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THIS PROJECT
IS PART-FINANCED
BY THE
EUROPEAN UNION
REPORT
2016
FISHING FOR PROTEINS
How marine fisheries impact on global food security up to 2050.
A global prognosis
This publication has been produced with the financial contribution of the European Union.
The contents of this publication are the sole responsibility of WWF and can in no way be taken
to represent the views of the European Union.
Publisher:
WWF Germany; International WWF Centre for Marine Conservation, Hamburg
Date:
October 2016
Authors:Prof. Dr. Martin Quaas, Dr. Julia Hoffmann, Katrin Kamin (all from Kiel University, Resource
Economics Working Group), Dr. Linda Kleemann (Kiel Institute for the World Economy, GFA
Consulting Group Hamburg); Karoline Schacht (WWF Germany)
Translation:
Katrin Kamin, Ann Marie Bohan
Editing:
Karoline Schacht (WWF)
Coordination: Karoline Schacht (WWF), Thomas Köberich (WWF)
Contact:
[email protected]
Design:
Wolfram Egert/Atelier für Graphic Design
Production:
Maro Ballach (WWF)
Print:
Paper:
Credits:
F. Larrey/WWF
Content
Summary of the Report
Background
4
4
Results
10
Key Findings of the study and WWF Comments
12
WWF Conclusion
14
Fishing for proteins. How marine fisheries impact on global food security up to 2050.
15
1 Structure of the Report
15
15
Climate Change
2 Fish Consumption
16
2.1 Fish Consumption in Selected Case Study Countries
18
2.2 Global Importance of Fish as a Protein Source
22
2.3 Food Security and Fish
25
2.4 Dependence on Fish
27
2.5 Fish Dependence Index
28
2.6 Current Fish Consumption and Fish Supply
32
3 The Bio-Economic Model
34
3.1 The Model Approach
34
3.2 Data and Estimation of Model Parameters
35
3.3 Global and Regional Demand Systems
37
3.4 Scenarios: Socio-Economic Pathways and Fishery Management
40
4 Results and Discussion
43
Appendix 50
National Recommended Intakes for Fish
50
Fish Supply Model
51
List of Large Marine Ecosystems
54
List of Protein-Rich Non-Fish Food Substitute Goods
54
List of Figures and Tables
55
Footnotes
56
References57
Acknowledgement59
Fishing for Proteins | 3
Summary of the Study
Background
The world’s population is growing and caring for it
is now placing the earth’s natural resources under
severe pressure. One of the most pressing questions concerning the future centres
on the food security of what will soon be nine billion people: how will we all have
enough to eat? Can we change fishing and agriculture in such a way that they will
feed us but their negative effects on the environment will remain limited to an
absolute minimum? Will we be in a position to resolve distribution issues fairly
and peacefully?
According to estimates, global food requirements are set to double in the next 35
years. From a technological perspective, it seems possible that enough food can
be produced for up to 10 billion people (Evans 1998). In terms of calories, farmers
around the world harvest around one-third more food than is needed to feed the
world’s population (BMEL 2015). Nevertheless, around a billion people go hungry
every day. Their hunger is the result of a distribution problem and is a consequence of poverty and not of the lack of food availability.
Something that is lacking in some regions is needlessly wasted in others: globally,
around 30 to 40% of all food along the production and supply chain ends up in the
bin (WWF 2015). The possibility of expanding cultivated land for the agricultural
production of staple foods seems very unlikely; on the contrary, this option has
reached its limits, or has already exceeded them in many areas. Many farming
systems generate huge harvests of products like corn, rice, cereals and meat while
simultaneously degrading resources such as soil and water.
And what about fish? Fish plays a hugely important role in global food security. It
provides more than 3.1 billion people with at least 20% of their animal protein but
above all it is an important source of fatty acids and micronutrients (Thilstedt et
al. 2016; FAO 2016; Béné et al. 2015). Fish currently supplies 17% of all the protein consumed in the world. This share will continue to grow because the rising
income of consumers is accompanied by an increase in demand for high-quality
fish (World Bank 2013). In addition to its importance as a source of food, fish is
also of great socioeconomic importance: approximately 500 million individuals
throughout the world make their living in some shape or form in the fishing
industry (FAO 2014).
Yet the state of global fish stocks is cause for concern. Among the scientifically
assessed fish stocks, 31% are considered to be overfished and another 58% to
be yet fully fished (FAO 2016; Costello et al. 2016). A further increase in fishing
pressure could gravely jeopardise the health of the fully fished stocks (FAO 2016).
In WWF’s view, the discussion about supplying the world’s population with
high-quality protein neglects the fact that both food productions systems – the
sea and the land – are closely interconnected and in terms of their capacity
and natural limits must be viewed as one. Protein-rich soya is used in fish food
whereas fish meal and fish oil are in turn part of the animal food of pigs and
poultry. Marine catch rates can obviously not be increased, they have in fact been
stagnating for almost 30 years. The demand for fish is currently much greater
than can be covered by marine fish alone and already today half of all fish in the
world is farmed or comes from aquaculture. This branch of the food industry,
which has grown hugely over the last 40 years, requires both sea and land (see the
box: Aquaculture).
4
The purpose of fishery management is to safeguard fish resources and ensure their
sustainable and environmentally friendly use in the long term. It is the responsibility of policy makers to ensure that this happens. A number of researchers are
convinced that this management must be improved significantly to strengthen
global food security and prevent the imminent collapse of fish stocks (Pauly et al.
2005; Worm et al. 2006, 2009; Branch 2008; Branch et al. 2010; Allison et al.
2012; Quaas et al. 2016). Such reforms in management could prove very costly in
the short term. However, the measures would be ultimately worthwhile if stocks
were to reach a healthy size again (Quaas et al. 2012; Sumaila et al. 2012). Consistent, effective fishery management that pursues an ecosystem-based approach,
ensures enforcement of the rules, severely restricts illegal fishing and embeds
the concept of sustainable management in all fisheries will improve the global
fish supply. This is vital in meeting the continuing growth in demand for fish and
maintaining marine biodiversity and ecosystem functions (Worm et al. 2009; Froese and Proelss 2010). After all, healthy fish stocks can only live in healthy seas.
In this study, WWF is seeking to find and consolidate answers to three questions:
» What is the maximum quantity of fish that can be obtained from the seas in
2050 under sustainable conditions?
» How will fish demand develop gloablly and regionally up to 2050?
» How will these projections affect the consumption of fish? For example, do we
face the threat of a fish protein gap?
Aquaculture
Growing numbers of people are eating increasing volumes of fish. In order to meet
the growing worldwide demand, fish is also farmed. In fact, were it not for the strong
expansion in aquaculture seen in recent decades, the demand for fish could not have
been met as the yields from global marine fishery have been stagnating for around
30 years. With an average annual growth of 9% since 1970, aquaculture is the fastest
growing branch of the global food industry. The Food and Agricultural Organization of the
UN (FAO) calculated total aquaculture production of over 90 million tons in 2014. Today,
more than half the edible fish consumed in the world is farmed.
However, the enormous growth in the farmed sector is problematic for several reasons.
For one thing, aquaculture is overwhelmingly practised in countries that have little or no
statutory frameworks for regulating aquaculture or protecting the environment. For another, it causes major marine pollution, if chemicals, food remains, faeces and medications
from the open cages reach the rivers and seas.
Feeding predatory fish in breeding facilities requires primarily wild fish; herbivorous fish
rely more on agricultural protein. In the past, as a result of the construction of facilities
for shrimp farming in the coastal regions of tropical and subtropical countries, valuable
habitats like mangrove forests were lost. Their destruction had huge consequences for
the operation of coastal ecosystems, coastal protection and fishing.
In this study, we are focusing on the future of fish from the sea. The future of aquaculture
is dealt with in a separate report.
Fish in the Diet
The unique combination of high-quality protein and important nutrients makes
fish an exceptionally valuable food. For one thing, it is a good source of animal
protein – 150 g of fish provides approximately 50 to 60% of an adult’s daily
Summary | 5
requirements. It also provides fatty acids, vitamins and other vital nutrients like
iodine and selenium, which do not exist in this quantity or variety in any other
cereal or meat (Beveridge et al. 2013; Kawarazuka and Béné 2011; WOR2 2013).
Food diversity and quality are important elements in the fight against hunger and
malnutrition. Poverty is correlated with an excessive intake of staple foods like
rice, corn and cereals and an insufficient share of proteins, fats and nutrients.
Fish is frequently the only available and affordable source of animal protein in
the coastal regions of developing countries. In a worldwide comparison, rather
less fish is consumed in poorer countries (approximately 10 kg per capita per
year), whereas the per capita consumption of around 22 kg per year in Asia,
North America and Europe is higher than the global average of 20 kg. This
reflects the various factors that affect fish consumption: how available fish is,
how expensive it is, whether there are dietary traditions in relation to fish and
how developed the country is. Generally, the lower the income, the lower the
consumption of fish.
The World Health Organization (WHO) recommends the regular consumption of
fish – one to two portions a week (WHO 2002).1 With an average portion size of
150 g, this results in a worldwide recommended annual consumption of 11.7 kg
fish per capita. Several national nutrition guidelines were also analysed for this
study. They operate within a similar range, averaging 10.6 kg of fish per capita
per year (see Table 6 in the appendix).
However, this rough guideline only applies in Africa and Latin America; all other
regions in the world consume significantly more fish (Figure Z1). Globally, an
average of more than 20 kg of fish is currently consumed per capita per year
(FAO 2016). The average German also consumes roughly 14 kg of fish per year,
more than the recommended intake.2 In general, Germans eat too much protein.
Depending on the individual age group, they consume between 130 and 160%
of the recommended amount (MRI 2008). We are thus eating more protein and
more fish than we really need. As the world’s population grows and the population density constantly increases in coastal areas, the question arises of whether
we are satisfying our need for fish at the expense of those who actually need it.
Viewed at a global level, fish is already distributed unequally and too much fish
per capita is eaten in the Northern hemisphere.
Staple foods like corn, rice and other cereals account for a large share of the dietary pattern of poor people. The consumption of fish is important in correcting
the imbalance between calories and protein. Fish is generally not only cheaper
than other animal protein but is also often a basis of local and/or traditional
recipes. In countries like Senegal or Indonesia, fish accounts for up to 40% of the
total intake of animal protein.
In absolute figures, the consumption of animal protein in developing countries is
lower than in developed countries. However, the share of animal protein in total
protein is growing very rapidly. This is due primarily to economic development
and the way in which developing countries in Africa and Asia are ‘catching up’. If
we make a distinction between fish and meat in the consumption of animal protein, it becomes clear that the contribution made by fish to the supply of animal
protein has fallen slightly since 1990 – primarily in favour of meat.
In poor countries, where fish is traditionally eaten, rising income leads to an
increase in the consumption of meat and higher-quality fish species. Conse-
6
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Fig. Z1
Total protein intake in the
eight case study countries,
subdivided into total protein
(excluding fish, light blue)
and fish protein (dark blue).
Source: FAOSTAT
120
100
80
Fish protein
in gr/caput/d
60
Non-Fish pr
in gr/caput/d
Non-Fish protein
(2011)
(in g/caput/day)
40
World avera
protein supp
Nutrition
recommendation
for total protein intake
(in g/caput/day)
20
Nutrition rec
for total prot
Fish protein
(2009-11).
(in g/caput/day)
....
World average
protein supply
(g/caput/day)
0
South
Africa
Senegal
Peru
USA
Indonesia
Germany
France
China
quently, small pelagic fish (those that live in the open sea between the surface of
the water and the bottom of the sea) are replaced by larger demersal species.
Between 1990 and 2012, the consumption of wild-caught fish remained almost
constant while the consumption of fish cultivated through aquaculture increased
five-fold. In 2015, half the volume of fish produced for human consumption came
from aquaculture, compared with just 5% in 1962 and 37% in 2002 (FAO 2015).
From a global perspective, there is enough food to feed everyone in the world. If
we also take the current protein supply as a basis, there is no protein gap. Food
distribution problems are actually at the heart of hunger issues.
The global average supply of protein was 79 g per capita per day in 2011, while
the average protein requirement was 49.6 g per capita per day. The latter figure
was calculated on the basis of the recommended 0.8 g per kilogram of body
weight and the average weight of a person in 2011 (62 kg). Measured against the
WHO’s recommended intake, the 79 g corresponds to an oversupply of protein of
around 30%.
Figure Z1 shows the protein supply in the countries that were selected as examples for this study: South Africa and Senegal, Peru and the USA, China and
Indonesia, Germany and France. The height of each bar represents the total
supply of protein, subdivided into dark blue areas for fish and light blue areas for
other proteins.
The New Fish Dependence Index
Our fish dependence index measures the level of dependence on fish as a source
of income and nutrition (especially protein). It is based on the composition
of a number of factors: a) food security (incidence of malnutrition in % of the
population); b) fish consumption (share of fish in the total consumption of
animal protein in %); c) national catch quantity per capita; and d) gross domestic
product (GDP) (in USD; capacity to replace fish by other protein-rich food). See
section 2.5 for further details on the index.
Summary | 7
Fig. Z2
Overview of global
fish dependence.
high
medium high
medium
low
no data
In Figure Z2, we link the country-specific situation of food (in)security and the
general situation regarding health and hunger with the value of fish and fisheries
to the country’s socioeconomic status and the livelihoods of its citizens in order
to describe the fish dependence of individual countries. The index shows that
countries with a high share of fish in their diet are particularly dependent on
fish. More importantly, however, these countries (in dark blue) are precisely the
countries that tend to have a large fisheries sector and are neither wealthy nor
particularly food secure.
According to this index, Senegal, for example, appears to be particularly
dependent on fish. At the same time, Senegal is also an example of the complexity reflected in this statement. According to estimates based on FAO figures,
approximately one million people are directly or indirectly dependent on fishing
in the country. Fish accounts for 44% of animal protein intake but just 12% of
total protein. If the global recommendation of 11.7 kg of fish per person per year
is taken as a reference, the annual average per capita consumption of 24 kg of fish
in Senegal is ‘too much’. At 60 g per capita per day, protein supply is also above
the required value of 49 g. Thus, on the one hand, a moderate decline in fish
intake would not lead to a protein gap in Senegal. Nevertheless, 10% of the population is undernourished and fishing is the main source of income in rural coastal
regions (Thiao et al. 2012). So even though the protein supply would be sufficient,
a shrinking fisheries sector would probably see an increase in poverty and hunger
in the coastal regions (Lam et al. 2012) with the potential consequence of political
instability.
Fish Demand and Fish Supply
We wanted to know which regions in the world can meet their requirements
through their own production now and in the future and where there is a growing
dependency on imports to meet demand. To do this, we subdivided the world’s
seas into 64 large marine ecosystems (LMEs). These 64 ecosystems supply up to
95% of the annual global fish catch (Sherman et al. 2009) and present quite specific challenges for regional, and in some cases, multinational management. Then
we calculated whether the fish catches in these regions in 2010 were able to meet
the local demands of people in the neighbouring countries for fish. To this end, we
drew on the data from the Sea Around Us project conducted by the University of
Vancouver (Sea Around Us database).
8
Fig. Z3
Amount of per capita fish
consumption, fish catches
and population size on an
LME basis in 2010.
Data: Sea Around Us
database/own maps
Pop. 2010 Scenario (mn)
> 50
50–150
150–500
500–1,000
Fraction
0–80%
80–100%
100–500%
> 500%
Catches (mn tons)
no data
0.01 – 0.60
0.61 – 1.50
1.51 – 4.00
4.01 – 8.00
8.01 –12.86
Figure Z3 shows the LMEs. The productivity of the regions differs significantly:
red or yellow means ‘does not supply enough fish to meet local demand’; light
green and green mean ‘supplies enough/more than enough fish to meet local
demand’.
