.

Czarnoleski M & Kozlowski J (1998) Do Bertanlan¡y’s growth curves result from optimal resource allocation? Ecological Letters 1, 5^7.

Day T & Taylor P.D (1997) Von Bertalan¡y’s growth equation should not be used to model age and size at maturity American Naturalist 149, 381^393.

De Graaf G & Prein M (2005) Fitting growth with the von Bertalan¡y growth function: a comparison of three approaches of multivariate analysis of ¢sh growth

in aquaculture experiments Aquaculture Research 36, 100^109.

de Lange C.F.M., Morel P.C.H & Birkett S.H (2003) Modeling chemical and physical body composition of the growing pig Journal of Animal Science 81(Suppl 2), E159^E165.

de Lange C.F.M., Morel P.C.H & Birkett S.H (2008) matical representation of the partitioning of retained en- ergy in the growing pig In: Mathematical Modelling in Animal Nutrition (ed by J France & E Kebreab), pp 316^

Mathe-338 CAB International,Wallingford, Oxfordshire, UK Dijkstra J & France J (1995) Modelling and methodology

in animal science In: Proceedings of the IVth national Workshop on Modelling Nutrient Utilization

Inter-in Farm Animals (ed by A Danfaer & P Lescoat), pp 9–18 National Institute of Animal Science, Foulum, Denmark.

Dijkstra J., Mills J.A.N & France J (2002) The role of dynamic modelling in understanding the microbial contribution to rumen function Nutrition Research Reviews 15, 67^90 Dijkstra J., Kebreab E., Mills J.A.N., Pellikaan W.F., Lo¤pez S., Bannink A & France J (2007) Predicting the pro¢le of nu- trients available for absorption: from nutrient require- ment to animal response and environmental impact Animal 1, 99^111.

Dumas A & France J (2008) Modelling the ontogeny of totherms exhibiting indeterminate growth Journal ofThe- oretical Biology 254,76^81.

ec-Dumas A., France J & Bureau D.P (2007) Evidence of three growth stanzas in rainbow trout (Oncorhynchus mykiss) across life stages and adaptation of the thermal-unit growth coe⁄cient Aquaculture 267, 139^146.

Dumas A., de Lange C.F.M., France J & Bureau D.P (2007) Quantitative description of body composition and rates

of nutrient deposition in rainbow trout (Oncorhynchus mykiss) Aquaculture 273, 165^181.

Dumas A., Dijkstra J & France J (2008) Mathematical elling in animal nutrition: a centenary review Journal of Agricultural Science 146, 123^142.

mod-Einen O., Holmefjord I., —sgrd T & Talbot C (1995) Auditing nutrient discharges from ¢sh farms: theoretical and prac- tical considerations Aquaculture Research 26,701^713 Elliott J.M (1975) The growth rate of brown trout (Salmo trut-

ta L.) fed on maximum rations Journal of Animal Ecology

44, 805^821.

Elliott J.M (1976a) The energetics of feeding, metabolism and growth of brown trout (Salmo trutta L.) in relation to

body weight, water temperature and ration size Journal of

Animal Ecology 45, 923^948.

Elliott J.M (1976b) Body composition of brown trout (Salmo

trutta L.) in relation to temperature and ration size Journal

of Animal Ecology 45, 273^289.

Elliott J.M., Hurley M.A & Fryer R.J (1995) A new, improved

growth model for brown trout, Salmo trutta Functional

Ecology 9, 290^298.

Embody G.C (1934) Relation of temperature to the

incuba-tion periods of eggs of four species of trout Transacincuba-tions

of the American Fisheries Society 64, 281^292.

Emmans G.C & Kyriazakis I (1997) Models of pig growth:

problems and proposed solutions Livestock Production

Science 51, 119^129.

Emmans G.C & Kyriazakis I (1999) Growth and body

com-position In: A Quantitative Biology of the Pig (ed by I

Kyr-iazakis), pp 181^197 CAB International, Oxon, UK.

Encarnacao P., de Lange C., Rodehutscord M., Hoehler D.,

Bureau W & Bureau D.P (2004) Diet digestible energy

content a¡ects lysine utilization, but not dietary lysine

re-quirements of rainbow trout (Oncorhynchus mykiss) for

maximum growth Aquaculture 235, 569^586.

FAO (2006) The State of the World Fisheries and Aquaculture

2006 Food and Agriculture Organization of the United

Nations, Rome, Italy.

Fauconneau B & Paboeuf G (2001) Muscle satellite cells in

¢sh In: Fish Physiology Series: Muscle Development and

Growth 18 (ed by I.A Johnston), pp 73^101 Academic

Press, London, UK.

France J & Kebreab E (2006) Role of modeling in animal

nutrition In: 42nd Eastern Nutrition Conference (ed by

Animal Nutrition Association of Canada) pp 145^154.

Animal Nutrition Association of Canada, Guelph, ON,

Canada.

From J & Rasmussen G (1984) A growth model, gastric

eva-cuation, and body composition in rainbow trout, Salmo

gairdneri Richardson, 1836 Dana 3, 61^139.

From J & Rasmussen G (1989) Fish growth In: Mathematical

Submodels inWater Quality Systems (ed by S.E Jrgensen

& M.J Gromiec), pp 331^369 Elsevier Science Publishers,

Amsterdam, the Netherlands.

Gayon J (2000) History of the concept of allometry

Ameri-can Zoologist 40,748^758.

Ge¤lineau A., Bolliet V., Corraze G & Boujard T (2002) The

combined e¡ects of feeding time and dietary fat levels on

feed intake, growth and body composition in rainbow

trout Aquatic Living Resources 15, 225^230.

Gerrits W.J.J., Dijkstra J & France J (1997) Description of a

model integrating protein and energy metabolism in

pre-ruminant calves Journal of Nutrition 127, 1229^1242.

Gilbert G (1993) Thomas Malthus: An Essay on the Principle of

Population Oxford University Press, Oxford, UK.

Gill M., Beever D.E & France J (1989) Biochemical bases

needed for the mathematical representation of whole

ani-mal metabolism Nutrition Research Reviews 2, 181^200.

Gill M.,Thornley J.H.M., Black J.L., Oldham J.D & Beever D.E (1984) Simulation of the metabolism of absorbed energy- yielding nutrients in young sheep BritishJournal of Nutri- tion 52, 621^649.

Glencross B (2006) The nutritional management of mundi, Lates calcarifer ^ a review Aquaculture Nutrition

barra-12, 291^309.

Groves T.D.D (1970) Body composition changes during growth in young sockeye (Oncorhynchus nerka) in fresh water Journal of Fisheries Resources Board of Canada 27, 929^942.

Halas V., Dijkstra J., Babinszky L.,Verstegen M.W.A & Gerrits W.J.J (2004) Modelling of nutrient partitioning in grow- ing pigs to predict their anatomical body composition 1 Model description British Journal of Nutrition 92, 707^ 723.

Halver J.E & Hardy R.W (2002) Nutrient £ow and retention In: Fish Nutrition, 3rd edn., ed by J.E Halver & R.W Hardy), pp.755^770 Academic Press, San Diego, CA, USA Hamilton M.C., Hites R.A., Schwager S.J., Foran J.A., Knuth B.A & Carpenter D.O (2005) Lipid composition and con- taminants in farmed and wild salmon Environmental Science and Technology 39, 8622^8629.

Hardy R.W & Barrows F.T (2002) Diet formulation and ufacture In: Fish Nutrition, 3rd edn., ed by J.E Halver & R.W Hardy), pp 505^600 Academic Press, San Diego,

man-CA, USA.

Hayes F.R (1949) The growth, general chemistry, and perature relations of salmonids eggs Quarterly Review of Biology 24, 281^308.

tem-Hemre G.-I & Sandnes K (2008) Seasonal adjusted diets to Atlantic salmon (Salmo salar): evaluations of a novel feed based on heat-coagulated ¢sh mince, fed throughout 1 year in sea: feed utilisation, retention of nutrients and health parameters Aquaculture 274, 166^174.

Hernandez-Llamas A & Ratkowsky D.A (2004) Growth of

¢shes, crustaceans and molluscs: estimation of the von Bertalan¡y, Logistic, Gompertz and Richards curves and

a new growth model Marine Ecology Progress Series 282, 237^244.

HMSO (1975) Technical Bulletin 33: Energy Allowances and Feeding Systems for Ruminants Ministry of Agriculture, Fisheries and Food, Department of Agriculture and Fish- eries for Scotland, Department of Agriculture for North- ern Ireland The Hillingdon Press, Uxbridge.

Hocquette J.-F., Richardson R.I., Prache S., Me¤dale F., Du¡y

G & Scollan N.D (2005) The future trends for research on quality and safety of animal products Italian Journal of Animal Science 4(Suppl 3), 49^72.

Hubbell S.P (1971) Of sowbugs and systems: the ecological bioenergetics of a terrestrial isopod In: Systems Analysis and Simulation in Ecology, Vol 1 (ed by B.C Patten), pp 269^324 Academic Press, New York, NY, USA.

Huisman E.A (1976) Food conversion e⁄ciencies at nance and production levels for carp, Cyprinus carpio L.,

mainte-and rainbow trout, Salmo gairdneri Richardson

Aquacul-ture 9, 259^273.

Imamidoost R & Cant J.P (2005) Non-steady-state modeling

of e¡ects of timing and level of concentrate

supplementa-tion on ruminal pH and forage intake in

high-produ-cing, grazing ewes Journal of Animal Science 83, 1102^

1115.

Ivlev V.S (1939) Transformation of energy by aquatic

ani-mals International Review of Hydrobiology 38, 449^458.

Iwama G.K & Tautz A.F (1981) A simple growth model for

salmonids in hatcheries Canadian Journal of Fisheries and

Aquatic Sciences 38, 649^656.

Jensen A.J (1990) Growth of young migratory brown trout

Salmo trutta correlated with water temperature in

Norwe-gian rivers Journal of Animal Ecology 59, 603^614.

Jobling M (1985) Growth In: Fish Energetics: New

Perspec-tives (ed by P Tytler & P Calow), pp 213^230 The John

Hopkins University Press, Baltimore, MD, USA.

Jobling M (1994) Fish Bioenergetics, Fish and Fisheries Series

13 Chapman & Hall, London, UK.

Jobling M (2001) Nutrient partitioning and the in£uence of

feed composition on body composition In: Food Intake in

Fish (ed by D Houlihan,T Boujard & M Jobling), pp 354^

375 Blackwell Science, Oxford, UK.

Jobling M (2003) The thermal growth coe⁄cient (TGC)

model of ¢sh growth: a cautionary note Aquaculture

Re-search 34, 581^584.

Johnston I.A., Manthri S., Alderson R., Smart A., Campbell

P., Nickell D., Robertson B., Paxton C.G.M & Burt M.L.

(2003) Freshwater environment a¡ects growth rate and

muscle ¢bre recruitment in seawater stages of Atlantic

salmon (Salmo salar L.) Journal of Experimental Biology

206, 1337^1351.

Katsanevakis S (2006) Modelling ¢sh growth: model

selec-tion, multi-model inference and model selection

uncer-tainty Fisheries Research 81, 229^235.

Kaushik S.J (1998) Nutritional bioenergetics and estimation

of waste production in non-salmonids Aquatic Living

Re-sources 11, 211^217.

Kellner O (1911) The Scienti¢c Feeding of Animals The

Mac-Millan Company, NewYork, NY, USA.

Kim K.I., Grimshaw T.W., Kayes T.B & Amundson C.H.

(1992) E¡ect of fasting or feeding diets containing

di¡er-ent levels of protein or amino acids on the activities of

the liver amino acid-degrading enzymes and amino acid

oxidation in rainbow trout (Oncorhynchus mykiss)

Aqua-culture 107, 89^105.

