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.
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Platessa L), Dab (Limanda-Limanda L) and Turbot (Scophthalmus-Maximus L) in
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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:
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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.
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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-
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Kjorsvik E., Hoehne-Reitan K. and K.I. Reitan, 2003. Egg and larval quality criteria as
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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
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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
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in 1st Feeding of Turbot (Scophthalmus-Maximus L) Larvae. Aquacult. 118, 257-275.
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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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of the welfare issues for sows and piglets in relation to housing. Australian Journal of
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Bouza, C., Sanchez, L., Martinez, P., 1997. Gene diversity analysis in natural populations and
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Bouza, C., Presa, P., Castro, J., Sanchez, L., Martinez, P., 2002. Allozyme and microsatellite
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(Scophthalmus maximus). Genetics 177, 2457-2467.
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
(Scophthalmus maximus): 1. A preliminary study of protein and lipid utilization.
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Castro, J., Bouza, C., Presa, P., Pino-Querido, A., Riaza, A., Ferreiro, I., Sanchez, L.,
Martinez, P., 2004. Potential sources of error in parentage assessment 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.
de Vries, A., Conlin, B.J., 2003. Design and performance of statistical process control charts
applied to estrous detection efficiency. Journal of Dairy Science 86, 1970-1984.
de Vries, A., Conlin, B.J., 2005. A comparison of the performance of statistical quality
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Estoup, A., Gharbi, K., SanCristobal, M., Chevalet, C., Haffray, P., Guyomard, R., 1998.
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Gall, G.A.E., Crandell, P.A., 1992. The Rainbow-Trout. Aquaculture 100, 1-10.
Gelineau, A., Medale, F., Boujard, T., 1998. Effect of feeding time on postprandial nitrogen
excretion and energy expenditure in rainbow trout. Journal of Fish Biology 52, 655-
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Hawkins, D.M., Olwell, D.H., 1997. Inverse Gaussian cumulative sum control charts for
location and shape. Statistician 46, 323-335.
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Imsland, A.K., Jonassen, T.M., 2001. Regulation of growth in turbot (Scophthalmus maximus
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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-
Maximus L) on Cultured Food Organisms and Post-Metamorphosis Growth on
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
ration on growth performance and nitrogen metabolism of greenback flounder. Journal
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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
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