LMEs with several neighbouring countries (such as the Mediterranean, Caribbean Sea and Baltic Sea) appear to be less able to cover local demand, whereas
LMEs with only one or a few neighbouring states perform better. Moreover,
the highly productive LMEs in the North Atlantic and East Pacific are generally
better able to meet local demand. This also applies to Europe, the East and
West Coast of the USA and the western coast of Latin America. By contrast, fish
production in the LMEs around Africa (with the exception of Northwest Africa)
and along the Asiatic and Australian coasts is inadequate when compared to
current demand.
The Bio-economic Model
Looking ahead to 2050, we are projecting future global fish catches and possible
effects on fish consumption. As fish catches are generally affected by fishery
activity and the productivity of stocks, we need to apply a bio-economic model
to determine future catches. This model combines an ecological aspect, which
describes the productivity of fish stocks, and an economic aspect, which describes
the economic incentives for carrying out fishery activity and the distribution of
fish catches across the markets.
The model is designed to explain how the total volume of fish catches changes
in different economic and fishery management scenarios and how the total
global catch is distributed in terms of regional catches and regional consumption
quantities.
We based the modelling framework on various current fishery management systems. A new element of this approach is that we include interactions in the sea.
The fish include predator and prey species and both are caught. Previous studies
with comparable global research approaches ignored the biological interactions
and either included all fish species in one model (World Bank 2009) or considered stocks that are biologically independent of one another (Quaas et al. 2016;
Costello et al. 2016).
Summary | 9
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8. Aqua
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Results
Base Colours
For our projection we assume a Maximum Sustainable Yield (MSY) management
scenario for all fisheries. The projection calculates the MSY, which provides an
indication of the maximum contribution that global fish stocks could theoretically
make in supplying the world’s population with protein in 2050. The estimated
MSYs for the global fish stocks using three different model approaches are
presented below.
Fig. Z4
Estimated MSY that the
global fish stocks could
supply using three different
model approaches.
(in million tons)
Global catch quantities
250
200
150
100
50
0
Yield-oriented
predator-prey model
Global surplus
model
Total regional
surplus models
The first bar shows the global catch quantity for a purely yield-oriented predator-prey model. The first model determines the productivity of global fish stocks
based on the interactions between the predatory and prey fish: only when the
stocks of large predatory fish are depleted can the catch of their prey fish be
increased significantly, thus increasing the total volume of the overall catch. Consequently, the objective of fishery management in this model is to maximise catch
quantities. However, an MSY of 160 million tons in 2050 can in principle only be
achieved at the expense of marine biodiversity. The increased catch quantity is
accompanied by a high level of uncertainty (+/- 90 million tons). This is a typical
effect following the destabilisation of the predator-prey balance. If all other target
values for healthy seas as a prerequisite for healthy fish stocks are disregarded –
for example intact habitats or the minimisation of unwanted bycatch – a higher
catch quantity would be possible but would be neither desirable nor sustainable
from an ecological perspective.
The second and third bars, on the other hand, show a stable maximum catch for
Schaefer surplus models. Such a model specifies that the utilisation rate may not
be higher than the natural growth rate of renewable resources. We first calculated
the surplus model for the entire sea and assumed one global stock (second bar);
we then calculated it for the 64 individual LMEs (third bar) and assumed one
stock for each of them. When added up, the result of the third model agrees with
the result of the second model: both project around 112 million tons of fish for
2050. We use the surplus model to analyse the potential contribution that the
LMEs could make to meeting the global and regional demand for fish protein.
We retrospectively calculated a total global catch of 101 million tons of fish for 2010.
This means that current catch quantities cannot be increased by more than 10% in
the future. Accordingly, marine resources already seem to be almost fully exploited.
10
2
30
20
1
10
0
1960
1970
1980
1990
2000
2010
0
1960
30
140
25
120
1980
1990
2000
2010
100
20
80
15
60
10
40
5
0
1970
20
20
30
40
50
60
70
80
Management effectiveness (%)
Fig. Z5
Global fish catches in 2050
according to global bio-economic predator-prey model.
The model shows varying
degrees of management
effectiveness and assumes
GDP growth according to
baseline scenario SSP1
(see section 4 for details
on the model).
(in million tons)
Global catches
of predatory fish
Global catches
of prey fish
90
100
0
20
30
40
50
60
70
80
90
100
Management effectiveness (%)
We also studied how various levels of fishery management effectiveness affect
catch quantities. Our analysis concluded that if fishery management effectiveness
reached 100%, marine biodiversity would be secured, global catches of predatory
and prey fish would reach levels of 21 and 116 million tons, respectively, and a
total of 137 million tons of fish would be caught sustainably (Figure 23).
If management took into consideration all potential effects of fishery activities
on future fishing opportunities, 100% effectiveness would be achieved. Optimum
management from an economic perspective would also stipulate the total allowable catch for individual stocks in such a way that it actually regulates and restricts
fishery activities.
We come to the conclusion that only a management system that focuses on
relationships in the ecosystem can meet the various needs of a sustainable fishing
industry: to achieve high catch volumes while increasing ecosystem resilience by
protecting marine biodiversity and habitats.
The effectiveness of fishery management is currently estimated at an average of
between 50 and 60% (Mora et al. 2009; Watson et al. 2009; Quaas et al. 2016).
There is thus considerable scope for development here. Current catch quantities
could be maintained at this level of effectiveness. However, the large predatory
fish would have to be heavily fished in order to reduce the pressure on smaller
prey fish, thus allowing for slightly larger catches. In fact, that is currently
common practice. Compared to the model of best-possible management, this
would cause a loss of stability in the balance of the ecosystem and shift future fish
consumption in the direction of prey fish.
If management effectiveness slipped below the current level, this would be
expressed in a sharp reduction in the catches of both predatory and prey fish.
This means that ensuring the maximum possible effectiveness of fishery management is critical for maintaining catch yields in the face of simultaneous increases
in the global demand for fish.
In the last step, we analyse how the LMEs can help to cover the global requirement for protein. To do this, we use the estimates from the third model (surplus
model for 64 LMEs) and compare them with projected regional fish consumption.
We use international estimates of future socioeconomic development for the
projections, e.g. population trend and economic growth (Shared Socioeconomic
Pathways, SSP – see footnote 3).
Summary | 11
In the SSP1 scenario with the lowest assumed population growth, the global fish
supply in 2050 will be able to meet approximately 81% of the global requirements
of what will then be almost 8.5 billion people. In the SSP3 scenario, where
population growth is strongest, only 75% of fish requirements will be met by wildcaught fish by the same date.
It is generally assumed that the huge growth rates in aquaculture seen over the
last 30 years were needed to meet the world’s growing appetite for fish. In terms
of numbers, half of the world’s fish currently comes from aquaculture production.
If the results of our projections are accurate and the volume of fish catches in
2050 could meet around 80% of the world’s requirements, the need for further
growth in aquaculture production would abate if the fish was distributed in a
more equitable manner.
And the distribution problems are continuing to grow: fish consumption in the
regions along the East Asian coast could decline significantly by 2050. Fish
is traded globally and prices depend on global demand. If fish prices increase
accordingly based on this demand, fish will become unaffordable for a large
swathe of the population of LMEs along the East Asian coast. These people would
have to switch to affordable alternative sources of protein and fish would be
exported at a higher export price.
Key Findings of the Study and WWF Comments
1 According to the projections made by this study, it will be possible to fish
approximately 112 million tons of fish around the world in 2050 if the current moderate level of fishery management effectiveness remains the same. Thus, it would
appear that marine resources are already close to fully exploited (2010: total catch
of 101 million tons), leaving little room to increase catch volumes in the future.
There is only one way to increase global catch quantities that is both relevant and
sustainable and thus meets the growing demand: fishery management must be
improved significantly worldwide and any decisions made must place far greater
emphasis on ecological interactions than has been the case to date. The interactions between predatory and prey fish is one such example. This type of differentiated, economically optimised and fully enforced management system could
enable sustainable catches of approximately 137 million tons worldwide in 2050.
12
WWF Comment
Improve Management
Fishing is exerting considerable pressure on fish stocks and their habitats in all areas of
the world’s oceans. WWF is committed to an ecosystem-based fishery management that
safeguards the future of marine ecology and the human population. Part of this strategy
is not only to conserve vital stocks of large predatory fish but also to protect habitats and
endangered species. Total allowable catch limits are set to actually regulate the fishing
industry. From the current perspective, this management model would constitute a major
improvement in quality and one that is urgently required to make fishing sustainable.
Ultimately, it would lead to more fish, which could then be distributed more equitably.
Illegal fishing, which accounts for an estimated 30% of the global catch, is evidence of a
particularly damaging consequence of poor management. It reflects increased competition and higher demand accompanied by weak controls. The European Union has a
particular responsibility to solve this problem. Firstly, EU Member States must be more
consistent in implementing the existing regulation against illegal fish imports. Secondly,
they must ensure that any fishing activity they conduct in waters outside the EU is both
fair and sustainable. Furthermore, EU agreements with third countries must focus on
prioritising regional fishing and first and foremost guarantee that local populations are
provided with local fish.
2 If the quality of fishery management, at a minimum, stays at its current
moderate level, there would be enough wild-caught fish available in 2050 (112
million tons) to theoretically supply each world citizen with 12 kg (per person per
year). This roughly matches the average quantity currently recommended by the
WHO and a large number of countries.
WWF Comment
More ‘Fish Fairness’
WWF considers that the belief that there is enough fish for everyone requires closer
scrutiny. Firstly, maintaining the status quo is simply not an option for global fisheries as
tolerable limits have already been reached for 58% of stocks and exceeded for 31%,
the latter being classified as overfished. In addition, there is currently no fair distribution
mechanism for fish that is geared towards real needs. Secondly, the WHO’s recommended intake of fish primarily focuses on the valuable micronutrients rather than on the
protein. In many countries, the current demand for fish is well above the average WHO
recommendation because the affected areas actually rely on fish for their basic protein
supply and very few alternatives are available. In Senegal, 24 kg of fish are consumed
per capita per year and fish provides almost half of the animal protein consumed. In
Germany and France, per capita consumption of 14 and 32 kg respectively also exceeds
the WHO’s recommended 11.7 kg. However, in these countries, fish provides just 7% of
the animal protein consumed. Even if we were to abstain completely from fish in northern
Europe, we would not suffer from protein deficiency. The situation is very different in
poorer regions with high levels of fish consumption.
3 We can assume that developed countries will use the option of importing fish
at higher prices when they are confronted with a shortage in fish supply in 2050.
Developing countries with abundant fish stocks will then export their fish rather
than eat it themselves. Rich countries would thus still be able to afford ‘their’
fish in the future while poorer nations would not. For poor coastal countries, the
probability that poverty and hunger will become more widespread within their
borders increases.
Summary | 13
In 2050, LMEs in Africa and in Latin America (with the exception of northwest
Africa and Peru) and those along the Asian coast will not be able to meet the local
demand for wild-caught fish. Neighbouring countries of LMEs in East Asia, West
Africa and in western South America could export their fish due to high fish prices
and low prices for substitute goods. On the other hand, developed countries with
high purchasing power such as Australia and the USA would probably increase
their fish imports. Germany, France or South Africa could import fish from other
marine regions to offset the major shortfalls that will sometimes occur in their
own supply.
WWF Comment
I Can Have Your Fish and Eat it
Today, Europe imports about a quarter of the world’s total fish catch and represents the
largest market for fish and fish products globally. More than half of the fish imported
into the EU originates in developing countries. In statistical terms, we in Europe have
already eaten all of the fish from our own waters by the middle of any given year. For the
remainder of the time, we eat imported fish which is then missing elsewhere as a source
of nutrition and/or as the cornerstone of local economic structures. The high demand for
imported fish would almost certainly decrease if fish stocks in the European Union’s own
waters were once again at healthy levels.
We must assume that fish consumption in the Northern hemisphere will have an even
more serious impact in the future on the living conditions of those who depend on fish
in various ways. Moreover, our analysis of distribution flows clearly suggests that any
additional catches will not be used to meet the growing demand in fish-dependent countries. However, the increasing scarcity of resources and unequal distribution of marine
fish must not be borne by the poorest countries. This would fuel conflicts and exacerbate
instability particularly if the fishing sector is not better regulated.
WWF Conclusion
Our report ‘Fishing for Proteins – Impacts of marine fisheries on global food
security to 2050’ identifies the key factors driving a sustainable future fish supply.
It also highlights that consistent changes are required in the fishing industry
and in its administration to ensure that the worldwide problems of hunger and
poverty do not continue well into the future. That would be contrary to the
commitments set out in the United Nations plan of action for the future: ending
hunger and poverty by 2030 are two of the 17 Sustainable Development Goals
(SDGs). To achieve these goals, fishery management, amongst other things,
must be improved significantly everywhere. Apart from bad management, fish
stocks also suffer from the effects of climate change as well as the pollution and
destruction of their habitats. Investment in improved fishery management, in
sustainable aquaculture, in the protection of vital marine habitats and in fair
trade policies would restore the productivity of our seas and pay off for billions of
people in developing countries. Our results clearly show that the world’s growing
population must not serve as an excuse for even more reckless exploitation of our
seas. In fact, the solution to these problems can be achieved by implementing and
enforcing ecosystem-based and sustainable fishery management. In addition, fair
access rights and prices must be guaranteed. An increasing supply of sustainably
produced, fair trade fish is not merely intended to ease the conscience of European consumers; it must also benefit fishermen and fish farmers in developing
countries with measurable effects.
The responsibility for this rests with us – not only politically but also as consumers.
14
FISHING FOR PROTEINS
Impacts of Marine Fisheries on Global
Food Security to 2050. A Global Prognosis
1.
Structure of the Report
We begin by describing developments in recent
years, current fish consumption, and explain facts
about fish, food security and the fish supply using
a newly developed index of fish dependency. Alongside a global perspective, we
also consider individual regions and selected representative countries.
Next, we calculate the quantity of wild-caught fish on a global as well as selected
regional basis that will be available in 2050. For modelling, we consider various
economic scenarios and levels of management quality, and compare the results
with the future demand for fish. In this comparison as well, we consider both the
global and the regional persepctive at the level of large marine ecosystems.
We calculate future demand based on shared socioeconomic pathways, (SSPs3)
and the regional supply of alternative protein sources – and thereby generate
an entirely novel approach for forecasting demand. For calculating potential
fish production, we adopt a global predator-prey model. This means that we
additionally take into consideration the biological interactions and amplify the
current model with an element of ‘ecological realism’.
Finally, we combine regional fish production in the large marine ecosystems
(LMEs) with the regional and global demand for fish. In the last section we present the findings from our model on the future of the ocean fishery and its effects
on fish consumption, and discuss issues related to the distribution of resources
and the challenges for trade.
All calculations and models are based on data from
»»Sea Around Us (http://www.seaaroundus.org/) for global fish landings and
prices in the large marine ecosystems (LMEs);
»»FAO (http://faostat3.fao.org/home/E) for consumption levels and
import-and-export prices for protein-rich foods:
»»scientific literature on estimates for preference parameters;
»»Shared Socioeconomic Pathways’ (SSPs) for income and population scenarios.
Climate Change
This study focuses on the biological and economic effects and on the effects of
fishery management quality on future fish catches and consumption levels. However,
climate change is also likely to play an important role in the future of fishing (Cheung
et al. 2010; Lam et al. 2012; Merino et al. 2012). While ocean warming may increase
productivity for some stocks (Kjesbu et al. 2014; Voss et al. 2011), ocean acidification
and warming (Voss et al. 2015; Blanchard et al. 2012) generally decrease the productivity of stocks. Against this background of mostly adverse climate change effects on
fisheries, estimates for future fish catches may be regarded as somewhat optimistic.