Kitchell J.F., Stewart D.J & Weininger D (1977)

Applica-tions of bioenergetics model to yellow perch (Perca

£avescens) and walleye (Stizostedion vitreum vitreum).

Journal of Fisheries Resources Board of Canada 34, 1922^

1935.

Kleiber M (1961) The Fire of Life: An Introduction to Animal

Energetics John Wiley & Sons, NewYork, NY, USA.

Knap P.W (2000) Variation in maintenance requirements of

growing pigs in relation to body composition: a simulation

study PhD thesis, Wageningen Agricultural University, Wageningen.

Knight W (1968) Asymptotic growth: an example of sense disguised as mathematics Journal of Fisheries Re- sources Board of Canada 25, 1303^1307.

non-KotarbinŁska M (1969) Badania nad przemiana energii u naacych sŁwinŁ.WydawnictwaW3asne no 238, Instytut Zoo- techniki, Krako¤w.

ros-Krogh A (1914) On the in£uence of the temperature on the rate of embryonic development Zeitschrift fuer Allgemein Physiologie 16, 163^177.

Kyriazakis I (1999) A Quantitative Biology of the Pig CABI Publishing, Oxon, UK.

Kyriazakis I & Emmans G.C (1992) The growth of mammals following a period of nutritional limitation Journal of The- oretical Biology 156, 485^498.

Landgraf S., Susanbeth A., Knap P.W., Looft H., Plastow G.S., Kalm E & Roehe R (2006) Developments of carcass cuts, organs, body tissues and chemical body composition dur- ing growth of pigs Animal Science 82, 889^899.

Le Dividich J.,Vermorel M., Noblet J., Bouvier J.C & Aumaitre

A (1980) E¡ects of environmental temperature on heat production, energy retention, protein and fat gain in early weaned piglets British Journal of Nutrition 44, 313^323 Lerner M.I (1939) Allometric studies of poultry.World’s Poul- try Congress Proceedings 1939, 85^88.

Lester N.P., Shuter B.J & Abrams P.A (2004) Interpreting the von Bertalan¡y model of somatic growth in ¢shes: the cost of reproduction Proceedings of the Royal Society

of London Series B 271, 1625^1631.

Lo¤pez S (2008) Nonlinear functions in animal nutrition In: Mathematical Modelling in Animal Nutrition (ed by J France & E Kebreab), pp 47^88 CAB International,Wall- ingford, Oxfordshire, UK.

Love R.M (1980) The Chemical Biology of Fishes,Volume 2: vances 1968^1977 Academic Press, London, UK Lupatsch I & Kissil G.W (1998) Predicting aquaculture waste from gilthead seabream (Sparus aurata) culture using a nutritional approach Aquatic Living Resources 11, 265^268.

Ad-Lupatsch I & Kissil G.W (2005) Feed formulations based on energy and protein demands in white grouper (Epinephe- lus aeneus) Aquaculture 248, 83^95.

Lupatsch I., Kissil G.W., Sklan D & Pfe¡er E (2001) E¡ects of varying dietary protein and energy supply on growth, body composition and protein utilization in gilthead seab- ream (Sparus aurata L.) Aquaculture Nutrition 7,71^80 Lupatsch I., Kissil G.W & Sklan D (2001) Optimization of feeding regimes for European sea bass Dicentrarchus lab- rax: a factorial approach Aquaculture 202, 289^302 Lupatsch I., Kissil G.W & Sklan D (2003) Comparison of en- ergy and protein e⁄ciency among three ¢sh species gilt- head sea bream (Sparus aurata), European sea bass (Dicentrarchus labrax) and white grouper (Epinephelus ae- neus): energy expenditure for protein and lipid deposition Aquaculture 225, 175^189.

Lyndsay D.B (1976) Amino acids as sources of energy EAAP

Publication 16, 183^195.

Machiels M.A.M & Henken A.M (1986) A dynamic

simula-tion model for growth of the African cat¢sh, Clarias

garie-pinus (Burchell 1822) I E¡ect of feeding level on growth

and energy metabolism Aquaculture 56, 29^52.

Martell D.J & Kie¡er J.D (2007) Persistent e¡ects of

incuba-tion temperature on muscle development in larval

had-dock (Melanogrammus aegle¢nus L.) Journal of

Experimental Biology 210, 1171^1182.

May R.M (1976) Simple mathematical models with very

complicated dynamics Nature 261, 459^467.

McMeekan C.P (1941) Growth and development in the pig,

with special reference to carcass quality characters Part

IV Journal of Agricultural Science 31, 1^17.

Meyer-Burgdor¡ K.-H., Osman M.F & Gˇnther K.D (1989)

Energy metabolism in Oreochromis niloticus Aquaculture

79, 283^291.

M˛hn S & de Lange C.F.M (1998) The e¡ect of body weight

on the upper limit to protein deposition in a de¢ned

popu-lation of growing gilts Journal of Animal Science 76, 124^

133.

Moza¡arian D & Rimm E.B (2006) Fish intake,

contami-nants, and human health: evaluating the risks and the

bene¢ts Journal of American Medical Association 296,

1885^1899.

Murray J.A (1914) The Chemistry of Cattle Feeding and

Dairy-ing Longmans, Green, & Co, London, UK.

Naylor R.L., Goldburg R.J., Primavera J.H., Kautsky N.,

Bev-eridge M.C.M., Clay J., Folke C., Lubchenco J., Mooney H &

Troell M (2000) E¡ect of aquaculture on world ¢sh

sup-plies Nature 405, 1017^1024.

Nelson D.L & Cox M.M (2000) Lehninger Principles of

Bio-chemistry, 3rd edn.Worth Publishers, NewYork, NY, USA.

Norris T (1868) American Fish-Culture Porter & Coates,

Phi-ladelphia, PA, USA.

NRC (1981) Nutritional Energetics of Domestic Animals &

Glossary of Energy Terms, Second Revised Edition The

Na-tional Academy Press,Washington, DC, USA.

NRC (1993) Nutrient Requirements of Fish Committee on

Ani-mal Nutrition, National Research Council The National

Academic Press,Washington, DC, USA.

NRC (1996) Nutrient Requirements of Beef Cattle, Seventh

vised Edition Committee on Animal Nutrition, National

Re-search Council The National Academic Press,Washington,

DC, USA.

NRC (1998) Nutrient Requirements of Swine,Tenth Revised

Edi-tion Committee on Animal Nutrition, National Research

Council The National Academic Press, Washington, DC,

USA.

NRC (1999) Sustaining Marine Fisheries The National

Acad-emy Press,Washington, DC, USA.

Oldham J.D., Emmans G.C & Kyriazakis I (1997) Limits and

limitations to nitrogen use in farm animals Proceedings of

the Nutrition Society 56, 525^534.

Oslage H.J & Fliegel H (1965) Nitrogen and energy lism of growing-fattening pigs with an approximately maximal feed intake In: Energy Metabolism: European As- sociation for Animal Production, Publication No 11 (ed by K.L Blaxter), pp 297^306 Academic Press, London, UK Papatryphon E., Petit J., van der Werf H.M.G., Kaushik S.J & Claver K (2005) Nutrient-balance modeling as a tool for environmental management in aquaculture: the case of trout farming in France Environmental Management 35, 161^174.

metabo-Parker L.A & Larkin P.A (1959) A concept of growth in

¢shes Journal of Fisheries Resources Board of Canada 16, 721^745.

Parker R.R & Vanstone W.E (1966) Changes in chemical composition of central British Columbia pink salmon dur- ing early sea life Journal of Fisheries Resources Board of Ca- nada 23, 1353^1384.

Parks J.R (1982) A Theory of Feeding and Growth of Animals Springrer-Verlag, Berlin, Germany.

Pettigrew J.E., Gill M., France J & Close W.H (1992) A ematical integration of energy and amino acid metabo- lism of lactating sows Journal of Animal Science 70, 3742^3761.

math-Phillips A.M., Jr, Livingston D.L & Dumas R.F (1960) E¡ect

of starvation and feeding on the chemical composition of brook trout Progressive Fish-Culturist 22, 147^154 Pˇtter A (1920) Studien ˇber physiologische hnlichkeit.VI Wachstumshnlichkeiten P£gers Archiv fuer die gesamte Physiologie des Menschen und derTiere 180, 298^340 Rasmussen R.S (2001) Quality of farmed salmonids with emphasis on proximate composition, yield and sensory characteristics Aquaculture Research 32,767^786 Rasmussen R.S & Ostenfeld T.H (2000) E¡ect of growth rate

on quality traits and feed utilisation of rainbow trout corhynchus mykiss) and brook trout (Salvelinus fontinalis) Aquaculture 184, 327^337.

(On-Rasmussen R.S., Ostenfeld T.H & McLean E (2000) Growth and feed utilisation of rainbow trout subjected to changes

in feed lipid concentrations Aquaculture International 8, 531^542.

Ratkowski D.A (1990) Handbook of Nonlinear Regression Models Marcel Dekker, New York, NY, USA.

Reinitz G (1983) Relative e¡ect of age, diet, and feeding rate

on the body composition of young rainbow trout (Salmo gairdneri) Aquaculture 35, 19^27.

Ricker W.E (1968) Methods for Assessment of Fish Production

in FreshWaters Blackwell Scienti¢c Publications, Oxford, UK.

Ricker W.E (1979) Growth rates and models Fish Physiology

8, 677^743.

Robertson T.B (1923) The Chemical Basis of Growth and nescence J.B Lippincott Company, Philadelphia, PA, USA Ro¡ D.A (1980) A motion for the retirement of the von Ber- talan¡y function CanadianJournal of Fisheries and Aquatic Sciences 37, 127^129.

Se-Rombough P.J (1994) Energy partitioning during ¢sh

devel-opment: additive or compensatory allocation of energy to

support growth? Functional Ecology 8, 178^186.

Roth B., Dorenfeld Jenssen M., Magne Jonassen T., Foss A &

Imsland A (2007) Change in £esh quality associated with

early maturation of Atlantic halibut (Hippoglossus

hippo-glossus) Aquaculture Research 38,757^763.

Rowe D.K.,Thorpe J.E & Shanks A.M (1991) Role of fat stores

in the maturation of male Atlantic salmon (Salmo salar)

parr Canadian Journal of Fisheries and Aquatic Sciences 48,

405^413.

Rowlerson A & Veggetti A (2001) Cellular mechanisms of

post-embryonic muscle growth in aquaculture species.

In: Fish Physiology Series: Muscle Development and Growth

18 (ed by I.A Johnston), pp 103^140 Academic Press,

San Diego, CA, USA.

Sandberg F.B., Emmans G.C & Kyriazakis I (2005a)

Parti-tioning of limiting protein and energy in the growing pig:

description of the problem, possible rules and their

quali-tative evaluation British Journal of Nutrition 93, 205^212.

Sandberg F.B., Emmans G.C & Kyriazakis I (2005b)

Parti-tioning of limiting protein and energy in the growing

pig: testing quantitative rules against experimental data.

British Journal of Nutrition 93, 213^224.

Sargent J.R.,Tocher D.R & Bell J.G (2002) The lipids In: Fish

Nutrition, 3rd edn., (ed by J.E Halver & R.W Hardy), pp.

181^257 Academic Press, San Diego, CA, USA.

Schinckel A.P & de Lange C.F.M (1996) Characterization of

growth parameters needed as inputs for pig growth

mod-els Journal of Animal Science 74, 2021^2036.

School¢eld R.M., Sharpe P.J.H & Magnuson C.E (1981)

Non-linear regression of biological temperature-dependent

rate models based on absolute reaction rate theory

Jour-nal of Theoretical Biology 88,719^731.

