Aus dem Institut für Tierzucht und Tierhaltung

der Agrar- und Ernährungswissenschaftlichen Fakultät

der Christian-Albrechts-Universität zu Kiel

___________________________________________________________________________

MODELLING OF GROWTH AND MORTALITY OF TURBOT

(Psetta maxima) REARED IN MARINE RECIRCULATION

AQUACULTURE SYSTEMS

Dissertation

zur Erlangung des Doktorgrades

der Agrar- und Ernährungswissenschaftlichen Fakultät

der Christian-Albrechts-Universität zu Kiel

vorgelegt von

Master of Science

ANDREAS BAER

aus Bremen

Dekan: Prof. Dr. U. Latacz-Lohmann

Erster Berichterstatter: Prof. Dr. J. Krieter

Zweiter Berichterstatter: Prof. Dr. Carsten Schulz

Tag der mündlichen Prüfung: 15. Juli 2010

___________________________________________________________________________

Die Dissertation wurde mit dankeswerter finanzieller Unterstützung aus FIAF Fördermitteln der EU angefertigt

Table of Contents General Introduction ............................................................................................................... 1 Chapter One: Management information and decision-support system for a closed aquaculture recirculation system......................................................................................................................................... 4

Chapter Two:

The use of CUSUM charts for early detection of increasing mortality in a turbot (Psetta maxima) recirculation aquaculture system............................................................................... 21

Chapter Three:

Analysing the growth of turbot (Psetta maxima) in a commercial recirculation system with the use of 3 different growth models.............................................................................................. 44

Chapter Four:

The combined effect of feeding time and diet composition on growth performance and metabolism of juvenile turbot (Psetta maxima) ....................................................................... 65 General Discussion................................................................................................................. 85 General Summary.................................................................................................................. 94 Zusammenfassung.................................................................................................................. 96

1

General Introduction

Aquaculture compared to agriculture is a relatively young field of intensive animal

husbandry. However, aquaculture is the fastest growing food-producing sector in the world,

which had an annual growing rate of 8.8% up until 2004 (FAO, 2005-2010). Marine

aquaculture currently comprises one-third of global seafood farming by weight, and

cultivation of marine finfish and shellfish has become the fastest growing segment within

aquaculture (FAO, 2000a). The major amount of marine aquatic organisms is produced in net

pens, but due to environmental pollution and other negative effects of offshore farms (Read

and Fernandes, 2003; Wu, 1995) the development of ecologically friendly and successful

recirculation systems is steadily increasing (Blancheton, 2000). Because intensive indoor fish

farming will increase in the future, a high efficiency is necessary to survive in the global

market and compete with other producers.

The focus in the present study is on the flatfish species turbot (Psetta maxima). Turbot is a

high-value species and its white meat with low lipid levels is appreciated by consumers

(Regost et al., 2001). The global aquaculture production of turbot was approximately 9.500t

in 2008 (FAO, 2005-2010) with the tendency to increase in the future. Since the commercial

production of turbot started approximately 20 years ago (FAO, 2005-2010), there are still

many potential bottlenecks in the large-scale industrial production of marine species (Planas

and Cunha, 1999). In Germany, the first commercial marine RAS opened in 2001 and was a

pioneer in turbot aquaculture. The present study deals with turbot aquaculture with the aim to

broaden knowledge in rearing this species and thus improve production. Data from the turbot

farm mentioned above was used the basis for the present study.

The first chapter gives attention to artificial intelligence in aquaculture, more precisely in

management information and decision-support systems. These systems help to improve

economic output and farm management (Montgomery, 1997). Therefore, a short introduction

to decision-support systems is presented in Chapter One.

In Chapter Two data from the commercial turbot recirculation aquaculture system (RAS) was

analysed and a statistical control chart was developed to monitor the mortality rate. Statistical

control charts are a useful tool in management control. They are common in industry to

monitor shifts in production processes (Wiklund, 1994). If the supervised process runs out of

control and the process is significantly different to the average process mean, an alarm is

triggered by the control chart. Different settings of control charts were tested to find out the

2

optimal performance in regard to the highest detection rate of periods with increased mortality

in the RAS.

Weight gain is by far the most important factor in commercial aquaculture in regard to

economic benefit. Therefore it is essential to know the growth performance of the species

reared. Each fish species has its own growth characteristics depending on environmental

rearing conditions (Pickering, 1993). Environmental conditions favouring optimal growth of

turbot have been known for some time (Person-Le Ruyet, 2002). The growth data of the

commercial turbot farm was analysed with the aid of different growth models as presented in

Chapter Three. New findings on the growth characteristics of turbot reared at the RAS were

attained.

During the juvenile stage, growth performance can be influenced by different feeding regimes

and varying diet compositions. In trout (Oncorhynchus mykiss) the feeding time and diet

composition has been shown to have a significant influence on body composition and growth

performance (Gelineau et al., 2002; Reddy et al., 1994). To examine whether comparable

effects on growth performance could also be detected in turbot, a feeding trial was conducted,

described in Chapter Four. The effects of feeding time and different feed compositions on

growth performance and metabolism of juvenile turbot were also analyzed.

References

Blancheton, J.P., 2000. Developments in recirculation systems for Mediterranean fish species.

Aquacultural Engineering 22, 17-31.

FAO, 2000a. The State of the World Fisheries and Aquaculture 2000. Rome: FAO.

FAO, 2005-2010. Cultered Aquatic Species Information Programme. FAO Fisheries and

Aquaculture Departement. Rome.

Gelineau, A., Bolliet, V., Corrraze, G., Boujard, T., 2002. The combined effects of feeding

time and dietary fat levels on feed intake, growth and body composition in rainbow

trout. Aquatic Living Resources 15, 225-230.

Montgomery, D.C., 1997. Introduction to statistical quality control. John Wiley & Sons, New

York.

Person-Le Ruyet, J., 2002. Turbot (Scopthalmus maximus) grow-out in Europe: practices,

results, and prospects. Turkish Journal of Fisheries and Aquatic Science 2, 29-39.

Pickering, A.D., 1993. Growth and Stress in Fish Production. Aquaculture 111, 51-63.

Planas, M., Cunha, I., 1999. Larviculture of marine fish: problems and perspectives.

Aquaculture 177, 171-190.

3

Read, P., Fernandes, T., 2003. Management of environmental impacts of marine aquaculture

in Europe. Aquaculture 226, 139-163.

Reddy, P.K., Leatherland, J.F., Khan, M.N., Boujard, T., 1994. Effect of the daily meal time

on the growth of rainbow trout fed different ration levels. Aquaculture International 2,

165-179.

Regost, C., Arzel, J., Cardinal, M., Robin, J., Laroche, M., Kaushik, S.J., 2001. Dietary lipid

level, hepatic lipogenesis and flesh quality in turbot (Psetta maxima). Aquaculture

193, 291-309.

Wiklund, S.J., 1994. Control-Charts and Process Adjustments. Departement of Statistic,

University of Urea.

Wu, R.S.S., 1995. The environmental impact of marine fish culture: Towards a sustainable

future. Marine Pollution Bulletin 31, 159-166.

4

Chapter One

Management information and decision-support system

for a closed aquaculture recirculation system

A. Baera,b, C. Schulza,b, J. Krietera

aInstitut für Tierzucht und Tierhaltung, Christian-Albrechts-Universität,

D-24098 Kiel, Germany

bGMA – Gesellschaft für Marine Aquakultur mbH,

D-25761 Büsum, Germany

5

Abstract

The development of automatic control systems for aquaculture has increased during the last

decade. There have been attempts to improve the productivity and efficiency of recirculation

systems with the help of artificial intelligence. Some management information systems (MIS)

exist for RASs but still they need to be improved. The present study presents some

approaches for improving automatic control systems for closed recirculation systems. It

presents results of different scientific studies in a review and tries to integrate these findings

into the development of improved artificial control systems. The potential bottlenecks of a

commercial RAS are manifold (e.g.: fish welfare, water treatment) and are also presented

below. Beside the feasibility of integrating the findings into an MIS it is important to keep

these decision-support tools as user-friendly as possible.

Keywords: Aquaculture recirculation system, management information system, decision

support

6

Introduction

The worldwide harvest of fish has stagnated at around 90 million tons per year and is not

expected to rise (FAO, 2007). At the same time the demand for fish products is increasing.

The result is a fast-growing aquaculture industry with the highest growth rates in the animal

food-producing sector, which had an average annual growth rate of 8.8% up until 2004 (FAO,

2006).

The environmental impact of the aquaculture industry depends on the species reared, the

production system and many other circumstances. To keep pollution at low levels modern

recirculation systems can become a key solution in regard to intensive and simultaneously

sustainable fish production in the future (Blancheton, 2000). These kinds of production

systems treat the rearing water biologically and mechanically and recycle the water back to

the fish tank (Figure 1). Closed recirculation aquaculture systems (RAS) can be managed with

a diurnal water exchange rate of lower than 10%. Therefore these systems can be operated in

nearly every region in the world and are less dependent on fresh water compared to other

production systems. Furthermore, there are some other potential advantages for the usage of a

closed RAS.

In theory, the fish farmer can control the rearing conditions, increase the productivity and the

quality of the aquatic organism produced and is independent of environmental impacts. The

risk of introducing diseases into the system is minimised as well as the chance of fish

escaping. Due to improved water purification systems the release of nutrients and waste water

can be reduced.

Nevertheless, an RAS is a dynamic system which can be affected by instable situations, e.g.

uncontrollable accumulations of waste particles in the rearing water, instable product quality

(e.g. off-flavour effect) etc. Therefore the technical equipment of an RAS needs to be

adjustable to changing situations and highly skilled employers are needed to run it.

While in other fields of industry a high degree of automatism and process control is common,

this development is still at the beginning in commercial aquaculture. The introduction of

automatic control systems into an RAS started in the 1980s (Schlieder, 1984). Until today few

standardised MISs exist for RASs. The solutions available on the market for monitoring

production processes are mostly computer models fitted exactly to the production conditions

and the problems of an existent facility. The possibility to transfer these special management

tools to other aquaculture facilities is low since these RASs probably deal with their

individual problems. The daily management of an RAS still relies on engineering rules-of-

thumb (Halachmi et al., 2005).

7

Some attempts have been made to develop decision-support software for an RAS. Ernst et

al.(2000) designed a software tool that simulates different parts of biological, chemical and

physical processes and is useful for design and management planning of an RAS. It does not

provide a complete simulation model of the entire system and hence no entire optimisation of

the day-to-day management can be provided. Overall, a migration to large and intensive

rearing systems is taking place as has occurred in agriculture in the last few decades ([Anon],

2004; Rao, et al., 1992).

This article focuses on the general difficulties and the advantages of the use of decision-

support systems in an RAS. The development of decision-support systems in aquaculture is

described in the review part and some perspectives of the use of MISs in fish culture for the

future are presented.

Figure 1: Illustration of the basic compositions of a recirculation aquaculture system. Modified according to Brinker et al.(2006).

Farm Management

According to Huirne (1990) and Turban and Aronson (2001), managerial tasks consist of

three categories: (1) strategic planning – long-term planning to direct future activities based

on available knowledge, (2) implementation – conversion of plans into reality, (3) control –

measuring process performance and comparing it to standards. Due to the diversity of skills, a

farm manager has to have different areas of particular interest: (1) production, (2) marketing,

and (3) finance (Boehlje and Eidmann, 1984). While production is the most basic area,

8

marketing is also important. Since profit maximisation is a common goal in business it is

important to keep in view the current market price of the produced product and of agricultural

commodities (Bowring et al., 1960). Furthermore, financial activities require management

decisions on capital acquisition and financial funds need to be available on demand (Boehlje

and Eidmann, 1984). By means of data recordings the farm manager is able to compare the

actual outcome of the production process with the average performance data (Huirne et al.,

1992). A successful farmer will combine the different areas of management to achieve a

maximum overall result.

Management Information Systems

The task of management information systems is to collect information on the process being

monitored, to locate weak points in production process and to optimise and control the

production. Briefly, the MIS consists of automatic decision-support systems which support

the process of decision-making (Turban and Aronson, 2001).

Management is affected by decision-making. Therefore, it is important for the farm manager

to make the right decisions at the right moment in order to attain a high profit. The decision-

making process in farm management was described by Boehlje and Eidmann (1984) as a

sequence of five actions: (1) define the problem or opportunity, (2) identify alternative

courses of action, (3) gather information and analyse each of the alternative actions, (4) make

the decision and take action and (5) accept the consequences and evaluate the outcome.

If a problem in production occurs, the farm manager can use the sequence of actions

developed by Boehlje and Eidmann (1984) to identify and solve the problem. Decision-

support systems imply the use of artificial intelligence, mainly computer programs, and

incorporate a variety of models (Huber et al., 1982). They monitor the process and compare

the average production quality with a defined standard, which was determined with the help

of a dataset previously recorded and evaluated.

To sum up, with help of an MIS, the farm manager is able to find possible production

problems and the solutions by evaluating the input data.

Problems in RAS

Although it has to be said that an RAS will be the optimal choice for production of aquatic

organisms in the future, there are still opportunities for improvement. An RAS has the

potential advantage of producing fish under optimal production conditions. It is possible to

perfectly meet the biological needs of the species reared. All important biological parameters

9

(e.g.: temp., salinity, pH, rearing density etc.) can be adjusted until they fit the optimal growth

conditions for the reared species. Nevertheless, an RAS can become instable concerning the

constancy of the parameter settings. A running system can be disturbed by changing just one

production parameter, e.g. the water flow rate. Because of this sensitivity, well-educated and

well-trained employees are needed to handle the challenging situations, which can occur

daily.

The potential advantages of an RAS are:

1) creation of optimal biological conditions

2) bio-security

3) minimization of effluent waste

4) water-saving

5) production at customer’s site

6) year-round production

7) higher stocking densities

Since an RAS is fairly capital-intensive, its profitable efficiency depends on profit

maximisation per rearing unit. Due to unwanted losses or other major problems, some farms

have failed in the past. When a farm became bankrupt, the knowledge of the bottlenecks and

problems in intensive fish farming increased. Today many biological, technical and physical

coherencies are known and in the majority of cases operators know where to turn an

adjustment screw to keep the whole system in balance. Overall improvement still has to be

made not only in a biological sense, but also engineering and economic senses in order to

achieve a positive benefit on the investment.

Automatic control systems

The use of a computer-controlled MIS in an RAS has advantages compared to conventional

operating farms. The major advantages are: (1) higher productivity, (2) reduced labour costs,

(3) less water and energy losses and (4) reduced stress and disease, and due to the application

of an artificial intelligence system the farm manager probably (5) can improve the

understanding of the rearing system (Lee, 2000).

Of course, the initial cost for artificial process control is not to be neglected but the

investment can greatly help to create safer operating conditions and the economic benefit may

well increase. Different control systems are available on the market. Most of them focus on

10

special parts of the rearing process (e.g.: monitoring the biological filtering unit). Some

different types of computer control systems and other decision-support tools available for the

aquaculture industry are presented in the following chapter.

Mathematical models

The simplest types of artificial control systems are mathematical models which apply diverse

mathematical techniques (e.g. dynamic programming, queuing networks etc.) to solve small

problems such as finding the perfect production routine (Huntley et al., 2002) or the optimal

dimension for a bio-filter (Losordo and Hobbs, 2000). These models are inflexible, which

means that they cannot adapt to complex problems which can occur in an RAS. Common

tools in an RAS resulting from mathematical modelling are various spreadsheets, which are

used for monitoring the profitability of a production process (Spradlin et al., 2000). An

advantage of mathematical models is the cost-benefit equation. They are easy to install and

they can improve the management of an RAS significantly. A major disadvantage of these

models however is that they have to be adjusted individually to each RAS with its own

specifications. Nevertheless, these mathematical models have to be evaluated in practice

before they are installed in the RAS and the whole production depends on them.

Computer models

The use of computer models in aquaculture increased in the last few years of the 20th century

(Lee, 1995). They benefit from their capability to solve complex situations in contrast to the

relatively static and simplified mathematical models. Due to their flexible simulation

techniques, they can be used to test different scenarios in an RAS. For example, different

settings of water parameters can be simulated and the resulting growth performance of the

reared fish can be evaluated. It is possible to repeat many different settings for different

simulated time frames (days, weeks, month etc) within seconds of real time with the help of

these simulation models. These models can be used to test different experimental set-ups

without doing any harm to the animals. Of course, these simulation models are complicated to

program and skilled manpower is needed but they probably help to save money since they can

simulate situations with negative impacts on the business and can be used to predict adverse

conditions.

Knowledge-based expert systems are programs that copy the action of a stated expert

(Bechtold, 1993; 1994). The expert formulates well-defined rules which have to be

programmed clearly. The advantage of these expert systems is that every decision made by

11

the system can be reproduced by the expert and the knowledge of the expert can be

transported to other employers or another RAS quickly. The disadvantage of these systems is

their inflexibility. They cannot be easily transferred to another RAS since these facilities

underlie other rearing conditions (e.g.: other threshold values or different monitoring

parameters) and other management goals (e.g.: quality vs. quantity).

A more advanced and transferable computer expert system is the fuzzy-logic-based system. A

fuzzy-based system is also based on defined rules such as the knowledge-based system. In

general it is comparable to the knowledge-based expert system, but the great difference lies in

the distribution of the answers of the defined rules. In contrast to knowledge-based expert

systems the answers of fuzzy-logic-based programs do not show a discrete (e.g.: 0 or 1, Yes

or No) distribution. The results of the rules are therefore not as definite as the results of the

expert-based system. They can be somewhere between 0 and 1. Due to the fuzzy-results of the

fuzzy-logic-based system, this computer system seems to be more appropriate for an RAS

compared to knowledge-based expert systems. The results of a fuzzy-logic query are more

detailed and can be better interpreted than the simple answer of the knowledge-based expert

system. In general, fuzzy-logic systems provide more detailed information about the process

compared to the expert-based systems. Fuzzy-logic models are used in agriculture for

management improvement. In dairy cattle farming, fuzzy-logic models were developed for the

detection of mastitis (Cavero et al., 2006; Firk et al., 2003; Kramer et al., 2009). In

aquaculture, a fuzzy system was used for supervision of the denitrification process due to its

ability to deal with changes and react using control actions relative to the degree of the

problems (Lee et al., 2000).

A complete different computer system for process controlling is a neural net. These types of

artificial intelligence systems do not need any rules to be defined in contrast to the other two

systems described above. The neural net makes decisions by calculating probabilities

comparable to statistical process control. Neural networks need large real or training

databases to be able to control a process. Therefore it is difficult to implement a neural net

computer model into newly opened facilities. Different kinds of neural nets exist but the most

interesting one for aquaculture is the process control neural net. With this kind of neural net it

is possible to define different limits for production parameters (e.g.: temperature, water level,

etc.) to control the process and detect process situations when a critical process level has been

reached (Plummer, 1993).

12

Practise - determining potential bottlenecks

The potential weakness of the production facility should be investigated before implementing

an MIS. Therefore, a critical analysis of each production part is essential for a successfully

operating monitoring system. The following paragraphs describe the main parts of a closed

recirculation system and the potential weak points and how they can be supervised by an MIS.

The optimal procedure to investigate the potential bottlenecks of the rearing system is to

follow the water flow starting at the fish tank.