Fishing for Proteins | 15
6. Brown
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BlueMedium
AquaDark
AquaMedium
PinkDark
PinkMedium
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BerryMedium
RedMedium
GreyDark
OrangeDark
GreyMedium
OrangeMediu
BaseColoursBackground
YellowDark
BaseColoursT
YellowMedium
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GreenMedium
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EarthMedium
BrownDark
BrownMedium
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BlueMedium
AquaDark
AquaMedium
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PinkMedium
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BerryMedium
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GreyMedium
BaseColoursBackground
BaseColoursT
7. Blue
8. Aqua
9. Pink
10. Berry
1. Red
2.
Fish Consumption
11. Grey
2. Orange
The term fish consumption is defined here as the amount
of fish that is available for consumption in a specific
Base Colours
3. Yellow
country: production (excl. non-food uses) plus imports
4
minus exports plus or minus changes in stocks.
4. Green
Fish consumption considered here includes pelagic, demersal and other marine
5. Earth
fish, freshwater fish, molluscs, crustaceans and cephalopods from marine and
aquaculture production. The data covers the 50 years from 1961 to 2011. Figure
6. Brown
1 shows the increase in global fish consumption, which totalled 130 million tons
in 2011. While Africa, America, Europe and Oceania only show slight increases in
7. Blue
consumption over 50 years, fish consumption in Asia strongly increased from the
1980s on. This increase is mainly driven by China and its expanding fish produc8. Aqua
tion (mainly aquaculture).
9. Pink
Fig. 1
World fish consumption
between 1960 and 2010
(in million tons).
Source: FAO FishStatJ
database
140
Africa
America
Asia
Europe
Oceania
World
40
10. Berry
120
100
11. Grey
80
Base Colours
60
20
0
1960
1970
Africa
1980
Americas
1990
Asia
Europe
2000
Oceania
2010
World
The development of the world’s per capita fish consumption in the same period
is shown in Figure 2. Within 50 years it more than doubled and reached more
than 19 kg per capita in 2013. Per capita fish consumption increased in every
continent. However, the absolute amount of fish eaten by one person differs
between regions. Africa has the lowest value of fish consumption per person, with
4.5 kg in 1961 increasing to 10.8 kg in 2011. Up to 1990, Europe had the highest
level of per capita fish consumption (21.3 kg); since then, Oceania has led the
field (26.5 kg in 2011). The steepest increase in fish consumption per person can
be observed for Asia, obviously also driven by the strong increase in Chinese
aquaculture production (see Fig. 2).
Figure 3 shows differences in current fish consumption levels at country level.
Developed countries show the highest (a mean of 26.8 kg in 2013) while low-income food-deficit countries (LIFDCs) have the lowest per capita consumption (a
mean of 7.6 kg in 2013). These differences in consumption depend on fish prices
Fig. 2
Per capita world fish
consumption between
1960 and 2010.
(kg/per capita/year).
Source: FAO FishStatJ
database
Africa
America
Asia
Europe
Oceania
World
30
25
20
15
10
5
0
1960
1970
Africa
16
1980
Americas
1990
Asia
Europe
2000
2010
Oceania
World
and availability of fish as well as substitutes, income and socioeconomic factors
(FAO 2016).
In terms of world consumption patterns, the share of demersal, pelagic and other
marine fish decreased over time while the share of freshwater fish increased (see
Figure 4). Again, the strong growth in aquaculture production, especially in China,
accounts for this shift and has led to increased consumption of species such as catfish, tilapia, pangasius (a freshwater fish), shrimps and bivalves (shellfish such as
1. Red
molluscs, crustaceans and cephalopods). The consumption of freshwater species
grew from 1.5 kg per capita to 6.5 kg per capita in the period studied.
RedDark
2. Orange
OrangeDark
The consumption pattern at continental level reflects the global trend (see Fig.
3. Yellow
5). However, the share of consumption of demersal and pelagic fish decreased in
Asia, America and Europe. One reason for this decrease might be that aquacul4. Green
ture products serve as a cheap alternative to wild catches. The picture in Africa
looks different. Here, the consumption pattern is relatively constant over time
5. Earth
with only a slight increase in the consumption of pelagic species.
YellowDark
GreenDark
EarthDark
6. Brown
Northern Europe and North America favour demersal fish while the Mediterranean and East Asian countries prefer cephalopods. Overall, 74% of the 19.77.kg
Blue
global per capita fish consumption in 2010 were finfish, 25% or 4.9 kg per capita
were shellfish (FAO 2016).
8. Aqua
BrownDark
BlueDark
AquaDark
9. Pink
PinkDark
10. Berry
BerryDark
Fig. 3
Global per capita
fish consumption
(average 2008 to 2010)
(in kg/year).
Source: FAO 2014
11. Grey
GreyDark
Base Colours
BaseColoursBackground
0–2
2–5
5–1
10–20
20–30
30–60
>6
Fig. 4
Global fish consumption
pattern (in million tons).
Source: FAO FishStatJ
database
120
80
Demersal a
Marine Fish
Pelagic Fish
Demersal and
other Marine Fish
Freshwater Fish
Molluscs, Other
Crustaceans
Cephalopods
60
Freshwate
40
Molluscs, O
20
Crustacean
Pelagic Fis
100
0
1961
1970
1980
1990
2000
2011
Fishing for Proteins | 17
Cephalopo
Pelagic Fish
Demersal and other
Marine Fish
Freshwater Fish
Molluscs, Other
in Mio. tons
Crustaceans
Cephalopods
Africa
12
America
10
8
6
4
2
0
1961
1970
1980
1990
2000
2011
16
14
12
10
8
6
4
2
0
1961
1970
1980
1990
2000
2011
2000
2011
Europe
Asia
100
18
16
14
12
10
8
6
4
2
0
80
60
40
20
0
1961
1970
1980
Fig. 5
Fish consumption pattern in
the case study continents
(in million tons).
Source: FAO FishStatJ
database 2016
Pelagic Fish
Demersal and
other Marine Fish
Freshwater Fish
Molluscs, Other
Crustaceans
Cephalopods
1990
2000
2011
1961
1970
1980
1990
2.1 Fish Consumption in Selected Case Study Countries
In addition to the broad overview provided above, we focused on eight countries in
order to provide further and more detailed insights. The case study countries were:
»»France and Germany (Europe),
»»Peru and the United States of America (America),
»»China and Indonesia (Asia),
»»Senegal and South Africa (Africa).
The choice was based on the following criteria: (1) each continent (except Oceania)
should be represented, (2) developed as well as developing countries should be
included, (3) fish and fisheries should play an important role for those countries.
The African countries Senegal and South Africa have the lowest fish consumption overall, starting with 0.06 million tons (Senegal) and 0.1 million tons
(South Africa) increasing to roughly 0.3 million tons in both countries. Peru’s fish
consumption slightly exceeds African values with 0.14 million tons in 1961 and
0.65 million tons in 2011. In contrast, fish consumption in the USA started much
higher in 1961 with 2.5 million tons and increased to 6.8 million tons in 2011.
Although Indonesia’s fish consumption was less than 1 million tons in the early
1960s, it has reached values similar to the USA in recent years with a maximum of
6.9 million tons in 2011. The leader in fish consumption is China. It doubled its
fish consumption from 3.4 million tons in 1961 to 6.9 million tons in 1984. From
the 1980s until today, China’s fish consumption grew at very high rates, leading
to 46 million tons in 2011. In comparison, the development of fish consumption
was quite moderate in the two European countries Germany and France. Fish
consumption levels in Germany stayed relatively constant over time at 0.7 million
tons in 1961 and 1.2 million tons in 2011. France experienced a stronger increase,
rising from 0.7 million tons in 1961 to 2.2 million tons in 2011.
18
10.
11. Berry
Grey
BerryDark
GreyDark
11. Grey
Base
Colours
GreyDark
BaseColoursBackground
BerryMedium
GreyMedium
BerryLight
GreyLight
5. Brown
Earth
6.
GreyMedium
BaseColoursTintedBox
GreyLight
Black
6.
7. Brown
Blue
BaseColoursTintedBox
Black
7. Blue
Base Colours
BaseColoursBackground
0
1960
GreenMedium
EarthMedium
GreenLight
EarthLight
EarthDark
BrownDark
EarthMedium
BrownMedium
EarthLight
BrownLight
BrownDark
BrownMedium
BrownLight
8
0.5
0.4
0.5
0.3
0.4
0.2
0.3
0.1
0.2
0
0.1
1960
GreenDark
EarthDark
Africa
America
6
8
4
6
2
4
1970
1970
1980
1980
1990
1990
2000
2000
2010
0
2
1960
1970
1980
1990
2000
2010
2010
0
1960
1970
1980
1990
2000
2010
Asia
Europe
3
50
40
50
30
40
20
30
10
20
0
101960
1970
1980
1990
2000
2010
1
0
1960
1970
1980
1990
2000
2010
0
1960
1970
1980
1990
2000
2010
0
1960
1970
1980
1990
2000
2010
3
2
2
1
Fig. 6
Senegal
Fish consumption
South Africa in the
eight case
study countries.
Senegal
(in million
Peru
South
Africatons).
Source:
USAFAO FishStatJ
database
Peru
Indonesia
USA
Senegal
China
South Africa
Indonesia
France
China
Peru
Germany
USA
Differences between the case study countries can again be found in the per capita
fish consumption over time (see Fig. 7). While South Africa’s per capita consumption is similar to the African average, Senegalese people consume four to five
times more fish than people in South Africa. Peru and the USA both exceed their
continent’s average. Also, these two countries show a similar development in per
capita fish consumption over time. However, Peru shows a high fluctuation. This
variability is mainly driven by the strong dependence (up to 80%) of the Peruvian fishery on anchoveta (Engraulis ringens). For example, in the early 1980s,
anchoveta stocks west of South America drastically decreased – mainly because
of an El Niño event (FAO 2016a). This decrease was reflected in the Peruvian per
capita consumption.
France
Germany
China and Indonesia experienced a strong increase in per capita consumption,
similar to the overall Asian development. Per capita consumption in Germany
and Europe is relatively steady over time. Germany lies below the European
average. In contrast, France exceeds the European average in terms of fish intake
per person and experienced a relatively strong increase over time from 18 kg per
person to 35 kg per person.
France
GermanyIndonesia
China
When comparing consumption patterns at country level, differences between the
developing countries (South Africa, Senegal, Indonesia and Peru) and the developed countries (China, France, Germany and the USA) can be noticed (see Fig.
8). In the developing countries, marine fish form the biggest share of consumed
fish with pelagic fish clearly dominating. Also, except for Indonesia, the share of
freshwater fish consumption is very small. Indonesia is one of the biggest aquaculture producers in the world. The main freshwater species produced are carp,
tilapia and gourami; shrimp are also cultivated (FAO 2016b). A similar argument
might hold true for Peru, which shows increased consumption of freshwater
species and molluscs from 1990 on. Towards the end of the 1980s, Peru initiated
aquaculture production of trout, tilapia, shrimps and scallops which developed
successfully in subsequent years (FAO 2016c).
Among the developed countries considered in this report, Germany and France
Fishing for Proteins | 19
Africa
USA
America
Peru
USA
Indonesia
America
China
Asia
Indonesia
China
France
Asia
Germany
Europe
France
Germany
Europe
30
40
30
40
20
30
10
20
0
101960
0
1960
Africa
America
20
30
10
20
1970
1980
1990
2000
2010
1970
1980
1990
2000
2010
Asia
40
1970
1980
1990
2000
2010
0
1960
1970
1980
1990
2000
2010
Senegal
South Africa
Peru
USA
Indonesia
China
France
Germany
0
1960
1970
1980
1990
2000
2010
1960
1970
1980
1990
2000
2010
Europe
40
30
40
20
30
10
20
0
10 1960
Fig. 7
Per capita fish consumption
in the eight case study
countries and the
corresponding continent
(black line)
(in kg/year).
Source: FAO FishStatJ
database
0
10
30
40
20
30
10
20
0
10 1960
0
1960
1970
1980
1990
2000
2010
1970
1980
1990
2000
2010
have the highest intake of marine fish. However, over time this share has
decreased in France in favour of molluscs and crustaceans. In Germany freshwater fish consumption has increased. In the USA, overall consumption has stayed
relatively stable, while, as in France and Germany, the share of marine fish has
decreased while the share of freshwater fish and crustaceans has increased. The
increase in the consumption of freshwater species, crustaceans and molluscs may
be driven by imports of aquaculture products, which are cheaper compared to
wild catches.
China is the world´s leading country in aquaculture production and shows the
highest share of freshwater fish and molluscs consumption in recent decades. In
contrast, its consumption of pelagic and demersal fish is the smallest of all the
eight case study countries.
The rapid increase in fish consumption in the developing economies in Asia can
be explained by the correlation between increasing fish consumption and increasing wealth: per capita intake of fish increases fastest where wealth and urbanisation are combined and where domestic supply is increasing as well (HLPE 2014).
Overall, the share of marine fish and seafood in world consumption has declined
over time, while the share of freshwater fish has shown an increase. However,
marine fish still forms the majority of the world´s fish consumption, and some
countries, for example South Africa, rely almost entirely on wild catches. Nevertheless, although Sub-Saharan Africa currently contributes less than 1% of global
aquaculture production, it registers as the fastest-growing aquaculture industry
measured by its growth rate (World Resources Institute 2013). African aquaculture production is still very low. There are a number of reasons for this, including
difficult market conditions and a focus on smallholder aquaculture, which is
very important for local food security but cannot meet the goal of increased fish
production at national level (Beveridge et al. 2010).
Monoculture of fish is increasingly replacing the traditional consumption of
small fish species in some low-income countries (FAO COFI 2014). Small pelagic
20
5. Earth
EarthDark
EarthMedium
EarthLight
BrownDark
BrownMedium
BrownLight
BlueDark
BlueMedium
BlueLight
AquaDark
AquaMedium
AquaLight
PinkDark
PinkMedium
PinkLight
BerryDark
BerryMedium
BerryLight
6. Brown
7. Blue
8. Aqua
Pelagic Fish
9. Pink
Demersal and other
Marine Fish
10. Berry
Freshwater Fish
Fig. 8
Fish consumption
pattern
Molluscs, Other
in the eight case study
countries
(in million tons).
Crustaceans
Source: FAO FishStatJ
database
Cephalopods
11. Grey
in Mio. tons
Base Colours
Senegal
0.4
0.3
0.2
0.2
0.1
0.1
1961
1970
1980
1990
2000
2011
China
50
0
1980
1990
2000
2011
2000
2011
2000
2011
2000
2011
Indonesia
4
20
2
10
1961
1970
1980
1990
2000
2011
Peru
0.8
0
6
0.4
4
0.2
2
1961
1970
1980
1990
2000
2011
France
2.5
1961
1970
0
1980
1990
USA
8
0.6
1961
1970
1980
1990
Germany
1.2
1
2
0.8
1.5
0.6
1
0.4
0.5
0
1970
6
30
0
1961
8
40
0
South Africa
0.4
0.3
0
Pelagic Fish
Demersal and
GreyDark
GreyMedium
other Marine Fish
Freshwater Fish
BaseColoursBackground
Molluscs,BaseColoursTintedBox
Other
Crustaceans
Cephalopods
0.2
1961
1970
1980
1990
2000
2011
0
1961
1970
1980
1990
Fishing for Proteins | 21
GreyLight
Black
fish in particular have a unique nutritional composition and are hence of great
nutritional importance. Moreover, small fish are more affordable and more
easily accessible than larger fish or other animal-based foods and vegetables
(Kawarazuka and Bené 2011). Consumption of these should be encouraged and
promoted, while the use of small pelagic fish for fish meal and fish oil should
be reconsidered (Tacon and Metian 2013). In addition to Germany, pelagic fish
play an important role in consumption for all developing countries in the case
studies (see Fig. 8). The availability of marine fish in the future is very important,
especially for those countries with low aquaculture production.
Conclusion
»»Total fish consumption has increased over time, but the share of marine fish
and seafood has declined.
