Assessing the Impact of Policy Changes in the Icelandic Cod Fishery
using a Hybrid Simulation Model
Sigríður Sigurðardóttir
, Björn Johansson
, Sveinn Margeirsson
and Jónas R. Viðarsson
Faculty of Ind. Eng, - Mech. Eng. and Computer Science, University of Iceland, Hjarðarhagi 2-6 107 Reykjavík, Iceland
Product and production development, CHALMERS University of Technology, 412 96 Gothenburg, Sweden
Matís, Icelandic Food and Biotechnology Research, Vínlandsleið 12, 113 Reykjavík, Iceland
Keywords: Hybrid Simulation Modelling, System Dynamics, Discrete Event Simulation, Fisheries Management, Life
Cycle Assessment.
Abstract: Most of the Icelandic cod is caught in bottom trawlers or longliners. These two fishing methods are
fundamentally different and have different economic, environmental and even social effects. In this paper
we present a hybrid-simulation framework to assess the impact of changing the ratio between cod quota
allocated to vessels with longlines and vessels with bottom trawls. It makes use of conventional bio-
economic models, discrete event modelling and provides a framework for simulating life cycle assessment
(LCA) for a cod fishery. The model was constructed in AnyLogic and consists of two models, a system
dynamics model describing the biological aspect of the fishery and a discrete event model for fishing
1.1 Icelandic Cod Fisheries
Historically, the seafood sector has been the single
most important industry in the Icelandic economy
with cod fishery as its backbone. Even though other
industries have been growing larger during the
years, the seafood industry is still considered the
most important one. National accounts show that in
the year 2011, exported seafood accounted for more
than 40% of total exports, with cod explaining more
than 12% (Iceland, 2013a). Figure 1 shows value of
exported seafood as a percentage of total exports.
Moreover, it has been estimated that the contribution
of the fisheries sector and related industries, or the
so called fisheries cluster, to the GDP in the year
2010 is 26% (Sigfusson et al., 2013).
In the 1980’s, recruitments of cod began to
reduce drastically while at the same time fishing
effort remained higher than recommended by the
Marine Research Institute. Stock levels of cod
reached a critical level and to contain the situation a
harvest control rule was developed to determine total
allowable catch (TAC). In 1984, a comprehensive
system of individual transferable quotas (ITQ) was
introduced. In the beginning, quota was allocated
Figure 1: Ratio of seafood of total value of exports and
ratio of cod in total value of seafood in during 1990-2011.
based on vessel’s previous catch records. The ITQ
system resulted in an improved economic efficiency
of the fisheries as well as biological viability
(Arnason, 1993), (Arnason, 2006). The merits of the
quota system have however been heavily debated
since its establishment due to the consolidation of
quotas and the effect it has had on fisheries
communities short of quota (Eythorsson, 2000).
The Icelandic government has defined objectives
with its fisheries management system which are to
promote conservation and efficient utilisation of the
exploitable marine stocks of the Icelandic fishing
banks and thereby ensure stable employment and
1990 1995 1999 2000 2001 2002 2003 2004 2005 2006 20072008200920102011
Seafood Cod
rardóttir S., Johansson B., Margeirsson S. and R. ViÃ
rarsson J..
Assessing the Impact of Policy Changes in the Icelandic Cod Fishery using a Hybrid Simulation Model.
DOI: 10.5220/0004488102940301
In Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2013),
pages 294-301
ISBN: 978-989-8565-69-3
2013 SCITEPRESS (Science and Technology Publications, Lda.)
settlement throughout the country (Ministry of
Fisheries and Agriculture, 2006).
1.2 Purpose of Study
Considering the aforementioned objectives, new
policies for managing the fisheries have to be
assessed in the three dimensions of sustainability;
economic, environmental and social. In this paper
we present a hybrid-simulation framework to assess
the impact of changing the ratio between cod quota
allocated to vessels with longlines and bottom
trawls. It makes use of conventional bio-economic
models, discrete event modelling (DES) and
provides a framework for simulating life cycle
assessment (LCA) for a cod fishery. The model was
constructed in AnyLogic and consists of two
models; a system dynamics model describing the
biological aspect of the fishery and a discrete event
model for fishing activities.