Fish tank

First of all it is important to know the optimal rearing conditions for the farmed species. The

observation of the water parameters is mandatory in aquaculture to create the necessary

biological requirements. The standard parameters (e.g.: temperature, pH, salinity,

conductivity etc.) are monitored with appropriate measuring tools. Furthermore, a parameter

is needed to describe animal welfare. Stress is a major factor influencing the welfare of the

cultured organisms. During the last few years fish welfare has become an increasing concern

in aquaculture and research (Huntingford et al., 2006). Until today no real-time method exists

to describe the status of a farmed fish in regard to welfare. The animals have had to be

examined in regard to physiological (e.g.: blood parameters, hormone level) and pathological

(e.g.: basic changes in histology) signs caused by exposure to chronic stress. In contrast to

short-term stress situations, a fish cannot go back to its healthy state when exposed to chronic

stress situations. It is even possible for the fish to compensate and to adapt to the new

situation and react with a new allostatic load (McEwen, 2000). The reason for chronic stress

occurrence can be found most of the time in inappropriate abiotic conditions (e.g.: water

temperature, oxygen concentration) (Ashley, 2007). At the moment, researchers are just able

to detect if a fish is stressed but not how much (Davis, 2010). Therefore, a new idea has been

put forward to measure the reflex impairment of the reared fish (Davis, 2010). Davis (2010)

described a method of how to measure the stress of a fish in real time. This method could be

implemented in automatic control systems to monitor the welfare of the farmed organisms.

The behaviour and the size of the farmed species determine the dimension, the material and

the layout of the rearing unit most of the time. Flatfish for example need more surface area of

the bottom compared to free-swimming fish such as salmonids. Therefore, the tanks of e.g.

turbot have only a low water level and large the surface area (Labatut and Olivares, 2004).

Common tanks in aquaculture are raceways because of their construction simplicity, ease-of-

use for husbandry and high surface area per volume of water (Watten and Johnson, 1990).

13

The problems in raceways can be found in the longitudinal distribution of the chemical water

parameters. A gradient in the dissolved oxygen and other metabolites can lead to a disparity in

the distribution of fish and can result in increased mortality and poor growth (Watten and

Beck, 1987).

The stocking density of turbot depends on the type of rearing unit as well as on the individual

body weight. Juvenile turbots are generally stocked in densities of 25-30kg/m3 (Iglesias et al.,

1978) and at maximum densities of up to 75kg/m3 for mature fish (Jones et al., 1981).

The critical rearing conditions and threshold levels for important water parameters have to be

analysed as well as correlations between the parameters before the MIS is installed.

Water pipes

The most important fact in transporting the water through the pipes of the RAS is the

transported volume per time. In addition to the pumps required for the transportation of the

water, the pipe diameter has to be chosen at the right dimension to ensure effective transport.

The optimal control for the proper transport is a flowmeter. The influence of the water stream

on important production parameters (e.g. self-cleaning effect) (Westers and Pratt, 1977) is

enormous. The lower the water flow rate the better the conditions are for fouling (Kukulka

and Devgun, 2007). Inside the pipes, organic and inorganic compounds and micro-organisms

can be found on the surface. This phenomenon is called fouling and occurs in nearly every

fish farm due to the prevailing conditions (Timmons et al., 2002). The fouling agent layer is

only a few millimetres thick but can cause major problems in regard to fungal and bacterial

load (Edberg et al., 2007). Since the growth rate of the bacteria is difficult to predict, the

pipes have to be cleaned from time to time. The fouling-problem has not yet been solved, but

because of its influence on the growth performance of the fish it is important to face this

problem in the near future and implement an automatic control system to observe the

occurrence of bacteria and fungi inside the pipes.

Mechanical filter

The waste production of intensive fish farms is based mainly on faeces production and feed

loss (Bergheim and Asgard, 1996; Pillay, 1992). Due to increased production in intensive fish

farming the amount of waste will rise in the future (Davenport et al., 2003). Suzuki et al.

(2003) reported that one ton of fish produced in aquaculture produces the same amount of

waste produced by 73 humans in a single day. Two possibilities exist to eradicate this waste:

1) increase the feed conversion ratio to lower the amount of faeces produced (Cho and

14

Bureau, 1997), or 2) remove the particles from the whole system and prevent them from

ending up in the environment (Brinker and Rosch, 2005). Since fish feeds nowadays are

characterised by high conversion ratios, most commercial farms try to remove waste particles

by use of mechanical filters. One of the most frequently used filter types in turbot aquaculture

is the so-called drum filter. The waste particles are sieved by microscreen filters with a mesh

size of 60µm and then removed from the system (Borges et al., 2003).

To use a monitoring control system, a parameter has to be observed which reflects the

efficiency of the drum filter automatically. One possible method was discovered by Brinker et

al.(2005). This method can be used for continuous measurements of the particle size in the

effluent of the drum filter. If the particle size exceeds a defined limit, the control system

triggers an alarm.

Biological filter

Biological filter units are one of the most important parts in recirculation systems because

they reduce the effluent stream volume and make the water suitable for the reared species

(Chen et al., 2006). These kinds of filters are used to reduce the total ammoniac nitrogen

(TAN) in the rearing water. TAN is the sum of ionised (NH4+) and unionised (NH3) forms of

ammonia in solution. Bacteria oxidise the waste products of the reared species (mainly

ammonia) over nitrite to nitrate, which is harmless for fish even at high concentrations. The

goal of biological filters is to remove as much TAN as possible out of the system to reduce

the negative effect of the TAN on the biological performance of the cultured species (Guerdat

et al., 2010).

Before setting limits for possible monitored parameters for management information, it is

important to define the moment when a “steady state” level of TAN removal has been

reached. Relatively few studies exist where this status is described (Sandu et al., 2002; Zhu

and Chen, 2002). In general, this moment is achieved when a constant flow of TAN in the

effluent has been reached (Colt et al., 2006).

The amount of TAN would be a suitable parameter to monitor the filtration rate of a bio-filter

automatically with an MIS. Colt et al. (2006) mentioned a possible approach to describe the

filtration rate of the bio-filter statistically. They suggested measuring the TAN in the effluent

and with the help of linear regression to test whether the amount of TAN changes

significantly over time.

15

Water treatments

Water quality influences the welfare of the fish, hence the growth rate. Therefore it is

important to control the water quality before entering the system. The amount of bacterial

load and other pathogens is reduced with the use of different treatments.

In addition to the application of ultraviolet light, ozone (O3) has become popular in

aquaculture for disinfection and for oxidation of organic and inorganic compounds (Krumins

et al., 2001). It has also been stated that ozone helps to oxidise nitrite, natural organic matter

and finely suspended particles more efficiently (Krumins, Ebeling and Wheaton, 2001; Singh

et al., 1999). The amount of ozone needed for disinfection is determined by the amount of

natural organic matter and nitrite in the water (Tango and Gagnon, 2003). Nevertheless, the

process water in the system will not be totally disinfected because the dissolved ozone reacts

very fast and the concentrations of ozone are low and therefore not enough ozone can be

provided to disinfect the complete water body. A negative by-product occurring from

ozonation in marine recirculation systems is bromate. It is toxic to aquatic organisms and to

humans and is produced by bromide in the presence of oxygen. A positive alliance between

the amount of natural organic matter in the water and the formation of bromate was suggested

by Hofmann (2000).

To keep the potential of bromate poisoning at a low level, the supervision of natural organic

matter is important. Automatic control measurements of organic matter are reasonable for

implementation of an MIS. A possible parameter to be measured is the total organic carbon in

the water, since the natural organic matter incorporates all forms of organic carbon.

Conclusion

The development of artificial intelligence for aquaculture rearing facilities has increased

significantly in the last few decades. Farm managers have now recognised the advantages of

using decision-support tools for day-to-day management. Hence, the demand for automatic

control systems is increasing. They provide a more even production compared to an RAS

without any artificial intelligence support. In addition to the acquisition costs, such a system

has numerous advantages. The increased process efficiency is probably the most important

one. But there is one thing developers have to be aware of: the user-interface has to be user-

friendly. If the handling is too difficult and not self-explanatory, the potential user will not use

these systems.

Overall, fuzzy-logic models seem to be adequate for further investigations for MISs.

Nevertheless, further investigations have to be made in the field of decision-support systems

16

for the RAS. Some ideas and suggestions for different parts of the RAS are presented in the

present study to improve the implementation of artificial intelligence in commercial

aquaculture facilities. Probably the development of new technologies and advanced artificial

intelligence programs will accelerate the implementation of an MIS in aquaculture in the

future.

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21

Chapter Two

The use of CUSUM charts for early detection of increasing

mortality in a turbot recirculation aquaculture system

A. Baera,b, C. Schulza,b, I. Traulsena, J. Krietera

aInstitut für Tierzucht und Tierhaltung, Christian-Albrechts-Universität,

D-24098 Kiel, Germany

bGMA – Gesellschaft für Marine Aquakultur mbH,

D-25761 Büsum, Germany

22

Abstract

Cumulative sum control charts (CUSUM) are widely used in industry for process control.

They are effective tools for statistical process control since they are able to detect small

deviations from a monitored process level. They are little used in agriculture and have still no

importance in aquaculture. In this paper CUSUM charts were designed to predict mortality

rates in a commercial turbot (Psetta maxima) aquaculture recirculation system in Germany.

Data from two rearing modules of a commercial turbot recirculation system were recorded

from 2001 to 2007. Turbots were reared from 5-2000g in 8 different weight classes. Daily

numbers of dead fish were recorded and analysed with different settings of the CUSUM chart

to reveal a shift in mortality rate. The CUSUM charts were adjusted to detect daily mortality

rates which exceed a tolerated value of 0.008 %. This tolerated daily mortality rate is

equivalent to a total amount of 5% dead fish of the initial stock during the whole production

period. In average the fish need a production period of 600 days to reach marketable size. For

each weight class the optimal setting for the CUSUM chart was designed and the best

sensitivity rates of the CUSUM charts fluctuate between 26-52% depending on the weight

class. The sensitivity increased with increasing fish size. The CUSUM charts were effective

tools for detecting small deviations in mortality rate data. Hence they can be used as an

extension of the existing decision support systems in the examined turbot farm. Furthermore a

close connection between water temperature and survival rate was detected. The mortality rate

increased when the water temperature reached 18 °C.

Keywords: statistical analyses, turbot, CUSUM charts, decision support system

23

Introduction

In marine aquaculture the cultivation of turbot (Psetta maxima) began in the 1970s in

Scotland (FAO, 2009) and turbot was introduced in Germany in the 1980s (Kuhlmann et al.,

1981). The rearing of turbot is still difficult; especially the cultivation of larval stages (e.g.

feeding regime, stocking density, rearing facilities) (Kjorsvik et al., 2003; Reitan et al., 1993;

Bromley and Howell, 1983; Kuhlmann and Quantz, 1980). Nevertheless, Spain produced

close to 4000t in 2002 which was 75% of the global production (www.fao.org). One of the

problems occurring in turbot aquaculture are high mortality rates due to varying reasons (e.g.

environmental rearing conditions, diseases) (Beverton and Iles, 1992a; b; Rosenberg and

Haugen, 1982; Kuhlmann and Quantz, 1980), but the farming of turbot is still increasing

worldwide (www.fao.org).

To optimize turbot production and create high productivity production systems it is necessary

to know about the ongoing biological processes (e.g. state of health, water treatment, feeding

regime) on the farm. Because of the complexity of a recirculation aquaculture system (RAS)

an optimal managing of these facilities is challenging. Every day the farm manager has to

make decisions which have an impact on the economics of the aquaculture facility. Therefore

it would be an advantage if the manager has got some support regarding critical decisions.

During the last decades some attempts have been made to develop management information

systems for aquaculture rearing systems. The software AquaFarm was developed for planning

and design of aquaculture facilities (Ernst et al., 2000). A decision support system for pond

aquaculture was also designed (Bolte et al., 2000). These support systems serve as a

framework for aquaculture facilities to offer special solutions for complex management

scenarios. Especially for marine RAS decision support systems have to be developed. In each

partition of the rearing process (e.g. feeding regime, stocking density, growth) and of facility

components (e.g. filtration unit, energy consumption, construction of rearing units)

improvements are still necessary to gain higher benefits out of the process. Most available

research referred to single processes belonging to the rearing process, the design of facilities

or biological, chemical and mechanical mechanisms and interactions in a RAS (Piedrahita and

Verreth, 2000).

In contrast to Ernst et al. (2000) or to Bolte et al. (2000) the present study concentrated on

one single process and did not want to develop a decision support system for the whole RAS.

With the use of statistical process control charts a prototype of a decision support tool for the

supervision of the mortality rate was developed.

24

Statistical process control charts are used to monitor a production process and lead to an early

detection of a weak link in the system. Different charts are available for decision support

systems. They are mainly used to supervise industrial processes and to give alarm if the

process is running out of control (Montgomery, 1997). The main charts used in industry are

the Shewart chart, the EWMA chart and the CUSUM chart (Wiklund, 1994). Because of the

simple construction and their ability to detect also small deviations from a target value

CUSUM charts were used for the prediction of mortality rates in the present study. CUSUM

charts were firstly introduced 1954 (Page, 1954). They entered the field of agriculture to

detect diseases (de Vries and Conlin, 2003; 2005) or to monitor the state of health of pigs

(Engler, 2007; Krieter et al., 2009; Madsen and Kristensen, 2005). CUSUM charts have not

been introduced in aquaculture as far as we know.

The aim of the present study was to develop an approach for computer based mortality

analysis to support the farm manager in decision making. Different settings of CUSUM charts

were examined to develop a prototype of statistical process control for monitoring the

mortality rate in the examined commercial turbot RAS. Because of the recurring of unwanted

high mortality rates in the examined commercial turbot farm and the involving expenses it is

important to know about increasing mortality rates as early as possible.

25

Material and Methods

Recirculation Aquaculture System (RAS)

The commercial turbot farm is located in northern Germany at the shoreline of the North Sea.

It is divided into two identical modules, module 1 and module 2. Each module owned its own

water treatment unit (mechanical and biological filtering units) and both recirculation systems

were separated of each other completely and worked independently. Module 1 contains 18

ponds and module 2 13 ponds. Turbots were reared from 5-2000g in 8 different weight classes

(

Table 1). In module 1 primarily, weight classes 1-5 were reared where else in module 2 the

bigger fish (weight class 6-8) were kept.

Table 1: Mean mortality rate (%), standard deviation (s) per weight class, the number of rearing groups for each weight class and the average rearing time

weight class

weight category (g)

mortality rate x (%) s

number of rearing groups

average rearing time (days)

1 5-20 0.2 0.99 81 84 2 21-50 0.46 1.88 67 79 3 51-100 0.32 0.99 62 94 4 101-200 0.34 1.19 68 79 5 201-400 0.42 1.51 116 58 6 401-800 0.70 2.49 155 57 7 801-1200 0.70 2.62 62 53 8 1201-2000 0.58 1.52 42 55

Data

Data were recorded between October 2001 and November 2007. During this period daily

measurements of mortality rate and more parameters regarding water quality were recorded

(Table 2). In total, there were 45,189 observations.

In the RAS turbots were reared in 8 weight classes. For each weight class a different number

of rearing groups existed (

Table 1) and for each group within weight classes a CUSUM chart was calculated.

Furthermore, water parameters were selected to determine their influence on the survival of

the turbots (Table 2). Since water temperature has a huge impact on growth and welfare of

fish a more detailed analysis was done to describe the influence of temperature on growth in

turbot in the examined commercial RAS.

26

Table 2: Measured and analysed daily water parameters (n: total number of observations, x : average value per observation, s: standard deviation, 0*: values below detection limit)

module 1

measured parameters n x s min max pH 19,462 6.84 0.36 5.7 7.9

ammonium (mg/l) 19,115 0.67 1.22 0* 4.6 Nitrite (mg/l) 19,289 0.97 0.93 0* 30 salinity (‰) 11,883 28.52 2.02 20 31

number of dead fish 20,849 7.8 32.66 0 2,245 feed (g) 20,847 2,327 3,117 0 95,000

number of total fish stock 20,849 2,917 2,743 15 16,851 temperature (C°) 19,346 18.65 2.33 14 24.8

module 2

measured parameters n x s min max pH 20,355 6.99 0.32 5.7 7.9

ammonium (mg/l) 20,050 0.6 0.91 0* 30 nitrite (mg/l) 20,297 1.02 1.9 0* 30 salinity (‰) 12,359 26 3.03 18 32

number of dead fish 24,340 4.5 19.21 0 1,595 feed (g) 24,333 2,241 2,790 0 38,900

number of fish stock 24,347 1,762 1,729 1 12,213 temperature (C°) 20,361 18.57 2.12 11.7 24.2

Statistical process control (SPC) charts

Statistical control charts can be used to monitor the consistency of production processes

(Montgomery, 1997). Wieringa (1999) described charts as a tool for the detection of

particular causes of variation that might be covered in the variation of common causes.

SPC charts are designed from a centre line which corresponds with the target value of the

monitored process. The upper and lower control line (UCL, LCL) define the range of the

natural variation of the plotted data. If the variation is higher, than can be referred to the effect

of a common cause an alarm signal occurs to indicate that the process is out of control and

investigations are required to improve it.

There are diverse types of control charts available for different types of datasets. CUSUM

charts are suitable for the detection of small deviations of the process mean (Montgomery,

1997). Since the variation of the mortality rate in the examined turbot farm is low, the present

study used CUSUM charts for the detection of small deviations in mortality rate.

27

CUSUM charts

Cumulative sum control charts (CUSUM) were first introduced by Page (1954). CUSUM

charts plot the cumulative sums of the deviations from a target value and incorporate all the

information in the sequence of sample values using information from all prior observations.

Due to the inclusion of the sum of all prior observations of the data the memory of the

CUSUM chart is relatively long.

The CUSUM charts work by accumulating the deviations from the measured value (xi) minus

the mean value (µ0) that are above or under the target value. There are two statistics Ci+ and

Ci- whereas Ci

+ accumulates deviations from µ0 that are above the target value and is called

one-side-upper CUSUM and Ci- accumulates deviations which are below the target value and

is called one-side-lower CUSUM.

They are computed as follows:

Ci+ = max [0, xi – (µ0 + k) + Ci

+]

Ci- = max [0, (µ0 - k) - xi + Ci

--1]

The starting values for Ci+ and Ci

- are zero. By the use of the reference value k the CUSUM

chart can be adjusted. Montgomery (1997) and Hawkins and Olwell (1997) recommended

that the k-value needs to be chosen relative to the size of the shift that is to be detected. They

suggested a k-value that is half the size of the deviation that needs to be detected. The upper

(UCL) and lower control limit (LCL) are determined by the h-value. The h-value describes

the factor of σ0 defining the distance between µ0 and the control limits (UCL, LCL).

LCL = -h*σ

UCL = h*σ

If one of the two statistics (Ci+, Ci

-) exceeds the control limits, an alarm signal occurs. Due to

the fact that the observed mortality rates in the dataset can only become positive values only a

one-sided-upper CUSUM chart had to be constructed.

Performance of CUSUM charts

Statistical control charts are classified by their average run length (ARL). The ARL for the

CUSUM chart describes the expected number of samples to be taken before the CUSUM

chart indicates a shift in the process. At the present data the ARL describes the expected

number of days until the chart detect a change in mortality rates. If no change in the

monitored process occurs, the ARL should be large and vice versa if the process has

undergone a change. Usually, the so called “in-control ARL” is evaluated for zero shifts in the

process level. In 1997, Montgomery published the legality that the rate of error (type 1 error:

28

process is in-control but the CUSUM charts exceed the control line and give alarm) increases

when the ARL is small but the time until the chart detects a shift in the process will be short.