Schwarz F.J & Kirchgessner M (1995) E¡ects of di¡erent

diets and levels of feeding on retention and e⁄ciency of

utilization of energy and protein by carp (Cyprinus carpio

L.) Journal of Applied Ichthyology 11, 363^366.

Sebens K.P (1987) The ecology of indeterminate growth in

animals Annual Review of Ecological Systems 18, 371^407.

Seber G.A.F & Wild C.J (2003) Nonlinear Regression

JohnWi-ley & Sons, NJ, USA.

Sharpe P.J.H & DeMichele D.W (1977) Reaction kinetics of

poikilotherm development Journal of Theoretical Biology

64, 649^670.

Shearer K.D (1994) Factors a¡ecting the proximate

compo-sition of cultured ¢shes with emphasis on salmonids.

Aquaculture 119, 63^88.

Shearer K.D, Parkins P., Gadberry B., Beckman B &

Swan-son P (2006) E¡ects of growth rate/body size and a low

lipid diet on the incidence of early sexual maturation in

juvenile male spring Chinook salmon (Oncorhynchus

tsha-wytscha) Aquaculture 252, 545^556.

Shul’man G.E (1974) Life Cycles of Fish, Physiology and

Bio-chemistry Halsted Press, Jerusalem, Israel.

Silverstein J.T., Shearer K.D., Dickho¡ W.W & Plisetskaya E.M (1998) E¡ects of growth and fatness on sexual devel- opment of chinook salmon (Oncorhynchus tshawytscha) parr Canadian Journal of Fisheries and Aquatic Sciences 55, 2376^2382.

Simmons A.L.A (1997) Dietary methionine requirement of venile Arctic charr (Salvelinus alpinus) MSc thesis Univer- sity of Guelph, Guelph.

ju-Solomon D.J & Bra¢eld A.E (1972) The energetics of feeding, metabolism and growth of perch (Perca £uviatilis L.) Jour- nal of Animal Ecology 41, 699^718.

Stead S.M & Laird L (2002) Handbook of Salmon Farming Springer, NewYork, NY, USA.

Steinbacher P., Haslett J.R., Obermayer A., Marschallinger J., Bauer H.C., Snger A.M & Stoiber W (2007) MyoD and myogenin expression during myogenic phases in brown trout: a precocious onset of mosaic hyperplasia is a prere- quisite for fast somatic growth Developmental Dynamics

236, 1106^1114.

Stoll B., Burrin D.G., Jahoor F., Henry J., Yu H & Reeds P.J (1998) Dietary protein is the major source of energy for the portal-drained viscera of fed piglets In: Energy Meta- bolism of Farm Animals (ed by K McCracken, E.F Uns- worth & A.R.G Wylie), pp 23^26 CAB International, Oxon, UK.

Swatland H.J (1994) Structure and Development of Meat mals Prentice-Hall, Englewood Cli¡s, NJ, USA.

Ani-ThompsonW.R., Meiske J.C., Goodrich R.D., Rust J.R & Byers F.M (1983) In£uence of body composition on energy re- quirements of beef cows during winter Journal of Animal Science 56, 1241^1252.

Thorbek C (1969) Studies on the energy metabolism of growing pigs In: Energy Metabolism of Farm Animals: Eur- opean Association forAnimal Production, Publication No.12 (ed by K.L Blaxter, J Kielanowski & G Thorbek), pp 281^

289 Oriel Press Limited, New Castle upon Tyne, UK Thornley J & France J (2007) Mathematical Models in Agri- culture: Quantitative Methods for the Plant, Animal and Eco- logical Sciences CAB International, Wallingford, Oxfordshire, UK.

Tobin D., Kause A., Mntysaari E.A., Martin S.A.M., han D.F., Dobly A., Kiessling A., Rungruangsak-Torrissen K., Ritola O & Ruohonen K (2006) Fat or lean? The quan- titative genetic basis for selection strategies of muscle and body composition traits in breeding schemes of rainbow trout (Oncorhynchus mykiss) Aquaculture 261, 510^521 Torstensen B.E., Bell J.G., Roselund G., Hendersen R.J., Gra¡ I.E., Tocher D.R., Lie O & Sargent J.R (2005) Tailoring of Atlantic salmon (Salmo salar L.) £esh lipid composition and sensory quality by replacing ¢sh oil with a vegetable oil blend Journal of Agriculture and Food Chemistry 53, 10166^10178.

Houli-van Dam A.A & De Vries F.W.T.P (1995) Parameterization and calibration of a model to simulate e¡ects of feeding level and feed composition on growth of Oreochromis nilo-

ticus (L.) and Oncorhynchus mykiss (Walbaum)

Aquacul-ture Research 26, 415^425.

Van der Meer M.B & van Dam A.A (1998) Modelling growth

of Colossoma macroponum (Cuvier): comparison of an

em-pirical and an explanatory model Aquaculture Research

29, 313^332.

von Bertalan¡y L (1957) Quantitative laws in

meta-bolism and growth Quarterly Review of Biology 32, 217^

231.

Wallich C (1901) A method of recording ¢sh egg development for

use of ¢sh-culturists U.S Commission of Fish & Fisheries,

Commissioner’s Report for 1900: pp 185^195.

Warren C.E & Davis G.E (1967) Laboratory studies on the

feeding, bioenergetics, and growth of ¢sh In: The

Biologi-cal Basis of Freshwater Fish Production (ed by S.D

Gerk-ing), pp 175^214 Blackwell Scienti¢c Publications,

Oxford, UK.

Warren C.E & Davis G.E (1968) Estimation of food

consump-tion rates In: Methods for Assessment of Fish Producconsump-tion in

Fresh Waters (ed by W.E Ricker), pp 204^225 Blackwell

Scienti¢c Publications, Oxford, UK.

Watanabe T (2002) Strategies for further development of

aquatic feeds Fisheries Science 68, 242^252.

Weatherley A.H & Gill H.S (1983) Protein, lipid, water and

caloric contents of immature rainbow trout, Salmo

gaird-neri Richardson, growing at di¡erent rates Journal of Fish

Biology 23, 653^673.

WeatherleyA.H & Gill H.S (1987) The Biology of Fish Growth.

Academic Press,Washington, DC, USA.

Weis R.N., Birkett S.H., Morel P.C.H & de Lange C.F.M (2004)

E¡ects of energy intake and body weight on physical and

chemical body composition in growing entire male pigs.

Journal of Animal Science 82, 109^121.

Whittemore C.T (1995) Modelling the requirement of young growing pig for dietary protein Agricultural Systems 47, 415^425.

Wieser W (1989) Energy allocation by addition and by pensation: an old principle revisited In: Energy Transfor- mations in Cells and Animals (ed by W Wieser & E Gnaiger), pp 98^105 Thieme Verlag, Stuttgart, Germany Willoughby S (1999) Manual of Salmonid Farming Fishing News Books, Blackwell Science, Oxford, UK.

com-Winberg G.G (1956) Rate of metabolism and food requirements

of ¢shes Fisheries Resources Board of Canada Translation Series No 194, Nanaimo.

Wol¡ E.V (1895) Farm Foods: or the Rational Feeding of Farm Animals, 6th edn D Van Nostrand Company, New York,

NY, USA.

Wright S (1926) The biology of population growth; the ural increase of mankind ^ reviews Journal of the Ameri- can Statistical Association 21, 493^497.

nat-Xu X.-M (1996) On estimating non-linear response of fungal development under £uctuating temperatures Plant Pathology 45, 163^171.

Yamamoto T., Shima T., Furuita H & Suzuki N (2002) ence of dietary fat level and whole-body adiposity on vo- luntary energy intake by juvenile rainbow trout Oncorhynchus mykiss (Walbaum) under self-feeding con- ditions Aquaculture Research 33,715^723.

In£u-Young L.L., Northcutt J.K., Buhr R.J., Lyon C.E & Ware G.O (2001) E¡ects of age, sex, and duration of postmortem aging on percentage yield of parts from broiler chicken carcasses Poultry Science 80, 376^379.

Zhou Z., Xie S., Lei W., Zhu X & YangY (2005) A bioenergetic model to estimate feed requirement of gibel carp, Caras- sius auratus gibelio Aquaculture 248, 287^297.

Effects of transplants and extracts of thoracic nerve cord–ganglia on gonad maturation of penaeoid shrimp

Jorge Alfaro & Lu|¤s Vega

Estacio¤n de Biolog|¤a Marina, Escuela de Ciencias Biolo¤gicas, Universidad Nacional, Puntarenas, Costa Rica

Correspondence: J Alfaro, Estacio¤n de Biolog|¤a Marina, Escuela de Ciencias Biolo¤gicas, Universidad Nacional, Puntarenas, Costa Rica E-mail: jalfarom@una.ac.cr

Abstract

It has been established recently that interspeci¢c and

intraspeci¢c thoracic ganglia transplants from

Pe-naeidae are gradually absorbed by the host without

activating an encapsulation mechanism Therefore,

this research was designed to evaluate the thoracic

ganglia extracts and implants from maturing

Trachy-penaeus byrdi (Burkenroad), XiphoTrachy-penaeus riveti

(Bou-vier) and Penaeus (Litopenaeus) occidentalis (Streets)

females as potential inducers of sexual maturation

in Penaeus (Litopenaeus) stylirostris (Stimpson),

Pe-naeus (LitopePe-naeus) vannamei (Boone) and T byrdi,

from the Gulf of Nicoya, Costa Rica Our ¢ndings

sug-gest that interspeci¢c and intraspeci¢c thoracic

ganglia extracts or implants from maturing penaeoid

females are not capable of inducing a clear response

in sexual maturation in males or females Tissues

were tested at increasing doses from 137, 386, 525

and 1500mg g 1body weight, without any positive

response It is proposed that a hypothetical hormone,

vitellogenesis-stimulating hormone, from the

thor-acic ganglia, is under the strong negative control of

eyestalks, by the gonad-inhibiting hormone in the

subgenus Litopenaeus Therefore, the use of thoracic

ganglia extracts or implants would be ine¡ective

when compared with injecting serotonin alone, as

the present results seem to support

Keywords: penaeoid, shrimp, thoracic ganglia,

transplants, extracts, gonad maturation

Introduction

The shrimp mariculture industry is based on

eye-stalk ablation for inducing ovarian maturation and

spawning This is a detrimental procedure that fects the entire physiology of shrimp (Quackenbush1986; Benzie 1998); therefore, alternatives were con-sidered to be a long-term goal for the industry(Quackenbush 1991) Recently, Alfaro, Zu¤niga andKomen (2004) reported a new procedure as a substi-tute for eyestalk ablation in Penaeus (Litopenaeus)stylirostris (Stimpson) and Penaeus (Litopenaeus)vannamei (Boone), based on repetitive injections ofserotonin and spiperone Their ¢ndings indicate thatthis technique induced ovarian maturation as well as

af-a maf-aturaf-ation e¡ect in non-treaf-ated shrimp, possiblyvia unidenti¢ed soluble pheromones Serotonin alsoinduced maturation and spawning at rates similar toeyestalk ablation (Wongprasert, Asuvapongpatana,Poltana,Tiensuwan & Withyachumnarnkul 2006) inPenaeus monodon (Fabricius)