1.3 Fisheries Models
Most simulation research in fisheries management is
based on continuous multi-parameter models. Tools
that have been used previously for assisting in
fisheries management are for example the multi-
parameter models FLR (Fisheries Library for R) and
EcoSim. The FLR framework is a development
effort directed towards the evaluation of fisheries
management strategies (Kell et al., 2007). Ecopath
with EcoSim (EwE) is an ecosystem modelling
software suite that allows for spatial and temporal
modelling for exploring impact and placement of
protected areas and policy assessment. It is probably
the best known ecosystem model and has been
applied widely in fisheries around the world.
Atlantis (Fulton et al., 2011) is a modelling
framework developed to evaluate ecosystem based
management strategies. It consists of a number of
different linked modules: biophysical, industry and
socioeconomic, monitoring and assessment.
Many other modelling frameworks exist
including Gadget (Begley, 2004) and BEMMFISH
(Arnason, R. and Koholka, 2003
Most of these modelling frameworks allow for
great details in the biological aspect of fisheries
modelling but may lack overview in the three
aforementioned dimensions of sustainability. The
need for holistic modelling in fisheries has been
emphasized (Dudley, 2008). System Dynamics
(SD) is a good tool for creating holistic models and
understanding how things affect one another.
Dudley (2008) has demonstrated the benefits of
using SD for modelling fisheries and represented a
framework that can be adapted to most fisheries. A
number of system dynamics models in fisheries
exist. A SD model of individual transferable quota
system was constructed in order to differentiate ITQ
from total allowable catch effects and identify areas
where policy changes and management
improvements may be most effective (Garrity,
2011). Other SD models include a model for the
management of the Manila clam, a shellfish fishery
in the Bay of Arcachon in France (Bald et al., 2009),
a model of the management of the gooseneck
barnacle in the marine reserve of Gaztelugtxe in
Northern Spain (Bald et al., 2006) and a SD model
of the Barents Sea capelin (Yndestad, 2002).
Finally, a hybrid model combining SD and agent
based modelling has been constructed for
understanding competition and cooperation between
fishers (Bendor et al., 2009).
1.4 Combining DES and LCA
Life Cycle Assessment (LCA) standardized by ISO
14040:2006 and 14044:2006 (Finnveden et al.,
2009) is by far the most commonly used analysis
method for evaluation of environmental footprint.
LCA, however, holds drawbacks, which reduce its
preciseness and limit its value for producing reliable
results. The main associated problems with
traditional LCA analyses are (Reap J. et al., 2003)
Use lumped parameters and site-independent
Static in nature and disregard the dynamic
behaviour of industrial and ecological systems.
Focuses only on environmental considerations, not
economic or social aspects.
Hence, it can be beneficial to complement LCA
with other analysis tools, in order to effectively
combine environmental and economic analysis. An
example of such a combination is discrete event
simulation (DES) and LCA. Various different
examples of successful LCA-DES combinations
have been carried out and presented before (Thiede
et al., 2011), (Heilala, J. et al., 2009), (Solding, P. et
al, 2009); (Wohlgemuth et al., 2006).
Nowadays most of the Icelandic cod is captured in
bottom trawls or with longlines. Use of gillnets used
to be more widespread than of longlines but that has
Figure 2: Total landings (thousand tonnes) of cod by fishing gear 1993-2011.
changed as figure 2 confirms. In 2011 46% of the
total allowable catch for cod was captured with
bottom trawls and 32% with longlines (Iceland,
2013a), so around 78% of the total allowable catch
is under consideration in this study.
Bottom trawls and longlines are very different
fishing gears and have different economic and
environmental impacts, and potentially social
impacts which are harder to quantify and measure
Economic Impact
Data from operating accounts of fishing companies
collected by Statistics Iceland reveal that the larger
vessels are more economically viable (Iceland,
2013b). During the years 2002-2007, the operation
of smaller vessels was unstable, partly due to
external factors such as high interest rates and strong
exchange rate of the Icelandic krona (Agnarsson,
Environmental Impact
When comparing bottom trawls and longliners in
terms of minimising environmental impact, the
longliner is a far better choice. In 2009, a life cycle
assessment was applied to compare the
environmental impact made when producing 1 kg of
frozen cod caught with a bottom trawl on the one
hand and a long line on the other. The conclusion
from that study was that a trawled cod has a higher
impact within all categories assessed such as climate
change, respiratory organics/inorganics, eco-
toxicity, acidification and fossil fuel (Guttormsd,
It has been reported that the distribution of corals
around Iceland began to decline when bottom
trawling was initiated (Institute, 2004). The biggest
drawback of longlines however are danger to marine
animals such as sea birds that get stuck in the hooks
of the longlines (Valdemarsen and Suuronen, 2003).