»»Marine catches still play an important role in fish consumption, some countries
rely on wild catches at 100%.
»»Increasing fish consumption is driven mainly by Chinese aquaculture.
»»Aquaculture products are not a substitution option for all countries.
2.2 Global Importance of Fish as a Protein Source
A unique combination of high-quality protein and vital nutrients make fish an invaluable food. Fish is not only a source of animal protein – 150 g of fish provides
about 50 to 60% of an adult’s daily protein requirements – but also of fatty acids,
vitamins and other essential elements such as iodine and selenium, which do not
occur in such quantity and diversity in cereals, other crops or meat (Beveridge et
al. 2013; Kawarazuka and Béné 2011; WOR2 2013). Dietary diversity and dietary
adequacy are high on the agenda in the fight against hunger and malnutrition.
Severe poverty is highly correlated with the intake of ‘too many’ staples and too
little protein, fat and micronutrients.
In coastal regions of developing countries fish is often the only affordable and
relatively easily available source of animal protein. In countries like Sierra Leone,
which has a very low overall food security, the share of fish in animal protein is
over 50%. Marked differences exist between and within countries and regions
in terms of the quantity and variety consumed per capita and the subsequent
contribution to nutritional intake. In a global comparison, Africa and Latin
America consume relatively little fish (around 10 kg per person per year), whereas
per capita consumption in Asia, North America and Europe is above the global
average (20 kg) at around 22 kg per year.5 This reflects the factors that affect fish
consumption: how available fish is, how expensive it is, whether there are dietary
traditions in relation to fish and how developed the country is. Generally speaking, the lower the income the lower the fish consumption. Dietary tradition relates
to the fact that countries with a strong fishing tradition due to a long coastline,
many fish-rich rivers or islands tend to still consume more fish (FAO 2016).
The WHO recommends on average an annual intake of 11.7 kg fish per person
(about 32 g per day or 225 g per week). Averaged over world regions, only Africa
and Latin America come close to meeting this reference value. Globally speaking,
however, there is an unequal distribution of fish and Northern hemisphere
consumes too much fish per capita.
In 2013, fish accounted for 6.7% of all protein consumed and for 17% of the global
22
4. Green
GreenDark
5. Earth
EarthDark
6. Brown
BrownDark
7. Blue
BlueDark
8. Aqua
AquaDark
9. Pink
PinkDark
population’s intake of animal protein. In developing countries, this share was
10. Berry
19.6% and 24.7% in low-income food-deficit countries (LIFDCs) (see Fig. 9). For
3.1 billion people, fish accounts for 20% of their animal protein; for 4.3 billion
11. Grey
people, this share is 15% (FAO 2016). Some small island states such as Kiribati,
Micronesia and the Maldives depend almost exclusively on fish as a proteinBase Colours
source (FAO 2016). The average daily dietary contribution of fish in terms of
calories is about 34 calories per capita. In countries where there is a lack of
alternative protein food and where there is a traditional preference for fish (e.g.
Senegal), as well as in several small island states such as the ones named above,
the daily fish calorie intake reaches 130 calories per capita or more (FAO 2016).
However, based on this data, we may underestimate the importance of fish as a
protein and nutrient provider, in particular for countries with low food security
and/or poor populations. There are a number of reasons for this.
»»There is a huge variation between and within countries, in particular coastal regions within small island countries depending to a much higher extent on fish
as a source of animal protein. When these coastal regions are remote, i.e. far
away from major markets and not easily accessible, and have a high prevalence
of poverty, substitution possibilities are limited in the short run. Consumption
in this case is supply driven.
»»Consumption data is likely to be underestimated in view of the under-recorded
contribution of subsistence fisheries and small-scale fisheries in official statistics (FAO 2014; Pauly 2016). Hence, actual fish consumption in developing
Fish
consumption
countries
is probably
higher than official data reports.
per capita (kg)
»»Economic
dependence
Fish
contribution on
to fish as a source of income plays an important role in
total animal
protein (%)
coastal areas
in developing
countries with repercussions on food security.
Fig. 9
Total fish consumption and
fish contribution to total
animal protein.
Source: FAOSTAT.
Fish consumption
per capita (kg)
30
25
20
15
Fish contibution to total
animal protein (%)
10
for fish (kg/caput/year)
5
.... WHO recommendation
0
Oceania
Europe
Northern
America
Asia
Africa
Latin America
and Caribbian
World
The dietary pattern of poor people typically has a very strong component of
staple food (in particular maize, rice and other cereals), with fish consumption
an important factor in helping to correct an imbalanced calorie/protein ratio.
Fish often represents an affordable source of animal protein that may not only be
cheaper than other animal protein sources, but also part of local and/or traditional recipes. In countries with a long coastline such as Senegal and islands such
as Indonesia, fish accounts for, or exceeds, 40% of total animal protein intake
(see Tab. 1 next page).
Fishing for Proteins | 23
BerryDark
GreyDark
BaseColoursBackground
Tab. 1
Dependence on fish in
the diet in the eight case
countries in this study.
Source: FAO
Prevalence of
undernourishment
(% of population)
Fish consumption
(kg per capita
per year)
Fish contribution
to total animal
protein (%)
2013 – 2015
2011
2011
China
9.3
33.5
20.56
Indonesia
7.6
28.9
54.82
10.0
23.5
43.73
<5
5.7
5.2
Senegal
South Africa
Peru
7.5
22.7
22.28
France
<5
34.8
13.3
Germany
<5
14.2
7.28
USA
<5
21.7
7.37
In Figure 10 we provide a graphical representation of ‘the role of fish in the diet
in relation to economic development’. While very low income tends to be associated with hunger, rising income first leads to adequate availability of calories and
subsequently to adequate nutritional quality. At low income levels (bottom left)
fish contributes to an unbalanced diet: fish tends to be consumed in very small
or very large quantities depending on availability. At very high income levels (top
right), fish also tends to contribute to an unbalanced, overly protein-rich diet.
Economic development is associated with structural change in which the share of
people involved in fishing declines in line with increasing development.
Fig. 10
Importance of fish in the
diet in relation to economic
development of a country
or population.
Strong fish
dependency as
source of income
(jobs, foreign
exchange)
hunger
Low
adaptive
capacity
Fish contributes to an
unbalanced diet
Structural change
Adequate
number of
calories
Adequate
nutrition
Fish contributes to an
unbalanced diet
Fish contributes to
a balanced diet
Economic
developement
While in absolute numbers animal protein intake is lower in developing countries
than in developed countries, the growing share of animal protein worldwide is
driven mainly by developing countries catching up, particularly in Africa and
Asia. Splitting up animal protein intake into fish and meat, the contribution of
fish to total animal protein intake has been declining slightly since 1990 at the
expense of other animal proteins.
In poor countries where fish is a traditional food, increasing income leads to
increased meat consumption and greater consumption of more valuable fish
24
GreenDark
5. Earth
EarthDark
6. Brown
BrownDark
7. Blue
BlueDark
8. Aqua
AquaDark
9. Pink
PinkDark
species, e.g. demersal instead of pelagic fish. Not surprisingly, consumption10.of
Berry
fish from aquaculture is growing fast. Between 1990 and 2012, fish consumption
from wild sources remained almost the same, but consumption of fish from11. Grey
aquaculture has multiplied by five. Half of fish produced for human consumption
came from aquaculture in 2012, compared to just 5% in 1962 and 37% in 2002
Base Colours
(FAO 2015).
BerryDark
GreyDark
BaseColoursBackground
Just as there is enough food on average to feed everyone on the planet and hunger is a question of distribution, there is also no protein gap considering current
world average protein supply.
Fig. 11
Total protein consumption
in the eight case study
countries subdivided into
total protein (excluding fish,
light blue) and fish protein
(dark blue).
Source: FAOSTAT
Fish protein
(2009-11).
(in g/caput/day)
Non-Fish protein
(2011)
(in g/caput/day)
.... Nutrition
recommendation
for total protein intake
(in g/caput/day)
Global average
protein consumption
(g/caput/day)
120
100
80
Fish protein
in gr/caput/d
60
Non-Fish pr
in gr/caput/d
40
World avera
protein supp
20
Nutrition rec
for total prot
0
South
Africa
Senegal
Peru
USA
Indonesia Germany
France
China
Globally, the average protein supply in 2011 was 79 g per capita per day, while
the average protein need was 49.6 g per capita per day. The latter was calculated
from the recommended 0.8 g per kg of body weight and the average weight of a
person (62 kg) in 2011. Figure 11 shows protein consumption in the eight case
study countries. The height of each column represents total protein consumption:
the darker blue parts represent fish and the lighter blue parts represent all other
protein. Based on this data, all countries except Liberia, Guinea-Bissau, Mozambique, Haiti and Madagascar had enough protein available in 2011. Nevertheless,
the distribution within and between countries is highly unequal.
2.3 Food Security and Fish
Undernourishment is a major problem worldwide, with one in seven people
undernourished and more than one-third of infant mortality attributable to
undernutrition. This is especially the case in many developing countries, with
the bulk of undernourished people living in rural areas (see Tab. 1). Most of the
world’s undernourished people live in South Asia, closely followed by Sub-Saharan Africa and East Asia.
In addition to the prevalence of undernourishment, researchers often use several
other indicators in order to assess the food security level of a country such as
stunting, measured by height-for-age, and wasting, measured by weight-for-age.
Fishing for Proteins | 25
A common multidimensional index used is the Global Hunger Index (GHI, see
Tab. 2). To reflect the multidimensional nature of hunger, the GHI combines the
following four component indicators into one index:
»»1/3 for undernourishment: the proportion of undernourished people as a
percentage of the population, reflecting the share of the population with
insufficient caloric intake
»»1/6 for child wasting: the proportion of children under the age of five who suffer
from wasting, i.e. low weight for their height, reflecting acute undernutrition
»»1/6 for child stunting: the proportion of children under the age of five who suf-
fer from stunting, i.e. low height for their age, reflecting chronic undernutrition
»» 1/3 for child mortality: the mortality rate of children under the age of five, partially
reflecting the fatal synergy of inadequate nutrition and unhealthy environments
(all standardised based on thresholds set slightly above the highest country-level
values observed worldwide for that indicator between 1988 and 2013.6
This indicator emphasises the nutrition situation of children – a vulnerable subset of the population for whom a lack of dietary energy, protein or micronutrients
(i.e. essential vitamins and minerals) leads to a high risk of illness, poor physical
and cognitive development or death. It also combines independently measured
indicators to reduce the effects of random measurement errors (GHI 2015).
The GHI categorises countries according to hunger severity into low (values
<10), moderate (scores of 10 – 20), serious (20 – 35), alarming (35 – 50) and
extremely alarming (>50). In our target countries, Senegal and Indonesia fall
into the serious category, South Africa has moderate levels, and all others are in
the low category. The index is not calculated for Germany, France and the USA,
which are considered to be generally food secure.
Tab.2
(Multidimensional) Global
Hunger Index 1995 to 2015
for case study countries.
Source: Welthungerhilfe
(WHH); International Food
Policy Research Institute
(IFPRI); and Concern
Worldwide 2015
1995
2005
2015
With data from
1993–1997
2003–2007
2010–2016
China
23.2
13.2
8.6
Indonesia
32.5
26.5
22.1
Senegal
36.9
28.5
23.2
South Africa
16.5
21.0
12.4
Peru
25.0
18.8
9.1
Conclusion:
»»The relevance of fish for food and nutrition security manifests itself in its value
as a protein and micronutrient source.
»»Poor people generally consume too few micronutrients and too little protein.
»»Globally speaking, there is no protein gap.
26
2.4 Dependence on Fish
In this section we combine the country-specific situation of food (in)security
and general health/hunger situations with the socioeconomic value of fish and
fisheries to the livelihoods in these countries in order to describe the countries´
dependence on fish.
The following factors determine dependence on fish for food and nutrition:
»» Fish as a food source, i.e. as a source of calories. This is particularly relevant in countries such as Senegal with food insecurity and a high fish intake.
»»Fish as a protein and micronutrient source. This is particularly relevant
in countries where the protein share of fish in the diet is high or low. It is also
particularly relevant in almost all poor countries that have an imbalanced
diet, where people eat too much staple food and not enough micronutrients
(e.g. Senegal, Indonesia). The fisheries and aquaculture sector plays, and can
continue to play, a particularly prominent role in diversified and healthy diets.
While average per capita fish consumption may be low, even small quantities of
fish can have a significant positive nutritional impact, given that it is a concentrated source of essential dietary components. Micronutrient deficiencies7 affect
hundreds of millions of people, particularly women and children in the developing world. More than 250 million children worldwide are at risk of vitamin A
deficiency (leading to blindness), more than 30% of the world’s population are
iron deficient, 200 million people have goitre with 20 million suffering from
learning difficulties as a result of iodine deficiency and 800,000 child deaths
per year are attributable to zinc deficiency.
»»Economic dependence on fish as an income source to buy (healthy)
food. This is particularly relevant in countries with high poverty rates and in
countries where people working in fisheries are comparably poor. When they
lose their jobs in the fish industry, poverty rises and this affects diets in two
ways: hunger and quality of the diet. A lack of money to buy food leads first
to a less balanced diet, with a tendency to eat less fish and meat, hence less
protein and micronutrients and in even more severe cases to a lower intake
of calories overall. The severity of this effect depends on the strength of the
formal or informal social security systems in place. As an example, if Senegal
stopped fishing completely, this would have adverse impacts on the livelihoods
of fishermen with widespread hunger as a likely consequence, at least in the
short run. This is a typical distribution problem: globally speaking, enough is
available, but it is distributed highly unequally.8
Hence, fish contributes to the nutritional security of poor households in developing countries in various ways. These include a consumption pathway where the
direct consumption of fish increases intakes of not only calories, but more importantly protein, micronutrients and omega-3 oils, and a cash-income pathway,
whereby the fish industry contributes to employment and higher overall food
consumption in poor countries (see Fig. 12). Commercialisation, fish processing
and small-scale aquaculture offer important livelihood opportunities, particularly
for women in developing countries, through their direct involvement in the production, processing and sale of fish. In particular, countries where a large share
of the population is dependent on fishing (e.g. Senegal) may suffer from higher
hunger rates and political instability if the fishing sector declines. According to
estimates by the FAO, a total of 660 to 820 million people are directly or indi-
Fishing for Proteins | 27
rectly dependent on fisheries and aquaculture. The FAO estimates the number of
fishermen alone at 54 million, of which 87% live in Asia. In developing countries,
the majority of them work in small-scale fisheries with low fish production per
person: on average 1.5 tons per year compared to 25 tons per fisherman and year
in Europe (FAO 2014).
Fig. 12
Main drivers of
dependence on fish.
Source: Own presentation.
Nutrition dependence
Economic dependence
Protein and micronutrient source
Catch values
Availability and
price of substitutes
Number of people
employed
2.5 Fish Dependence Index
Our fish dependence indicator measures the degree of dependence on fish as a
source of income and food, in particular as a source of protein. It is based on a
composite ranking of the following factors:
»»food security: prevalence of undernourishment for the period from 2011 to
2013 in %, data from the FAO;
»»fish consumption: fish share of total animal protein intake in % for 2011, data
from the FAO;
»»capture production per capita for 2011, data from FAOfishstat;
»»gross domestic product (GDP) per capita as a proxy for substitution capacity for
2011, data from the World Bank.9
The indicator measures the short-run dependence. In the long run, the possibilities
for compensating through new industries (e.g. building up an aquaculture industry) and new sources of protein (e.g. plant-based sources) have to be considered.
The indicator is similar in some ways to the indicator developed by Allison et al.
(2009a, 2009b) and used in Badjeck et al. (2013). The main difference is the more
recent data used in this index and the stronger focus on fish as food. Using more
recent data (from 2011) is only possible because this index is composed of fewer
components. The results of the two indices are in most cases very similar.