The sinus gland from eyestalks, the brain and thethoracic ganglia have been considered to be thesource of a vitellogenesis-stimulating hormone(VSH), which has not yet been characterized (Huber-man 2000; Huang, Ye, Li & Wang 2008) However,thoracic ganglia implants or extracts have producedincreases in gonadal growth in shrimps, crabs, lob-sters and cray¢sh (Otsu 1960; Oyama 1968; Hinsch &Bennett 1979; Quackenbush 1986; Takayanagi,Yama-moto & Takeda 1986, for review; Fingerman 1987, forreview; Kulkarni, Glade & Fingerman 1991) It wasdemonstrated that thoracic ganglion extracts fromsexually active females of Uca pugilator (Bosc) in-duced precocious ovarian maturation in intact andeyestalk-ablated crabs, whereas extracts from sexu-ally inactive females did not stimulate ovariangrowth (Eastman-Reks & Fingerman 1984)

Serotonin is present in the central nervous system

of crustaceans and its role in the mechanism of

ovar-ian maturation seems to be that of a stimulator for

the postulated release of a putative VSH from the

brain and thoracic ganglia in Procambarus clarkii

(Girard) (Fingerman 1997) However, serotonin and

its receptors have been identi¢ed in the ovary of

P monodon, suggesting a direct role of serotonin in

ovaries (Ongvarrasopone, Roshorm, Somyong,

Pothiratana, Petchdee, Tangkhabuanbutra,

Sopha-san & Payim 2006; Wongprasert et al 2006)

Recently, gonadotropin-releasing hormone (GnRH)

was identi¢ed in the protocerebrum of P monodon,

and this molecule may be involved in the regulation

of serotonin, as well as a possible VSH

(Ngernsoung-nern, Ngernsoung(Ngernsoung-nern, Kavanaugh, Sobhon, Sower

& Sretarugsa 2008) Gonadotropin-releasing

hor-mone has also been found in the follicular cells

sur-rounding developing and mature oocytes The

exogenous administration of GnRHs in P monodon

shortened the ovarian maturation period, at a lower

rate than eyestalk ablation (Ngernsoungnern,

Ngern-soungnern, Weerachatyanukul, Chavadej, Sobhon &

Sretarugsa 2008)

Studies of transplants and extracts of thoracic

ganglia in penaeoid shrimps are

limited.Yano,Tsuki-mura, Sweeney andWyban (1988) implanted thoracic

ganglia sections (15 mm in length) of the lobster,

Ho-marus americanus (Milne-Edwards), with vitellogenic

ovaries into P vannamei recipients Tissue

recogni-tion was not evaluated and the authors reported

in-duction of ovarian maturation (4/6 maturing

females) compared with abdominal ganglia

implan-tation (1/6) 18 days after treatment Hooi (1991)

eval-uated intraspeci¢c thoracic ganglia extracts at 400

and 1000mg g 1from spent spawners for inducing

ovarian maturation and spawning in P monodon,

without success This author stated the importance

of evaluating interspeci¢c thoracic extracts from less

valuable crustacean species

Alfaro, HernaŁndez, Zu¤niga, Soto and Mej|¤a-Arana

(2008) reported that interspeci¢c and intraspeci¢c

thoracic ganglia transplants from Penaeidae are

gra-dually absorbed by the host without activating an

en-capsulation mechanism Because implants are not

rejected by haemocytes, neurosecretory cells remain

alive for a few days and possibly release a putative

VSH, thereby inducing gonad maturation, as

ob-served by Yano et al (1988) The e¡ects may also be

due to other neuropeptides in the transplants

How-ever, the extents of the induction as well as the

com-mercial relevance of this technology have to be

investigated This research was designed to evaluate

thoracic ganglia extracts and implants from maturing

females as potential inducers of sexual maturation inpenaeoid shrimps

Material and methodsExperimental designExperiments were conducted in maturation tanks(3 m in diameter) with a water depth of 0.50 m and atotal daily water replacement of 100%, using newwater pre-treated by high-pressure silica sand ¢ltra-tion and sedimentation (temperature 5 27 1C; sal-inity 5 34 g L 1) The photoperiod was natural (13 hlight:11h dark), with a reduced light intensity (10^

43 lx) Adult P stylirostris and Trachypenaeus byrdiwere captured from the Gulf of Nicoya, and P vanna-mei were grown in tanks after a commercial pondculture, to be used as recipient species Donor speciesfor nerve tissues obtained from the Gulf of Nicoyaincluded T byrdi, Xiphopenaeus riveti and Penaeusoccidentalis

Experiments were started after 1 week of tization in the experimental tanks The animals werefed at 15% b.w day 1, with fresh-frozen squids andsardines at a 1:2 ratio Ovarian maturation was regis-tered once a week for every female, and was based onthe external observation of ovarian size and colour asdescribed by King (1948) and Yano et al (1988) withslight modi¢cations:

acclima-Stage I The ovary is transparent with no guishable outline

distin-Stage II The ovary is visible as a thin opaque linealong the dorsal central axis

Stage III The ovary is visible as a thick yellow orgreen band, depending on the species

Stage IV The ovary is turgid, broad and dark red ^orange or green, depending on the species Spawning

is imminent

Spermatophore condition was evaluated in terms

of general appearance as normal morphology, acterized by white colour and normal sperm cells, or

char-a deteriorchar-ating condition (Alfchar-aro &char-amp; Lozchar-ano 1993),characterized by brown pigmentation and spermresidues

Experiment 1Two T byrdi thoracic ganglia extracts were preparedfrom fresh tissues of mature females in stages III^IVand immature females in stage I (11g b.w.) The tis-sues were dissected from living animals on board

the boat, then washed and stored in cold (4 1C)

abso-lute ethanol, and kept at 18 1C until further

extrac-tion Dissection of tissues was performed within a

few seconds to avoid any endocrine alteration

Etha-nol dehydration is a common technique for

preser-ving the biological activity of neurohormones

(Zanuy & Carrillo 1972)

The extraction protocol was based on the

proce-dures provided by Quackenbush and Keeley (1988)

and Hooi (1991) for neuropeptides (Yano1993;

Finger-man 1997) Thirteen thoracic ganglia stage III^IV

(approximately 249 mg fresh tissue weight,

treat-ment A) and the same weight of thoracic ganglia

stage I (treatment B) were ground in a glass

homoge-nizer and extracted in a sterile saline solution (0.85%

NaCl) Extracts were gently boiled for 5 min and then

centrifuged at 2000 g for 30 min at 4 1C The

super-natants of the extracts (49.8 mg mL 1, Treatments

A and B) and a saline control (Treatment C) were

injected immediately at 100mL female 1, and the

remaining extracted samples were stored frozen

at 18 1C until four more doses (137mg

equiva-lents g 1b.w or 0.26 thoracic ganglia equivalents

female 1) were applied, at weekly intervals Each

treatment had eight stage I P vannamei females

(36.4 g b.w.), and the ovarian stages were monitored

for 5 weeks

Experiment 2

Xiphopenaeus riveti females (11g b.w.) in stage IV of

ovarian maturation were collected and transported

alive to EBM Two hours after capture, females

were dissected to remove the entire thoracic ganglia

chain (approximately 15 mm in length and 13 mg in

weight) and the abdominal ganglia chain of similar

size Entire chains were maintained in a chilled

(12^14 1C), sterile crustacean physiological solution

(C.P.S., 18 mM HEPES, Ro, Talbot, Leung-Trujillo &

Lawrence 1990) for 5^10 min before implantation

Twenty male P vannamei (33.7 g b.w.) were

ran-domly assigned to two treatments: (A) thoracic

gang-lia stage IV implant and (B) abdominal ganggang-lia stage

IV implant Each male received an entire nerve tissue

section (15 mm in length), using an implantation

de-vice The implanter was a syringe (1mL) modi¢ed

to carry replaceable sterile glass tubes (2 mm in

diameter) to load tissue sections Each recipient was

implanted with a new glass tube to avoid

contamina-tion; alcohol was applied at the surface and a

small hole was made with the tube through the soft

epidermis at the dorsal junction between the lothorax and the abdomen, and then the tissue wasexpelled into the cavity located at the left side fromthe heart Spermatophore condition was evaluated

cepha-at weeks 2 and 4 from implantcepha-ation

Experiment 3Penaeus occidentalis females (45 g b.w.) in stage IV ofovarian maturation were collected and transportedalive to EBM Two hours after capture, females weredissected to remove the entire thoracic ganglia chain(approximately 30 mm in length) and the abdominalganglia chain of similar size Tissues were cut into

10 mm sections (approximately 21mg) and tained in a chilled (12^14 1C), sterile crustacean phy-siological solution (C.P.S., 18 mM HEPES, Ro et al.1990) for 5^10 min before implantation

main-Twenty adult female P stylirostris (40 g b.w.) instage I of ovarian maturation, captured from thewild, were randomly assigned to two treatments: (A)thoracic ganglia stage IV implant and (B) abdominalganglia stage IV implant Each female received onetissue section, following the previously describedprotocol The ovarian stages were monitored for 4weeks

Experiment 4Both the groups from experiment 3, at the end of 4weeks, were treated with serotonin plus spiperone.The dose used was based on Alfaro et al (2004): seroto-nin at 25mg g 1b.w and spiperone at 2.0mg g 1b.w.Females received 100mL of a mixed solution of bothchemicals, freshly prepared by dissolving 4.0 mg of spi-perone (Sigma, St Louis, MO, USA) in 1.0 mL of 95%ethanol; a water bath at 50 1C was used to facilitatedissolution Then 50 mg of serotonin (Sigma) was dis-solved and mixed with spiperone in 5.0 mL of sterilesaline solution (0.85% NaCl) Only one dose was ad-ministered at week 0, and the ovarian stages wereregistered for 3 weeks

Experiment 5Trachypenaeus byrdi thoracic ganglia and abdominalganglia extracts were prepared from fresh tissues ofmature females in stages III^IV (12 g b.w.), as indi-cated in experiment1 Seventeen thoracic ganglia (ap-proximately 374 mg fresh tissue weight, treatment A)

and similar weights of abdominal ganglia (treatment

B) were processed

Both supernatants were injected immediately

at 100mL female 1and stored frozen at 10 1C until

a further injection after a week with a dose of

1500mg equivalents g 1b.w or 0.85 thoracic ganglia

equivalents female 1 Each treatment had ¢ve T

by-rdi females in stage I of ovarian maturation (12 g b.w.),

and the ovarian stages were recorded after 2 weeks

Results

Table 1 summarizes our observations of the intents to

induce sexual maturation by thoracic ganglia

trans-plants and extracts Experiment 1 registered only one

(1/8) P vannamei female reaching a maturing

condi-tion stage III^IV for the thoracic ganglia stage III^IV

treatment, injecting an extract at 137mg

equiva-lents g 1b.w (0.26 thoracic ganglia equivalent

female 1) at week 1; this female spawned in the

fol-lowing days, returning to ovarian stage I Treatment

B (thoracic ganglia stage I) and the saline control did

not register any maturation

Experiment 2 evaluated thoracic ganglia stage IV

implants in P vannamei males at 386mg g 1; no

im-provement in the spermatophore condition was

ob-served as compared with abdominal ganglia stage

IV implantation The males used in this study showed

deteriorating spermatophores in both the treatments

after 2 weeks from surgery At 4 weeks, thoracic

ganglia-implanted males showed the same condition

as before

Experiment 3 evaluated thoracic ganglia stage IV

(10 mm in length) and abdominal ganglia stage IV

implants in females at 525mg g 1

No P stylirostrisfemale was induced to mature after 4 weeks from

surgery in both treatments A recovered thoracic

ganglia graft from a dying female revealed its

mor-phology without encapsulation and partial

absorp-tion (4 mm in length) after 5 weeks from surgery On

the other hand, a single injection of serotonin plus

spiperone (Experiment 4) did induce ovarian

matura-tion in both groups implanted with nerve tissue One

week after injection, females were observed with

growing ovaries (stage II) and at week 2 they (3/9)

reached stages III^IV of maturation in the 3A group

The other group (3B) also reached stages III^IV of

maturation at weeks 1 (2/9) and 2 (2/8) Maturing

fe-males eventually spawned their eggs, returning to

ovarian stage I

Experiment 5 evaluated intraspeci¢c nerve tissueextracts in T byrdi at 1500mg g 1 No female was in-duced to mature after 2 weeks from the ¢rst injection

in both treatments

DiscussionThe sequence of experiments developed for this re-search suggests that interspeci¢c and intraspeci¢cthoracic ganglia extracts or implants from maturingpenaeoid females are not capable of inducing a clearresponse in sexual maturation Implants and ex-tracts were tested at doses of 137, 386, 525 and