A hybrid simulation model of the Icelandic cod
fishery was constructed to assess the difference
between the two fishing gears. The model consists of
a System Dynamics model that describes the growth
of the cod stock. Fishing activities were simulated
with a Discrete Event model. Figure 3 shows a
diagram of the model. The discrete event model
simulates fishing trips of four different vessel types.
Before a vessel starts a trip, it sends a query to the
SD model to see if there is still catch quota
available. If the total allowable catch is reached, no
further fishing trips are planned until the TAC is
updated for the following fishing year.
One of the key assumptions made in the model is
that every year, the vessels reach their catch quota.
This is a valid assumption as the system holds a lot
of fishing capacity and there is a demand for catch
quotas and landing records confirm that they are
always met (Marine Research Institute, 2004).
3.1 A System Dynamics Model
The SD model describes the dynamics of the
biological stock and provides the total allowable
Natural Biomass Growth Function
A simple biological model was applied to describe
the biomass of cod. It accounts for no age-structure
and the population dynamics are described
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Figure 3: A diagram describing the hybrid-simulation model and the interaction between the SD model and the DE model.
with a logistic function (Clark, 1985):
Were is the stock size of the fishable cod, K, is the
carrying capacity and r the intrinsic growth rate of
the stock.
Total Allowable Catch
The total allowable catch at a year y+1 is determined
with the following harvest control rule:
where a represents harvest rate, and B
is the
fishable biomass at year y+1, which consists of cod
large enough to be caught (Ministri of Fisheries and
Agriculture, 2010).
3.2 Discrete Event Model
The discrete event model simulates fishing trips of
three different types of longliners and a bottom
trawler. Ideally the model would make use of
information from logbooks and use data on trip
basis, information such as duration of trip, distance
sailed, and amount of catch and oil consumption as
an input. In this study only public data on quota
allocation and landings were used and scaled over
the whole fishing fleet under consideration.
The model outputs are catch numbers, economic
performance and CO
Catch numbers for each vessel are estimated
with data over quota allocations published by the
Directorate of Fisheries (Directorate of Fisheries
Economic Impact
Economic performance is measured by multiplying
revenue with the ratio of net profit and revenue but
this information is available from Statistics Iceland
for different vessel types (see equation 3). Figure 4
shows the economic performance of the four
different vessels during 2006-2011. This shows
clearly how unstable the operating results have been
for the small vessels. Average numbers dating back
to 1997 were used in the model.
Figure 4: Profits as a ratio of total revenue by vessel type.
  
Environmental Impact
The environmental impact of each of the fishing
gear was measured in CO
equivalences and based
on results from an LCA carried out in 2009. That
study showed that a one kilo of trawled cod had a
5.14 kg CO
equivalence while a long lined cod
added up to 1.58 kg CO
eq (Guttormsd., 2009). In
the same study, it was revealed that the hot spot in
the life cycle of cod is the fishing phase.
Social Impact
It is not an easy task to simulate social impact of
changing management policies. In this study the
only social factor taken into account are number of
jobs on each vessel. It might also be relevant to take
jobs on-shore into account since many of the
longliners do not have baiting machines on-board
and thus create jobs on land.
3.3 Model Validation
The model was validated using available historical
data as an input.
Biological growth
Stock assessment data from the Icelandic Marine
Research Institute was fitted to the logistic model
(equation 1) with a linear regression. With 57 data
points, the following fit was obtained:
Table 1: Results from fitting stock data to a logistic model
with linear regression.
0.4700 6.6559
2654.44 2.5561
These results are not far from results obtained by
(Arnason et al., 1993). Moreover, running the model
with historical catch data as an input, results are
shown in figures 5 and 6.