Therefore, for minimizing the type 1 error, the ARL should be high. In contrast, the type 2

error (process is out-of-control but the chart does not detect it) will increase by a high ARL.

The performance of each CUSUM chart was distinguished in five different categories. By

means of the five categories (A-E) it was possible to establish the performance of the

CUSUM charts for the rearing groups. Each category represents a different time-frame where

the CUSUM chart exceeds the UCL and an alarm occurs. Group A reflects an alarm which

take place 1-3 days before the mortality rate excess 0.008% (A: day -1 to -3). The Category B

stands for an alarm occurring at the same day when the mortality rate exceed 0.008% (B =

day 0). Category C represents the CUSUM charts which indicate an exceeding earlier than 3

days before the mortality rate reach 0.008% (C: < -3). On the contrary, category D stands for

CUSUM charts where an alarm occurred after the exceeding of the tolerated mortality rate (D

> 0). Category E represents the wrong alarms, where an alarm occurred but no exceeding of

the tolerated mortality rate occurred (E: false positive, type 1 error). Only with a high amount

of correct forecasts of category A and B the CUSUM charts are a useful tool for a manager in

regard to support decision making. Rearing periods shorter than 14 days were excluded from

the present analysis.

Designing of the CUSUM charts

In order to detect deviations caused by high mortality rates a target value as well as an UCL

had to be defined. To construct the CUSUM charts the target value as well as the standard

deviation was needed. Since the turbot are reared around 600 days to reach the individual

marketable size of approximately 2000g and the farm manager tolerated a 5 % mortality rate

during these 600 days, a daily mortality rate of 0.008 % is accepted. Hence, the target value of

the CUSUM chart takes the value of 0.008. The standard deviations were calculated out of the

means of the whole period during the data was taken (Table 1). For establishing the UCL,

different values of h (from 0.05 to 3) were tested to determine the best performance of the

charts.

Due to exponentially distributed data (daily mortality rate), the calculation of the reference

value k could not be done as Montgomery (1997) and Hawkins and Olwell (1998) suggested,

since their data was distributed normally. Gan (1994) described a method how to calculate the

k-value for exponential distributed data. Hence this formula was used for the calculation of k

in the present study.

29

k = (ß0 x ß1)/(ß1 – ß0)x ln (ß1/ß0)

The parameter ß0 describes the target value (in the present study ß0: 0.008) and ß1 describe the

deviation in the process level which one is interested in (in the present study ß1 was chosen to

be 0.004 which represented a change in process level of 2.5%). Therefore, k took a fixed

value of 0.0055. For more detailed information of the calculation of k see Gan (1994).

The calculation of the ARL of the CUSUM charts had also to be adjusted because of the

exponential distribution of the data. The instructions of Zhang et al. (2005) were applied to

calculate the most precise ARL. They described a method for determining the ARL-unbiased

form for exponential charts. The results of the ARL calculation will be compared with the

actual reaction times for the tested CUSUM charts based on the data from the RAS. The data

was analysed using the statistical computer software SAS (SAS, 2005).

An example for the calculated CUSUM charts is shown in Figure 1Figure . This CUSUM

indicates an “out of control” of the process at day 18. In this example the chart visualize the

deviation from the process mean 3 days in advance (the exceeding of the UCL occurred at day

21; Figure 1).

-1

5

11

17

23

1 5 9 13 17 21 25

day

mor

talit

y ra

te (

%)

target value

measured mortality rate

CUSUM

UCL

Figure 1: Example for a CUSUM chart. The arrow marks the first out of control value (day 21).

30

Results

Average run length

The calculated unbiased ARL for the exponential CUSUM chart is 19.8 using the formula of

Zhang et al (2005). That means the chart indicates a shift in the process level after 20 days.

The real average time until detection was determined on the basis of the evaluated data

depending on weight class. With increasing weight class a decrease in ARL was observed.

Two different approaches were used to determine the real ARL. First, all rearing groups were

taken into account for the calculation of the real ARL. The second approach was to include

only the rearing groups where the CUSUM chart exceeded the UCL (Ci+ > UCL). The real

ARL increased significantly compared to the ARL when all rearing groups were considered,

but the decreasing trend with increasing weight class persisted (Table 3). This decrease in real

ARL can be explained by a temporally change in the occurrence of deviations from the target

mortality rate. The bigger the fish grow the earlier a change in mortality rate appeared

because the fish were reared in lower densities compared to younger fish and therefore also

few dead fish can have a significant influence in the mortality rate.

Table 3: The real average run length (ALR) analysed for each weight class.

weight class

no. of all rearing groups

x ARL (days)

no. of rearing groups with

Ci+ > UCL

x ARL (days)

1 81 23 35 53 2 67 24 38 41 3 62 19 34 34 4 68 17 35 34 5 116 18 60 35 6 155 16 101 24 7 62 16 34 29 8 42 13 33 17

Performance CUSUM charts

The results of category A and B supposed to be the ideal forecasts with regard to practical

application of the CUSUM charts. In Figure 2 the sensitivity for the best performance

(category A and B) of the CUSUM charts for each weight class is visualized. The sensitivity

is the number of CUSUM charts with desired prediction times (category A and B) divided by

the total number of performed CUSUM charts for each weight class. The sensitivity increased

with increasing weight class.

31

The results of the CUSUM charts taking all rearing groups into account were compared with

the results of the CUSUM charts where rearing periods were removed from the dataset which

had high mortality rates during the first two days after stocking. That is because of the weak

ability of the CUSUM chart to detect the out-of-control situation right from the start of the

process control. To obtain the best results for category A and B different h values were tested

for each weight class (Table 5).

Remarkable are the constant lower detection accuracies (except for weight class 3) when

rearing groups with high mortality rates were removed from the dataset (Figure 2). When

removing rearing periods with high mortality rates during the first two days after stocking, the

total number of CUSUM charts involved in the calculation for the sensitivity decreased. The

removed CUSUM charts should be classified with the category C-E. Hence the expected

sensitivity should be improved. A closer analyse of the removed CUSUM charts showed that

also few CUSUM charts of category B were removed from the dataset when removing rearing

periods with high mortality rates right after stocking. In some cases the CUSUM chart was

able to detect deviations in the mortality rate already at day 2 after stocking (detection at the

same day = category B). The early detection of a deviation from the process mean already at

day 2 was only possible when extreme mortality rates occurred (more than 25%) due to

extreme situations (diseases, power failure).

32

0

10

20

30

40

50

1 2 3 4 5 6 7 8

weight class

sens

itivi

ty in

%all rearing groupswithout rearing groups showing mortality rates > 0.008 % at day 1 or 2 after stocking

Figure 2: Highest sensitivity for category A and B (0-3 days forecast) of tested CUSUM charts.

Table 4 shows the percentage distribution of the sensitivity for category A and B. After

removing the rearing groups from the dataset with high mortality rates right after stocking

only the sensitivity for category A increased. Due to the loss of some CUSUM charts with the

category B when removing rearing periods with high mortality rats at day 1 or day 2, the

overall performance for the sensitivity for category B became worse compared to the

performance of the CUSUM charts when all rearing groups were considered in the dataset.

With increasing weight class the sensitivity increased in category B. A decreasing trend is

observable in category A.

33

Table 4: Sensitivity of category A (1-3 day forecast) and B (same day forecast) for all rearing groups compared to the results of category A and B for all rearing groups excluding rearing periods with high mortality rates during the first 2 days after stocking

results for A (%)

results for B (%)

without rearing

groups with without rearing

groups with weight class

all rearing groups

mortality rate > 0.008% at day 1 or 2

all rearing groups

mortality rate > 0.008% at day 1 or 2

1 17 20 15 10 2 21 22 19 18 3 13 14 13 14 4 15 16 18 14 5 11 12 25 23 6 8 8 34 29 7 11 15 23 18 8 11 13 41 33

A summary about the best sensitivity for each weight class is shown in Table 5. The optimal

h-values fluctuated between 0.1 and 1.75. A negative trend is visible in the results of category

A (1-3 day forecast). With increasing weight class the results changed for th worse (Table 4),

while in category B (same day) the opposite was true. The sensitivity improved with

increasing weight classes (except for weight class 7). Category C (forecast earlier than 3 days)

always stayed on a high level between 30-40%. In contrast, category D (later than same day)

stayed in each weight class on a low level and the results were always lower than 20%. The

type 1 error (category E) ranged from 10 to 33%.

Table 5: Sensitivity (%) of the best CUSUM charts (optimal h values with highest values for category A+B) for each weight class. Rearing periods with high mortality rates at day 1 or day 2 after stocking were removed from the dataset. Category A: 1-3 days prediction, category B: same day detection, category C: earlier than 3 days detection, category D: later than same day detection, category E: error type 1 (error type 2 did not occur).

category weight class h-value A (1-3days) B (0) C (<-3) D (>0) E (error type1)

1 1.0 20 10 33 5 33 2 0.5 22 18 34 4 22 3 1.75 14 14 32 14 27 4 0.75 16 14 35 4 31 5 0.5 12 23 39 2 24 6 0.1 8 29 43 0 20 7 0.25 15 18 28 18 21 8 0.7 13 33 30 13 10

34

Table 6 shows the time to signal for the categories C and D. The categories C and D

represented the CUSUM charts with inadmissible detection accuracies where the signal was

much too early (category C) or too late (category D) compared to the real shift in process

level. Category C is characterized by a relatively long prediction time in every weight class

ranging from 20.7 to 46.7 days (Table 6). In contrast category D showed only little delay in

signalling an exceeding of the control line with a maximum average delay of 7 days.

Table 6: Average time to signal for category C (signal < -3 days) and for category D (signal later than day 0).

category C weight class 1 2 3 4 5 6 7 8

average time (days) to signal -44.3 -30.5 -32.1 -30.0 -46.7 -30.8 -38.9 -20.7 Median (days) -34.6 -17.1 -22.2 -17.7 -27.8 -21.7 -24.3 -13.8

category D weight class 1 2 3 4 5 6 7 8

average time (days) to signal 0 7.0 3.2 7.0 2.0 1.3 7.2 4.3 Median (days) 0 3.6 2.6 6.7 2.0 1.2 2.9 3.4

To provide a standard h value for practical application of the CUSUM charts a fixed standard

h-value of 0.5 for each CUSUM chart was compared with the optimal h values for each

weight class. Results are shown in Figure 3. They demonstrate that a value of 0.5 for h is an

acceptable option to start with, since only two weight classes differed in the results with more

than 100% compared to the optimal h-values (weight class 1 and 6). Where else all other

results of a fixed standard h-value of 0.5 differ only up to 25% from the optimal results.

35

h=

0.7

h=0

.25

h=0

.1

h=

0.3

5

h=

0.7

5

h=1

.75

h=

0.5

h=

1

0

5

10

15

20

25

30

35

40

45

50

1 2 3 4 5 6 7 8

weight class

sens

itivi

ty in

%

h = 0.5 optimal h values

Figure 3: A comparison of a fixed h-value of 0.5 against h-values with the best performance. Only rearing periods with low mortality rates (<0.008) at the first 2 days after stocking were considered.

Temperature and mortality rate

An important part in aquaculture plays the water temperature regarding fish welfare. Hence

the temperature gradient and its relationship with the mortality rate were examined more

detailed.

In average the temperature fluctuated in the examined RAS between 15 to 22.3°C during the

seasons (depending on module 1 or 2). The warmest periods (water temperature > 18°C) are

throughout the summer time between Mai and September. In general, module 2 is cooler than

module 1. Fifty two percent of the dead fish (2001-2007) in the RAS died in the time from

June to August. Obviously, turbots are highly sensitive to warm water periods (Figure 4).

They also react on daily temperature changes of 0.2 to -0.2 °C with increasing mortality

(Figure 5). Furthermore the bigger the fish grow the more sensible they become against

temperature changes (Figure 5).

36

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10 11 12

month

tem

pera

ture

(°C

)

0

5

10

15

20

25

dead

fish

(%

)

percentage distribution of deadfishtemperature module 1temperature module 2

Figure 4: Average annual temperature gradient in module 1 and 2 together with the percentage distribution of the dead fish died during the observed time.

0

0.2

0.4

0.6

0.8

1

1.2

1 2 3 4 5 6 7 8

weight class

mor

talit

y ra

te (

%)

daily temperature change < = -0.2 °Cdaily temperature fluctuation between -0.2 to 0.2 °Cdaily temperature change > = 0.2 °C

Figure 5: Daily temperature changes and their influence on mortality rate depending on weight class of turbot.

37

Other water parameters like nitrite, ammonia or salinity were not tested for their influence on

mortality. These parameters were always in the optimal rearing range for turbot as published

in literature (Eddy, 2005; Imsland et al., 2007; Irwin et al., 1999) (Table 2). Therefore, no

negative affect on growth or health was assumed. Since the temperature overshadows and

interacts with all other factors which potentially have an influence on the mortality rate, the

main focus of attention was on the main influencing factor the temperature.

Discussion

CUSUM - Performance

The use of decision support systems in aquaculture will steadily increase in the future and

therefore the knowledge of non-experts which are using this kind of artificial intelligence will

rise. But it is still difficult to set up an optimal general CUSUM chart for decision support

with the data from a single RAS, because of the natural variance occurring in biological data.

A sufficient amount of data is required to use statistical methods for analysing the data.

With regard to practical application of CUSUM charts in commercial RAS the detection of

high mortality rates have to occur before the fish start to die. A forecast up to three days

allows the farm manager an appropriate time frame to react to increasing mortality rates. The

results of the CUSUM charts reflect the difficulty of a reliable forecast of mortality rates. At

first, CUSUM charts are not able to detect high mortality rates at the first two days right after

stocking, except the mortality rates are very high (mortality rates of > 25% / standing stock).

Therefore the rearing groups with mortality rates above 0.008 % during the first two rearing

days were not considered. From the practical point of view the delay of the CUSUM charts

right after stocking can be neglected, because the farm manager would supervise the freshly

stocked fish with higher attention during the first couple of days anyway to recognize the

welfare of the fish.

In Figure 2 the best results of the CUSUM charts for all rearing groups were compared to the

best results of the CUSUM charts for all rearing groups excluding the rearing groups with

high mortality rates during the first two days right after stocking. By ignoring the rearing

groups which showed high mortality rates during the first two days after stocking the overall

performance of the CUSUM charts should increase but the opposite is true. The results

become worse when applying CUSUM charts to this set of rearing groups (Figure 2). The

reason for this unexpected result is the fact that some of the CUSUM charts for the rearing

groups with high mortality rates at day one or two indicated a change in mortality rate at the

same day (day 2) which is equivalent to the category B. Because of removing also these

38

rearing periods where the CUSUM chart actually detected the deviation from the process

mean at the same day (category B) the overall performance of the CUSUM charts

deteriorated. Only the frequency of category A increased where else the frequency of category

B decreased (Table 4). Highest sensitivity of deviations from mean mortality rate in category

A (-1 to -3 days forecast) fluctuate between 8 to 22 % (Table 4). Nevertheless the ’same day’

(category B) forecasts were also taken into account for an acceptable prediction of mortality

rates. Due to the addition of category B to an acceptable time frame for forecasts of deviations

in mortality rate the prediction rate of 5 % mortality rate / production cycle increased to 26 up

to 52 % depending on weight class (Figure 2). For practical decision support for the farm

manager, the `same day` detections are a little too late, because there is no time left to react.

Nevertheless, the CUSUM chart visualizes a trend in mortality rate which adverts to a rising

mortality rate (see Figure 1 time before CUSUM exceeds UCL). Because of that, it can

become a useful tool for day-to-day management and monitoring the mortality rate.

CUSUM - average run length

Zhang et al. described (2005) a method how to calculate an ARL-unbiased design for

exponential CUSUM charts. The calculated ARL of 19.8 is very low in the present data.

Therefore, the expected number of samples to be taken before the chart detects a shift is very

low.

Comparing the calculated ARL with the real average run length identified by the actual data

showed that they are about the same range taking all rearing groups into account. The real

ARL time decreased with increasing weight class (Table 3). There are two possible reasons

for this. First, the turbot died faster after stocking with increasing age. The more reasonable

assumption is that due to lower stocking densities in higher weight classes, the CUSUM chart

reacted more sensitive to little changes in mortality rate. Because of lower stocking densities

the target value of 0.008% for the CUSUM charts is much faster fulfilled than with higher

stocking densities in smaller weight classes.

The lower the ARL is the more false positive (type 1 error) alerts will occur, but the chance

for detecting a real deviation in process increases. On the contrary, high values of ARL let the

numbers of type 2 errors increase. In the present study, no false negative (type 2 error) alert

occurred. Due to the low value of ARL and therefore the increased sensitivity for process

deviations, the CUSUM charts reacted sometimes very early (category C). The time between

indication of a deviation in mean mortality rate and the real process shift was up to 46.7 days

(Table 6). Probably, the setting for the h and k - values for these CUSUM charts was too low.

39

Since around 30 % of the indications of the CUSUM chart performance end up in category C

(Table 5), an approach for further studies should be to test different settings of the h and k-

value of CUSUM charts especially for these charts.

CUSUM - Control lines

To calculate the UCL the standard deviation (s) is used. The present s of mortality rate

fluctuates between 0.99 up to 2.62 per rearing period. Due to this wide range of s, the h values

have to be adjusted for each weight class to determine the optimal level for the UCL. The h

values decreased with increasing s. Probably the performance of the CUSUM charts can be

improved by lowering the s of the mortality rate of each rearing period. But that means to

synchronize the mortality rates of each fish group which is impossible. Nevertheless

optimizing the whole rearing process leads to higher survival rates and therefore to decreasing

s. De Vries and Conlin (2005) described that the specific variability and dynamics of a

production system affect the performance of the control chart. Therefore for practical purpose

the starting h value for the calculation of CUSUM charts depends from the s of mortality rate

but in regard to our results an h value of 0.5 is probably a promising attempt to start with

(Figure 3).

The developed CUSUM charts are effective in detecting small shifts from the target value. As

Gan (1992), reported a CUSUM chart is more effective in detecting small shifts when the k

value is close to the target mean. A value of 0.008 for the target value is very low but the

calculated k value is also very low and compared to the results these values are obviously

effective to detect also small shifts in the monitored process. The false alarm rate (error type

1) is also low and indicated a good setting of the CUSUM chart. The number of false alarms

depends on the performance of the charts and the tolerance of the farm manager to accept

false alarms and a delay in signalling. Of course, the more the false alarm rate increases, the

more sceptical the manager will become and will not trust in the charts any longer.

Temperature and dead fish

One of the main advantages of a RAS should be the controllability of each single rearing

parameter (e.g.: water temperature, light conditions, salinity etc.) influencing the cultivation

of aquatic organisms. The RAS the present data comes from is located at the shoreline of the

shallow Wadden Sea. During the summer times water temperatures can reach temperatures

above 20 °C and will increase in future up to 1°C in the year 2020 (Perry et al., 2005). Former

studies about optimal rearing temperatures for turbot documented that best individual growth

40

can be obtained in 14-18°C water temperature depending on biotic and abiotic factors (Brett,

1979; Burel et al., 1996; Planas and Cunha, 1999). Therefore in the future the water has to be

cooled down to at least 18°C to lower the mortality rate during the summer month. Our

studies also demonstrated that turbot is susceptible to daily temperature changes.

Nevertheless, juvenile turbots apparently have got a better ability to adapt to comparatively

rapid temperatures changes compared to bigger fish (Figure). This is probably due to their

evolutionary adaption to fast changing environmental conditions which are present in their

nursery grounds, the extreme habitat Wadden Sea. In nature adult turbots live in deeper

waters with more stable conditions. To meet the requirements of the bigger turbot in the RAS

they were reared in module 2 which had lower water temperatures than module 1 (Figure 4).