1500mg g 1b.w in di¡erent experimental animals,without any positive response

Our previous ¢ndings (Alfaro, HernaŁndez et al.2008) demonstrated for the ¢rst time that thoracicganglia implants within Penaeidae are immunologi-cally accepted by the recipient species, but grafts aregradually absorbed before disappearing completely.Therefore, it is expected that transplanted nervecord^ganglia sections will remain physiologically ac-tive for a short time On the contrary, grafts from aPalaemonidae (Macrobrachium tenellum [Smith])were encapsulated by the host

This active phase of grafts seems to be too short toproduce reliable sexual maturation in penaeoidshrimps in males or females In experiment 1, therewas only one maturing female, which is a negligiblee¡ect Other techniques have yielded signi¢cant im-provements in spermatophore quality in P vannamei,such as injection of 17-alpha-methyltestosterone (Al-faro 1996) and methyl farnesoate injection (Alfaro,Zu¤niga, Garc|¤a & Rojas 2008)

In the ¢ddler crab, U pugilator, ¢ve injections of athoracic ganglia extract at a dose equivalent to 0.25thoracic ganglia per injection, during 10 days, in-duced an increased gonad index in intact as well as

in eyestalk-ablated females (Eastman-Reks & man 1984)

Finger-The report by Yano et al (1988) is the only lished evidence for induced ovarian maturation in apenaeoid shrimp by interspeci¢c thoracic ganglia im-plantation Based on the present knowledge, we hy-pothesized that lobster grafts were not encapsulatedimmediately after surgery, given the maturation re-sponse in P vannamei observed by the authors.The evidence from di¡erent decapods suggests that

pub-a substpub-ance from thorpub-acic gpub-anglipub-a is cpub-appub-able of cing ovarian growth However, in penaeoid shrimpsthis expected e¡ect was not measured in any of our

experiments Serotonin plus spiperone has given

more predictable responses in ovarian maturation as

discovered in a previous contribution (Alfaro et al

2004) as well as in the present one

Other penaeoid shrimps may give di¡erent

re-sponses as observed in P monodon, because this

spe-cies was induced to mature similar to eyestalk

ablation with serotonin alone (Wongprasert et al

2006); however, the injection of GnRHs was less

ef-fective than eyestalk ablation (Ngernsoungnern,

Ngernsoungnern,Weerachatyanukul et al 2008)

It is proposed that a putativeVSH is present and it is

under a strong negative control of the eyestalks, by

gonad-inhibiting hormone in the subgenus

Litope-naeus, as suggested previously by Vaca and Alfaro

(2000) The combined e¡ect of serotonin and

spiper-one (Alfaro et al 2004) was much higher than

seroto-nin alone (Vaca & Alfaro 2000) on ovarian

maturation in the subgenus Litopenaeus, suggesting

that the negative control of dopamine is stronger

than the positive control of serotonin

Based on our ¢ndings, the use of thoracic ganglia

extracts or implants is not a practical approach for

in-ducing reliable gonad maturation in penaeoid

shrimps Moreover, the recently reported mechanism

of implant absorption is a biological constraint for the

implementation of transplant technology Therefore,

the injection of serotonin alone or in combination

with spiperone is a much better alternative for the

traditional unilateral eyestalk ablation

Acknowledgments

The authors wish to thank the sta¡ of Estacio¤n de

Biolog|¤a Marina This research was supported by Ley

de Pesca from the Government of Costa Rica The

authors also acknowledge unknown referees for

their valuable comments

References

Alfaro J (1996) E¡ect of 17-alpha-methyltestosterone

and17-alpha-hydroxyprogesterone on the quality of white

shrimp, Penaeus vannamei spermatophores Journal of the

World Aquaculture Society 27, 487^492.

Alfaro J & Lozano X (1993) Development and deterioration

of spermatophores in pond-reared Penaeus vannamei.

Journal of theWorld Aquaculture Society 24, 522^529.

Alfaro J., Zu¤niga G & Komen J (2004) Induction of ovarian

maturation and spawning by combined treatment of

ser-otonin and a dopamine antagonist, spiperone in

Litope-naeus stylirostris and LitopeLitope-naeus vannamei Aquaculture

236, 511^522.

Alfaro J., HernaŁndez L., Zu¤niga G., Soto R & Mej|¤a-Arana F (2008) Thoracic nerve cord - ganglia recognition in in- traspeci¢c and interspeci¢c transplants in the white shrimp, Penaeus (Litopenaeus) vannamei Aquaculture

288, 126–131 doi: 10.1016/j.aquaculture.2008.11.006 Alfaro J., Zu¤niga G., Garc|¤a A & Rojas E (2008) Preliminary evaluation of the e¡ect of juvenile hormone III and methyl farnesoate on spermatophore quality of the white shrimp, Litopenaeus vannamei Boone,1931 (Decapoda: Penaeidae) Revista de Biolog|¤a Marina y Oceanograf|¤a 43, 167^171 Benzie J.A.H (1998) Penaeid genetics and biotechnology Aquaculture 164, 23^47.

Eastman-Reks S & Fingerman M (1984) E¡ects of docrine tissue and cyclic AMP on ovarian growth in vivo and in vitro in the ¢ddler crab, Uca pugilator Comparative Biochemistry and Physiology 79A, 679^684.

neuroen-Fingerman M (1987) The endocrine mechanisms of ceans Journal of Crustacean Biology 7, 1^24.

crusta-Fingerman M (1997) Roles of neurotransmitters in ing reproductive hormone release and gonadal matura- tion in decapod crustaceans Invertebrate Reproduction and Development 31, 47^54.

regulat-Hinsch G.W & Bennett D.C (1979) Vitellogenesis stimulated

by thoracic ganglion implants into destalked immature spider crabs, Libinia emarginata Tissue and Cell 11, 345^ 351.

Hooi C.H (1991) E¡ects of eyestalk ablation, thoracic lion extract and gonad extract from ‘spent spawners’ on ovarian maturation in pond-reared shrimps, Penaeus monodon Fabricius Aquaculture and Fisheries Management

gang-22, 463^471.

Huang H.,Ye H., Li S & Wang G (2008) Immunocytological evidence for the presence of vertebrate FSH- and LH-like substances in the brain and thoracic ganglion of the swimming crab, Portunus trituberculatus Progress in Nat- ural Science 18, 1453^1457.

Huberman A (2000) Shrimp endocrinology A review Aquaculture 191, 191^208.

King J.E (1948) A study of the reproductive organs of the common marine shrimp, Penaeus setiferus (Linnaeus) The Biological Bulletin 94, 244^262.

Kulkarni G.K., Glade L & Fingerman M (1991) Oogenesis and e¡ects of neuroendocrine tissues on in vitro synthesis

of protein by the ovary of the red swamp cray¢sh barus clarkii (Girard) Journal of Crustacean Biology 11, 513^522.

Procam-Ngernsoungnern A., Procam-Ngernsoungnern P., kul W., Chavadej J., Sobhon P & Sretarugsa P (2008) The existence of gonadotropin-releasing hormone (GnRH) immunoreactivity in the black tiger shrimp Penaeus monodon Aquaculture 279, 197^203.

Weerachatyanu-Ngernsoungnern P., Weerachatyanu-Ngernsoungnern A., Kavanaugh S., Sobhon P., Sower S.A & Sretarugsa P (2008) The pre- sence and distribution of gonadotropin-releasing hor-

mone-liked factor in the central nervous system of the

black tiger shrimp, Penaeus monodon General and

Com-parative Endocrinology 155, 613^622.

Ongvarrasopone C., Roshorm Y., Somyong S., Pothiratana

C., Petchdee S., Tangkhabuanbutra J., Sophasan S &

Payim S (2006) Molecular cloning and functional

expres-sion of the Penaeus monodon 5-HT receptor Biochimica et

Biophysica Acta 1759, 328^339.

Otsu T (1960) Precocious development of the ovaries in the

crab, Potamon dehaani, following implantation of the

thor-acic ganglion Annotationes Zoologicae Japonenses 33, 90^

96.

Oyama S.N (1968) Neuroendocrine e¡ects on ovarian

develop-ment in the crab Thalamita crenata Latreille studied in vitro.

Doctoral dissertation University of Hawaii, Honolulu,

Ha-waii, USA.

Quackenbush L.S (1986) Crustacean endocrinology; a

re-view Canadian Journal of Fisheries and Aquatic Sciences

43, 2271^2282.

Quackenbush L.S (1991) Regulation of vitellogenesis in

pe-naeid shrimp In: Frontiers in Shrimp Research (ed by P.

DeLoach, W.J Dougherty & M.A Davidson), pp 125^140.

Elsevier, Amsterdam, Netherlands.

Quackenbush L.S & Keeley L.L (1988) Regulation of

vitello-genesis in the ¢ddler crab, Uca pugilator The Biological

Bulletin 175, 321^331.

Ro S., Talbot P., Leung-Trujillo J & Lawrence A.L (1990) Structure and function of the vas deferens in the shrimp Penaeus setiferus: segments1^3 Journal of Crustacean Biol- ogy 10, 455^468.

Takayanagi H., Yamamoto Y & Takeda N (1986) An ovary stimulating factor in the shrimp, Paratya compressa The Journal of Experimental Zoology 240, 203^209.

Vaca A & Alfaro J (2000) Ovarian maturation and ing in the white shrimp, Penaeus vannamei, by serotonin injection Aquaculture 182, 373^385.

spawn-Wongprasert K., Asuvapongpatana S., Poltana P., wan M & Withyachumnarnkul B (2006) Serotonin sti- mulates ovarian maturation and spawning in the black tiger shrimp Penaeus monodon Aquaculture 261, 1447^ 1454.

Tiensu-Yano I (1993) Ultraintensive culture and maturation in tivity of penaeid shrimp In: CRC Handbook of Mariculture, Crustacean Aquaculture, Vol 1 (ed by J.P McVey), 2nd edn,

cap-pp 289^313 CRC Press, Boca Raton, FL.

Yano I.,Tsukimura B., Sweeney J.N & Wyban J.A (1988) duced ovarian maturation of Penaeus vannamei by im- plantation of lobster ganglion Journal of the World Aquaculture Society 19, 204^209.