Fish as a share of animal protein in food and capture production as a proxy for
economic relevance are the main factors and weighted equally. GDP per capita
(in USD) and food security (prevalence of undernourishment) are the moderating
factors. A high income reduces the dependence created by a high level of the two
main factors due to compensation effects. This means that a high income increases
possibilities for compensating for a loss of jobs or the loss of fish as a source of
food, e.g. through imports and social security. A high level of food security reduces
the potentially harmful effects of losing fish as a food source.
28
Figure 13 shows the worldwide distribution of fish dependence. Asian island
states and West African coastal countries are most dependent on fish, whereas
dependence in Europe is comparably low. Figures 13a to 13d show the effects of the
application of one of the four factors: a) catch per capita, b) share of fish in total
consumption of animal protein, c) per capita GDP, d) share of undernourishment.
The index reveals that, not surprisingly, countries with a very high share of fish
in the diet are highly dependent on fish (see Tab. 3, page 31). One important
aspect is that these countries usually also have a comparably large fish industry
and are not wealthy or particularly food secure countries. At the same time, the
fish protein in high-income food-secure countries is, on average, relatively lower.
Nevertheless, poverty and fish dependence do not necessarily go hand in hand
(see Tab. 3, left). Among the poorest countries are countries with no access to the
ocean (e.g. Ethiopia, Central African Republic).
Germany – with a low catch per person, a low share of fish protein, high income
and high food security – has very little dependence on fish. South Africa and
United States are also not particularly dependent on fish, even though they have
Fig. 13
Fish dependence
scores worldwide.
Source: Own presentation.
high
medium high
medium
low
no data
This world map is a composite ranking of the following factors:
Fig. 13a
Per capita catch by country.
high
medium high
medium
low
no data
Fishing for Proteins | 29
Fig. 13b
Share of fish in total consumption of animal protein.
high (> 20%)
medium high
(betw. 10 and 20%)
medium
(betw. 5.6 and 10%)
low (< 5.6%)
no data
Fig. 13c
Per capita GDP in USD.
high (> 26,932)
medium high
(betw. 11,360 and 26,932)
medium
(betw. 4,133 and 11,360)
low (< 4,133)
no data
Fig. 13d
Share of undernourishment
in population.
high
medium high
low
no data
30
Tab. 3
Fish dependence of the
eight poorest countries and
the eight countries with the
highest share of fish protein
consumption (own results).
Tab. 4
Fish dependence in the
eight case study countries.
South Africa
Senegal
8 poorest countries
Fish
dependence
8 countries with the
highest share of fish in
total animal protein intake
Fish
dependence
Liberia
medium
Sri Lanka
very high
Malawi
high
Bangladesh
very high
Niger
low
Solomon Islands
very high
Central African Republic
medium
Kiribati
very high
Mozambique
high
Micronesia
very high
Ethiopia
low
Cambodia
very high
Guinea
very high
Sierra Leone
very high
Togo
high
Maldives
very high
Per capita GDP
(USD)
Prevalence of
undernourishment (%)
Fish share in
animal protein
Catch per
person (tons)
2011
2011-13
2011
2011
12,291
5.0
5.02
0.01
Dependence
score
medium
2,163
12.3
43.73
0.03
very high
Peru
10,429
9.6
22.28
0.28
high
United States of America
49,804
5.0
7.37
0.02
medium
Indonesia
8,438
9.3
54.82
0.02
very high
Germany
42,080
5.0
7.28
0.00
low
France
37,325
5.0
13.30
0.01
high
China
10,286
11.0
20.56
0.01
high
a very large absolute catch and low consumption (see Tab. 4, page 31). The target
countries also include Senegal and Indonesia, where many people are involved
in the fishing industry, fish is an essential part of the diet and there is a relatively
low level of food security.
As an example showing the complexity of fish dependence, Senegal is considered
highly dependent on fish.10 According to FAO figures, an estimated one million
people are directly or indirectly dependent on fish. Fish accounts for 44% of
animal protein intake, but only 12% of total protein. With a consumption of, on
average, 24 kg of fish per year per head, Senegal ‘over-consumes’ fish if the WHO
recommendation of 11.7 kg is taken as a reference. At 60 g per head per day,
protein availability is also above the level needed (the recommended level is 49
g). So, even with a moderate decline in fish intake, there would be no protein gap
in Senegal. Nevertheless, 10% of the population is undernourished. In coastal
rural areas, the fishery sector is the main source of income (Thiao et al. 2012).
However, despite an adequate protein supply, a decline in the fishing sector
would likely result in increasing poverty and hunger in Senegal (Lam et al. 2012).
Conclusion:
»»Poverty and fish dependence do not necessarily coincide.
»»However, poor countries with a comparably large fishing sector have a very
high risk of becoming food insecure if fish is lost as an income source.
Fishing for Proteins | 31
2.6 Current Fish Consumption and Fish Supply
Since fish consumption and fish supply differ between regions it is interesting to
see which regions are able to meet their demand with their own production and
which regions depend on imports to meet their demand for fish. Using population and catch data for 64 LMEs, we first calculate to what extent fish landings
from the LMEs meet local fish consumption in 2010 for the population living
in the respective neighbouring countries.11 Up to 95% of the annual global fish
catch originates from these 64 areas (Sherman et al. 2009). They pose particular
challenges for regional, and in some cases, multinational management. We used
data from the University of Vancouver’s Sea Around Us project for these calculations (Sea Around Us-database).
Fig. 14
Population, catches and
share of local consumption
for population in 2010
that was covered by LME
catches in 2010
Source: Sea Around Us
database/own mapping.
Pop. 2010 Scenario (mn)
> 50
50–150
150–500
500–1,000
Fraction
0–80%
80–100%
100–500%
> 500%
Catches (mn tons)
Keine Daten
0.01 – 0.60
0.61 – 1.50
1.51 – 4.00
4.01 – 8.00
8.01 –12.86
32
The regions depicted in Figure 14 refer to LMEs. Data on catches per LME in
2010 is taken from the Sea Around Us database. Data on population per country
in 2010 is taken from the IPCC Shared Socioeconomic Pathways Scenario 1. Fish
consumption per capita and country in 2010 is taken from the FAOStat database.
(Important: According to the FAO, fish consumption includes all kinds of fish
and seafood products including aquaculture and inland fisheries products while,
according to Sea Around Us, the catches only cover marine fish and seafood in
the LMEs. High seas catches are not included in our model. Land-locked countries are not included.
Figure 14 shows how LMEs differ in the extent to which local landings are sufficient to meet local needs in terms of fish consumption. LMEs in which landings
are not sufficient to meet local fish demand are indicated in red (coverage 0 to
80%) or yellow (coverage of 80 to 100%). LMEs in which landings are more than
sufficient to meet local fish demand are indicated in light green (coverage between
100% and 500%). Extremely over-supplied LMEs are able to meet much more
than local demand and are indicated in dark green (coverage greater than 500%).
There is a substantial variation: in very Arctic waters, e.g. the Canadian High
Arctic, North Greenland, the Beaufort Sea or the Insular Pacific Hawaiian LME,
fish production covers less than 1% of local consumption while the Scotian Shelf,
Newfoundland-Labrador Shelf, Icelandic Shelf and Sea and the Faroe Plateau
stand out with their massive production, which exceeds local needs and leads to
coverage of more than 1,000%.
Landings in 9 of the 64 LMEs are much higher than local needs. The north east
coast of North America and the northwest coast of Europe, including Iceland and
the Faroe Plateau, are characterised by very high catches relative to consumption
in the local population. In the remaining 16 LMEs, landings are sufficient to cover
local consumption of fish.
Thirty-nine LMEs have landings that are not sufficient to cover local consumption; of these 34 have a fraction of less than 80% and 21 have a fraction below
50%. The weakest LMEs are the areas in the High Arctic, north of Canada and
Russia. LMEs around Australia struggle to meet at least half of the local demand.
The ratio of landings to local needs for all LMEs considered is 82%. This means
that 82% of the aggregated fish consumption in all LMEs is covered by marine
catches from LMEs. So, obviously those catches are not sufficient to satisfy consumption. As mentioned earlier, the catches do not include aquaculture, inland
or high seas production. Catches from these sectors probably account for the 18%
of fish demand that is not covered by LMEs.
A similar calculation using the WHO recommendation for fish consumption of
11.7 kg per capita per year, instead of the actual fish consumption levels of 2010,
lead to a ratio of landings to local needs of 144% for all LMEs considered. This
indicates that in 2010 LME catches were sufficient to satisfy the basic needs in
terms of fish consumption according to the WHO. However, a redistribution of
the resource is required. In this scenario, 39 LMEs are in the under-supplied
category while 9 LMEs are in the extremely over-supplied category.
Overall, it seems that LMEs with many bordering countries tend to have a lower
potential to meet demand. Examples include the Mediterranean, the Caribbean
Sea and the Baltic Sea. In contrast, LMEs with only one or a few coastal countries
perform better. Also, LMEs located in the North Atlantic and the East Pacific
seem to have a stronger potential to meet local demand. This also holds true for
Europe, the east and west coast of the USA and the west coast of Latin and South
America. In contrast, LMEs around Africa (North West Africa being the exception) and along the Asian coasts as well as in Australia show a deficit in marine
fish production compared to local demand.
In our case studies, only Indonesia and China are facing a clear under-supply in
terms of marine catches. However, both countries might find suitable substitutes
through their aquaculture production.
Conclusion:
»»Catches vary strongly at LME level.
»»In approximately two-thirds of all LMEs, the total demand for fish
in 2010 could not be covered by local marine catches.
»»Marine catches can cover 82% of global demand for fish.
»»Excess catches are mainly reported for LMEs in the North Atlantic
and East Pacific.
Fishing for Proteins | 33
3.
The Bio-economic Model
We are interested in future global marine catches
and effects on consumption levels, specifically focusing on the year 2050. Fish markets are globalised to
a very large extent (Smith et al. 2010; Asche et al.
2015). Global markets allocate worldwide catches so that they equal total demand
for both human consumption and animal feed. Global catches are determined by
the fishing effort applied and the productivity of the fish stocks. Thus, an assessment of potential future catches requires a bio-economic modelling approach
that combines the ecological approach describing productivity of fish stocks with
the economic part describing the economic incentives to exerting fishing effort
and markets that allocate fish catches to different consumers. The literature
suggests that the efficiency of fishery management plays a central role in this
respect (Costello et al. 2008; Quaas et al. 2016).
The model is designed to tell us how the overall size of the fish catches changes
under different economic and fishery management scenarios and how the total
global catch is allocated in terms of regional catches and regional consumption
quantities. To address these questions, we use a nested modelling approach.
Here we briefly sketch the modelling approach; details are given in the technical
appendix.12
3.1 The Model Approach
Questions 1 and 2 are addressed in a global model that separates predatory and
prey fisheries using Lotka-Volterra stock dynamics. With this model approach
we take biological interaction into account. The model assumes that the change
in biomass over time depends on the natural growth of a stock, the interaction
between predator and prey and fishing activities. The predator has a negative
impact on the prey biomass, meaning that if the predator stock increases, the
prey stock will decrease because the predator feeds on prey. On the other hand,
if the prey stock increases, the predator stock will also increase. Hence, the
prey stock has a positive impact on the predator’s biomass. Fishing activities
are influenced by the demand parameter and reduce the change of a stock over
time. Available studies investigating a similar research question at the global
level have so far not taken into account such ecological interactions and lumped
all fish into one aggregate surplus production model (World Bank 2009), or
considered several biologically independent stocks (Quaas et al. 2016; Costello
et al. 2016).
Furthermore, we look into the interactions of predatory and prey fisheries on
the global fish markets, as well as into the interaction between fish and non-fish
protein food, by means of a stylised consumer demand system, where different
types of fish and non-fish protein food are imperfect substitutes (Anderson 1985;
Quaas and Requate 2013). Besides predatory fish and forage fish, we consider
protein-rich non-fish food, including beans, dairy products, eggs, lentils, peas,
maize, meat, nuts and rice.
Questions 1 and 3 are addressed in a regionalised model where each large
marine ecosystem hosts one individual fish stock. In this model we abstract from
ecological interactions between the stocks and consider a generalised Schaefer
surplus production model for each LME, but include economic interactions that
are mediated by the global fish market. Thus, we do not differentiate between
predator and prey but assume that all fish in one LME can be seen as one stock.
34
The change of biomass over time is determined by the natural growth of a stock
and the fishing activity. Again, fish production is affected by the LME-specific
demand parameters for fish goods. As fish is a traded commodity on a worldwide
market, demand in one region of the world will affect the production in another
region: a higher world-market price makes it more attractive to exert fishing
effort. In our regional demand model we consider regional consumption of three
commodities at the LME level: domestically produced fish, imported fish and
protein-rich non-fish substitution goods, again including beans, dairy products,
eggs, lentils, peas, maize, meat, nuts and rice.
3.2 Data and Estimation of Model Parameters
We use data from three main sources for the estimating parameters of the
bio-economic fishery models:
»»Sea Around Us (www.seaaroundus.org),
»»FAO database FAOStat (http://faostat3.fao.org/home/E) and
»»FAO Fisheries and Aquaculture Department’s database FishStatJ
(http://www.fao.org/fishery/statistics/software/fishstatj/en).
Data on catches and landed values of fish is taken from the Sea Around Us database. From this database we use time series of catches and landed values from
1950 to 2010 for 64 LMEs (see Figure 15).
Fig. 15
Large marine ecosystems
considered in this study.
54
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1. East Bering Sea
2. Gulf of Alaska
3. California Current
4. Gulf of California
5. Gulf of Mexico
6. Southeast U.S. Continental Shelf
7. Northeast U.S. Continental Shelf
8. Scotian Shelf
9. Newfoundland-Labrador Shelf
10. Insular Pacific-Hawaiian
11. Pacific Central-American
12. Caribbean Sea
13. Humboldt Current
14. Patagonian Shelf
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
South Brazil Shelf
East Brazil Shelf
North Brazil Shelf
Canadian Eastern Arctic West Greenland
Greenland Sea
Barents Sea
Norwegian Sea
North Sea
Baltic Sea
Celtic-Biscay Shelf
Iberian Coastal
Mediterranean
Canary Current
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
Guinea Current
Benguela Current
Agulhas Current
Somali Coastal Current
Arabian Sea
Red Sea
Bay of Bengal
Gulf of Thailand
South China Sea
Sulu-Celebes Sea
Indonesian Sea
North Australian Shelf
Northeast Australian Shelf
East-Central Australian Shelf
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
Southeast Australian Shelf
Southwest Australian Shelf
West-Central Australian Shelf
Northwest Australian Shelf
New Zealand Shelf
East China Sea
Yellow Sea
Kuroshio Current
Sea of Japan/East Sea
Oyashio Current
Sea of Okhotsk
West Bering Sea
Northern BeringChukchi Seas
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
Beaufort Sea
East Siberian Sea
Laptev Sea
Kara Sea
Iceland Shelf and Sea
Faroe Plateau
Antarctic
Black Sea
Hudson Bay Complex
Central Arctic Ocean
Aleutian Islands
Canadian High ArcticNorth Greenland
Fishing for Proteins | 35
The Sea Around Us database includes differentiated data on catches, landings
and estimated discards of large-scale, small-scale, subsistence and recreational
fishery. The data in the Sea Around Us database combines reported values from
the FAO and additional unreported data estimated by Sea Around Us. Additional
information on missing data was collected for this estimate (Pauly and Zeller
2015). The main sources were governmental websites and publications, statistical
agencies responsible for the fishing industry, international research organisations such as the FAO, the International Council for the Exploration of the Seas
(ICES) or regional fisheries management organisations such as the Northwest
Atlantic Fisheries Organization (NAFO) as well as academic literature. Based on
this information, anchor-points in time are derived and used for interpolation.