When daily water temperature changes exceed 0.2°C the mortality rate increases with

increasing weight class (Figure).

Due to the climate change the average water temperature probably will rise in future (Perry et

al., 2005). Therefore, the examined RAS has to cool down the water in future. This will

increase the costs and the whole enterprise will probably become very challenging.

Conclusion

In the examined commercial RAS turbots start to die when the water temperature reached

18°C. The prototype of CUSUM charts developed for detecting high mortality rates in turbot

farming demonstrate a good overall performance with an acceptable prediction rate of correct

forecasts up to 52 %. By use of these CUSUM charts the farm manager has got an additional

decision support system and can complement the impression of the monitored process.

Nevertheless, this is a first attempt to introduce CUSUM charts into an aquaculture

recirculation system as far as we know and it still has to be demonstrated under practical

conditions that they will provide acceptable results when using in daily routine with latest

data.

Since the water temperature was very high for turbot aquaculture in the examined RAS during

the summer time it had a huge influence on the progress of the mortality rate. Therefore the

prediction of mortality rate with CUSUM charts via the temperature would be an other

possible attempt which has to be investigated. Overall it should be considered to monitor also

other parameters (e.g. feeding rate) to predict the mortality rate with the use of CUSUM

charts.

It is difficult to provide a general recommendation for the setting of CUSUM charts. In each

RAS different conditions influence the production process and hence rearing processes are

41

very farm specific. However, we suggest testing CUSUM control charts because they can

help to increase the performance of a supervised process and therefore supporting the

economical benefit of fish cultivation.

References

Beverton R.J.H. and T.C. Iles, 1992a: Mortality-Rates of 0-Group Plaice (Pleuronectes-

Platessa L), Dab (Limanda-Limanda L) and Turbot (Scophthalmus-Maximus L) in

European Waters .2. Comparison of Mortality-Rates and Construction of Life Table

for 0-Group Plaice. Neth. J. Sea Res. 29, 49-59.

Beverton, R.J.H. and T.C. Iles, 1992b: Mortality-Rates of 0-Group Plaice (Platessa-Platessa

L), Dab (Limanda-Limanda L) and Turbot (Scophthalmus-Maximus L) in European

Waters .3. Density-Dependence of Mortality-Rates of 0-Group Plaice and Some

Demographic-Implications. Neth. J. Sea Res. 29, 61-79.

Bolte, J.; Nath, S. and D.Ernst, 2000: Development of decision support tools for aquaculture:

the POND experience. Aquacult. Eng. 23, 103-119.

Brett, J.R., 1979: Environmental factors and growth. Fish Physiol. 8, 599-675.

Bromley, P.J. and B. R. Howell, 1983: Factors Influencing the Survival and Growth of Turbot

Larvae, Scophthalmus-Maximus L, during the Change from Live to Compound Feeds.

Aquacult. 31, 31-40.

Burel C., PersonLeRuyet J., Gaumet F., LeRoux A., Severe A. and G. Boeuf, 1996: Effects of

temperature on growth and metabolism in juvenile turbot. J. Fish Biol. 49, 678-692.

de Vries A. and B. J. Conlin, 2003: Design and performance of statistical process control

charts applied to estrous detection efficiency. J. Dairy Sci. 86, 1970-1984.

de Vries, A. and B.J. Conlin, 2005: A comparison of the performance of statistical quality

control charts in a dairy production system through stochastic simulation. Agric. Syst.

84, 317-341.

Eddy, F.B., 2005: Ammonia in estuaries and effects on fish. J. Fish Biol. 67, 1495-1513.

Ernst D.H., Bolte J.P. and S.S. Nath, 2000: AquaFarm: simulation and decision support for

aquaculture facility design and management planning. Aquacult. Eng. 23, 121-179.

FAO, 2009. Cultured Aquatic Species Information Programme. FAO Fisheries and

Aquaculture Department. Rome.

Hawkins D.M. and D.H. Olwell, 1997: Inverse Gaussian cumulative sum control charts for

location and shape. Statistician 46, 323-335.

42

Imsland, A.K., Schram E., Roth B., Schelvis-Smit R. and K. Kloet, 2007: Improving growth

in juvenile turbot (Scophthalmus maximus Rafinesque) by rearing fish in switched

temperature regimes. Aquacult. Int. 15, 403-407.

Irwin S., O'Halloran J. and R.D. FitzGerald, 1999: Stocking density, growth and growth

variation in juvenile turbot, Scophthalmus maximus (Rafinesque). Aquacult. 178, 77-

88.

Kjorsvik E., Hoehne-Reitan K. and K.I. Reitan, 2003. Egg and larval quality criteria as

predictive measures for juvenile production in turbot (Scophthalmus maximus L.).

Aquacult. 227, 9-20.

Krieter J., Engler J., Tölle K.-H., Timm H.H. and E. Hohls, 2009: Control charts applied to

simulated sow herd datasets. Livestock Sci. 121, 281-287.

Kuhlmann D. and G. Quantz, 1980: Some Effects of Temperature and Salinity on the

Embryonic-Development and Incubation-Time of the Turbot, Scophthalmus-Maximus

L from the Baltic Sea. Meeresforschung-Reports on Marine Research 28, 172-178.

Kuhlmann D., Quantz G. and U. Witt, 1981: Rearing of Turbot Larvae (Scophthalmus-

Maximus L) on Cultured Food Organisms and Post-Metamorphosis Growth on

Natural and Artificial Food. Aquacult. 23, 183-196.

Madsen T.N. and A.R. Kristensen, 2005: A model for monitoring the condition of young pigs

by their drinking behaviour. Comput. Electron. Agric. 48, 138-154.

Montgomery, D.C., 1997: Introduction to statistical quality control. John Wiley & Sons, New

York.

Page, E.S., 1954: Continuous Inspection Schemes. Biometrika 41, 100-&.

Perry A.L., Low P.J., Ellis J.R. and J.D. Reynolds, 2005: Climate change and distribution

shifts in marine fishes. Science 308, 1912-1915.

Piedrahita R.H. and J. Verreth, 2000: Developments in recirculation aquaculture system

technology - Selected papers from the special session on recirculation aquaculture

systems held at 'Aquaculture Europe 98' in Bordeaux, France. Aquacult. Eng. 22, 1-1.

Planas M. and I. Cunha, 1999. Larviculture of marine fish: problems and perspectives.

Aquacult. 177, 171-190.

Reitan K.I., Rainuzzo J.R., Oie G. and Y. Olsen, 1993. Nutritional Effects of Algal Addition

in 1st Feeding of Turbot (Scophthalmus-Maximus L) Larvae. Aquacult. 118, 257-275.

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Larval Turbot (Scophthalmus-Maximus) Reared in Enclosures. Mar. Biol. 72, 73-77.

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43

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44

Chapter Three

Analysing the growth of turbot (Psetta maxima) in a commercial

recirculation system with the use of 3 different growth models

A. Baera,b, C. Schulza,b, I. Traulsena, J. Krietera

aInstitut für Tierzucht und Tierhaltung, Christian-Albrechts-Universität,

D-24098 Kiel, Germany

bGMA – Gesellschaft für Marine Aquakultur mbH,

D-25761 Büsum, Germany

45

Abstract

The growth data of a commercial aquaculture recirculation system was analyzed to investigate

the growth performance of reared turbot (Psetta maxima). Three common growth models

(von Bertalanffy, Gompertz and Schnute) were fitted to the growth data documented over a

time period of 6 years. To determine the most suitable model, 3 different criteria were used:

(1) the Akaike index criterion, (2) the sum of squared residuals and (3) the average daily

deviation between the estimated final weight and the observed final weight. The evaluation of

the growth models showed that the Schnute model had the lowest Akaike index, the lowest

sum of squared residuals and the lowest daily deviation between estimated and real weight of

all tested growth models.

The Schnute model produced sigmoid growth curves. The estimated growth coefficients were

the most realistic ones in regard to biological interpretation. In contrast the von Bertalanffy

growth model and the Gompertz model estimated inaccurate exponential growth curves and

are therefore unable to simulate the growth data as well as the Schnute model. The results

indicate that the von Bertalanffy growth model is not the optimal model to simulate the

present growth data and that the growth potential of reared turbot has probably not yet been

fully exploited in the aquaculture system(s) examined (so far).

Keywords: growth model, von Bertalanffy, Schnute, Gompertz, turbot

46

Introduction

The cultivation of turbot (Psetta maxima) in marine aquaculture began in the 1970s in

Scotland (FAO, 2005) and was introduced in Germany in the 1980s (Kuhlmann, et al., 1981).

Because of the relatively short time period of domestication of turbot there are still processes

which can be optimized in the rearing process (e.g. feeding regime and diet composition for

larval stages) (Bromley and Howell, 1983; Imsland, et al., 2007; Kuhlmann and Quantz,

1980; Mallekh, et al., 1998; Turker, 2006). In commercial aquaculture facilities, the growth

performance of the aquatic organisms is the most important influence factor with regard to

economical benefit. For rearing purposes, it is crucial to know the limits of growth in

captivity since the growth of fish in aquaculture production systems is much faster than and

differs from the growth of fish in the wild. Not only growth rate per unit of time and feed

coefficient but also the knowledge of growth curve parameters of the growth model is

important to improve the efficiency of the production of marketable turbot. In regard to

maximizing the economic benefit, it is also of interest to know about the inflection point

where growth starts to decrease. Therefore, information about the shape of the growth curve

of the fish can be useful to be able to interpret and monitor the growth performance of the

standing stock more precisely. The information given by the growth curves can be used to

improve the rearing process. The time period of increased growth can be determined exactly.

Hence, the farmer has information about the optimal moment to harvest and sell the fish in

regard to profit maximization and do not waste resources (e.g.: space, feed).

Growth can be defined as individual weight gain per unit of time. Different mathematical

models are available to predict the individual growth rate. These estimate the mean individual

body growth. Up to now, one of the widely used models in fishery science and aquaculture to

predict the individual growth rate of fish via empirical relationships (e.g.: length-weight data)

has been the von Bertalanffy growth model (VBGM) (von Bertalanffy, 1938). This model has

been favoured by many fish scientists because of its simplicity and its possibility to interpret

the parameters biologically. There are also other well-known models common in fishery

science such as the logistic model (Ricker, 1975), the Gompertz model (Gompertz, 1825) or

the generalized VBGM (Pauly, 1979). In the past, the VBGM has often been chosen to be the

optimal model for the dataset before even testing other, perhaps more suitable growth models.

This has often been criticised since the use of VBGM could lead to over- and underestimation

or even unrealistic estimates of the growth parameters and therefore to incorrect conclusions

regarding the biological interpretation of the growth parameters (Katsanevakis, 2006;

Katsanevakis and Maravelias, 2008; Rabaoui, et al., 2007).

47

Another model developed for estimating growth of organisms is the Schnute model (Schnute,

1981). It is a flexible model which estimates four parameters in contrast to the Gompertz and

VBGM, which estimate three parameters.

The aim of the study was to examine the growth performance of turbot reared in a commercial

turbot recirculation system. Different growth models (VBGM, Gompertz, Schnute) were

tested and evaluated while searching for the model with the most realistic reflection of growth

performance.

Material and Methods

Data

Turbot data were collected at a recirculation aquaculture system (RAS) between October 2001

and November 2007. The commercial fish farm is located in northern Germany at the

shoreline of the North Sea. It is divided into two identical modules, Module 1 and Module 2.

Each module had its own water treatment unit (mechanical and biological filtration) and both

recirculation systems were completely separate from each other and worked independently.

Module 1 contained 18 tanks and Module 2 13 tanks. Turbots were reared from 5-2000g in 8

different weight classes (Table 1). The weight of the fish stock was taken at the beginning and

at the end of each rearing period. The average individual fish weight was calculated by

dividing the measured total weight of each tank by the number of fish reared in this tank. To

ensure homogenous size groups the fish were graded when necessary. Therefore the average

end weight of each class was different to the start weight of the following weight class as

shown in Table 1.

The total number of rearing groups was equal to the number of tanks stocked with fish of the

same weight range during the examined time period. The average rearing time fluctuated

between 53 and 94 days. Weight classes 1-5 were reared primarily in Module 1 while the

larger fish were kept in Module 2 (weight classes 6-8) (see Table 1).

48

Table 1: Number of rearing periods and the average body weights at the beginning and the ending of each weight class.

fish with

slow growth rate

fish with normal growth

rate

fish with fast growth

rate

weight class

weight category

(g)

no. of rearing groups

average rearing time

(days)

average starting weight

(g)

average end

weight (g)

average starting weight

(g)

average end

weight (g)

average starting weight

(g)

average end

weight (g)

1 5-20 81 84 16 44 13 40 7 22 2 21-50 67 79 34 84 34 74 27 56 3 51-100 62 94 77 98 75 164 72 195 4 101-200 68 79 160 249 153 289 109 194 5 201-400 116 58 318 352 297 418 258 378 6 401-800 155 57 570 547 594 758 507 657 7 801-1200 62 53 926 1068 995 1252 896 1154

8 1201-2000

42 55 1397 1440 1538 1548 1429 1639

Specific growth rate

The specific growth rate (SGR) was used to identify the growth rate of the fish within the

turbot population and to distinguish between slow-, normal- and fast-growing fish. The

datasets were thus divided into slow (S), normal (N) and fast- (F) fish according to growth

rate. Averages for all 8 weight classes are shown in Table 1. With the use of SGR it is

possible to compare the growth performance of fish with different individual body weights,

respectively different age classes.

The average SGR of each weight class was compared to the SGR of each rearing group

belonging to this weight class and the fish were arranged in the three groups (S, N, F). Group

S contained rearing groups with growth rates lower than the average SGR – 1 standard

deviation (STD) (slow growth rate < average SGR – 1 STD). Group N contained rearing

groups with an average growth performance of the average SGR ± 1 STD. The last group F

contained the fast-growing rearing groups where the performance was better than the average

SGR + 1 STD.

In general the SGR decreased with increasing weight class. The growth rate of the fish with

slow SGR was at least twice as slow and sometimes even slower than the growth speed of the

fast-growing fish (Table 2). In order to achieve more detailed growth information about the

fish reared in RAS, three growth curves were estimated for each tested growth model, one for

slow (S), one for normal (N) and one for fast (F).

49

Table 2: Specific growth rates for each weight class (average SGR) and standard deviation in brackets.

specific growth rate for each weight class

growth rate 1 2 3 4 5 6 7 8

slow 0.67

(0.11) 0.55 (0.10)

0.23

(0.09) 0.26 (0.04)

0.15 (0.04)

0.11 (0.05)

0.08

(0.05) 0.04

(0.01)

normal 1.18 (0.21)

0.88 (0.12)

0.65 (0.13)

0.54 (0.10)

0.34 (0.07)

0.29 (0.06)

0.20 (0.05)

0.10 (0.02)

fast 1.95

(0.35) 1.28 (0.15)

1.15 (0.18)

0.82 (0.07)

0.64 (0.08)

0.48 (0.06)

0.34 (0.02)

0.22 (0.03)

Growth models

Schnute (1981) developed a versatile growth model in which several traditional growth

models are incorporated as special cases. The complete model was used in the present study

including four parameters with a biological meaning:

when a ≠ 0 and b ≠ 0,

when a ≠ 0 and b = 0,

when a = 0 and b ≠ 0, and

when a = 0 and b= 0

where:

Wt : live weight (g) at time t (days)

T1 : first specified age (days) in dataset

T2 : last specified age (days) in dataset

a : constant relative rate of relative growth rate (days-1)

b : incremental relative rate of relative growth rate

y1 : theoretical estimated live weight (g) at age T1

y2 : theoretical estimated live weight (g) at age T2

Wt = [ y1b

+ (y2b – y1

b) 1- exp – a (t-T1) ] 1/b

1-exp – a (T2 –T1)

Wt = y1 exp [ln(y2/y1) 1- exp – a (t-T1) ]

1-exp – a (T2 –T1)

Wt = [y1b

+ (y2b – y1

b) t-T1 ] 1/b

T2 –T1

Wt = y1 exp [ln(y2/y1) t-T1 ]

T2 –T1

50

The ages T1 and T2 are fixed whereas the other parameters have to be estimated. Among

others, the system of the 4 equations includes the asymptotic growth functions such as the von

Bertalanffy (a > 0, b = 1/3) and the Gompertz function (a > 0, b = 0). Therefore, it is possible

to transform the four equations into traditional growth functions with a different set of

parameters.

To evaluate the growth models the growth parameters a was compared in the growth models.

Furthermore, the estimated weights at the beginning (y1) and the end (y2) of the model were

compared together with the deviations of the estimated body weight from the real body

weight. Finally, the inflection points (y*), the estimated age at these inflection points and the

asymptotic weight (yinf) were estimated and discussed.

Multi Model inference

One approach towards analysing fish growth is to work with more than one growth model

instead of choosing a priori VBGM or another model (Katsanevakis and Maravelias, 2008).

This so-called multi-model inference is a relatively new method of determining a

representative model for a given dataset. Different growth models are fitted to the same

dataset and the model providing the best fit is selected. A suitable criterion for model

evaluation is the use of the Akaike Information Criterion (AIC) (Akaike, 1973). Furthermore,

the residual sum of squares can also be used to identify the best model as well as the

deviations between the estimated weight and the real weight (Urban, 2002). The formula for

calculating the daily deviation in the present study was: daily deviation = (∑=

n

i 1

│real weighti -

estimated weighti│) / days.

By means of these 3 criteria [(1) AIC, (2) residual sum of squares (SSE), (3) daily deviation

between estimated and real weight] the following three growth models were evaluated and a

ranking was defined to identify the ‘best’ model.

The descriptive statistic was performed with SAS (SAS, 2005). The growth models were

estimated using the PBS modelling-package of the Open Source software R (R

DEVELOPMENT CORE TEAM, 2004).

51

Results

Parameter estimates

Fish with slow growth

The parameter a for fish with slow growth rates was negative for the Schnute model. The

Gompertz and the VBGM estimated values close to 0 for parameter a. The estimated values of

y1 for the Schnute and the Gompertz model were more realistic compared to the VBGM. The

VBGM estimated a fish weight close to 0g (y1), whereas the Schnute and Gompertz model

estimated values of 22g, respectively 13g for y1. The Schnute model estimated the highest

value of 1,602g for y2, followed by the Gompertz model with 1,581g and the VGBM with

1,348g. For fish with slow growth rates the time and the weight of inflection point and the

maximum estimated weight were missing because the curves of the estimated growth were

exponential (Table 4).