In-Zanuy S & Carrillo M (1972) Aplicacio¤n de las hormonas nadotro¤picas en piscicultura Publicacio¤nTe¤cnica Direccio¤n General de Pesca (Espana) 10, 221^231.

go-Effects of turbidity on feeding of the young-of-the-year

Priit Zingel1,2& Tiit Paaver1

The e¡ect of water turbidity on the prey selection and

consumption of the young-of-the-year (YOY)

pike-perch in the planktivorous feeding stage was studied

Attention was paid particularly to the question of

how the food selectivity depends on the size of YOY

pikeperch and how the turbidity a¡ects feeding in

dif-ferent size classes Studies were carried out in ponds

of two ¢sh farms in Estonia over 4 years Small

clado-cerans were the most preferred prey in the smallest

pikeperch size class In larger size classes, the most

selected prey were the large cladocerans Water

tur-bidity a¡ected the prey selection of the planktivorous

pikeperch signi¢cantly In more turbid environments,

the larger zooplankters were more positively selected

than the smaller ones Turbidity decreased both total

zooplankton consumption and Fulton’s condition

fac-tor of ¢sh only in the largest size class of pikeperch

The e¡ect of turbidity on foraging and growth, and

thus on the size of juvenile pikeperch of a particular

year class is substantial under conditions where

juve-niles cannot shift from planktivory to piscivory

Keywords: planktivory, pikeperch, turbidity, food

selectivity

Introduction

Pikeperch (Sander lucioperca) is an important ¢sh

both in recreational and in commercial ¢sheries of

eastern Europe The annual yields are highly variable

(Svrdson & Molin 1981; Buijse 1992; Buijse,Van

Den-sen & Schaap 1992; Lehtonen, Hansson & Winkler

1996) because the survival and growth rate of

juve-nile ¢sh of every year-class depend considerably onthe feeding conditions Juvenile pikeperch must shift

to piscivory after achieving a total length of 5^7 cm(Buijse & Houthuijzen 1992) This accelerates thegrowth rate and is one of the key factors that in£u-ences their recruitment to the ¢shery (Post & Evans1989) Attempts have been made to stabilize the £uc-tuations of stock in natural waters by means of stock-ing of juvenile ¢sh, reared in ponds Therefore, it iscrucial to understand the factors that determine thegrowth of the pikeperch during their ¢rst year of life.Most ¢sh use vision for orientation towards prey(Guthrie & Muntz 1993) The process of predation in-cludes the following steps: location of prey, pursuit,attack and retention (Clarke, Buskey & Marsden2005) The location of prey may be highly in£uenced

by the transparency of the water (O’Brien 1987b;Utne-Palm 2002) Therefore, the ability of the preda-tor and the prey to detect each other may be impaired

by turbidity (Abrahams & Kattenfeld 1997)

Predation pressure on zooplankton is suggested to

be low in eutrophic and turbid lakes because ity may o¡er zooplankton refuge from visual hunting

turbid-by ¢sh (Jeppesen 1998) Several studies have shownthat in lakes with high productivity and turbidity,the feeding conditions for pikeperch are good (Lehto-nen et al 1996) The underlying mechanisms are notclearly understood, although several authors suggestthat it is related to the visual adaptations of this spe-cies to the low transparency of water (Svrdson

& Molin 1973; Persson, Diehl, Johansson, Andersson

& Hamrin 1991) Pikeperch is known for its ability tofeed at low-light levels and is also active during twi-light and night It is supported by the presence oftapetum lucidum, a re£ective material in the retina,

which induces additional light sensitivity and

confers an advantage to this ¢sh in turbid and

low-light environments (Ali, Ryder & Anctil 1977)

Prey characteristics, such as size, pigment and

mo-tion, play an important role in the detection of prey

for visual foraging by ¢sh (O’Brien 1987a; O’Keefe,

Brewer & Dodson 1998) In most studies that have

aimed to determine the e¡ects of turbidity on the

be-haviour of the predator, the reactive distance has

been measured The reaction distance of

planktivor-ous ¢sh to its planktonic prey decreased with

in-creasing turbidity (Vinyard & O’Brien 1976; Barrett,

Grossman & Rosenfeld 1992; Gregory & Northcote

1993; Miner & Stein 1993; Ben¢eld & Minello 1996;

Utne 1997; Utne-Palm 1999) Estimations of feeding

e⁄ciency of the predator in turbid environments

have revealed that the e¡ect of turbidity on feeding

e⁄ciency is negative (De Robertis, Ryer, Veloza

& Brodeur 2003; Horppila, Liljendahl-Nurminen

& Malinen 2004; Nurminen & Horppila 2006)

The main objective of our study was to evaluate

the e¡ect of water turbidity on the prey selection

and consumption of the young pikeperch during the

¢rst summer in ponds We also wished to determine

how the food selectivity depends on the size of the

pikeperch and how turbidity a¡ects feeding in

di¡er-ent size classes

Materials and methods

Our studies were carried out in Estonia, where the

feeding of the young-of-the-year (YOY) pikeperch

was investigated in six ¢shponds over 4 years

(2003^2006) The data for the ponds used are given

in Table 1 Pikeperch larvae were stocked to the ponds

in the beginning of June The plankton and ¢sh

sam-ples were taken approximately every third week until

the ponds were emptied in October Both plankton

and ¢sh samples were collected at each sampling

occasion Turbidity was measured as Secchi disk

visibility to the nearest 5 cm Zooplankton was

col-lected by ¢ltering of10 L depth-integrated pond water[(samples were taken from the whole water columnwith an interval of 0.5 m using a Ruttner sampler(HYDRO-BIOS Apparatebau GmbH, Kiel-Holtenau,Germany) and mixed in one tank)] using a planktonnet (mesh size 48mm), ¢xed with Lugol’s solution andcounted in three 2.5^5 mL subsamples, which com-prised 10^20% of the whole sample volume The sam-ples were counted under a stereomicroscope (NikonSMZ645, Nikon Instruments Europe B.V., Amstelveen,the Netherlands) and enumerated at 32^56 mag-ni¢cations The length of at least 20 individuals ofeach species was measured in every sample forbiomass calculation The individual weights of roti-fers were estimated from average lengths according

to Ruttner-Kolisko (1977) The lengths of crustaceanswere converted to the wet weights according toBalushkina and Winberg (1979) The zooplankterswere divided into four principal groups: rotifers,small cladocerans (e.g Bosmina, Chydorus), large cla-docerans (e.g Daphnia, Leptodora, Polyphemus) andcopepods (Eudiaptomus, Mesocyclops)

The YOY pikeperch were sampled using a liftnet(1m2, mesh size 1mm), a sweepnet or a gillnet The

¢sh were measured (Ltot) and weighted They werepreserved in ethanol and their whole digestive tractcontent was extracted and analysed under thestereo-microscope at 16 40 magni¢cation Whereavailable, the stomach content of at least 20 speci-mens of pikeperch was inspected However, on foursampling occasions, only 15 specimens were gath-ered The diet was evaluated using the numericalmethod (Hyslop 1980) Daily food consumption wascalculated according to a 4-h gut passage time(Sutela & Huusko 1997) and a 20-h diel feeding time(Karjalainen 1992) The average reconstructed gutcontent was calculated based on the Eldridge, Whip-ple, Eng, Bowers and Jarvis (1981) method: C 5 RH/T,where C is the daily food consumption (zooplanktersingested ¢sh 124 h 1), R is the average recon-structed gut content (zooplankters ingested ¢sh 1),

H is the hours of active feeding (h) and T the gut sage time (h) of actively feeding ¢sh

pas-Feeding selectivity of the ¢sh was assessed usingIvlev’s selectivity index (Ivlev 1961):

Ei¼ ðri niÞ  ðriþ niÞ1

where riis the relative abundance (%) of prey gory i in the diet of ¢sh and niis the relative abun-dance (%) of prey category i in the environment.Index values between  0.3 and 10.3 are generally

cate-Table 1 The surface area, mean zooplankton (ZP) biomass

and range of ZP biomass found in our study ponds in the

Hrjanurme (1, 2 and 3) and Haaslava (4,5 and 6) ¢sh farms

considered to be not signi¢cantly di¡erent from 0 and

represent nonselective feeding (Lazzaro 1987)

Fulton’s condition factor (Tesch 1971) was

calcu-lated for each ¢sh as follows:

KL tot ¼ ð100 000  WÞ=Ltot3

where W is the weight in grams and Ltotis the total

length in millimetres

Based on LtotYOY, ¢sh were classi¢ed into four

size classes: 31^45, 46^60, 61^75 and 76^90 mm

The programSTATISTICAfor Windows (Statsoft, Tulsa,

Oklahoma, USA) was used for statistical analyses

Results

To analyse how the prey selection is a¡ected by the

size of theYOY pikeperch all available feeding

selectiv-ity data for each size class were pooled (Fig 1) The

di¡erences among ponds and years (pikeperch and

zooplankton community data) were checked using

one-way analysis of variance As the di¡erences were

not signi¢cant (in all cases P40.05), the pooling of all

feeding selectivity data seemed justi¢ed We did not

observe pikeperch cannibalism in our study ponds

and all ¢sh fed on zooplankton Rotifers were

nega-tively selected (Eio  0.3) and all other zooplankton

groups were positively (Ei40.3) selected For rotifers,

there was a negative correlation between ¢sh size

and selection (R250.71, Po0.01) Small cladocerans

were the most preferred prey in the smallest pikeperch

size class In larger size classes, the most selected prey

was the large cladocerans The selectivity index for

small and large cladocerans increased with the size of

the pikeperch (R250.59, Po0.01 and R2

50.64,

Po0.01 respectively) The same trend applied to the

copepods (R250.76, Po0.01)

For rotifers and small cladocerans, there was a

negative linear correlation (Po0.001) between water

turbidity and selection (Figs 2 and 3) In the case of

rotifers, the correlation weakened gradually with thesize of the YOY For large cladocerans and copepods,

we found a positive linear correlation (Po0.001)between water turbidity and selection (Figs 4 and 5).The correlation strengthened gradually with the size

of theYOY

On comparing the daily food consumption andwater turbidity, a negative linear trend was found,but the correlation was statistically signi¢cant(Po0.01) only for the largest size class of the pike-perch (Fig 6) A similar trend was found when wemeasured Fulton’s condition factor at di¡erent levels

of water turbidity A statistically signi¢cant (Po0.01)negative correlation was found only for the largestpikeperch size class (Fig.7)

DiscussionOur data show clearly that the water turbidity af-fected the prey selection of the planktivorous pike-perch Usually, this kind of relationship is explainedwith the decrease in the reactive distance, which al-ters the encounter rates with prey (Hecht & van derLangen 1992) Fish have di¡ering reactive distancesfor zooplankters of di¡erent sizes, which results in adi¡erent relative density assessment by a foragingplanktivore (Gliwicz 2002) In our study, the smallerzooplankters were gradually less selected in moreturbid water Large zooplankters were more posi-tively selected in the more turbid environment Thisindicates that the YOY pikeperch behaved as generalpredators and switched from one zooplankton sizegroup to another depending on the relative abun-dance of the prey At the same time, it was seen thatthe most conspicuous prey objects were not the mostselected on every occasion One such example wasthe selective feeding on small cladocerans that ex-ceeded the feeding on large-sized copepods The pre-ferred selection of small-sized prey instead of largerones may be explained by the reduced handling timeincluding high capture success (Heath 1993) Cope-pod species are known to be good escapers (e.g Dren-ner, Srickler & O’Brien 1978); and thus, feeding onpoor escapers like Bosmina (Szlauer 1965) was morepro¢table Our study showed that in the case of smal-ler pikeperch, the selectivity index for copepods was

in the range that represented unselective feeding (Fig.1).Only larger ¢sh size classes selected the copepods po-sitively in more turbid water This suggests thatcopepods were not the favourable prey item for plank-tivorous pikeperch

Figure 1 Ivlev’s selectivity index ( SD) for the di¡erent

pikeperch size classes and zooplankton groups

On comparing feeding of di¡erent ¢sh size classes,

it was found that the selectivity for cladocerans and

copepods increased gradually with the size of the

YOY while the selectivity for rotifers decreased

Feed-ing on rotifers by juvenile ¢sh, and particularly aswitch in a diet from copepods or cladocerans to roti-fers, has often been described only under conditions

of poor food availability (e.g Treasurer 1992) The

Figure 2 Linear correlation between water turbidity and Ivlev’s selectivity index for the di¡erent pikeperch size classesfeeding on rotifers Each point shows an average value for a given pikeperch size class available from a given pond on agiven sampling occasion