A linear interpolation between anchor-points is used to reconstruct commercial
catches. Either population trends or trends in the number of fishers over time are
used to interpolate between anchor points for non-commercial catches from subsistence and recreational fisheries. The reconstructed catches are then combined
with the reported FAO data.
The Sea Around Us catch data is disaggregated into catches by species, functional
groups and size groups of 0-30, 30-89 and >89 cm average length. We use this
information to group the catches into predator and prey categories. We take a
size-based approach and consider large fish to be most likely predatory and small
fish to be most likely forage fish. Specifically, we consider all fish greater than 90
cm to be predatory while fish smaller than 90 cm and invertebrates are considered to be prey. In the global predator-prey model and the global demand model,
the data is aggregated per year over all 64 LMEs, leading to 61 observations each
(= number of years from 1950 to 2010) for predator and prey catches. In the fish
supply model at LME-level we use total catches per year aggregated over size
groups. In this model the data has been aggregated by LME and year. In total, we
use a dataset with 3,904 observations.
Figure 16 shows the development in total catches and catches from 1950 to 2010.
The amount of total catches peaks in 1996 at 123 million tons. Since then, catches
are decreasing. This is in line with Pauly and Zeller (2016) who also calculated the
global peak in catches in 1996 at a level of 129 million tons. In contrast to the global
data of Pauly and Zeller (2016), we do not include high seas catches, which results
in the difference of 6 million tons. In addition, some small island states, such as
Wallis and Futuna Islands (France), Saint Helena (UK) or Nauru, are not included
because their geographical position does not lie within the area of an LME.
The biological parameters are estimated using the Catch-MSY method developed
by Martell and Froese (Martell and Froese 2013). This method allows biological
parameters, such as biomass based on catch data, to be estimated. It requires
time series of catch data and prior ranges for the parameter values as well as
possible ranges of stock sizes from the initial and the final period.
After specifying initial stock sizes and limits for the final stock size, a parameter
set is randomly drawn from the prior parameter distribution. Then, the underlying fish supply model is used to calculate the biomass corresponding to the level
of harvest given the parameter set. If this biomass is in a reasonable range, the
parameter set is stored. In our analysis, we repeat this procedure 10,000,000
times for each LME. We use samples of 1,000 randomly picked accepted parameter values in our model computations to compute mean estimates and confidence
intervals. Thus, all results reported below are based on averages and standard
deviations obtained from 1,000 separate model runs.
36
Fig. 16
Development of global total
catches and predatory/prey
catches from 1950 to 2010.
(in million tons)
Source: SeaAroundUs
Total
Prey
Predator
150
100
50
0
1940
1960
Total
Prey
1980
2000
2020
Predator
We use the above-mentioned approach by Martell and Froese with a Schaefer
surplus production model for each LME and for one global stock of aggregated
fish. In the global predator-prey model we extend the approach by Martell and
Froese and determine parameter values for a Lotka-Volterra predator-prey
model (Hannesson 1983). In each case, initial parameter sets are randomly
drawn from a uniform distribution to be tested. Economic theory predicts a
positive relationship between fish stock biomass and market supply of fish (or
no relationship at all in the case of a pure schooling fishery), and thus a negative
relationship between stock biomass and the fish price. We use price data in
each run for a tested parameter13 set to check whether this requirement is met.
Biological parameters that do not pass this test are rejected. Otherwise, we use
the resulting information on the relationship between price and stock biomass to
obtain an estimate for economic parameter values.
3.3 Global and Regional Demand Systems
To quantify the demand systems, we use data on fish prices from Sea Around Us.
The data on landed values is used to derive prices. The ex-vessel fish prices, used
to calculate the landed values, are derived using two approaches. Local ex-vessel
prices, converted to US dollars, form the starting point. These are combined with
ex-vessel prices calculated from reported landed values and catches.
Real prices are determined by deflation using the consumer price index of 2005
(Sumaila et al. 2015). We use this price data for the calculation of production
values in the global demand model. Figure 17 shows the development of ex-vessel
prices over time for predator and prey fish. Since prey also include valuable invertebrates such as shrimp, lobster or sea urchins, the prey price does not deviate
much from the predator price.
In addition to fish production quantities and fish prices, the global demand model
also requires data on total expenditures and consumption levels for the three
commodities: predatory fish, prey fish and non-fish protein-rich substitution
goods. Total national expenditures are calculated from production, export and
import values, while national consumption is calculated from production, export
and import quantities (see Fig. 19).
Data on substitution goods is taken from the FAO Statistics division, with a
Fishing for Proteins | 37
11. Grey
GreyDark
GreyMedium
GreyLight
BaseColoursBackground
BaseColoursTintedBox
Black
Base Colours
1. Red
RedDark
RedMedium
OrangeDark
OrangeMediu
YellowDark
YellowMediu
GreenDark
GreenMedium
EarthDark
EarthMedium
BrownDark
BrownMedium
BlueDark
BlueMedium
AquaDark
AquaMedium
PinkDark
PinkMedium
BerryDark
BerryMedium
GreyDark
GreyMedium
BaseColoursBackground
BaseColoursT
2. Orange
3. Yellow
2600
2400
4. Green
2200
5. Earth
2000
1800
6. Brown
1600
7. Blue
1400
1200
1000
8. Aqua
1950
1955
1960
Fig. 17
Prey
Global real ex-vessel
Predator price
per year in 2005
(in USD per ton).
Source: Sea Around Us/
Own graphic
Prey
Predator
1965
1970
1975
1980
1985
1990
1995
2000
20059. Pink ’10
10. Berry
time series running from 1961 to 2013. Collection of the data is restricted by the
availability of both trade and production data and the length of the corresponding
11. Grey
time series. Considering all these limitations, the following commodities are
included in the group of substitution goods: beans, dairy products, eggs, lentils,
Base Colours
peas, maize, meat, nuts and rice.14
The global demand model reflects total global production (quantity in tons),
global export price (per ton in current USD) and global export value (in current
USD) for the above-mentioned commodities. In contrast to this, the demand
model at LME-level does not require data on predatory fish and prey fish, but on
domestically produced fish and imported fish.
We use the FAO database to provide data on total national domestic production
(quantity in tons), exports (quantity in tons, value in current USD) and imports
(quantity in tons, value in current USD) from 1976 to 2010. The FAO FishstatJ
database does not contain information on production values for fish goods hence
we calculate the production value as the product of production quantities and
export prices. Export (import) prices for fish as well as for substitution goods are
calculated by dividing export (import) values by the corresponding quantities.
Sea Around Us only provides deflated landed values (real prices) of predatory and
forage fish and is hence not comparable to the nominal FAO price data for nonfish substitution goods. For this reason, data on global export values and global
export quantities of fish is also taken from the FAO FishStatJ database for the period from 1976 to 2010. Global nominal export prices per ton are calculated using
this data. The FAO FishstatJ database does not differentiate between forage and
PreyFor this reason, we were not able to calculate the price for these
predatory fish.
two types ofPredator
fish separately. Instead, a common global export price per year for
Substitution Goods
Fig. 18
Global expenditure per
year (in billion USD
from 1976 to 2010).
Source: FAO FishstatJ/
Own graphic
Predator
Prey
Substitutiongoods
38
250
200
150
100
50
0
1976
1981
1986
1991
1996
2001
2006
’10
8. Aqua
AquaDark
9. Pink
PinkDark
10. Berry
BerryDark
11. Grey
GreyDark
Base Colours
BaseColoursBackground
Fig. 19
Global production per year
(in million tons from 1976
to 2010.
Data from FAOSTAT,Sea
Around Us/Own graphic
Predator
Prey
Substitutiongoods
Exponentiell
Substitution
Goods
120
7 Predator and Prey Production
Substitution Goods Production 3
3000
100
2500
80
2000
60
1500
40
1000
20
500
0
0
1975
1980
1985
1990
1995
2000
2005
2010
both types of fish is calculated. Furthermore, both wild-caught fish and fish from
aquaculture are included in the FishstatJ database. Since we calculate a global
price for all Prey
fish commodities in this database, this price does not differ between
wild-caught Predator
fish and fish from aquaculture either. Hence, the information entered
Substitution
into our database
from Goods
these two sources relates to:
Exponentiell (Substituion Goods)
»»total global marine catches of predatory and forage fish (in tons),
»»the global export value of predatory and forage fish (in current USD),
»»the global export price for fish (per ton in current USD).
Finally, total national expenditures are calculated as the sum of production and
import values minus export value.
National consumption is calculated as the sum of domestic production and imports minus exports. For some observations, the resulting value for consumption
is negative. In these cases, we set the negative values equal to zero.
We assume one global consumer who has preferences regarding the quantities of
each commodity she consumes. We further assume that the consumer prefers to
substitute fish with another species of fish rather than with non-fish substitute
goods.
To calculate global demand for a) predatory fish, b) forage fish and c) protein-rich
non-fish substitute goods, we use the following yearly input data at global level:
export prices, production quantities, total expenditures for all three commodities
and a parameter that expresses the above-mentioned preferences of our consumer. With the given information on predatory and forage fish and substitute goods,
we estimate the demand parameters for each commodity for each year from 1976
to 2010. The demand parameters indicate the estimated share each commodity
has in the consumption of protein-rich food.
For the projections of global fish demand in 2050, we then calculate the mean of
these demand parameters over time per LME and commodity. To match catch
data from Sea Around Us at LME level with FAO country data, we estimated each
country’s share in a particular LME based on the spatial overlap of the country’s
coastal waters with the LME. Some countries included in the FAO data do not
Fishing for Proteins | 39
have coastal waters in any LME, either because they are landlocked or because
there is no LME defined in their coastal waters. We remove countries without
access from our dataset.15
Some countries have coastal waters in more than one LME. For these countries
we assumed that the fraction of trade and production associated with an LME is
equal to the fraction of the country’s area of coastal waters in the corresponding
LME. The shares are calculated using GIS software and information on the area
of exclusive economic zones (EEZs) and LMEs.
Estimating regional consumption requires the aggregation of all input data
(domestic production quantities, etc.) at LME level. With regard to the regions,
we group input data by the 64 LMEs provided by Sea Around Us. In each LME,
the consumer has preferences regarding the quantities of each commodity she
consumes. We assume that each LME’s consumer prefers to substitute fish with
another species of fish rather than with a non-fish substitute good and that she
differentiates between imported and domestically produced fish.
To calculate regional demand for a) imported fish, b) domestically produced fish
and c) protein-rich non-fish substitute goods, we use the following yearly regional
level input data: export prices, import prices, domestic production quantities,
imported production quantities, total expenditures for all three commodities and
two parameters that express the above-mentioned preferences per LME.
Using this information, we estimate the demand parameters for each good and
each year from 1976 to 2010. The demand parameters specify the estimated share
of each of the three goods in the total consumption of protein-rich foods. To
project the demand for fish in 2050, we calculate the average value per LME and
per good from the time series of the demand parameters.
3.4 S
cenarios: Socioeconomic Pathways
and Fishery Management
Fish consumption crucially depends on income that consumers in different parts
of the world spend on fish and non-fish protein-rich food. It also depends on
population numbers.
With regard to income and population numbers, we base our scenarios on GDP
development data from the International Institute for Applied Systems Analysis
(IIASA) quantification of the Intergovernmental Panel on Climate Change (IPCC):
so called ‘shared socioeconomic pathways’ (SSPs) describing world futures in the
21st century.16 Five SSPs describe five scenarios of global future societal development. These SSPs are one component of the IPCC scenarios integrating future
changes in climate and society to investigate climate impacts and options for
mitigation and adaptation (O’Neill et al. 2015). Among those, SSP1 is deemed to
describe sustainable development. We use SSP1 for our baseline scenario.
Figure 20 shows the projection of GDP in each of the scenarios. In order to cover
the range of future GDP we also used scenarios SSP3 (minimum GDP scenario)
and SSP5 (maximum GDP scenario).
For the base case of the SSP1 scenario, global GDP increases by a factor of 3.757.
The income elasticity of demand for food is the parameter that determines what
40
GreyDark
Base Colours
BaseColoursBackground
1. Red
RedDark
2. Orange
OrangeDark
3. Yellow
YellowDark
4. Green
GreenDark
Fig. 20
Development of global
GDP from 2010 to 2050
for IPCC scenarios SSP1,
SSP3 and SSP5.
(in trillion USD)
SSP1
SSP3
SSP5
350
5. Earth
300
6. Brown
EarthDark
BrownDark
250
7. Blue
BlueDark
200
8. Aqua
AquaDark
150
9. Pink
PinkDark
100
10. Berry
50
0
BerryDark
11. Grey
2010
2020
SSP1
SSP2
2030
SSP3
2040
2050
Base Colours
BaseColoursBackground
SSP5
SSP4
fraction of additional income will be spent on food by 2050. The review by Cireira
and Masset (2010) indicates that, while the income elasticity of fish demand is
close to 1, the best global estimate for the income elasticity of food demand is 0.48.
For the base case, we assume that global expenditures for fish and non-fish
protein-rich food increase by a factor of 0.48 x 3.757. In addition, we consider a
very conservative scenario (SSP3) where food expenditures increase by a factor of
0.48 x 2.758 and no further technical progress is made in fishing technology; and
a high-pressure scenario (SSP5) where global food demand increases by a factor
of 4.534 and income elasticity is 1, which may be adequate for fish as well (Cireira
and Masset 2010).
The corresponding population development is depicted in Figure 21. To calculate
future fish demand, we will use the scenarios SSP1 and SSP3 in order to cover the
range of possible population numbers in 2050. While SSP1 assumes the smallest
population level in 2050 (8.5 billion) with a population increase factor of 1.23,
SPP3 refers to the biggest population level in 2050, namely 9.95 billion with a
population increase factor of 1.45.
With regard to the supply of non-fish protein-rich food, we estimate the trend
between 1976 and 2010, which was characterised by an annual growth rate of
2.09%. In all scenarios we assume that this growth rate can be sustained until
SSP1
SSP2
SSP3
SSP5
SSP4
11
Fig. 21
Development of the global
population from 2010 to
2050 in the IPCC scenarios
SSP1, SSP3 and SSP5.
(in billion people)
10
SSP1
SSP3
SSP5
8
9
7
6
2010
2020
2030
2040
GreyDark
2050
Fishing for Proteins | 41
2050. The economic parameters estimate gives an estimate of the improvement in
fishing technology and corresponding reduction in fishing cost by 2.4% per year
for predator fisheries and 1.1% per year for prey fisheries on average. This is in
line with previous findings in the literature (Squires and Vestergaard 2013). We
also assume that this trend will persist until 2050, except in our most conservative
scenario in which we assume that there will be no further technical progress.
Fishery management may have a very important effect on future stock development and catches (Froese and Proelß 2010; Quaas et al. 2016; Costello et al.
2016). We consider different scenarios with respect to management effectiveness.
One such scenario is that all fisheries will be managed according to the maximum
sustainable yield, i.e. in such a way that the long-term catches in tons are maximised (Froese and Proelß 2010).
Furthermore, we consider different scenarios where fishing effort is managed
by means of total allowable catches and effort regulations (Grafton et al. 2005).
In our modelling approach, we follow Quaas et al. (2016) and conceptualise
management effectiveness as the fraction of external costs of fishing that are
internalised in the fishermen’s decisions with regard to their catch effort. Such
external costs arise if individual fishermen do not fully take into account the
effects of fishing on future fishing opportunities. Economically optimal management would set the total allowable catch (TAC) so that 100% of external costs
of fishing are taken into account and fisheries are regulated. No management
at all would correspond to open access conditions and 0% of the external costs
of fishing would be taken into account. We quantify management effectiveness
based on Mora et al. (2009). We consider the case of perfect management
(management effectiveness at 100%) and eight cases of imperfect management
(management effectiveness from 20% to 90%, respectively). We neglect costs of
management, e.g. for monitoring and enforcement, throughout the analysis.