Fish with normal growth

For fish with a normal growth rate the Schnute model estimated the closest values for y1

(19.9g) regarding the input weight data of 12.9g. This estimation had a smaller deviation from

the input weight data compared to the estimations of the other growth models tested. The

Schnute model is the only growth model out of the ones tested which estimated the inflection

point (t*) within the examined time period at day 599, whereas the Gompertz model and the

VBGM estimated the inflection point at day 916, respectively day 8,253, which is beyond of

the examined time period of 732 days (average rearing period for fish with normal growth

rate). The values for parameter a for fish with normal growth rates fluctuated between the

growth models from 0.05 (VBGM) over 0.82 (Gompertz) to 6.14 (Schnute) (Table 4). The

resulting curve shape of the Schnute growth model was sigmoidal (Figure 1), whereas the

shape of the growth curves of the Gompertz growth model and the VBGM looked exponential

(Figure 2, Figure 3)

Fish with fast growth

Fish with fast growth rates had the highest values for parameter a. The Schnute model

calculated a value for parameter a of 9.98 followed by the Gompertz model of 1.79 and the

VBGM with a value of 0.25. The fish needed 513 days to reach the final weight. The Schnute

model estimated the most realistic value for the start weight (y1) of 15.8g whereas the

Gompertz and the VBGM estimated weights which were much lower and close to 0g. Schnute

estimated the final weight (y2) with the smallest deviation from the real weight at day 513

(1,638.5g). The VBGM had the lowest value of parameter a (0.25) followed by the Gompertz

52

model (1.79). The Schnute model had the highest calculated value of 9.98 for parameter a

(Table 4). The shape of the estimated curve had an exponential shape for the Gompertz and

the VBGM. The Schnute model had a sigmoid shape.

Table 4: Estimated parameters from three tested growth models (Schnute, von Bertalanffy and Gompertz) to describe the growth of turbot reared in the examined RAS. y1: start weight, y2: weight at the end of the growth period, t*: time at inflection point, y*: weight at inflection point, yinf: max. estimated weight

slow growth parameters Schnute Gompertz von Bertalanffy

a -0.97 1.2*10 -7 1.1*10-6 b 0.32 0 0.33

y1 (g) 21.8 13 1.5*10-19 y2 (g) 1602 1581 1348

t* (day) - - - y* (g) - - -

yinf (g) - - -

normal growth

parameters Schnute Gompertz von Bertalanffy a 6.14 0.82 0.05 b -2.39 0 0.33

y1 (g) 19.9 3.4 0.1 y2 (g) 1697 1800 1823

t* (day) 599 916 8253 y* (g) 1138.6 3127.9 669430

yinf (g) 1896.3 8502.5 2253000

fast growth parameters Schnute Gompertz von Bertalanffy

a 9.98 1.79 0.25 b -2.54 0 0.33

y1 (g) 15.8 0.22 0 y2 (g) 1696 1761 1829

t* (day) 412 467 1876 y* (g) 1096.5 1432.5 31909

yinf (g) 1802.3 3894 107390

53

The growth curve fitted by the Schnute model had a sigmoid shape for fish with a normal and

a fast growth rate (Figure 1). The growth curve for slow growing fish had an exponential

shape.

Schnute growth curves

0

200

400

600

800

1000

1200

1400

1600

1800

1 101 201 301 401 501 601 701

day

est

ima

ted

we

igh

t (g

)

s low growth dataslow growthnormal growth datanormal growthfast growth datafast growth

Figure 1: Growth curve for weight-at-age data for turbot with slow, normal and fast growth speed, fitted by the Schnute model.

The Gompertz growth model estimated exponential growth. The calculated inflection points

for normal growth and fast growth were out of range and are not shown in Figure 1.

The estimated growth curves of the VBGM looked similar to the Gompertz growth

estimations. The VBGM estimated exponential growth curves and the inflection points for the

estimated growth model of slow-growing fish did not exist (Figure 3). In contrast to the

Gompertz growth model, the estimated growth curve of slow-growing fish did not approach

the estimated growth curve for fish with a normal growth rate. No decrease in growth was

estimated for the given dataset.

54

Gompertz growth curves

0

200

400

600

800

1000

1200

1400

1600

1800

1 101 201 301 401 501 601 701

day

est

ima

ted

we

igh

t (g

)

slow growth dataslow growthnormal growth datanormal growthfast growth datafast growth

Figure 2: Growth curve for weight-at-age data for turbot with slow, normal and fast growth speed, fitted by the Gompertz growth model

von Bertalanffy growth curves

0

200

400

600

800

1000

1200

1400

1600

1800

1 101 201 301 401 501 601 701

day

est

ima

ted

we

igh

t (g

)

s low growth dataslow growthnormal growth datanormal growthfast growth datafast growth

Figure 3: Growth curve for weight-at-age data for turbot with slow, normal and fast growth speed, fitted by the von Bertalanffy growth model.

55

Goodness of fit

Fish with slow growth

The AIC value for growth models for fish with slow growth rate ranged from 20,035 for the

Schnute model to a value of 20,057 for the Gompertz model and the 21,056 for VBGM. The

Schnute model also had the lowest SSE values (2,279,709) followed by the Gompertz model

(2,320,563) and the VBGM (4,897,146). The average daily deviation (DD) between real and

estimated weight for normal-growing fish was 11% for all tested models and no significant

difference occurred (Table 3).

Fish with normal growth

The AIC values indicated the Schnute model as the one with the best goodness of fit (AIC

value: 20,824).The VBGM and the Gompertz model had higher AIC values of 21,472,

respectively 21,306. The SSE was lowest in the Schnute model with 1,785,556 compared to

the Gompertz model with 2,496,840, and the VBGM respectively 2,799,808.

The deviations of the growth models for fish with a normal growth rate had the greatest

differences during the time period from day 1 to day 300 (Figure 4). At the beginning of the

estimation (days 1-66) the Schnute model overestimated the real body weight for fish with a

normal growth rate. The VBGM and the Gompertz model underestimated the body weight

between day 1 and day 304 or day 334, respectively. The shape of the deviations of the

growth models looked alike after a rearing period of 300 days (Figure 4). Compared to the

other growth models tested the Schnute model fluctuated with the lowest amplitude around

the real fish weight. The highest deviation between estimated and real body weight occurred

at day 1 with an overestimation of the body weight of 51 %. This value is low compared to

the calculated deviations of the VBGM (99 % at day 1) and the Gompertz growth model (76

% at day 1) (Figure 4). The resulting values of the DD between estimated and real body

weight for fish with a normal growth rate were the lowest for the Schnute model (9 %)

followed by the Gompertz model (25 %) and the VBGM (29 %) (Table 3).

Fish with fast growth

Here, the models showed considerable differences between the AIC values. The Schnute

model had the lowest value (14,332) followed by the Gompertz model (14,924) and the

VBGM (15,150). The order of the models persisted when using the SSE or the DD for

evaluation. The Schnute model had the lowest value of SSE (1,325,720), followed by the

Gompertz model (2,122,664) and the VBGM (2,646,824) (Table 3). The gradient of the

56

deviations between real and estimated body weight was similar to the gradient of the

deviations of fish with normal growth rate. The Schnute model overestimated the real body

weight during the first 100 days, while the other tested models (Gompertz and VBGM)

underestimated the real fish weight. According to these estimations the Schnute model had the

lowest value of DD with 18 %, followed by the VBGM with 36 % and the Gompertz model

with 37 %.

Table 3: Akaike index criterion (AIC), sum of squared residuals (SSE) and the mean percentage deviation per day (DD) from estimated to real weight for growth models

slow growth Schnute Gompertz von Bertalanffy

AIC 20035 20057 21056 SSE 2,279,709 2,320,563 4,897,146 DD 11 11 11

normal growth

Schnute Gompertz von Bertalanffy AIC 20,824 21,306 21,427 SSE 1,785,556 2,496,840 2,799,808 DD 9 25 29

fast growth

Schnute Gompertz von Bertalanffy AIC 14,332 14,924 15,150 SSE 1,325,720 2,122,664 2,646,824 DD 18 37 36

Deviations between estimated and real body weight

The deviations between estimated and real weight data for fish with normal growth rate are

shown in Figure 4 below. The weight is expressed in percentage. The input weight data was

fixed as 100 % and the difference between the estimated weight and the input weight data is

shown for each growth model tested. The closer the distance between the estimated weight to

100 % (= input weight data) the better the estimation was.

57

0

20

40

60

80

100

120

140

160

0 100 200 300 400 500 600 700

day

est

ima

ted

bo

dy

we

igh

t (%

)

real fish weightSchnute modelGompertz modelvon Bertalanffy model

Figure 4: Illustration of the deviation between the estimated and the real body weight of fish grew with normal growth speed expressed in percentage of real body weight.

For fish with a slow growth rate the models showed similar performance. Highest deviations

between estimated and real body weight were found at day 500 (74 % under estimation) and

day 585 (143 % over estimation) in all 3 models, whereas for the fish with a normal and fast

growth rate the estimation of the Schnute model differed to the Gompertz and VBGM. The

highest overestimations occurred at the beginning of the growing period at day 2 (152 %

overestimation for fish with normal growth rate, 210 % for fish with fast growth rate) and the

lowest underestimation of the real body weight occurred at day 238 (83 %) (respectively day

176, 61%). For the Gompertz and VBGM it was vice versa. They started with the lowest

underestimations during the first 9 days (0 and 1 % for fast and normal growth rates for the

VBGM, 2.5 and 24 % for fast and normal growth rates for the Gompertz model) and the

highest overestimations occurred at the same days (day 461 for fish with a normal growth rate

with 134 % for the Gompertz model and 135 % for VBGM and at day 264 for fish with a fast

growth rate 137 % for the Gompertz model and 156 % for the VBGM).

The mean daily deviations between estimated and real body weight are shown in Table 3.

They were used for the evaluation of the growth models (Criterion no. 3).

58

Discussion

Data

All the models tended to overestimate the final weight (= y2). Only the VBGM estimated a

lower final weight (y2) for fish with slow growth rates compared to the real weight data

(Table 4). The final weights observed in the data were lower (Table 1). Probably the

heterogeneous distribution of the data was the reason for the overestimation. The estimated

parameter of the growth models would probably be better if the weight data were more

homogenous and the steps between the weight classes were smaller (Table 1).

Extreme differences between start and end weight within a weight class occurred in slow-

growing fish at weight class 6 (Table 1). The weight decreased during this rearing period.

This was because of repeated grading on the one hand and stagnating growth on the other

hand. Slow-growing fish remained in their weight class if they did not reach the aspired

weight to be transferred to the heavier weight class. In contrast to the slow-growing fish,

some individuals grew faster and skipped a size class. In the end it was impossible to

determine the exact time period an individual fish was reared in the RAS due to this high

variance in individual growth. Probably some individuals were much older than the average

age of the standing stock because of their slow growth rate. These fish should be identified as

early as possible and expelled from the stock because of their negative impact regarding

economic benefit. Certainly some non-homogeneity in growth performance is still existent in

turbot aquaculture. The aquaculture industry is still at the beginning of the domestication of

turbot. One solution to deal with this growth variance is to mark the fish individually and to

document the growth performance more accurately.

Another factor influencing growth is the RAS itself. After several years of operating a RAS a

decrease in growth performance of the reared fish is potential possible. This phenomenon can

hardly be explained and has probably several reasons. In the course of time organic matter

accumulates inside the whole rearing system (Schrader and Summerfelt, 2010). The organic

matter serves as media for microorganisms which produce metabolites that can cause

undesirable tastes and odours or can be biochemically active (Vining, 1992; Watson, 2003).

Some of the metabolites are probably responsible for several effects influencing growth

performance of the reared organisms. The metabolites influence the metabolism of fish

negatively resulting in decreased growth performance (Malbrouck and Kestemont, 2006).

Probably this negative effect of metabolites of microorganisms occurred also in the present

RAS, since sometimes an off flavour could be detected in the fish. This phenomenon probably

supported the overall low growth rate detected in the present RAS.

59

Because of the problems mentioned above the fish did not grow homogeneously in the

examined RAS. Therefore for the evaluation of the growth performance, the best method was

to divide the population into slow, normal and fast growth rates to estimate the most realistic

growth curves for different growth rates.

Growth models

Fish with slow growth

The estimation of the growth curves of the slow-growing fish were characterized by the

impossibility of the estimation of values for the parameters t* and y*. Schnute (1981)

described the transformation of his growth model into traditional growth models by different

settings of parameters. Nevertheless the estimation for parameter a for slow-growing fish in

the present study resulted in a negative value for the Schnute model (Table 4). Schnute (1981)

explained this case where parameter a < 0 and parameter b ≥ 0 represents unbounded

accelerated growth. A theoretical minimum existed in yinf while an age 0 cannot be

calculated because the curve can not be extrapolated back to the age 0. Therefore the time of

inflection point (t*) could not be calculated either as well as the body weight at the inflection

point (y*).

The estimation of the growth of the present data of slow-growing turbot can be interpreted by

the fact that the fish need some time until they reach a weight at which they start increasing in

growth. The slow-growing fish compensated their slow growth rate during the last part of the

rearing period (Figure 1, 2, 3). Endogenous and exogenous factors are responsible for the

different growth rates within the examined turbot population (Imsland, et al., 2001; Regost, et

al., 2003). The growth rate is influenced by endogenous factors such as gender, genetics and

the age of an animal. For example, sexual dimorphism in growth exists in flatfish and female

turbot grow faster than males (Imsland, et al., 1997). The reason can be found in a surplus

energy (energy in excess of maintenance requirements) accumulated in the gonads and liver

(Lozan, 1992). Both sexes have the same surplus energy level up to a special size and beyond

that size female fish grow faster because they have a greater surplus in energy compared to

males (Deniel, 1990). Female turbot grow significantly faster than males after reaching

maturity (Devauchelle, et al., 1988). Probably a higher number of males can be found within

the group of slow-growing fish and vice versa in the fast-growing group. Exogenous factors

such as type, level and the duration of undersupply of nutrients influence the growth rate as

well (Caballero, et al., 2002; Fountoulaki, et al., 2009). Small fish can be prevented from

feeding by stronger and larger individuals and cannot meet their metabolic needs.

60

The exponential shape of the curves of slow-growing fish supported this theory (see Figures

1, 2 and 3).

Fish with normal growth

Over 60% of the fish reared in the examined commercial turbot farm grew within the present

definition of a normal growth rate. The Schnute model estimated sigmoid growth curves for

fish with normal growth rates. This growth curve is a classical growth situation where the

organisms first slowly develop during the lower part of the s-shaped growth curve. Growth

rate increases until the point of inflection and decreases afterwards until it approaches the

limiting size yinf. Overall, the adjustment of the VBGM and Gompertz growth models to the

real input data differed from the Schnute model (Table 2). In contrast to the Schnute growth

model, the inflection points of the VBGM and the Gompertz growth model did not appear in

the calculated time period of 732 days. Both growth curves (Gompertz, VBGM) showed an

increasing trend and no decrease in growth could be observed during the examined time

period (Figure 2, Figure 3). Furthermore, the growth curves of the VBGM and Gompertz

models looked similar, which is affirmed by the close AIC values of both models (Table 3).

Both models overestimated the growth potential of the examined growth data. The VBGM

estimated a maximum final weight (yinf) of > 2 million kg which is above the possible growth

potential of turbot.

Up to the inflection point the run of the curve increased steadily. The SGR values in Table 2

also indicate a decrease in growth starting at weight class 4 (Table 2): According to the

growth biology of fish it is well known that fish have a high growth potential during the

juvenile stage (Boeuf, et al., 1999). In the present study, the SGR also indicated a high growth

potential of turbot during the first life period.

Nevertheless, while comparing the identified value of parameter a from the VBGM (Table 4)

with the k-values from the literature (parameter a in the VBGM equals the k-value of the

VBGM) wide fluctuations can be observed (Froese and Pauly, 2003). The published k-values

of turbot vary from 0.13 to 0.59. It is remarkable that all published k-values are higher

compared to the present value for parameter a for normal-growing fish (a = 0.05) since all

these data originate from wild fish. It should be the other way around since the rearing

conditions in a closed recirculation system should meet the biological needs of turbot in an

optimal way, leading to higher k-values. Overall, short-lived species which almost reach their

maximal estimated length in a relatively short life period have higher k-values compared to

species which need many years to reach their asymptotic size (e.g.: European Sprat (Sprattus

61

sprattus) average k-value of 0.3; Halibut (Hippoglossus hippoglossus) average k-value of 0.1;

Froese and Pauly, 2003). A possible explanation for the low values of parameter a calculated

in the present study might be the state of health of the turbot. During the time the data

recording was performed the fish had to be treated against multiple infections and bacteria.

Because of these medical treatments the appetite of the fish decreased, hence their growth was

influenced negatively. Since only the fast-growing fish (9-25% of each weight class) reached

an acceptable growth rate which is in the range of the growth rates named in the literature, it

can be assumed that the economic benefit of the whole RAS can be increased by calling the

true growth potential of the fish, which has probably not yet been fully exploited.

Fish with fast growth

For the data of fast-growing fish the Schnute model estimated a sigmoid growth curve. The

VBGM and the Gompertz model estimated exponential growth curves. These fish increased

their growth rate earlier compared to the fish with a normal or slow growth rate.

Some turbots were more aggressive during the feeding time and demonstrated more activity

compared to other individuals (Imsland, et al., 1997). These fish grew faster because of their

behaviour and simultaneously they excluded other fish from feeding (Imsland, et al., 1998).

Therefore, it is necessary to grade the fish from time to time to assure homogenously sized

groups and to avoid feeding stress.

Overall, the SGR is much higher compared to the fish with slow and normal growth rates

(Table 2). Fast-growing fish reached their final weight after ~17 months whereas the normal-

and slow-growing fish took ~22, respectively ~24 months to reach that size.

Evaluation of growth models

The present multi-model approach identified the Schnute model as the most precise growth

curve. Three different criteria were used to evaluate the 3 models: (1) the Akaike index

criterion, (2) the sum of squared residuals (SSE), and (3) the sum of the deviations between

the estimated and the real weight of the turbot population in the RAS.

According to the AIC, the models can be arranged in the following order (from lowest to

highest AIC): Schnute, Gompertz and the VBGM. The same ranking resulted from the

evaluation of the SSE: Schnute, Gompertz and the VBGM (Table 3). The last evaluation of

the models was done by comparing the deviations between the estimated weight and the real

measured weight. Since the fish in the RAS were only allowed to grow up to around 2 kg,

their maximal growth potential was not utilized. Hence, the estimated maximum weight (yinf)

62

from the present data cannot be compared with the true biological maximum weight (wmax)

found in literature. Nevertheless, the models tested tend to overestimate the final weight (y2)

for all tested weight data. The daily deviation was calculated for each model for fish with

normal growth rates and following ranking of the models could be determined for all growth

rates (in order of the smallest deviations): the Schnute model always achieved the lowest DD,

the VBGM had the second lowest values of DD (except for fish with normal growth rates)

and the Gompertz model had the highest DD values (except for fish with normal growth rates)

(Table 3).

Conclusion

To sum up the results of the present study, the Schnute growth model is the best model among

the tested growth models because it shows the estimates closest to the real data. This result

underlines that it is important to test different growth models instead of choosing VBGM a

priori.

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65

Chapter Four

The combined effect of feeding time and diet composition on

growth performance and metabolism of juvenile turbot

(Psetta maxima)

A. Baera,b, S. Wuertzb, J. Krietera, C. Schulza,b

aInstitut für Tierzucht und Tierhaltung, Christian-Albrechts-Universität,

D-24098 Kiel, Germany

bGMA – Gesellschaft für Marine Aquakultur mbH,

D-25761 Büsum, Germany

66

Abstract

Juvenile turbot (Psetta maxima) were fed twice a day with different diets containing varying

levels of protein and lipid but at the end of the day all fish were fed identical amounts of

dietary nutrients. In total 5 feeding regimes were tested. Two fish groups were fed with diets

containing same amounts of lipid (9 %) and changing protein levels (54 vs. 61 %) in the

morning and evening feed application. Two fish groups were fed with diets containing same

amounts of protein (58 %) and changing lipid levels (5 vs. 10 %) in the morning and evening

feed application and one control group was fed with the average amount of protein and lipid.