Figure 3 Linear correlation between water turbidity and Ivlev’s selectivity index for the di¡erent pikeperch size classesfeeding on small cladocerans Each point shows an average value for a given pikeperch size class available from a givenpond on a given sampling occasion

avoidance of rotifers indicates that YOY pikeperch

did not su¡er from lack of suitable planktonic food in

the ponds

The e¡ect of turbidity on the food selectivity wassimilar in all pikeperch size classes The e¡ects ofturbidity on foraging of YOY pikeperch are not widely

Figure 4 Linear correlation between water turbidity and Ivlev’s selectivity index for the di¡erent pikeperch size classesfeeding on large cladocerans Each point shows an average value for a given pikeperch size class available from a givenpond on a given sampling occasion

Figure 5 Linear correlation between water turbidity and Ivlev’s selectivity index for the di¡erent pikeperch size classesfeeding on copepods Each point shows an average value for a given pikeperch size class available from a given pond on agiven sampling occasion

investigated Ljunggren (2002) studied how the visual

conditions a¡ected pikeperch feeding on mysids and

found that turbidity had no negative e¡ect on the total

consumption rates Studies on the closely related

species walleye (Stizostedion vitreum) yielded similar

results (Vandenbyllaardt,Ward, Braekevelt & Mcintyre

1991; Bristow, Summerfelt & Clayton 1996) This can

be explained by the vision physiology of pikeperch,which is well adapted to turbid conditions (Ljunggren2002; Sandstr˛m & Kars 2002) Our study showedthat while the food consumption of the smallerpikeperch size classes was not signi¢cantly a¡ected

by turbidity, the negative e¡ect of poor visibility onplankton consumption was evident in the largest size

Figure 6 Linear correlation between water turbidity and daily food consumption for the di¡erent pikeperch size classes.Each point shows an average value for a given pikeperch size class available from a given pond on a given sampling occasion

Figure 7 Linear correlation between water turbidity and Fulton’s condition factor for the di¡erent pikeperch size classes.Each point shows an average value for a given pikeperch size class available from a given pond on a given sampling occasion

class (76^90 mm) The same applied to Fulton’s

condi-tion factor ^ turbidity a¡ected signi¢cantly only the

condition of the largest size class It is known that at

the end of ¢rst summer, pikeperch must shift to

pisciv-ory, which greatly accelerates their growth rate and

increases the condition factor (van Densen 1985;

Buijse & Houthuijzen 1992) This directly in£uences

the recruitment of a year class to a ¢shery, as the larger

individuals are more likely to survive the ¢rst winter

and contribute more to the adult stock than the

smal-ler ones (Post & Evans 1989) Without the shift from

planktivory to piscivory, the pikeperch growth is

halted (Biro 1972) In our study ponds, suitable prey

¢sh were absent and the pikeperch was forced to feed

only on zooplankton It resulted in a smaller size and

also a low condition factor at the end of the growth

period In cases like this, the water turbidity may act

as an important factor in£uencing the pikeperch food

consumption and growth.We suggest that while large

YOY pikeperch feed on zooplankton, the energetic

costs for foraging increase signi¢cantly in more turbid

environments This may result in reduced growth and

high mortality during the ¢rst winter Therefore, the

traditional stocking procedure late in the ¢rst season

should be questioned in cases where the rearing

ponds are highly turbid Instead, the stocking of

50 mm summer-¢ngerlings, large enough to shift to

piscivory, should be considered Of course, the

stock-ing habitat should also be carefully considered There

is no point in stocking summer-¢ngerlings in an

envir-onment that lacks suitable prey items

In conclusion, we can say this about the

plankti-vorous feeding period, water turbidity a¡ected the

prey selection of the pikeperch In a more turbid

environment, the larger zooplankters were more

positively selected than the smaller ones Turbidity

decreased the total zooplankton consumption and

Fulton’s condition factor only in the largest

plankti-vorous pikeperch size class The e¡ect of turbidity on

foraging and growth rate of juvenile pikeperch is

sub-stantial in cases where juveniles cannot shift from

planktivory to piscivory

Acknowledgments

This study was supported by the Estonian

target-¢nanced research projects SF0172103s02 and

SF1080022s07, and the Estonian Science Foundation

grant no 5425 We wish to express our gratitude to

the sta¡ of Hrjanurme and Haaslava ¢sh farms for

their help in carrying out our studies

ReferencesAbrahams M & Kattenfeld M (1997) The role of turbidity as

a constraint on predator^prey interactions in aquatic vironments Behavioral Ecology and Sociobiology 403, 169^174.

en-Ali M.A., Ryder R.A & Anctil M (1977) Photoreceptors and visual pigments as related to behavioural responses and preferred habitats of perches (Perca spp.) and pikeperches (Stizostedion spp.) Journal of Fisheries Research Board of Canada 34, 1475^1480.

Balushkina E.V & Winberg G.G (1979) Relation between body mass and length in planktonic animals In: Ob- shchiye Osnovy IzucheniyaVodnykh Ekosistem (ed by G.G Winberg), pp 169^172 Nauka, Leningrad, Russia (in Russian).

Barrett J.C., Grossman G.D & Rosenfeld J (1992) induced changes in reactive distance of rainbow trout Transactions of the American Fisheries Society 121, 437^443.

Turbidity-Ben¢eld M.C & MinelloT.J (1996) Relative e¡ects of turbidity and light intensity on reactive distance and feeding of

an estuarine ¢sh Environmental Biology of Fishes 46, 211^216.

Biro P (1972) First summer growth of pike-perch (Lucioperca lucioperca L.) in Lake Balaton Annales of Biology Tihany

39, 101^113.

Bristow B.T., Summerfelt R.C & Clayton R.D (1996) parative performance of intensively cultured larval wal- leye in clear, turbid, and colored water The Progressive Fish-Culturist 58, 1^10.

Com-Buijse A.D (1992) Recruitment-induced variations of the yield and consequences for the management of the gill net ¢shery on perch and pikeperch in Lake IJssel In: Dy- namics and Exploitation of Unstable Percid Populations (ed by A.D Buijse), pp 151^167 Landbouwuniversiteit, Wageningen, the Netherlands.

Buijse A.D & Houthuijzen R.P (1992) Piscivory, growth, and size-selective mortality of age 01 pikeperch (Stizostedion lucioperca) Canadian Journal of Fisheries and Aquatic Sciences 49, 894^902.

Buijse A.D., Van Densen W.L.T & Schaap L.A (1992) class strength of Eurasian perch (Perca £uviatilis L.) and pikeperch (Stizostedion lucioperca (L.)) in relation to stock size, water temperature and wind in Lake IJssel, the Neth- erlands, 1966^1989 In: Dynamics and Exploitation of Un- stable Percid Populations (ed by A.D Buijse), pp 31^69 Landbouwuniversiteit,Wageningen, the Netherlands Clarke R.D., Buskey E.J & Marsden K.C (2005) E¡ects of water motion and prey behavior on zooplankton capture

Year-by two coral reef ¢shes Marine Biology 146, 1145^1155.

De Robertis A., Ryer C.H., Veloza A & Brodeur R.D (2003) Di¡erential e¡ects of turbidity on prey consumption of piscivorous and planktivorous ¢sh Canadian Journal of Fisheries and Aquatic Sciences 6012, 1517^1526.

Drenner R.W., Srickler J.R & O’Brien W.J (1978) Capture probability: the role of zooplankter escape in the selective

feeding of planktivorous ¢sh Journal of the Fisheries

Re-search Board of Canada 35, 1370^1373.

Eldridge M.B.,Whipple J.A., Eng D., Bowers M.J & Jarvis B.M.

(1981) E¡ects of food and feeding factors on

laboratory-reared striped bass larvae Transactions of the American

Fisheries Society 110, 111^120.

Gliwicz Z.M (2002) On the di¡erent nature of top-town and

bottom-up e¡ects in pelagic food webs Freshwater Biology

47, 2296^2312.

Gregory R.S & Northcote T.G (1993) Surface, planktonic,

and benthic foraging by juvenile chinook salmon

(Oncor-hynchus tshawytscha) in turbid laboratory conditions.

Canadian Journal of Fisheries and Aquatic Sciences 50,

233^240.

Guthrie D.M & Muntz W.R.A (1993) Role of vision in

behaviour In: Behaviour of Teleost FISHES (ed by

T.J Pitcher), 2nd edn, pp 89^128 Chapman and Hall,

London, UK.

Heath M.R (1993) The role of escape reactions in

determin-ing the size distribution of prey captured by herrdetermin-ing

lar-vae Environmental Biology of Fishes 38, 331^344.

Hecht T & Van Der Langen C.D (1992) Turbidity induced

changes in the foraging strategies in estuaries South

African Journal of Zoology 27, 95^107.

Horppila J., Liljendahl-Nurminen A & Malinen T (2004)

E¡ects of clay turbidity and light on the predator^prey

in-teraction between smelts and chaoborids Canadian

Jour-nal of Fisheries and Aquatic Sciences 61, 1862^1870.

Hyslop E.J (1980) Stomach content analysis ^ a review of

methods and their application Journal of Fish Biology 17,

411^429.

Ivlev V.S (1961) Experimental Ecology of the Feeding Fishes.

Yale University Press, New Haven, NJ, USA.

Jeppesen E (1998) The ecology of shallow lakes PhD thesis.

Department of Lake and Estuarine Ecology National

Environmental Research Institute, Silkeborg, Denmark.

Karjalainen J (1992) Food ingestion, density-dependent

feeding and growth of vendace Coregonus albula (L.)

larvae Annales Zoologici Fennici 29, 93^103.

Lazzaro X (1987) A review of planktivorous ¢shes: their

evolution, feeding behaviours, selectivities, and impacts.

Hydrobiologia 146, 97^167.

Lehtonen H., Hansson S & Winkler H (1996) Biology and

exploitation of pikeperch, Stizostedion lucioperca (L.), in

the Baltic Sea area Annales Zoologici Fennici 33, 525^535.

Ljunggren L (2002) Feeding ecology of young-of-the-year

pike-perch (Stizostedion lucioperca) ^ implications for recruitment

and aquaculture PhD thesis Acta Universtatis

Agricultur-ae SueciAgricultur-ae, Silvestria 255, SLU, Vattenbruksinstitutionen,

Ume.

Miner J.G & Stein R.A (1993) Interactive in£uence of

turbid-ity and light on larval bluegill (Lepomis macrochirus)

fora-ging CanadianJournal of Fisheries and Aquatic Sciences 50,

781^788.

Nurminen L & Horppila J (2006) E⁄ciency of ¢sh feeding on

plant-attached prey: e¡ects of inorganic turbidity and

plant-mediated changes in the light environment ogy and Oceanography 51, 1550^1555.

Limnol-O’Brien D.P (1987a) Description of escape response of krill (Crustacea: Euphausiacea), with particular reference to swarming behavior and the size and proximity of the pre- dator Journal of Crustacean Biology 7, 449^457.

O’Brien W.J (1987b) Planktivory by freshwater ¢sh: thrust and parry in the pelagia In: Predation: Direct and Indirect Impacts on Aquatic Communities (ed by W.C Kerfoot & A Sih), pp 3^16 University press of New England, Hanover,

NH, USA.

O’Keefe T.C., Brewer M.C & Dodson S.I (1998) Swimming behavior of Daphnia: its role in determining predation risk Journal of Plankton Research 20, 973^984.

Persson L., Diehl S., Johansson L., Andersson G & Hamrin S.F (1991) Shifts in ¢sh communities along the productiv- ity gradient of temperate lake ^ patterns and the impor- tance of size-structured interactions Journal of Fish Biology 38, 281^293.