42
4. Green
GreenDark
5. Earth
EarthDark
6. Brown
BrownDark
7. Blue
BlueDark
8. Aqua
AquaDark
4.
Results and Discussion
9. Pink
The following section presents model output regarding fish supply and fish demand in 2050 based10.on
Berry
the global demand model and the global predatorprey model.
11. Grey
PinkDark
BerryDark
GreyDark
We assume maximum sustainable yield management for all fisheries. This allows
Base Colours
us to answer the question of the extent to which the fish stocks in the global oceans
could contribute to the supply of protein for the world population in 2050. Based
on the global predator-prey model, the global surplus production model and
aggregated results from the regional model, we present estimates of the maximum
sustainable yield (MSY) that the global fish stocks could supply. Figure 22 shows
the estimates for the three models; Table 5 shows the corresponding figures:
Fig. 22
Estimate of maximum
sustainable yield that the
global fish stocks could
supply according
to different models.
(in million tons)
250
200
150
Global MSY estimate
100
50
0
Predator-Prey
Surplus production
model type
LME surplus
production
The first bar shows the global catch quantity for a predator-prey model; the
second and third bars show the maximum catch for Schaefer surplus models. In
the second model, one global stock is assumed for the entire sea; the third model
represents the total maximum sustainable yields from 64 LMEs each of which has
one stock. In this third model, we analyse the potential contribution of the LMEs
to meet global and regional needs for fish protein.
The diagram indicates that the global MSY is 112 million tons in the Schaefer
surplus model. We calculate that total global catches reached 101 million tons
in 2010.17 This means that marine resources are already almost fully exploited,
which does not leave much space for an increase in catches in the future.
Tab. 5
Mean catches in 2050
according to three model
specifications.
Mean catch
(million tons)
Standard deviation
(million tons)
Yield-oriented, global predator-prey
160
91
Global surplus production
(global Schaefer model)
112
1
Aggregate of regional surplus production model
(64 LMEs)
111
3
Fishing for Proteins | 43
BaseColoursBackground
1. Red
RedDark
BlueLight
2. Orange
The globalBlueDark
MSY estimateBlueMedium
for the predatory-prey
model is much larger – almost
OrangeDark
OrangeMedium
160 million tons. If the management strategy focuses exclusively on yield, the
8. Aqua
AquaDark
AquaMedium
AquaLight
3. Yellowhigher. A growing global
total global
catch could therefore
be substantially
YellowDark
YellowMedium
population coupled with a rising demand for protein-rich food like fish could
9. Pink
PinkDark
PinkMedium
PinkLight
4. Green
justify a management
strategy
that is focused
on
maximising biomass.
However,
GreenDark
GreenMedium
10. Berry in doing this, all other conservations objectives of a sustainable fishing industry
BerryMedium
BerryLight
5. Earth
(ecologicalBerryDark
effects, ecosystem-related
eff
ects, socioeconomic
consequences)
would
EarthDark
EarthMedium
11. Grey have to be disregarded. A high maximum catch also comes with much higher
GreyDark
GreyMedium
GreyLight
6. Brown
uncertainty. This is in line with studies showing that the stability
of ecosystems
BrownDark
BrownMedium
Base Colours declines sharply if predatory populations are disproportionately reduced (Britten
BaseColoursBackground
BaseColoursTintedBox
Black
7. Blue
et al. 2014; Essington et al. 2015).
Senegal
South Africa
Peru
USA
Indonesia
China
France
Germany
Next, we analyse how global catches depend on the management effectiveness for
the three scenarios in the global bio-economic predator-prey model. Figure 23
shows the global catches in 2050 for the demand scenario based on the reference
scenario with GDP increase from SSP1 and an income elasticity of food demand
of 0.48.
8
0,5
0,4
0,3
0,2
0,1
0
1960
1970
1980
Fig. 23
Global fish catches accord50 ing to global bio-economic
predator-prey model based
40
on varying degrees of
30management effectiveness
(reference scenario, income
20
growth from SSP1).
(in million tons)
10
0
1960
Global catches
1970
1980
of predatory fish
Global catches
of prey fish
In a scenario where there is perfect
management (100% effectiveness), global
6
catches of predatory fish and prey fish reach levels of 21 and 116 million tons, re4
spectively. Together, this totals
137 million tons, significantly above current yields
(Figure 23). The current level2of fishery management effectiveness, however,
averages out at about 50-60% (Mora et al. 2009; Watson et al. 2009; Quaas et al.
0 global catch yields in 2050 would be only slightly
2016). If it remains at this level,
1960
1970
1980
1990
2000
2010
1990
2000
2010
above the current level. Current management does not sufficiently reflect on the
species´ interactions. Thus, predatory fish would be heavily fished, which would
alleviate predatory pressure on forage fish, enabling slightly higher total catches.
Compared to a scenario of perfect management, fish consumption would gradually shift towards smaller and smaller species.
3
A decrease in management effectiveness to levels below the current status leads
2
to a strong decrease in catches for both predatory and forage fish. Thus, in line
with the finding of Quaas et al. (2016), achieving a sufficiently high degree of
1
management effectiveness is essential for sustaining fish catches while global fish
demand continues to increase.
1990
2000
2010
0
1960
140
25
120
1980
1990
2000
2010
100
20
80
15
60
10
40
5
20
20
30
40
50
60
70
80
Management effectiveness (%)
44
1970
For the sake of comparison, we consider the high-pressure scenario with GDP
growth based on SSP5 and a unit income elasticity of fish demand. Results are
shown in Figure 24. If management was perfect, global catches of predatory fish
30
0
RedMedium
90
100
0
20
30
40
50
60
70
80
Management effectiveness (%)
90
100
RedLight
OrangeLight
YellowLight
GreenLight
EarthLight
BrownLight
2
30
20
1
10
0
1960
1970
1980
1990
2000
0
1960
2010
1970
1980
1990
2000
2010
1. Red
30
25
BlueDark
Senegal
South Africa
15
10. Berry
5
0
120
AquaDark
9. Pink
AquaMedium
PinkMedium
BerryMedium
11. Grey
GreyDark
GreyMedium
30
Base Colours
40
50
BerryLight
60
40
GreyLight
60
70
80
90
BaseColoursTintedBox
100
0
Black
20
30
Management effectiveness (%)
Fig. 24
Global fish catches according to global bio-economic
0,5
predator-prey model based
on varying degrees of
0,4
management effectiveness
0,3 (high-pressure scenario,
0,2income growth from SSP5,
unit income elasticity of
0,1
food demand).
(in
million tons)
0
1960
1970
1980
Fig. 25
Expected global fish catches
according to a global
bio-economic predator-prey
50 model based on varying
40 degrees of management
effectiveness (low-pressure
30 scenario, income growth
20 from SSP3, no technical
progress in fishing).
10
(in million tons)
0
1960
1970
1980
Global catches
of predatory fish
Global catches
of prey fish
RedLight
OrangeDark
OrangeMedium
OrangeLight
YellowDark
YellowMedium
YellowLight
GreenDark
GreenMedium
GreenLight
EarthDark
EarthMedium
EarthLight
BrownDark
BrownMedium
BrownLight
3. Yellow
4. Green
5. Earth
6. Brown
7. Blue
40
50
60
70
80
90
100
Management effectiveness (%)
and prey fish could be sustained at similar levels as in the reference scenario.
However, with decreasing management effectiveness, the catches decrease much
more strongly than in the baseline
scenario.
8
6
Results for the most conservative
scenario with demand growth derived from
SSP3, and assuming that there
will
be no further technical progress in fishing,
4
are shown in Figure 25. In this case, fish catches could be sustained even if
management did not improve2compared to current levels. However, the assumptions made here are not very realistic: in particular, the trend of improving
0
fi1990
shing technology
is2010
likely to 1960
continue 1970
in the coming
would lead
1980 decades,
1990which
2000
2010
2000
to strongly increased fishing pressure. Nevertheless, this scenario shows that
economic driving forces, in particular increasing demand and technical progress
in fishing technology, are central factors influencing the future fate of fisheries.
In the last step we analyse how
3 the LMEs within the global ocean can contribute
to meeting the needs of fish protein intake around the globe. To do this, we use
the MSY estimates for the diff2erent LMEs and compare them with regional fish
consumption, based on FAO data for fish consumption in 2010 and the population in 2050 assumed from 1the two scenarios used (SSP1 and SSP3). These
scenarios refer to the extremes in population development with SSP1 referring
to the smallest projected population
in 2050 and SSP3 referring to the largest
0
projected
population
among the
five scenarios.
1960
1970
1980
1990
2000
2010
1990
2000
2010
The results are depicted in Figure 26 and Figure 27. The LMEs are again colour-coded according to their potential to meet local needs. The red and yellow
30
140
25
120
100
20
80
15
60
10
40
5
0
RedMedium
2. Orange
20
BaseColoursBackground
20
PinkLight
80
BerryDark
France
Germany
AquaLight
100
PinkDark
Indonesia
China
BlueLight
140
8. Aqua
Peru
20
USA
10
BlueMedium
RedDark
20
20
30
40
50
60
70
80
Management effectiveness (%)
90
100
0
20
30
40
50
60
70
80
90
100
Management effectiveness (%)
Fishing for Proteins | 45
Fig. 26
Projected MSY catches (in
million tons), population
size (in millions) and share
of local needs (in %) that
could potentially be met by
LME in 2050 under ideal
conditions and population
development according to
SSP1 scenario.
Fig. 27
Projected MSY catches (in
million tons), population
size (in millions) and share
of local needs (in %) that
could potentially be met by
LME in 2050 under ideal
conditions and population
development according to
SSP3 scenario.
Pop. 2010 Scenario (mn)
> 50
50–150
150–500
500–1,000
> 1,000
Fraction
0–80%
80–100%
100–500%
> 500%
Catches (mn tons)
No Data
0.01 – 0.60
0.61 – 1.50
1.51 – 4.00
4.01 – 8.00
8.01 –13.15
46
dots indicate that the LME is not able to meet local needs – not even according
to the MSY management scenario assumed here. The green dots indicate that the
LME is able to meet more than local needs with MSY management.
Compared to 2010, the results are quite similar. In 2050, 38 LMEs in the SPP1
scenario and 37 LMEs in the SSP3 scenario are not able to meet the needs of the
local population. The extremes of 2010 can also be found in the 2050 scenarios.
In very Arctic waters, e.g. the Canadian High Arctic, North Greenland or Beaufort
Sea or the Insular Pacific Hawaiian LME, fish production meets less than 1% of
their demand while the Scotian Shelf, Newfoundland-Labrador Shelf, Icelandic
Shelf and Sea and the Faroe Plateau stand out with their massive overproduction,
which leads to coverage of more than 1,000%.
However, a few LMEs change their category. The North Sea and the Sea of Japan
will probably be able to meet local needs in 2050. In contrast, the Arabian Sea
and the California Current are likely to not be able to meet local needs in 2050.
Regarding the world’s potential to meet population needs in terms of fish, the
scenarios differ. In the SSP1 scenario with the lower population in 2050, the
Fig. 28
Net import and net export of
fish per LME in 2050
(in million tons).
Net exporter (mn ton)
> 5,0
5.0–1.0
1.0–0.5
< 0.5
Net Importer (mn ton)
> 5.0
5.0–1.0
1.0–0.5
< 0.5
world’s fish supply can meet 81% of the world’s fish needs. In the SSP3 scenario
with the higher population, only 75% of the world’s fish needs can be met by
the world’s fish supply. Similar to the situation in 2010, the missing 19% and
25%, respectively, are probably supplied by fish from aquaculture, high seas and
inland production.
In the SSP1 scenario with the slow population development, 28 LMEs experience
a decrease of more than 10% in their share of MSY catches and local needs. The
average decrease in these LMEs is 25%. In 20 LMEs the share increases by more
than 10% with an average increase of 37%. Overall, decreases and increases seem
to level out since the world’s share only decreases by 1%.
In the SSP3 scenario with the strong population development, the share reduces
by more than 10% in 19 LMEs. The average decrease in these 19 LMEs is 35%.
In contrast, 28 LMEs experience an increase in their share by more than 10%.
On average, the increase is 43%. However, since the world’s share shows a clear
decrease from 82% in 2010 to 75% in 2050, it seems that although more LMEs
will probably face a stronger deficit in fish supply in the SSP1 scenario, this total
deficit will be bigger in the scenario with the higher population.
Overall, Figure 26 and Figure 27 clearly show that the world’s future fish needs
will probably not be met by marine catches alone. Aquaculture will be required as
well, with the caveat that some aquaculture production uses wild captured fish as
well (Essington et al. 2015).
In order to find out which LMEs are likely to export fish products and which
LMEs are likely to depend on imports in the future, we calculate net import
and export quantities per LME in 2050 from estimates of the demand model at
LME-level. The distribution is shown in Figure 28.
Figure 29 and Figure 30 show the absolute deviation of fish consumption between
2010 and 2050 for all LMEs. The LMEs in Figure 29 experience a decrease in fish
consumption in 2050 compared to 2010 while Figure shows the LMEs where fish
consumption will increase.
According to Figure 29, waters along the East Asian coast will experience the
biggest decrease in fish consumption by 2050 although these areas struggle to
Fishing for Proteins | 47
GreenDark
GreenMedium
EarthDark
EarthMedium
BrownDark
BrownMedium
BlueDark
BlueMedium
AquaDark
AquaMedium
PinkDark
PinkMedium
BerryDark
BerryMedium
GreyDark
GreyMedium
BaseColoursBackground
BaseColoursT
5. Earth
6. Brown
7. Blue
8. Aqua
9. Pink
10. Berry
meet local demand with local supply (see Figure 26 and Figure 27). Since LMEs
interact on a global market, price levels for fish and substitution goods will influ11. Grey
ence people´s decision on whether they will consume fish or turn to a substitute
instead. If fish prices are sufficiently high, fish will become an unaffordable good
Base Colours
for a substantial share of the population in the LMEs along the East Asian coast.
These people will turn to the more affordable substitution goods and fish is going
to be exported for the higher export price. This might explain the leading position
of East Asian LMEs among the net exporters.
Fig. 29
LMEs with decreasing fish
consumption between
2010 and 2050
(in million tons).
Oyashio Current
Canadian Eastern Arctic-West
East Siberian Sea
Aleutian Islands
Laptev Sea
Humboldt Current
California Current
Sea of Okhotsk
Pacific Central-American
Arabian Sea
Black Sea
Kara Sea
Barents Sea
Beaufort Sea
Iberian Coastal
Caribbean Sea
Baltic Sea
Gulf of Alaska
Northern Bering - Chukchi
East Bering Sea
Sulu-Celebes Sea
North Sea
Sea of Japan / East Sea
Guinea Current
Mediterranean Sea
Kuroshio Current
Bay of Bengal
East China Sea
Yellow Sea
South China Sea
0
48
5
10
15
20
25
Base Colours
BaseColoursBackground
Fig. 30
LMEs with increasing
fish consumption between
2010 and 2050
(in million tons).
Southeast Australian Shelf
Northwest Australian Shelf
Indonesian Sea
Gulf of Thailand
East Central Australian Shelf
West-Central Australian Shelf
Benguela Current
North Brazil Shelf
Gulf of California
Canary Current
Agulhas Current
Newfoundland-Labrador Shelf
Northeast U.S. Continental Shelf
West Bering Sea
Celtic-Biscay Shelf
Southeast U.S. Continental Shelf
Greenland Sea
Southwest Australian Shelf
South Brazil Shelf
Gulf of Mexico
Somali Coastal Current
North Australian Shelf
Patagonian Shelf
Canadian High Arctic – North Greenland
Scotian Shelf
Northeast Australian Shelf-Great Barrier
Insular Pacific-Hawaiian
Faroe Plateau
Iceland Shelf and Sea
Red Sea
Hudson Bay Complex
New Zealand Shelf
East Brazil Shelf
Norwegian Sea
0
5
10
15
Fishing for Proteins | 49
Appendix
Tab. 6
National recommended
intakes for fish
(based on the WHO’s
recommended standards).