Juvenile turbot (initial body weight 7.9g) showed significant differences in specific growth

rate (SGR). The experimental groups fed with varying lipid levels over the day showed

significantly lower SGRs (2.34 - 2.37) compared to all other tested feeding regimes (SGR =

2.52 – 2.58). No significant differences occurred in the final body composition (protein,

lipid). Varying protein applications over the day influence the growth performance of juvenile

turbot significantly in contrast to constant protein application. The protein retention level

increased significantly (54.9 %) when the fish were fed with high protein levels in the

morning compared to feeding groups with low protein levels in the diet during the morning

feed application (protein retention of 46.2 – 50.5 %). Varying lipid levels over the day and

constant protein levels in the diets lead to a significantly decreased growth performance. The

present results of the feeding trial show that the growth performance of juvenile turbot can be

significantly influenced by a feeding regime adjusted to the physiological rhythm of the

animal. In order to detect possible metabolic and stress responses to the feeding regime blood

plasma parameters (cortisol, protein, triglyceride, glucose) were analyzed. It could be shown

that varying dietary lipid contents will significantly influence blood triglyceride contents and

other parameter did not vary between feeding groups. But fish of all feeding groups showed

higher cortisol levels in the morning indicating acute stress responses.

Keywords: Turbot, nutrition, feeding regime, lipid, protein

67

Introduction

In commercial aquaculture systems the growth of the aquatic organisms is the most important

part regarding the economical benefit. The degree of individual weight gain is mainly

influenced by the quantity and quality of food available. But the possible assimilation of

nutrients is significantly influenced by biotic and abiotic factors such as salinity, temperature,

stocking density etc. (Burel, et al., 1996; Imsland, et al., 2008; Imsland, et al., 2001; Mallekh,

et al., 1998). Former scientific studies showed that the growth performance of different fish

species is influenced by the time of nutrient supply and the feeding regime (Jorgensen and

Jobling, 1992; Tucker, et al., 2006). Fish fed with the same amount of feed at different

daytimes can show differences in growth performance (Gelineau, et al., 1998); (Verbeeten, et

al., 1999). This influence of feeding regime on growth performance is known in commercial

aquaculture and has received already some attention in turbot (Psetta maxima) production

(Saether and Jobling, 1999; Turker, 2006). Other former studies demonstrated optimal growth

performance of fish fed slightly below satiation (Eroldogan, et al., 2004; Van Ham, et al.,

2003). Hence, feeding the turbot to less than satiation without growth depression, improved

feed utilization and less water pollution leads to increased economical benefit. Therefore an

improved feeding regime would be a helpful tool for turbot aquaculture industry to improve

the growth performance, dietary nutrient retention and economical benefit.

Turbot shows two certain time periods during the day where its activity is increased (Waller,

1992). Furthermore the hormone and metabolite level involved in feeding and growth

processes fluctuate over the day and the fish should respond differently to offered nutrients

depending on time of the day (Boujard, 2001; Boujard, et al., 1993). This implies that fish are

in different physiological states over the day (Boujard, 2001). Therefore, Turker (2006)

recommended a feeding regime of two feed applications per day for turbot to improve growth

performance, but qualitative nutrient variations are not focussed yet.

The aim of the present study was to investigate the effects of daily dietary nutrient application

timing on growth performance and body composition of juvenile turbot. In addition blood

parameters were recorded to evaluate possible metabolic and stress responses.

68

Materials and Methods

Experimental diets

Five diets were formulated with changing lipid and protein contents. The protein level ranged

between 54.2 and 61.1% and the lipid content varied from 5.7 to 10.1%. The amount of

nitrogen free extracts (NFE) in the diets varied between 6.6 and 13.6%. The ash content

remained stable at around 15.5%. The experimental diets were composed of fish products

(herring meal and oil), blood meal (pig), cereal grains (wheat gluten and wheat starch) and a

vitamin-mineral mixture. All ingredients were pressed (L 14-175, AMANDUS KAHL,

Reinbek, Germany) to pellets of 4 mm diameter. Chemical composition and ingredients of

experimental diets are reported in Table 1.

Table 1: Proximate analysis of feed constituents in the experimental diets according to the AOAC (1995). NFE = nitrogen free extract, a Herring, crude protein > 680g kg-1, b Vitfoss, AA-Mix 507101, c P:E: relative contribution of protein energy to total energy expenditure.

Diet 2

Diets Diet 1 Diet 3 Control Diet 4 Diet 5

Ingredients (g kg-1)

fish meal a 650 650 650 650 650

fish oil 56 56 56 6 106

crude starch 75 77 77 77 77

wheat gluten 30 30 30 30 30

vitamin + mineral mixture b 20 20 20 20 20

blood meal 169 66 117 117 117

dextrose 0 101 50 100 0

Proximate composition

crude protein (%) 61.1 54.2 58.4 59.8 58.9

crude lipid (%) 8 9.5 8.9 5.7 10.1

NFE (%) 6.6 13.6 9.8 12.5 6.9

ash (%) 15.5 15.4 15.6 15.9 15.3

water (%) 7.2 9.3 8.4 8.4 6.5

energy (MJ/kg) 21.1 20.5 20.7 19.8 22

P:E (g/MJ) c 29 26.4 28.2 30.2 26.8

69

Growth study

The feeding trial consisted out of 5 different feeding groups (Table 2) and was conducted in

the experimental facilities of GMA in Büsum, Germany. Juvenile turbot were reared in a

closed recirculation system of 15 tanks each of 175 l. Water clearification system consisted of

a protein skimmer and a moving bed biofilter. The experiments run at constant seawater

temperature of 18 ± 0.5°C with a salinity of 28 ± 2 ‰ in an aquarium recirculation system

(water turnover rate 3 h-1, water exchange rate 25 %) for a period of 8 weeks. 300 turbot

originated from a Norwegian strain with an initial body weight of 7.9 ± 0.16g were randomly

distributed to 15 aquaria that each experimental group consisted of 20 individuals. The light

conditions were set to 12/12h light/dark cycle. The fish were fed on a fixed feeding level (at

approximately 3%/d of fish biomass) in order to provide identical amounts of experimental

diets and daily nutrient supply. The diets offered in the morning (8-9 a.m) and evening (16-17

p.m.) feeding varied only in the protein content in the experimental groups 1 and 2 and the

lipid content in the experimental groups 4 and 5, while group 3 served as control group

characterised by a constant nutrient supply over the day (table 2). Thus the sum of nutrients

offered over the day did not vary between the experimental groups. The fish were group

weighed every 2 weeks to follow growth and feed utilisation.

Table 2: Feeding regime in the different feeding groups

varying protein

Group 1 Group 2 Control Group 3

varying lipid

Group 4 Group 5

Diet no.(mornings) 1 3 2 4 5

Diet no. (evenings) 3 1 2 5 4

Sampling

For analyses of whole body composition five fish from the initial pool were sampled and

stored at -20°C. At the end of the trial, six fish were taken from each aquarium, 3 in the

morning and 3 in the evening. In total 90 fish were sampled. To ensure an identification of a

possible endocrine adaptation by the fish to the feeding interval the sampling duration did not

exceed 1h and lay exactly in the time period where the fish were normally fed. The fish were

slaughtered for analyses of body composition (protein, lipid, water, ash and energy content).

Furthermore body parameters (weight, size, liver weight) were taken for calculation of growth

performance. To compare the growth performance among the tested feeding groups the

specific growth rate (SGR) and the feed conversion ratio (FCR) were calculated.

70

Blood Physiology

To monitor possible influence of the feeding time on blood physiology, samples were taken

from 3 fish out of each aquarium during the two sampling times, mornings and evenings (non-

lethal sampling). The blood samples (0.5-2ml) were taken out of the caudal vene from

randomly chosen fish with heparinised 2ml syringes. The blood samples were centrifuged

(1000g, 5min) and the plasma was stored in a deep freezer at -80°C until analyses took place.

Glucose, cortisol, triglycerides and protein were measured with the microplate reader Tecan®

Infinite 200 with luminescence detection and different test kits (Table 3). Briefly, the

parameter standards of each test kit were diluted to create a standard curve. The plasma was

diluted depending on the parameter so that the values fall within the range of the standard

curve. A reagent was added and depending on the analysed parameter incubated for a few

minutes. After sample vortexing level of examined physiological parameter in the plasma

sample was determined with a microplate reader (Tecan® Infinite 200). Cortisol was

measured using an enzyme-linked Immunosorbent Assay (ELISA) kit (RE52611; IBL,

Hamburg, Germany) for the in-vito measurement of free cortisol in human saliva. With help

of the competitive principle of the used ELISA the amount of cortisol in the sample can be

determined. A defined amount of antigen in the sample and a defined amount of antigen

(enzyme conjugate) compete for the binding sites of the antibodies coated onto the wells. The

competitive reaction inside the wells is stopped after an incubation time of 1h by washing the

wells. A substrate is added to induce a colour reaction which is inversely proportional to the

amount of antigen inside the sample.

Table 3: Test kits used for analyses of blood parameters.

Parameter Company Kit Sample volume [µl] diluted Reagent [µl] glucose Greiner GOD-PAP 5 - 250

triglycerides Greiner GPO-PAP 15 1:2 200 proteins Roth RotiQuant 50 1:200 200

Chemical Analyses

For analyses of whole body composition the fish samples were freeze dried for 48h and

afterwards homogenized. Chemical analyses of diets and fish were conducted as follows:

protein (N x 6.25) according to the Kjeldahl method, decomposition and distillation was

performed with equipment by the Büchi company (Büchi Digestion Unit K-435 and

distillation Unit B-324); lipid by petroleum ether extraction in a Soxleth extraction system;

water content by drying in an oven at 104°C to constant weight; ash content by drying in an

muffle oven at 450°C for 18h. Gross energy was determined using a bomb calorimeter

71

(IKA®calorimeter 200c). All analyses were performed according to Naumann and Bassler

(1988) and guidelines of European Union (2009).

Statistical Analyses

The statistical analyses were performed with SAS (SAS, 2005) using ANOVA. Tukey test

with a p-value of 5% were used to evaluate differences of means (n = 3, parameter as mean

per replicate). If test for normality failed the non-parametric Kruskal-Wallis test was used to

identify significant differences of means (p-value of 5%). The blood plasma parameters were

also tested against the feeding time. The t-test was used to identify significant differences

between the morning and evening feed application of each feeding group (p-value of 5%).

Results

Growth

No mortality was observed during the experiment. The different feeding groups demonstrated

varying growth performance. Fish fed with constant lipid levels and varying protein levels

(group 1 + 2) showed a comparable high growth performance as the control group with SGR

of 2,52-2,54 and 2,58 respectively. Fish fed with constant protein and changing lipid levels in

their diets (groups 4 and 5) grew slower compared to the other experimental groups (Figure

1). The SGR of 2.3 % day-1-2.2 % day-1 was significant different (P<0.05) between groups 4

and 5 to the other feeding groups (Figure 1).

bb

aaa

2.10

2.20

2.30

2.40

2.50

2.60

1 2 control group 4 5

Feeding group

SG

R (

% d

ay-1

)

specific growth rate

Figure 1: Specific growth rates (SGR) of the different feeding groups as characterised in tab. 1 and 2. Different letters indicate significant differences between the SGR of the feeding groups (mean ± standard error, Tukey, P<0.05, n=3 replicates).

72

Weight gain was highest in the control group (weight gain = 549g). The feed conversion rate

(FCR) was the lowest in the control group with 0.93 (Table 4). No significant differences

could be observed among the calculated parameters.

The protein and productive protein value (PPV) of group 1 was significantly higher compared

to all other feeding groups. The control group showed average values in PPV and protein and

lipid retention. No significant differences could be detected in lipid retention.

Table 4: Effect of feeding regime on growth (initial weight, weight gain, protein and lipid retention, protein efficiency ratio, feed conversion and productive protein value); protein retention = body protein (g) / protein consumed (g) * 100; lipid retention = body lipid (g) / lipid consumed * 100; FCR (Feed conversion rate) = (dry weight of ingested feed / live weight gain); PPV (productive protein value) = ((body protein gain * 100) / Protein consumed); Different letters indicate significant differences between the feeding groups (mean ± standard error, P<0.05, n=3 replicates).

varying protein content varying lipid content Group 1 Group 2 Control Group 3 Group 4 Group 5

initial weight (g tank-1) 174 ± 1.5 172 ± 2.1 170 ± 1.5 175 ± 2.3 172 ± 2.2

final weight (g tank-1) 715 ± 22.6 713 ± 17.3 719 ± 5.0 672 ± 23.7 641 ± 6.5

protein retention (%) 54.9 ± 1.0a 48.4 ± 0.9b,c 50.5 ± 0.2b 47.3 ±1.0b,c 46.2 ± 0.7c lipid retention (%) 69.9 ± 1.3 70.7 ± 1.3 66.2 ± 0.2 68.4 ± 1.5 69.4 ± 1.1

feed conversion rate 0.97 ± 0.02 0.96 ± 0.03 0.93 ± 0.01 1.03 ± 0.04 1.05 ± 0.02

productive protein value 41.4 ± 0.6a 36.3 ± 0.1c 38.2 ± 0.3b 34.8 ± 0.2c,d 33.8 ± 0.5d Body composition and nutrient retention No significant differences were found in the body composition (protein, lipid, dry matter)

between the fish groups with varying diets (Table 5). The dry matter level fluctuated between

28.9 (Group 2) and 32.3 % (Group 1). The protein and lipid values varied also between the

feeding groups, but not significantly. Group no. 2 showed the lowest protein value (20.3 %)

and simultaneously the highest lipid value (3.18 %) and energy content (21.9 kJ g-1 body

weight) in the final body composition. The energy content was significant different to the

feeding group 4, which had the lowest energy content of 20.9 kJ g-1 body weight (Tukey,

P<0.05).

73

Table 5: Outcome of feeding regime on final body composition of the juvenile turbot (% of wet weight). Initial body composition was: dry matter = 30.9%, ash = 5.1%, protein = 20.1%, lipid = 2.9%, energy = 21.1kJ g-1 body weight. Data are shown as means ± S.D., n=3. Different letters indicate significant differences within the same row between the feeding groups (mean ± standard error, P<0.05, n = 3 replicates).

varying protein content varying fat content Group 1 Group 2 Control Group 3 Group 4 Group 5

Proximate composition (% ww) dry matter 32.3 ± 2.1 28.9 ± 0.7 29.3 ± 0.2 30.2 ± 0.6 30.4 ± 0.8

ash 4.8 ± 0.6 4.3 ± 0.4 4.6 ± 0.3 4.6 ± 0.4 4.8 ± 0.1 protein 23.3 ± 1.4 20.3 ± 0.3 21 ± 0.2 21.3 ± 0.5 21 ± 0.5 lipid 4.5 ± 0.4 4.5 ± 0.2 4.2 ± 0.1 4.1 ± 0.3 4.2 ± 0.1

energy kJ g-1 21.6 ± 0.01a,b 21.9 ± 0.2a 21.6 ± 0.04a,b 20.9 ± 0.31b 21.2 ± 0.2a,b

Blood plasma composition

Dietary treatment had some significant influence on the blood plasma composition (Table 6).

Due to the wide variation of the measured values of each analysed parameter only for cortisol

and triglyceride statistical significance between the feeding groups could be observed.

Varying lipid application over the day resulted in higher cortisol levels in the evening and

triglyceride amounts in the morning in contrast to remaining feeding groups. In addition fish

of all feeding groups showed higher cortisol levels in the morning in comparison to the

evening.

Table 6: Comparison between measured blood parameters sampled mornings (M) and evenings (E), different capital letters indicate significant differences within the same feeding group between mornings and evenings. Small letters in subscript indicate significant differences between the groups within one feeding time (mean ± standard error, P<0.05, n = 5).

varying protein content Varying lipid content Group 1 Group 2 Control Group 3 Group 4 Group 5

Blood parameters protein (mg ml-1) M 28.6 ± 1.8 31 ± 2.1 24.5 ± 1.4 26.3 ± 0.9A 31 ± 1.5 protein (mg ml-1) E 27.6 ± 1.0 28.3 ± 2.4 27.2 ± 1.0 28.5 ± 1.5B 28.8 ± 1.1

triglyceride (mg ml-1) M 1.2 ± 0.7a 3.3 ± 0.6b 3.1 ± 0.4b 3.4 ± 0.4b 2.1 ± 0.5a,b triglyceride (mg ml-1) E 1.9 ± 0.4 2.1 ± 0.5 1.8 ± 0.5 2.7 ± 0.4 1.7 ± 0.5

cortisol (ng ml-1) M 3.2 ± 1.4A 4.5 ± 2.2A 1.6 ± 0.8 3.9 ± 1.9A 2.2 ± 1.6 cortisol (ng ml-1) E 0.9 ± 0.3Ba,c 0.3 ± 0.1Bb 0.5 ± 0.3a,b 1.3 ± 0.5Bc 2.7 ± 1.5a,b,c

glucose (mg ml-1) M 0.2 ± 0.02 0.4 ± 0.06 0.4 ± 0.04 0.4 ± 0.08 0.3 ± 0.03 glucose (mg ml-1) E 0.3 ± 0.02 0.3 ± 0.04 0.4 ± 0.06 0.4 ± 0.07 0.2 ± 0.03

74

Discussion

Growth parameter

According to the nutritional requirements of turbot reported by Lee (2003) and Caceres-

Martinez (1984) the control diet in the present study was formulated to reach similar amounts

of lipid and slightly elevated levels of protein to ensure a good growth performance of

juvenile turbot. Cacerez-Martinez et al. (1984) observed a decline in growth rate in juvenile

turbot (weighing 10g) when fed with diets with lipid concentrations ranging from 10 to 20%.

Therefore, to obtain optimal growth of the juvenile turbot the present lipid levels of the

experimental diets were not exceeding 10.1 %.

Overall, the results indicated a good growth performance and the observed SGRs of the

different feeding groups are within the range, reported by earlier studies (Caceres-Martinez, et

al., 1984; Imsland, et al., 2001). The control group had the highest SGR with 2.58 % body

weight gain per day. The lowest SGR was detected at feeding group 5 with only 2.35 % body

weight gain per day, which is still within the reported range of the scientific studies

mentioned above. These results lead to the assumption that the fish did not suffer any lack of

nutrients and the environmental conditions were set in an optimal range.

A significant difference in SGR could be observed between the lipid-group (groups 4 and 5,

changing lipid and constant protein levels in the diets) and the protein-group (groups 1 and 2,

changing protein and constant lipid levels between the two feeding times) (Figure 1). No

significant effect of feeding time and nutrient supply on growth performance could be

observed within these two groups.

The significantly lower SGR of groups 4 and 5 compared to the other tested feeding regimes

is also reflected by decreasing productive protein values (PPV) of both groups compared to

the other feeding groups (Figure 1). The present feeding regime indicates a positive

correlation between the amount of protein in the diet, the feeding time and the growth rate.

Obviously, juvenile turbot digest protein more effective during the morning compared to

evening. Feeding groups 4 and 5 had constant protein levels in the two feed applications per

day and no significant differences in PPV could be observed. In contrast group 1 and 2

showed significant different PPVs. Group 1 was fed high amounts of protein during the

morning and showed the highest PPV. Group 2 was fed the lowest amounts of protein during

the morning and showed the lowest amount of PPV. Hence, the efficiency to digest protein is

highest during the morning in juvenile turbot.