Post J.R & Evans D.O (1989) Size-dependent overwinter mortality of young-of-the-year yellow perch (Perca £aves- cens): laboratory, in situ enclosure, and ¢eld experiments Canadian Journal of Fisheries and Aquatic Sciences 46, 1958^1968.

Ruttner-Kolisko A (1977) Suggestion for biomass tion of planktonic rotifers Archiv fˇr Hydrobiologie 8, 71^76.

calcula-Sandstr˛m A & Kars P (2002) E¡ects of eutrophication on young-of-the-year freshwater ¢sh communities in coastal areas of the Baltic Environmental Biology of Fishes 63, 89^101.

Sutela T & Huusko A (1997) Food consumption of vendace Coregonus albula larvae in Lake Lentua, Finland Journal of Fish Biology 51, 939^951.

Svrdson G & Molin G (1973) The impact of climate on Scandinavian populations of the sander, Stizostedion lu- cioperca (L.) Report of the Institute of Freshwater Research, Drottningholm 53, 112^139.

Svrdson G & Molin G (1981) The impact of eutrophication and climate on a warmwater ¢sh community Report of the Institute of Freshwater Research, Drottningholm 59, 142^151.

Szlauer L (1965) The refuge ability of plankton animals fore models of plankton-eating animal Polskie Archiwum Hydrobiologii 13, 89^95.

be-Tesch F.W (1971) Age and growth In: Fish Production in Fresh Waters (ed by W.E Ricker), pp 98^130 Blackwell, Oxford, UK.

Treasurer J.W (1992) The predator^prey relationship of perch, Perca £uviatilis, larvae and zooplankton in two Scottish lochs Environmental Biology of Fishes 35, 63^74.

Utne A.C.W (1997) The e¡ect of turbidity and illumination

on the reaction distance and search time of the marine planktivore Gobiusculus £avescens Journal of Fish Biology

50, 926^938.

Utne-Palm A.C (1999) The e¡ects of prey mobility, prey

con-trast, turbidity and spectral composition on the reaction

distance of Gobiusculus £avescens to its planktonic prey.

Journal of Fish Biology 54, 1244^1258.

Utne-Palm A.C (2002) Visual feeding of ¢sh in a

tur-bid environment: physical and behavioural aspects.

Marine and Freshwater Behaviour and Physiology 35,

111^128.

Vandenbyllaardt L., Ward F.J., Braekevelt C.R & McIntyre

D.B (1991) Relationships between turbidity, piscivory,

and development of the retina in juvenile walleyes Transactions of the American Fisheries Society 120, 382^390.

Van Densen W.L.T (1985) Feeding behaviour of major 01

¢sh species in a shallow, eutrophic lake (Tjeukemeer, the Netherlands) Journal of Applied Ichthyology 1, 49^70 Vinyard G.L & O’BrienW.J (1976) E¡ects of light and turbud- ity on the reactive distance of bluegill (Lepomis macro- chirus) Journal of the Fisheries Research Board Canada 33, 2845^2849.

Effects of dietary protein and lipid level, and water temperature, on the post-feeding oxygen consumption

of Atlantic cod and haddock

Juan C Pe¤rez-Casanova1, Santosh P Lall2& A Kurt Gamperl1

1 Ocean Sciences Centre, Memorial University of Newfoundland, St John’s, NL, Canada

2 Institute for Marine Biosciences, National Research Council Canada, Halifax, NS, Canada

Correspondence: J C Perez-Casanova, Aquaculture, Biotechnology and Aquatic Animal Health Section, Department of Fisheries and Oceans, Northwest Atlantic Fisheries Centre, 80 East White Hills Road, St John’s, Newfoundland, Canada A1C 5X1 E-mail: jucperea@ yahoo.com

Abstract

Tank respirometry was used to study the interactive

e¡ects of protein:lipid level (55%:11% vs 42%:16%;

both diets isoenergetic) and temperature (11, 6 and

2 1C) on the magnitude and duration of speci¢c

dy-namic action (SDA) in juvenile Atlantic cod and

had-dock The protein:lipid level did not a¡ect any

measured variable However, numerous temperature

and species e¡ects were observed For example,

although the maximum post-feeding oxygen

con-sumption (30^50% above routine metabolic rate;

RMR) and SDA duration ( 55^85 h; SDADUR) were

not a¡ected by temperature, SDADURg 1of food

in-creased from 11 to 2 1C (from  3 to 12 h g food 1)

While absolute SDA (mg O2) decreased by  60^65%

in cod and 75% in haddock from11to 2 1C, due to a

concomitant decrease in food consumption from

 2.0% to 0.6% body mass, SDA comprised between

3.3% and 5.2% of the dietary energy content at

all temperatures Finally, RMR at 11 and 2 1C and

SDADURat 2 1C were 25^35% and 25% greater in

cod, respectively, as compared with haddock These

results suggest that feeding reduced protein diets at

low water temperatures is unlikely to improve the

growth of these species

Keywords: speci¢c dynamic action, temperature,

diet, Atlantic cod, haddock

Introduction

In North America, the aquaculture of Atlantic cod

(Gadus morhua L.) and haddock (Melannogramus

aegle¢nus L.) is still at a pre-commercial stage, in largepart because seasonal £uctuations in several envir-onmental factors make the cage-culture of these

¢sh challenging Of these environmental factors,temperature is probably the most important as it canrange fromo0 to 20 1C, and changes in tempera-ture can a¡ect physiological processes such as meta-bolism, food intake and growth, immune statusand survival (Burel, Person-Le Ruyet, Gaumet, LeRoux, Severe & Boeuf 1996; Imsland & Jonassen2001; Luo & Xie 2008) With regard to cold tempera-tures, research shows that Atlantic cod decreasetheir food consumption and experience changes infeeding behaviour and decreases in the growth rateduring months when water temperatures are low(Brown, Pepin, Methven & Somerton 1989; Clark,Brown, Goddard & Moir 1995; Purchase & Brown2001) Further, it appears that the biggest challengefacing the aquaculture of haddock in the NorthAtlantic is overcoming slow growth rates associatedwith cold water temperatures (Frantsi, Lanteigne,Blanchard, Alderson, Lall, Johnson, Leadbeater,Martin-Robichaud & Rose 2002)

Decreases in ¢sh appetite (Mallekh & Lagarde're2002), growth (Clark et al 1995; Claireaux, Webber,Lagarde're & Kerr 2000) and activity (Clark et al.1995) at low temperatures (o5 1C) are likely to be atleast partly related to the inter-relationships betweenmetabolic scope (the di¡erence between maximumand standard metabolism), speci¢c dynamic action(SDA, the energy required for ingestion, digestionand nutrient absorption/assimilation) and ration(Muir & Nimmi 1972; Jobling 1981; Soo¢ani & Haw-kins 1982; Soo¢ani & Priede 1985; Claireaux et al

2000) For example, it appears that Atlantic cod

in cold water can only direct a limited amount of

metabolic energy to SDA due to a reduction in their

metabolic scope (Claireaux et al 2000), and it

has been hypothesized that there is a ‘metabolic

constraint’ on appetite in ¢sh (i.e ¢sh will not feed

again until their metabolic rate declines below a

certain level) (Jobling 1981) In order to achieve

signi-¢cant growth in Atlantic cod and haddock reared

at cold temperatures, it is clear that food ration

and composition must be optimized (Gotceitas,

Methven, Fraser & Brown 1999) However, there are

insu⁄cient data on which to base feeding protocols

or diet formulations for use at cold temperatures,

because studies that provide relevant information

on SDA in Atlantic cod, haddock and other ¢sh

(Jobling & Davies 1980; Soo¢ani & Hawkins 1982;

LeGrow & Beamish 1986; Lyndon, Houlihan & Hall

1992; Blaikie & Kerr 1996; Peck, Buckley & Bengtson

2003, 2005), on the relationship between Atlantic

cod husbandry and growth (Lambert & Dutil 2001)

or that provide detailed analyses on the optimization

of diets for these species (Lie, Lied & Lambertsen

1988; Kim & Lall 2001; Morais, Bell, Robertson,

Roy & Morris 2001; Nanton, Lall & McNiven 2001;

Tibbetts, Lall & Milley 2005) have rarely been

con-ducted at temperatures below 8 1C

During sea-cage rearing, Atlantic cod and haddock

juveniles are generally fed commercial diets that are

formulated for optimum growth at temperatures of

10^12 1C, and are expensive due to their high protein

(HP) content ( 50^58%) (De Silva & Anderson

1995; Me¤dale & Guillaume 2001; Morais et al 2001)

However, it may be possible to improve the growth

of these species at cold temperatures, and reduce

production costs, by feeding diets containing lower

protein levels This is because, with the exception of

a recent study by Eliason, Higgs and Farrell (2007)

on rainbow trout, Oncorhynchus mykiss (Walbaum),

most studies (e.g Cho, Bayley & Slinger 1976; Jobling

& Davies 1980; Jobling 1981; LeGrow & Beamish 1986)

on ¢sh have shown that SDA can be reduced

signi¢-cantly by reducing the dietary protein content In

ad-dition, Clark et al (1995) showed that sea-caged

Atlantic cod fed low-protein diets during the

fall/win-ter months had equivalent, or even higher (by 10%),

weight gain than ¢sh fed a high-protein diet

In this study, we acclimated Atlantic cod and

haddock juveniles to three temperatures (11, 6 and

2 1C) and investigated whether reducing the dietary

protein:lipid content would decrease the magnitude

and duration of SDA

Materials and methodsThese studies were conducted in accordance withthe guidelines published by the Canadian Council

on Animal Care, and approved by the Animal CareCommittee at Memorial University

Rearing conditionsThe Atlantic cod used in these experiments were com-munally reared in production tanks following stan-dard rearing protocols in place at the AquacultureResearch and Development Facility (ARDF) of theOcean Sciences Centre, Memorial University of New-foundland Haddock were reared from eggs at the Insti-tute for Marine Biosciences, National Research Council(IMB-NRC) Marine Research Station (Ketch Harbour,

NS, Canada) following the techniques described inFrantsi et al (2002), but were transferred to the ARDFapproximately 6 months before the research began Atthe ARDF, juveniles of both species (3000 Atlantic cod,

5000 haddock) were held in 4000^6000 L tanks plied with ¢ltered and oxygenated seawater (tempera-ture 11 1 1C; oxygen saturation 490%) and a 12 hlight:12 h dark photoperiod, and were fed twice daily(9:00 and16:00 hours) at a rate of 1.5% of average bodymass (BM) per day with a commercial diet (EWOSCanada, Surrey, BC, Canada; 55% protein,15% lipid)

sup-AcclimationTwo months before the respirometry experiments,the ¢sh were acclimated to one of three tempera-tures: 11 1C, which is within the optimum tem-perature range for juvenile Atlantic cod growth(Bjornsson, Steinarsson & Oddgeirsson 2001; Peck,Buckley, Caldarone & Bengtson 2003); 2 1C, whichposes signi¢cant challenges for these species incage-culture (Brown et al 1989; Clark et al 1995),and 6 1C, which is intermediate between the two tem-peratures However, no 6 1C experiments were per-formed on haddock, as insu⁄cient numbers of ¢shwere available to conduct experiments at all threetemperatures During this period, both species weremaintained at 12 h light:12 h dark, and fed the com-mercial diet at a rate of 1.5% BM every second day.This change in protocol was used to ensure that the

¢sh achieved an optimum size for the respirometerstudy, and to minimize size di¡erences between ¢shheld at the di¡erent temperatures At the end of theacclimation period, there was no signi¢cant di¡er-ence in the mass of any of the ¢ve groups (three