Figure 5: Comparison of output from model simulations
and actual stock assessment data for fishable biomass.
There we compare our results from simulation runs
with data from 1955. The model gives good results
in comparison with data from the mid-eighties until
present times which is the period when the demersal
stocks of Iceland have been controlled under a quota
management scheme and the cod stock has been
quite stable. The model however does not account
very well for the fluctuations in the stock due to
overfishing in the years before the ITQs were
imposed. These fluctuations are very visible in the
graphs where there is a large gap between the blue
and the red line. This we find acceptable as in the
foreseeable future, the stock will without a doubt
continue to be controlled with catch quotas, and thus
maintain its equilibrium.
Figure 6: Comparison of output from model simulations
and actual stock assessment data for landings.
Other results such as number of jobs, economic
performance and number of vessels were compared
to current numbers for validation purpose when
running the model with actual harvest rates from
historical data.
The main objective of the study was to use
simulation to determine the optimal ratio of quota
allocated to trawlers versus longliners with the
multi-objective aim of maximising profit and
number of jobs while minimising environmental
impact. The model was run multiple times over ten
years for different values of q which determines
division between quota allocated to bottom trawlers
and longliners. Figure 7 shows the results from these
runs. The results are displayed in such a way that for
each category, each value is displayed as a
proportion of the best possible outcome.
The best possible economic outcome is obtained
when the entire quota is allocated to bottom trawlers
whereas the best environmental outcome is at the
opposite end, where the entire quota is allocated to
longliners. The dashed line in figure 7 shows the
current allocation policy, which leans towards
maximizing profitability rather than minimizing
environmental impact. If the policy were to lean
more towards the intersection of the economic and
environmental components, we would get the best
possible outcome, assuming that the two
components have the same importance. The model
does not take into account jobs in baiting that are
created on-shore because of long liners.
By expanding the model boundaries, we are
likely to see even more positive effects of long liners
and a sharper contrast between longliners and
bottom trawlers in terms of social aspects. This also
leads to a more distribution of wealth which surely
would be accounted for as a positive social
Figure 7: Main results from running simulations with different quota allocations. To the far left we display the case where
the entire quota under consideration is allocated to bottom trawlers and the far right shows the opposite case with the entire
quota allocated to long liners.
100%BT 87.5%BT 75%BT 62.5%BT 5050 62.5%LL 75%LL 87.5%LL 100%LL
In this paper we have presented the first steps in
combining a SD and a DE model resulting in a
holistic model of a system while looking at parts of
it in more detail. In this study, we used publicly
accessible data on landings and quota allocations,
which were scaled over the whole fleet under
consideration. The output of this work is a simple
model which can be improved by adding more
system details. Next step is to add more species but
the model is easily scalable in terms of number of
species. Another obvious step to make in terms of
improving the analysis is to expand the system
boundaries, for instance to include jobs throughout
the whole value chain.
We present a simulation framework which makes
it possible to combine LCA data with a hybrid DES-
SD model. Using logbook data, as an input to the
DE model, the fishing phase could be modelled in
more detail but the fishing phase is the part of the
life cycle of cod which has the most negative
environmental impact due to fossil fuel
consumption. With data from logbooks and more
detailed operational data, the model could be more
realistic and used for further scenario evaluation on
quota allocation. In terms of future research, it
would be possible to model agent based vessels for
finding company operations revenue and equilibrium
based on different quota allocations. Such a model
could be used to identify opportunities to minimise
environmental impact and reduce cost by simulating
alternative fishing routs for the vessels.
To conclude, the findings made from the combined
SD DE model shows and confirm the results in
terms of clarification of economic and
environmental impact of longliners versus bottom
trawlers. The model also shows a need for a larger
more complete modelling approach including
logbooks from the vessels for increasing accuracy on
catch and redirection of traffic to minimize cost and
environmental impact while maintaining job
The authors would like to acknowledge support from
Matís through the EcoFishMan project (FP7-KBBE-
2010-4) and ProViking through the EcoProIT
project and VINNOVA (Swedish Agency for
Innovation Systems). This work has been carried out
within the Sustainable Production Initiative and the
Production Area of Advance at Chalmers. The
support is gratefully acknowledged.
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