Since the lipid level varied significantly between the diets 4 and 5 and no significant changes

could be detected neither in the final body composition nor in the daily plasma triglyceride

75

level of these fish, they were probably able to react physiologically to the applied feeding

regime. The same was suggested by Brown et al. (2010). They found a circadian cyclical fatty

acid deposition pattern in rainbow trout. Trout respond with significant different growth

performance to dietary treatments with changing lipids sources over the day (Brown, et al.,

2010). The response of the blood physiology to feeding time in the present experiment

supports the idea that feeding time might interact with some physiological and endocrine

circadian cycles involved in protein and energetic metabolism to affect growth. Bolliet et al.

(2000) described the role of protein metabolism on the effect of feeding time on growth in

rainbow trout. Furthermore, Boillet et al. (2004) reported an physiological adaptation of

nutrient metabolism of rainbow trout to daytime instead of applied nutrients. Therefore the

feeding regime for trouts has to be adapted to daytime. The same can be found in the present

experiment. The changes in daily feed application in regard to nutrient composition have been

probably too large for an efficient digestion by the fish and the metabolism was not able to

convert the offered energy efficiently into body tissue. The fish showed a slight tendency

towards decreased levels of measured blood parameters at the evening measurement

compared to the morning measurement disregarding the applied feeding regime. Furthermore,

no significant differences occurred in the final body composition (Table 4). Hence, the

observed significant differences in the specific growth rate can be traced back to the feed

composition and not only to the feeding time. Overall, turbot react to increased dietary lipid

levels with increased whole body lipid content (Andersen and Alsted, 1993).The same is true

for salmonids (Hillestad and Johnsen, 1994). In the present study analyses of body

composition showed no significant differences in body fat content between the feeding

groups. Probably differences will occur when performing the feeding trial over a longer time

period.

The present results suggested that blood meal is susceptible to digestion by fish proteinases.

Obviously the feed composition is influencing energy and protein retention, resulting in

varying growth performance of the feeding groups. High feed utilisation can only be achieved

if protein retention is high. These findings support earlier studies where Hevroy et al. (2004)

found out that growth rate was correlated to protein synthesis efficiency.

Furthermore, the protein retention of the juvenile turbot was better during the morning

compared to the evening. Feeding group 1 which was fed with the highest amounts of protein

during the morning showed highest protein retention (Tab. 4). Group 2 was fed with the

lowest amount of protein during the morning and showed significant lower protein retention

than group 1 (Tab. 4). Hence, it can be assumed that the protein metabolism of juvenile turbot

76

is increased during the early morning. To take advantage of this behaviour high protein diets

should be fed during the morning.

The feed intensity stayed at a constant level of approximately 3% biomass per day. The feed

efficiency would have become probably worse if the fish were fed to satiation. Erdogan et al.

(2004) and van Ham et al. (2003) showed that optimal growth performance of fish can be

obtained when fed slightly below satiation. In contrast (Carroll, et al., 2005) showed that

summer flounder (Paralichthys dentatus) showed no decrease in feed conversion ratio when

fed to satiation. A higher growth variation could be detected within the fish group being fed

restricted amounts of diets. This leads to the assumption that the fish have to compete for the

food and this result in slower growth with higher growth variation (Carroll, et al., 2005).

Turbot demonstrate two periods with increased activity during the day, mornings and

evenings. Probably during these times the feed intake increase. Therefore the fish were fed

during these times in the present experiment to ensure a high feed intake.

The varying lipid retention levels observed in the feeding groups indicated a different

energetic metabolism between the feeding groups (Table 5). The control group had the lowest

lipid retention, suggesting an increased proportion of lipid was used for energy supply.

Therefore a higher amount of ingested protein is available for synthesising body protein

resulting in best growth performance and probably increased digestibility of the diet.

The protein-group demonstrated a better growth performance than the lipid-group. The

protein requirement for turbot is reported to range between 49 – 65 % (Cacerez-Martinez et

al., 1984; (Cho, et al., 2005; Danielssen and Hjertnes, 1993; Lee, et al., 2003). The wide

difference in the protein requirement for growth of turbot found in the literature probably

resulted from the differences of protein quality, initial fish weight, rearing conditions and

feeding intensity. Although a homogenous body composition of the protein level could be

detected between the feeding groups a significant influence of enzymatic activities can be

assumed in regard to protein, because the dietary protein level fluctuated over the day but the

fish nearly grew identical.

Carter (1993) attempted to explain slight and no significant differences in growth

performance of identical fed Atlantic salmon with individual differences in anabolic protein

syntheses. Probably this phenomenon explains also the slight differences in SGR observed

between groups 1 and 2 and the control group. Overall the protein : energy ratios in the diets

were within the same range and the results of the present study do not indicate any influence

of the protein level in the diet on growth performance.

77

The energy content of the diets fluctuate more in the lipid-group (19.8 – 22 MJ kg-1) than in

the protein-group (20.5 -21.2 MJ kg-1). Bolliet et al. (2000) suggested that time of feeding and

dietary lipid content are linked with each other and influence growth of rainbow trout

(Oncorhynchus mykiss). Cacerez-Martinez et al. (1984) described a decrease in growth

performance as well when reaching dietary lipid levels of more than 10 %. Eventually the fish

are not able to adapt their enzymatic digestion effectively to the present energy or nutrient

variation between the two feed applications offered to the lipid-group. In experiments with

Atlantic salmon it was possible to influence the daily enzyme activity by feeding regime

(Harpaz, et al., 2005). More plausible seems to be the explanation of Erdolgan et al. (2004).

They reported if nutrients are applied in excess fish tend to metabolize the ingested diets

ineffectively.

Body composition and blood parameter

The dietary lipid amount is changing between morning and evening feed application

significantly between group 4 and 5. Nevertheless, no effect on body composition could be

detected between the tested feeding regimes (Table 5). Former studies of halibut

(Hippoglossus hippoglossus), another flatfish species, showed no significant effect of

increased dietary lipid levels and depressed growth performance (Berge and Storebakken,

1991). Studies of different fish species like rainbow trout (Oncorhynchus mykiss), sea bass

(Dicentrarchus labrax) and sea bream (Sparus aurata) (Alvarez, et al., 1998; Vergara, et al.,

1996), reported an increase in body lipid with increasing dietary lipid levels, the same positive

correlation was observed for turbot (Saether and Jobling, 2001) and Senegalese sole (Solea

senegalensis)(Borges, et al., 2009). The whole body lipid content of the examined fish in the

present study did not exceed 4.5 % and showed no significant differences between the feeding

regimes. Overall turbot has a general low level of whole body lipid content (lower than 5 %)

(Regost, et al., 2001).

The protein content of the juvenile turbot showed also no significant differences in body

composition. The calculated protein values were slightly higher than the values reported by

other studies for turbot (Regost et al., 2001; Olivia-Teles et al., 1999; Caceres-Martinez et al.,

1984). The increased values in the present study came probably due to the smaller size class

of the juvenile turbot and the high protein values in the diet compared to the studies

mentioned above.

The triglyceride concentrations found in the blood plasma are similar to the concentrations

reported by Regost et al. (2001). They found that a positive correlation exist between dietary

lipid levels and triglyceride concentrations The present experiment supports the statement of

78

Regost et al. (2001) since iso-nutritive amounts of feed were used for all feeding groups and

no significant differences among the triglyceride concentrations of the different dietary

treatment could be observed with regard to feeding time. This is in agreement to the findings

of Montoya et al. (2010). They found significant influence of feeding time on daily rhythms

of behaviour and digestive physiology in gilthead seabream (Sparus aurata). Feeding random

meal times over the day leads to a non statistical significant daily rhythm and changed

physiology, whereas periodical fed fish displayed statistical significant rhythms and

physiology over the day. Since the turbot in the present experiment were always fed at the

same day times it can be assumed that they were adapted to the feeding times physiologically.

Nevertheless, all groups showed slight tendency towards an increased ability to metabolize

dietary lipids more efficiently in the morning compared to the evening, since the triglyceride

levels measured evenings were slightly decreased compared to the morning-levels. Overall,

the time of nutrient application had also no significant influence on the triglyceride level in

the blood plasma.

Plasma protein level showed no significant differences between the feeding groups. The

feeding time had also no significant influence and the protein level in the blood plasma. The

detected plasma protein levels are similar to those reported by Waring et al. (1996) and Adron

et al. (1978).

The cortisol levels measured in the present study differed significantly between mornings and

evenings in feeding groups 1, 2 and 4. The increased cortisol level in the morning can be

explained by switching on the light during the morning and the occurrence of staff in front of

the aquarium. During the day the cortisol level is decreasing again. Probably the fish got used

to the disturbances by the staff. These results are contrary to the findings of Mazeaud et al.

(1977). The average cortisol level of 3.08 ± 1.19 ng ml-1 (mornings) and 1.14 ± 0.95 ng ml-1

(evenings) is lower than the values reported earlier by Person Le-Ruyet et al.(2002) and by

Waring et al. (1996) for turbot of 80g and 647g with an average cortisol level of 3.8 ng ml-1,

respectively 5-7 ng ml-1. Former studies of stressed salmonid species (Oncorhynchus

tshawytscha and Oncorhynchus mykiss) showed cortisol levels of >200ng ml-1 (Kebus, et al.,

1992; Strange and Schreck, 1978, Kebus 1992) For the marine sea bass (Dicentrarchus

labrax) peak levels of 80-100ng ml-1 were measured during density stocking experiments (Di

Marco, et al., 2008). Due to the low cortisol levels measured in the different treatment groups

it can be assumed that the fish were not significantly stressed during the experiment and while

taking the blood samples. Hence, no negative influence on the growth performance due to the

rearing conditions can be supposed.

79

Beside free lipid acids, glucose is the major energy substrate in animals. The present level of

glucose is in the range reported earlier for turbot (Waring, 1996; Imsland, et al., 2008). No

nutritional undersupply in regard to energy availability could be observed.

The stress response of teleost fish can be divided into primary and secondary effects

(Mazeaud, et al., 1977). The first part of the stress response can be recognized in an increased

cortisol level and secondary followed by long-lasting changes in glucose levels (Barton, 2000;

Mazeaud, et al., 1977). Some studies of marine teleost reported that these fish are not able to

respond to rearing stress in captivity with an elevated glucose level (Bourne, 1986; Fletcher,

1975). The plasma glucose level of turbot reacts significantly different to handling stress

compared to salmonids exposed to physical stress. (Biron and Benfey, 1994; Waring, et al.,

1992). Vijayan and Moon (1994) hypothesized that the low response of the glucose level to

stress may represent an adaptation of species with low activity to save energy. Since the

glucose level in the present study did not differ significantly between the feeding groups and

between the feeding times it can be assumed that the turbot were not stressed. However,

glucose is probably not the optimal blood parameter to indicate stress in turbot since they

react only with little changes to handling stress.

Conclusion

Juvenile turbot show significant different growth performance when being fed with varying

feeding regimes and iso-nutritious diets under the rearing conditions of the present study.

They showed lowered growth performance when being fed with varying lipid levels over day.

Probably the fish will demonstrate a varying range of growth performance when applying

decreased levels of protein. Varying protein applications over the day will significantly

influence protein retention in fish in contrast to constant protein supply over the day. Feeding

high protein amounts in the morning seems to be one effective way to increase protein

retention in fish, but further investigations have to be made on daily metabolism changes.

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85

General Discussion

The aim of the present study was to develop an automatic control system for mortality rate in

an aquaculture facility rearing turbot (Psetta maxima). Furthermore, the growth of turbot was

analyzed. Rearing data originating from a commercial recirculation aquaculture system (RAS)

were evaluated with the use of different growth models. Finally, a feeding trial was conducted

to identify the growth potential of juvenile turbot reared at optimal conditions. The influence

of different feeding times and varying diet composition on growth performance was

examined.

CUSUM chart

Different statistical control charts are available for decision support systems (Montgomery,

1997). The main charts used in industry are the Shewart chart, the EWMA chart and the

CUSUM chart (Wiklund, 1994). The present study worked with the cumulative sum control

(CUSUM) chart because of its flexible construction. Also in terms of practical application in

RAS it is important to keep the monitoring systems as simple as possible to guarantee a wide

acceptance among the users.

The charts are mainly used for process monitoring to detect shifts in production processes.

The advantage of these systems is that they visualize the monitored process. CUSUM charts

are build up by a centre line which represents the target value and two control lines, an upper

and an lower control limit (UCL, LCL). The observed process is recorded and if it exceeds

one of the control lines an alarm signal occurs. It is possible to select the settings of the

CUSUM chart to predict the trend of the monitored process.

A disadvantage of this kind of process monitoring systems is that the control chart can not

trace back the signal to the source of variation. No information about the reason for the shift

in process exists.

Statistical control charts were successful tested in agriculture to monitor the course of disease

and the behaviour of animals (de Vries and Conlin, 2003; 2005; Madsen and Kristensen,

2005; Pleasants, et al., 1998; Quimby, et al., 2001). Therefore, a transfer of these charts to

aquaculture suggests itself, but still, to our knowledge, this is the first attempt to implement

CUSUM charts in aquaculture.

The charts can be adjusted with the setting of different parameters. The detection rate of shifts

in the process (shifts in mortality rate in the present study) depends on the setting of the

parameters h and k. The h-value determines the width of the control limits and depends on the

86

standard deviation of the monitored process. Due to high variation in mortality rate and

standard variation within the examined RAS it was very difficult to choose an optimal h-value

which leads the CUSUM charts to highest possible detection rate.

The k-value had a much larger influence on the behaviour of the CUSUM chart. The smaller k

is chosen the earlier the chart detects a shift in the monitored process but also a high number

of false alarms occurs. Since the present data had an exponential distribution the general

recommendation of Montgomery (1997) and Hawkins and Olwell (1997) could not be

followed. They suggested setting the k-value half the size of the shift that has to be detected.

In the present study the k-value was fixed at 0.0055. This setting was chosen to ensure a

detection of daily mortality rate larger than 0.008 %, which represents 5% of the initial stock

(approximately rearing time to marketable size: 600 days, tolerated mortality during this

rearing period: 5% of initial stock � accepted daily mortality rate of 0.008%). The control

charts predicted the deviations in mortality rate in an acceptable time frame of up to 3 days

before they occurred. The detection rate of high mortality rates fluctuated between the 8

examined weight classes within the range of 26-52%.

Unfortunately, the mortality rate in the examined RAS was nearly permanent above the

tolerated value of 5% per production cycle. The reason for the increased mortality rate is

manifold. On the one hand the animals were infected with diverse bacteria and viruses and on

the other hand the rearing conditions were partly insufficient for the physiological needs of

turbot (e.g. during the summer time the water temperature exceeded 20°C which is very

unsuitable for turbot). Furthermore, the data of the RAS was partly incomplete. The resulting

analyses were difficult to conduct because of the inhomogeneity of the data. For the

development of a control chart it was not possible to provide general recommendations for the

settings of a control chart because of the wide fluctuations in the examined parameters,

especially in mortality rate.

The results showed that the CUSUM charts have the potential to support the decision making

of the farm manager. Nevertheless further investigations have to be made. In general it would

probably be much more accurate to predict the mortality rate with another parameter than by

the mortality rate itself as it was done in the second chapter. The mortality rate and the growth

rate depends on environmental factors like the temperature, salinity, oxygen (Brett, 1979;

Jobling, 1994) Since the mortality rate is negatively correlated with animal welfare it would

make sense to find one or more parameters to supervise animal welfare instead of mortality

rate to draw conclusions from the `welfare parameter` about the well-being (or mortality rate)

of the fish. Animal welfare is an important field of research in intensive animal production

87

(Barnett, et al., 2001; Sundrum, 2001) and is becoming a major topic in aquaculture, too

(Huntingford, et al., 2006). Therefore it is important to turn the attention to animal welfare

also in aquaculture in the future. Davis (2010) escribed a method how to measure the stress of

a fish in real-time. Present methods describing fish welfare rely on expensive and laboratory-

based measurements of changes in fish pathology and physiology (e.g.: histology, plasma

glucose). Since these methods often are not linked to fitness outcomes it would be much more

effective to measure a direct sign of stress. Reflex impairment describes a method to measure

the reflex of a fish exposed to peripheral stimuli like touching or sound. It is correlated with

stress and some reflexes may be impaired due to stimuli (e.g.: mouth gaping, fin erection).

With the help of reflex impairment of the reared fish it is possible to measure the stress level

of a fish (Davis, 2010). This method could be implemented in automatic control systems to

monitor the welfare of the farmed organisms.

In future, after successful implementation of a `welfare parameter`, the farm manager will be

able to detect in advance misleading production situations and protect the animals against

harm with support of artificial intelligence.

Growth

In chapter 3 the growth performance of the reared turbot was examined. Due to the analyses

in chapter 2 it was known that the fish mortality in the examined RAS demonstrated highly

variable. The logical result would be a highly growth variance within the standing stock. To

verify this theory the growth data was analysed by means of 3 different growth models.

Schnute (1981) developed a mathematical growth model which can be transformed by setting

the parameters into different common growth models (e.g. von Bertalanffy, Gompertz). Three

growth models (Schnute, von Bertalanffy and Gompertz) were tested and evaluated in regard

to the best fit to the data. These three models were chosen because of their wide application in

fishery science.

For growth analyses the data was divided into 8 weight classes since the fish were also reared

in groups of these weight classes. To get detailed information about the growth performance

each group was divided into slow, normal and fast growing fish.

The results showed that the Schnute model was the best model among the tested ones.

Probably this was due to the fact that the Schnute model had to estimate 4 parameters and is

more flexible compared to the Gompertz and the von Bertalanffy model which only estimate

3 paramters. Therefore the estimations of the Schnute model were more accurate compared to

the other two models.

88

The growth performance of the turbot showed big differences between the slow and the fast

growing fish. Slow growing fish needed in average 2 years to reach marketable size in

contrast to fast growing fish which needed 1.5 years on average. This temporal difference in

grow out can have several reasons. Due to high bacterial load in the rearing water some fish

had to be treated against diverse infections and diseases. During this medical treatment these

fish demonstrated decreased growth. Furthermore, the analyzed data of the examined RAS

was insufficient in regard to homogeneity. The different weight classes showed wide

variations in growth rate. This leads to the assumption that the whole RAS is not running

perfectly or that the fish were not able to fetch their potential growth performance because of

several problems. The specific growth rates of all fish sizes are lower compared to the average

growth rates of identical fish sizes found in literature (Caceres-Martinez, et al., 1984; Imsland

and Jonassen, 2001; Imsland, et al., 1996; Stefansson, et al., 2002; Van Ham, et al., 2003).

Another possible explanation for the weak growth performance can be the domestication

period of turbot. The production of turbot started in the 1970s in Scotland (FAO, 2009) and

the fish was introduced in Germany in the early 1980s (Kuhlmann, et al., 1981). The entering

of this flatfish specie in highly intensive production systems is comparatively young. Trout

(oncorhynchus mykiss) for example is one of the oldest fish (beside the common carp,

cyprinus carpio) in culture. It was introduced in aquaculture in the late 19th century (Gall and

Crandell, 1992). However, turbot culture is still in the beginning and a complete

domestication did not happened yet. Breeding programs exist but not for many generations

(Bouza, et al., 2007). Further investigations in regard to genetic improvement have to be

made.

Probably the turbot strains reared in the RAS had different growth potentials since they

originated from different origins. Imsland (2001) showed that fish from the same species but

different origins showed significantly different growth rates.

Differences between wild and domesticated turbot stocks were mentioned in different

scientific articles (Bouza, et al., 1997; Bouza, et al., 2002; Coughlan, et al., 1998). These

authors reported lower genetic variability in fish farms compared to wild populations. The

opposite was reported by Castro et al. (2004). They reported a high genetic variability of a

broodstock in Spain which is similar with a French population (Estoup, et al., 1998) and other

wild populations (Coughlan et al., 1998). Probably the genetic variability in a commercial fish

farm is still high, since breeding programs for genetic selection are existent just for a short

time.

89

Differences in juvenile growth and feed efficiency were also detected between farmed turbot

populations originating from Iceland, Norway, France and Scotland (Imsland et al, 2001). In

general the northern populations showed better growth and feed efficiency. A possible

solution can be found in the natural environment of the strains. The fish are adapted to short

growth periods in the northern parts of the world and react with increased growth rates during

these short time frames. If these fish are reared under artificial conditions (elongated daytime,

optimal nutrient supply, perfect rearing conditions) they demonstrate their growth potential

over the whole production period and grew faster compared to fish originating from southern

parts. Overall, the aquaculture industry has to evaluate the different growth potential of the

different strains to choose the ideal genetic material for breeding programs.

The individual identification of fish is another problem in fish farming in general and in detail

in the examined RAS. Because of the grading process it was impossible to retrace each

individual fish back to its stocking date, respectively its origin. It was not possible to

distinguish between individuals which where old and slow growing and fish which were

comparatively young and fast growing. The economical benefit would increase if the slow

growing individuals were identified and removed from the system. These animals block

important resources (space, food, manpower etc.) for animals with higher growth rates.

Tagging the fish with individual tags could solve this problem. This method is common in

livestock animals.

Feeding trial

To get more information about the growth potential of turbot the feeding trial described in

chapter 4 was performed. Because it is already known that fish fed with the same amount of

feed at different daytimes can show differences in growth performance (Gelineau, et al.,

1998; Verbeeten, et al., 1999), juvenile turbot (initial weight 7.9 ± 0.16g) were fed with diets

containing different amounts of lipid and protein at different daytimes. Turbot had two time

periods per day with increased activity, in the morning and the evening (Waller, 1992).

Because of their natural daily activity rhythm the fish were fed during these times of

increased activity (8-9 a.m. and 16 – 17 p.m.). The energetic level and the composition of the

diets differed between the feed applications in the morning and in the evening, but the total

energetic level and amount of offered nutrients to the different experimental fish groups were

identical at the end of the day.

The fish responded with different growth performance to the different feeding regimes. Fish

with different lipid content in the diet between the morning and evening feeding had a

90

significantly slower specific growth rate compared to all other feeding treatments. This result

showed that juvenile turbot were able to react to different protein ratios better compared to

changing lipid ratios in the diet. Juvenile turbot were not able to metabolize high lipid

contents in the diet as good as high protein ratios. This result is in agreement with the

observations of Cacerez-Martinez et al. (1984). They reported that juvenile turbot (10g) react

with depressed growth when fed high lipid levels (10-20%). Nevertheless, no significant

differences in final weight could be observed between the different feeding groups. Only a

tendency towards better growth performance of the control group and the feeding groups

where the fat content remained stable in both feed applications and the protein level

fluctuated. No significant effect on final body composition and blood plasma parameters

could be detected.

Overall the growth performance of the juvenile turbot was in the range reported in the

literature (Cacerez-Martinez et al., 1984; Imsland, et al. , 2001). Under the experimental

conditions the juvenile turbot demonstrated a good growth performance, but no significant

influence of the tested feeding regimes on final weight could be located. Nevertheless,

improvement in feeding regime as well as the diet itself is an actual topic in turbot

aquaculture and further investigations have to be made in the field of nutrient supply to

aquaculture organisms.

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Brett, J.R., 1979. Environmental factors and growth. Fish Physiology 8, 599-675.

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Caceres-Martinez, C., Cadena-Roa, M., Metailler, R., 1984. Nutritional requirements of turbot

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Coughlan, J.P., Imsland, A.K., Galvin, P.T., Fitzgerald, R.D., Naevdal, G., Cross, T.F., 1998.

Microsatellite DNA variation in wild populations and farmed strains of turbot from

Ireland and Norway: a preliminary study. Journal of Fish Biology 52, 916-922.

Davis, M.W., 2010. Fish stress and mortality can be predicted using reflex impairment. Fish

and Fisheries 11, 1-11.

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Gall, G.A.E., Crandell, P.A., 1992. The Rainbow-Trout. Aquaculture 100, 1-10.

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Imsland, A.K., Sunde, L.M., Folkvord, A., Stefansson, S.O., 1996. The interaction of

temperature and fish size on growth of juvenile turbot. Journal of Fish Biology 49,

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Jobling, M., 1994. Fish Bioenergetics. Chapman and Hall, London, 309 pp.

Kuhlmann, D., Quantz, G., Witt, U., 1981. Rearing of Turbot Larvae (Scophthalmus-

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Natural and Artificial Food. Aquaculture 23, 183-196.

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their drinking behaviour. Computers and Electronics in Agriculture 48, 138-154.

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quality control chart suitable for monitoring effects on ultimate muscle pH. New

Zealand Journal of Agricultural Research 41, 235-242.

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H.W., 2001. Application of feeding behaviour to predict morbidity of newly received

calves in a commercial feedlot. Canadian Journal of Animal Science 81, 315-320.

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heterogeneity in juvenile turbot Scophthalmus maximus (Rafinesque) under different

photoperiod regimes. Aquaculture Research 33, 177-187.

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conversion, body composition and nutrient retention of juvenile turbot (Scophthalmus

maximus). Aquaculture 217, 547-558.

Verbeeten, B.E., Carter, C.G., Purser, G.J., 1999. The combined effect of feeding time and

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Ichthyologie 8, 62-71.

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University of Urea.

94

General Summary

The present thesis dealt with management information systems in marine aquaculture. The

aim of the study was to develop a prototype of decision support systems for a closed

recirculation aquaculture system (RAS). RAS are highly intensive rearing facilities and it is

expected that the number of this type of aquaculture systems will continue to increase in the

future. High growth rates could be achieved due to the possibility to set the environmental

rearing conditions (e.g.: temperature, oxygen, salinity) within the optimal range of the reared

species.

In this thesis, production data in cooperation with commercial RAS rearing the marine flatfish

species turbot (Psetta maxima) were recorded between 2001 and 2007. The data set was

analysed and evaluated with the aim to set up a statistical control system for monitoring the

mortality rate and to evaluate the growth performance of the reared fish.

Chapter one presents an introduction to management information systems. A review of

scientific research literature illustrates important aspects of management and decision support

systems and possibilities to implement and use these systems in RASs. Since the application

of artificial intelligence systems in aquaculture started during the nineties of the last century,

many bottlenecks still exists in management information systems for RASs.

Chapter two deals with the development of a decision support system for the examined RAS.

The aim of this study was to predict the mortality rate of the reared turbot using a statistical

control chart. Currently, these statistical control charts are mainly used in industry for process

monitoring but can also be found in agriculture. To our knowledge, this was the first attempt

introducing these charts into aquaculture.

Different settings of the cumulative sum control chart (CUSUM) were evaluated to identify

the highest detection rate resulting in detection rates between 26 and 52% when using the

optimal settings. The level of detection rate depended on the size class of the fish. A farm

manager would be able to detect unwanted high mortality rates up to three days in advance

when using the CUSUM chart with the evaluated settings. Thus, the results indicated a benefit

for the fish farmer by using the control charts predicting high mortality rates in advance.

Chapter three analyzes the growth performance of the turbot reared in the examined RAS.

The production data from the RAS was evaluated by applying different mathematical growth

95

models for a whole production cycle. Three models were tested, the von Bertalanffy

(VBGM), the Gompertz and the Schnute model. The four parametric Schnute model

estimated the most realistic growth curve compared to the three parametric VBGM and

Gompertz growth model. The results indicated a highly variable growth performance among

the reared fish. In total three growth curves were estimated for slow, normal and fast growing

fish. In average the fast growing fish reached a final weight after 17 months whereas the

normal and slow growing fish needed 22, respectively 24 months to reach that size. Resulting

from these analyzes the Schnute model would be recommended for growth performance

estimation in the present RAS.

The growth performance of juvenile turbot with an initial weight of 7.9g was analyzed in

chapter four conducting a feeding trial. The aim of this feeding trial was to test the combined

effect of feeding time and diet composition on growth performance. Twice daily five feeding

groups were fed with different diets containing varying levels of protein and lipid. Only

qualitative differences in the diets existed between the applications and at the end of the day

each group was fed identical amounts of dietary nutrients.

The specific growth rate (SGR) of the different feeding groups showed significant differences

among the tested feeding regimes. The control group was fed both feed applications and the

identical diet demonstrated the best SGR with 2.58% body weight gain per day. This was in

contrast to the feeding groups with varying lipid levels between the two feed applications

which showed the lowest SGR with 2.35%, respectively 2.37%.

The fish were not able to adapt their metabolism efficiently towards changing nutritional

diets; hence it is important to set up a balanced feeding regime to exploit the growth potential

of turbot effectively.

96

Zusammenfassung

Das Ziel dieser Arbeit ist es, einen Prototyp von Management-Informations-Systemen (MIS)

für marine Aquakultur Kreislaufanlagen zu entwickeln und tiefere Einblicke in das Wachstum

von Steinbutt (Psetta maxima) zu gewinnen. Durch den Einsatz von MIS wird eine

konstantere Produktion von marinen Speisefischen, speziell dem Steinbutt, in kommerziellen

hoch intensiven Fischzuchtanlagen angestrebt.

Im ersten Kapitel gibt eine grundlegende Literaturrecherche die Entwicklung von MIS in der

Aquakultur wieder. Solch automatische Entscheidungshilfen für das tägliche Management

eines Produktionsprozesses sind in industriellen Fertigungsprozessen heutzutage

unentbehrlich, um eine gleichbleibend hohe Qualität des erzeugten Produktes zu

gewährleisten. Das Prinzip solcher MIS beruht auf statistischen und mathematischen

Berechnungen und Modellen. Im Agrarbereich werden solche Systeme z.B. zur

Krankheitsüberwachung bei Rindern benutzt. Durch eine fortschreitende Industrialisierung

der Aquakultur liegt es auf der Hand, solche Systeme künstlicher Intelligenz zu nutzen, um

Prozesse zu überwachen und zu steuern und somit einen höheren Output sowie eine bessere

Qualität zu erzielen.

Im zweiten Kapitel wurde ein MIS zur Sterblichkeitsüberwachung von Steinbutt entwickelt.

Reale Betriebsdaten standen von einer privatwirtschaftlichen Steinbutt-Kreislaufanlage zur

Verfügung. Diese Produktionsdaten bildeten die Grundlage für die Entwicklung eines

Überwachungsinstruments, das Mortalitäten anzeigen und bestenfalls vorhersagen kann, die

außerhalb eines definierten Bereichs liegen. Mit Hilfe einer statistischen Methode, dem

„cumulative sum control chart“ (CUSUM-chart) wurden die Mortalitätsverläufe innerhalb des

vorliegenden Datensatzes analysiert und ausgewertet. Dieses statistische Kontrollinstrument

erkennt eine Überschreitung eines vorher definierten Schwellenwertes und löst ein

Alarmsignal aus. Die Höhe der Alarmschwelle kann individuell gewählt werden, ebenso die

relative Abweichung des gemessenen Wertes vom Mittelwert. Wird ein Wert gemessen, der

größer als die vorher definierte Abweichung ist, wird das Alarmsignal ausgelöst. Die

Alarmschwelle wurde bei 0.008% des aktuellen Fischbestandes festgesetzt. Das entspricht 5

% des Anfangbestandes, bei einer angenommenen Haltungsdauer von 600 Masttagen bis zum

Erreichen des Marktgewichts.

Das entwickelte CUSUM-chart wurde so eingestellt, dass eine Früherkennung von

auftretenden Sterblichkeiten bis zu 3 Tage im Voraus angestrebt wurde. Die erzielten

97

Sensivitäten für unerwartet hohe Sterblichkeitsverläufe schwankten je nach Altersklasse der

beobachteten Fische zwischen 26 und 52%. Diese Variation der Erkennungsraten liegt an den

immer wieder auftretenden bakteriellen und viralen Erkrankungen der Tiere. Die durch diese

Erkrankungen hervorgerufenen hohen Sterblichkeiten führten teilweise zu erheblichen

Verlusten, die für das MIS nicht vorhersagbar waren. Diese Ergebnisse verdeutlichen jedoch

eine potentielle Praxistauglichkeit dieser Monitorringsysteme.

Das dritte Kapitel befasst sich mit dem Wachstum vom Steinbutt. Um eine zielgerichtete

Produktion dieser Speisefische zu ermöglichen ist es wichtig, den genauen Wachstumsverlauf

der Fische zu kennen. Die Wachstumsverläufe für langsam, normal und schnell wachsende

Steinbutt wurde mit Hilfe dreier nichtlinearer Wachstumsmodelle geschätzt. Als

Datengrundlage dienten die Produktionsdaten der Steinbutt-Kreislaufanlage. Die drei Modelle

(Bertalanffy, Gompertz und Schnute) schätzen jeweils eine Wachstumskurve für jede

Wachstumsgeschwindigkeit, wobei die vier-parametrische Schnutefunktion im Gegensatz zu

den drei-parametrischen vonBertalanffy- und Gompertzfunktionen die genauesten

Schätzungen erzeugte. Im Mittel benötigen die Steinbutt aus der untersuchten

Kreislaufanlage, je nach Wachstumsgeschwindigkeit 24, 22 oder 17 Monate, um das

Marktgewicht zu erreichen. Anhand dieser Ergebnisse ist zu erkennen, dass die Tiere über ein

sehr hohes individuelles Wachstumspotential verfügen und stark auseinander wachsen

können.

Im vierten Kapitel wurde der Einfluss von unterschiedlichen Fütterungszeiten mit

unterschiedlichem Nährstoffangebot in einem Fütterungsversuch an juvenilen Steinbutt

getestet. Es sollte herausgefunden werden, ob Steinbutt in der Lage sind, zeitlich

unterschiedlich verabreichte Nährstoffe unterschiedlich zu verwerten. Neben den

Wachstumsparametern (Wachstumsrate, Körperzusammensetzung, Futterverwertung etc.)

wurden auch Blutparameter gemessen, um eventuelle physiologische Unterschiede

festzustellen. Vier Fütterungsgruppen und eine Kontrollgruppe wurden verglichen. Die Tiere

wurden zweimal täglich gefüttert, morgens und abends. Außer bei der Kontrollgruppe

bestanden qualitative Unterschiede im Hinblick auf Nährstoffverabreichung zwischen den

morgendlichen und abendlichen Fütterungen, aber am Tagesende erhielt jede

Fütterungsgruppe den gleichen quantitativen und qualitativen Nährstoffanteil. Es zeigten sich

signifikante Unterschiede im Bereich der spezifischen Wachstumsrate zwischen den

Fütterungsgruppen. Fische, bei denen der Fettgehalt im Futter stärker zwischen den beiden

täglichen Fütterungen schwankte, wiesen eine signifikant geringere Wachstumsrate auf als

Fische, die mit konstantem Fettgehalt aber schwankendem Proteingehalt gefüttert wurden.

98

Die Kontrollgruppe, die identisches Futter zu beiden Fütterungszeiten verabreicht bekam,

wuchs am besten. Die Körperzusammensetzung wies keinerlei Unterschiede auf. Dagegen

zeigten sich bei den Blutparametern signifikante Differenzen im Tagesverlauf. Insbesondere

der Cortisolspiegel, ein Indikator für das Stressempfinden, zeigte signifikante Differenzen im

Tagesverlauf. Anscheinend sind die Tiere noch nicht vollständig an die künstliche

Haltungsumgebung gewöhnt. Zusammenfassend lässt sich ableiten, dass die Fische mit einem

angepassten Fütterungsregime die angebotenen Nährstoffe effektiver nutzen können und ihr

Wachstumspotential besser ausschöpfen können.

99

Danksagung

An dieser Stelle möchte ich allen Personen danken, die zum Gelingen dieser Arbeit

beigetragen haben.

Ich bedanke mich bei meinem Betreuer Herrn Prof. Dr. Joachim Krieter für die Überlassung

des interessanten Themas. Des Weiteren ermöglichte er mir die Teilnahme an internationalen

und nationalen Konferenzen und ließ mir viele Freiräume während der Erstellung der Arbeit.

Herrn Prof. Dr. Carsten Schulz danke ich für die ausgezeichnete fachliche und

wissenschaftliche Betreuung nicht nur während der Versuchsphase.

Herrn Dipl. Markus Griese danke ich für die lehrreiche Zeit in der Praxis und die tatkräftige

Unterstützung während der Versuchsdurchführung. Den Mitarbeitern der GMA Büsum, ins

besondere den Bewohnern des Rosengrundes, möchte ich ganz herzlich für die tatkräftige

Unterstützung während der Fütterungsversuche danken. Bedanken möchte ich mich auch bei

Herrn Dr. Sven Würtz. Er hat mir in der Probenanalyse tatkräftig geholfen und vieles

beigebracht.

Meinen Kollegen aus dem Container, insbesondere Andi und Stefan, möchte ich hiermit

ausdrücklich für die schöne Zeit danken. Die fachlichen und weniger fachlichen Gespräche

vor, nach und während der Mittagspausen trugen wesentlich zu der guten Arbeitsatmosphäre

bei!

Mein besonderer Dank gilt Anna, meiner Bürokollegin. Dröge und laaange Stunden vor dem

Rechner wurden ein-ums-andere Mal von lustigen Gesprächen unterbrochen und ließen die

Zeit wie im Fluge verstreichen.

Meiner Familie danke ich für die tolle Unterstützung, besonders Kristin für Ihre Hilfe bei der

Englischkorrektur. Ihr habt mich immer unterstütz und an mich geglaubt. Danke!

Dani, Du hast mir viel Kraft gegeben, an mich geglaubt und mir geholfen die Dissertation

erfolgreich abzuschließen. Danke für alles!

100

Lebenslauf

Name: Andreas Baer Geburtstag: 15.12.1979 Geburtsort: Bremen Eltern: Kristian Heiner Baer Bärbel Baer Staatsangehörigkeit: Deutsch Familienstand: verheiratet Schulausbildung: 1986-1992 Grundschule und Orientierungsstufe Sottrum 1992-1999 Ratsgymnasium Rotenburg / Wümme

Studium: 2001-2004: Bachelor-Studium der Agrarwissenschaften an der Humboldt Universität zu Berlin 2004-2007: Master-Studium „Fishery Science and Aquaculture“ an der Humboldt Universität zu Berlin Zivildienst 1999-2000: Zivildienst bei der Naturschutzgesellschaft „Schutzstation Wattenmeer e.V.“ auf der Nordseeinsel Amrum Berufliche Tätigkeit: Jun. 2006 – Dez. 2006: Wissenschaftliche Mitarbeit am Institut für Ernährung und Meereslebensmittel (NIFES), Bergen, Norwegen Apr. 2007 – Dez. 2008: Wissenschaftlicher Mitarbeiter am Institut für Tierzucht und Tierhaltung der Christian-Albrechts-Universität zu Kiel bei Herrn Prof. Dr. J. Krieter Seit Dez. 2008: Wissenschaftlicher Mitarbeiter bei der Gesellschaft für Marine Aquakultur (GMA) mbH in Büsum bei Herrn Prof. Dr. C.Schulz Praktika: Mai 2005 – Aug.2005: Praktikum auf einer Lachsfarm in Norwegen