National recommended intake
Recommended
quantity
Source
(g/week)
United Kingdom
2 portions (140 g each) per week,
one of which should be oily
280
Food Standards Agency (2010)18
Australia/New Zealand
2-3 servings (150g each)
375
Food Standards Australia New
Zealand (2013)20
Canada
At least 150g each week
150
Health Canada (2011)21
Austria
1-2 portions per week (total 150g)
150
WHO (2003)22
Germany
1-2 portions per week
150
Georgia
12,8-15g fish per day
97
WHO (2003)23
Ukraine
20g fish per day
140
WHO (2003)24
Estonia
2-3 servings per week (50g each)
150
WHO (2003)25
United States of America
8 oz per week
226
http://bit.ly/1nhRps6
Italy
100-240g per week
170
http://bit.ly/294BDQm
France
100-200g per week
150
http://bit.ly/29AcfCm
Ireland
2x per week
200
http://bit.ly/29Anq8D
Norway
2-3x per week
250
http://bit.ly/29KT48J
Denmark
2-3x per week
350 (explicit)
http://bit.ly/29xPV69
Sweden
2-3x per week
250
http://bit.ly/29AVhkg
Iceland
2-3x per week
250
http://bit.ly/29T6jU8
Eastern Mediterranean
(Cyprus, Lebanon, Turkey,
Greece, Jordan, Syria, Israel,
Palestine, Egypt, Libya)
2x per week
180
http://bit.ly/29t25Cn
Malaysia
2-3x per week (200-300g/week)
250
http://bit.ly/29T6leL
Sri Lanka
2-3x per week (fatty fish)
250
http://bit.ly/29t2F30
Barbados
2-3x per week
250
http://bit.ly/1TbViHR
Mexico
2x per week
200
http://bit.ly/29M12LC
Argentina
2-3x per week ( 75-100 g each)
244
http://bit.ly/1OLY18D
Total:
31 national recommendations
19)
50
to 25): from Thurstan et al. (2013)
Ø = 204,25
204.25 x 52 = 10.6 kg/capita x year
Fish Supply Model
Fish Supply Model
Fish
Supply
Model
a. Global
Predator-Prey
Model
Fish
Supply
a.
Predator-Prey
Model
WeGlobal
assume
aModel
Lotka-Volterra
type of predator-prey model (Hannesson 1983) where 𝑥𝑥 refers to the biomass of the predatory
Fish
Supply
a.
Global
Predator-Prey
Model
We
assume
a
Lotka-Volterra
type
of prey
predator-prey
model (Hannesson
where
refers
to the
biomass
𝑦𝑦̇ t) are
defined
as of the predatory
species
and
𝑦𝑦Model
to the biomass
of the
species. Changes
in biomass1983)
over time
(𝑥𝑥̇ t𝑥𝑥and
Fish
Supply
Model
a. Global
Model
We
assume
a
type
of prey
predator-prey
model (Hannesson
where
refers
to the
biomass
𝑦𝑦̇ t) are
defined
as of the predatory
species
and
𝑦𝑦 Lotka-Volterra
to the biomass
of the
species. Changes
in biomass1983)
over time
(𝑥𝑥̇ t𝑥𝑥and
Predator-Prey
a. Global
Predator-Prey
Model
2
We assume
of prey
predator-prey
model
1983)
where
refers
to the
biomass
the predatory
and
𝑦𝑦̇ t) are
defined
as of
species
and
𝑦𝑦 Lotka-Volterra
to the biomasstype
of the
species.
Changes
(𝑥𝑥̇ t𝑥𝑥
a
𝑥𝑥̇ 𝑡𝑡 =
𝑟𝑟𝑥𝑥 𝑥𝑥𝑡𝑡 −(Hannesson
𝑘𝑘in
− 𝐻𝐻𝑡𝑡time
𝑥𝑥 𝑥𝑥biomass
𝑡𝑡 + 𝑎𝑎𝑎𝑎𝑡𝑡 𝑦𝑦1983)
𝑡𝑡over
Fish
Supply
Model
We
assume
a
Lotka-Volterra
type
of predator-prey
model
(Hannesson
where
𝑥𝑥
refers
to
the
biomass
of the predatory
22
species and
𝑦𝑦 to the biomass of the prey species.
Changes
in
biomass
over
time
(𝑥𝑥̇
t and 𝑦𝑦̇ t) are defined as
𝑥𝑥̇
=
𝑟𝑟
𝑥𝑥
−
𝑘𝑘
𝑥𝑥
+
𝑎𝑎𝑎𝑎
𝑦𝑦
−
𝐻𝐻
𝑦𝑦
𝑦𝑦
−
𝑏𝑏𝑏𝑏
𝑦𝑦
−
𝐿𝐿
𝑦𝑦̇
𝑥𝑥
𝑥𝑥𝑦𝑦 biomass
𝑡𝑡𝑡𝑡
𝑡𝑡𝑡𝑡 𝑡𝑡𝑡𝑡over 𝑡𝑡time (𝑥𝑥̇ t and 𝑦𝑦̇ t) are defined as
𝑦𝑦 𝑡𝑡𝑡𝑡
a. Global
Model
species
andPredator-Prey
𝑦𝑦 to the biomass
of the prey species.𝑡𝑡 Changes
in
𝑥𝑥̇𝑦𝑦̇𝑡𝑡 =model
𝑟𝑟𝑥𝑥𝑦𝑦𝑥𝑥𝑦𝑦𝑡𝑡𝑡𝑡 −(Hannesson
𝑘𝑘𝑥𝑥𝑦𝑦𝑥𝑥𝑦𝑦𝑡𝑡2𝑡𝑡2 +
− 𝑎𝑎𝑎𝑎
𝑏𝑏𝑏𝑏𝑡𝑡𝑡𝑡𝑦𝑦𝑦𝑦1983)
− 𝐻𝐻
𝐿𝐿𝑡𝑡 where 𝑥𝑥 refers to the biomass of the predatory
𝑡𝑡𝑡𝑡 −
We assume
a Lotka-Volterra type of predator-prey
22
𝑟𝑟𝑥𝑥𝑦𝑦𝑥𝑥𝑦𝑦𝑡𝑡𝑟𝑟𝑡𝑡𝑦𝑦−denote
𝑘𝑘𝑥𝑥𝑦𝑦𝑥𝑥𝑦𝑦𝑡𝑡2𝑡𝑡 +
𝑎𝑎𝑎𝑎
𝑦𝑦 −
−the
𝑏𝑏𝑏𝑏𝑡𝑡intrinsic
− 𝐻𝐻
𝐿𝐿𝑡𝑡𝑡𝑡time
describe
the stock
size
in year
𝑡𝑡,𝑥𝑥̇𝑥𝑥̇𝑦𝑦̇𝑡𝑡𝑟𝑟𝑡𝑡𝑥𝑥=
and
growth
rates,
ky capture
Here,
𝑥𝑥𝑡𝑡 and
𝑡𝑡 𝑦𝑦𝑡𝑡𝑡𝑡 −
x and
𝑦𝑦̇ t) kare
defined
as densityspecies
and𝑦𝑦𝑦𝑦𝑡𝑡 to
the biomass
of the
prey
species.
Changes
(𝑥𝑥̇ t and
𝑟𝑟𝑥𝑥 𝑥𝑥𝑡𝑡 − 𝑘𝑘in
𝐻𝐻
𝑡𝑡 =
𝑥𝑥 𝑥𝑥biomass
𝑡𝑡2 + 𝑎𝑎𝑎𝑎𝑡𝑡 𝑦𝑦𝑡𝑡over
𝑟𝑟𝑦𝑦 𝑦𝑦𝑟𝑟𝑡𝑡𝑦𝑦−
𝑘𝑘𝑦𝑦𝑎𝑎𝑦𝑦𝑡𝑡2and
−the
𝑏𝑏𝑏𝑏
𝑦𝑦𝑡𝑡 − 𝐿𝐿𝑡𝑡𝑡𝑡 the
𝑡𝑡denote
𝑦𝑦𝑡𝑡 describe
in year
𝑡𝑡,𝑦𝑦̇𝑦𝑦̇ 𝑟𝑟𝑡𝑡𝑥𝑥=
and
denote
growth
rates, kparameters.
density- in the
Here,
𝑥𝑥𝑡𝑡 and
dependence
predator the
andstock
prey size
species,
respectively,
and
𝑏𝑏 intrinsic
interaction
An increase
x and ky capture
for
=
𝑟𝑟
𝑦𝑦
−
𝑘𝑘
𝑦𝑦
−
𝑏𝑏𝑏𝑏
𝑦𝑦
−
𝐿𝐿
𝑡𝑡
𝑦𝑦 𝑡𝑡
𝑦𝑦 𝑡𝑡
𝑡𝑡 𝑡𝑡
𝑡𝑡
𝑦𝑦𝑡𝑡 describe
the
size
in the
year
𝑡𝑡,𝑥𝑥̇𝑟𝑟𝑥𝑥 =
and
denote
the
growth
rates,
and kthe
densityHere,
𝑥𝑥𝑡𝑡 and
dependence
for
predator
andstock
prey
species,
respectively,
and
𝑎𝑎𝑥𝑥𝑡𝑡2and
𝑏𝑏 𝑡𝑡intrinsic
denote
the
interaction
An increase
biomass
of prey
has a positive
impact
on
development
of
which kisparameters.
interaction
termin𝑎𝑎𝑥𝑥the
x why
y capture
𝑡𝑡 𝑦𝑦𝑡𝑡
𝑟𝑟𝑥𝑥 𝑥𝑥𝑟𝑟𝑡𝑡𝑦𝑦−
𝑘𝑘𝑥𝑥the
+predator’s
𝑎𝑎𝑎𝑎
𝑦𝑦𝑡𝑡 − 𝐻𝐻biomass
𝑡𝑡
𝑡𝑡
𝑦𝑦𝑡𝑡 describe
the
in
year
𝑡𝑡,𝑦𝑦̇𝑟𝑟of
and
𝑟𝑟𝑦𝑦and
denote
the
growth
rates,
kisparameters.
and
kthe
densityHere,
𝑥𝑥𝑡𝑡 and
x why
y capture
dependence
for
predator
andstock
prey
species,
respectively,
𝑎𝑎𝑦𝑦 2and
𝑏𝑏aintrinsic
denote
the
interaction
Anof
increase
in𝑎𝑎𝑥𝑥the
biomass
of However,
prey
has aanpositive
impact
the
development
of𝑘𝑘𝑦𝑦the
predator’s
biomass
which
interaction
term
is
positive.
increase
insize
theon
biomass
the
has
negative
impact
on
the
development
the
prey’s
𝑡𝑡 𝑦𝑦𝑡𝑡
𝑟𝑟𝑦𝑦 𝑦𝑦predator
−
𝑏𝑏𝑏𝑏
𝑦𝑦
−
𝐿𝐿
𝑦𝑦𝑡𝑡 describe
the
stock
size
in
year
𝑡𝑡, 𝑟𝑟𝑡𝑡𝑥𝑥𝑥𝑥 =
and
𝑟𝑟𝑡𝑡𝑦𝑦−denote
the
intrinsic
growth
rates,
k
and
k
capture
densityHere,
𝑥𝑥𝑡𝑡 and
𝑡𝑡
𝑡𝑡 𝑡𝑡
𝑡𝑡
x
y
dependence
forispredator
and
prey
and
𝑎𝑎 and
denote
theimpact
interaction
parameters.
Anof
increase
in𝑎𝑎𝑥𝑥the
biomass
of
prey
has
positive
impact
therespectively,
development
of the
predator’s
which
islevels
why
the
interaction
term
is
positive.
However,
increase
inspecies,
theonbiomass
of
predator
has
negative
on the
development
the
prey’s
negative.
H𝑏𝑏
and
Lt biomass
denote
harvest
for predator
and
prey
which
whyaan
the
prey’s
interaction
term
𝑏𝑏𝑥𝑥the
𝑡𝑡 𝑦𝑦𝑡𝑡
𝑡𝑡 𝑦𝑦𝑡𝑡 is
dependence
for predator
and
prey
species,
respectively,
and
𝑎𝑎 and
𝑏𝑏at denote
the interaction
parameters.
An increase
in the
biomass
of
prey
has
positive
impact
development
of thehas
predator’s
which
why
interaction
term
𝑎𝑎𝑥𝑥𝑡𝑡 𝑦𝑦𝑡𝑡
is
positive.
However,
increase
insize
theon
biomass
the
predator
at intrinsic
negative
impact
on of
the
development
the
prey’s
Hthe
and
Lt biomass
denote
harvest
for
predator
and
prey
biomass
which
whya
the
prey’s
interaction
term
species
Thus,
the
change
in
biomass
is
by
biological
growth
the
stock,
minusof
catches,
plus
and
𝑦𝑦is
describe
the
stock
inthe
year
𝑡𝑡, of
𝑟𝑟𝑏𝑏𝑥𝑥
𝑟𝑟𝑦𝑦negative.
denote
the
growth
rates,
kis
and
kthe
densityHere,
𝑥𝑥respectively.
𝑡𝑡 𝑦𝑦determined
𝑡𝑡 is
y capture
𝑡𝑡of
𝑡𝑡 has
𝑥𝑥 and
biomass
prey
aan
positive
impact
on
the
development
of the predator’s
biomass
which
isxlevels
why
the
interaction
term
𝑎𝑎𝑥𝑥𝑡𝑡 𝑦𝑦𝑡𝑡
is
positive.
However,
increase
in species,
the biomass
of
the
predator
has
at denote
negative
on of
the
development
ofcatches,
the
negative.
Lt denote
harvest
levels
for predator
andprey’s
prey
biomass
which
is predator
whyan
the
prey’s
interaction
term
𝑏𝑏𝑥𝑥is
species
Thus,
theprey
change
biomass
byH𝑏𝑏the
biological
growth
the
stock,
minus
ordependence
minusrespectively.
the
interaction
term.
𝑡𝑡 𝑦𝑦determined
𝑡𝑡 is
for
and
respectively,
and
𝑎𝑎 and
theimpact
interaction
parameters.
An increase
inplus
the
is
positive.
However,
an
increase
in the in
biomass
of
the
predator
has
aand
negative
impact
on the
development
of the
prey’s
𝑦𝑦𝑡𝑡functions,
is negative.
Ltbiomass
denote
harvest
for interaction
predator
and
prey
biomass
which
is has
why athe
prey’s
interaction
term
𝑏𝑏𝑥𝑥is
t and
species
respectively.
the change
in biomass
byH
the
biological
growth
ofislevels
the
stock,
minus catches,
plus
or
minus
the
interaction
term.
We
assume
Schaefer
harvest
production
biomass
of generalised
prey
positive
impact
on
the
development
of
the
predator’s
which
why
the
term
𝑎𝑎𝑥𝑥
𝑡𝑡 𝑦𝑦𝑡𝑡
is
negative.
H
and
L
denote
harvest
levels
for
predator
and
prey
biomass
which
is whyThus,
the
prey’s
interaction
term
𝑏𝑏𝑥𝑥𝑡𝑡𝑡𝑡 𝑦𝑦determined
t
t
𝑡𝑡
species
respectively.
Thus,
the change
biomass
determined
by the
biological
growth
stock, minus
or
the
interaction
We
assume
generalised
Schaefer
harvest
production
functions,
However,
isminus
positive.
anterm.
increase
in
thein
ofis
predator has
a negative
impact
on of
thethe
development
of catches,
the prey’splus
species
respectively.
Thus,
the change
inbiomass
biomass
isthe
determined
by
the
biological
growth
of
the
stock,
minus
catches,
plus
or
minus
the
interaction
term.
We
assume
generalised
Schaefer
harvest production
which
= 𝑞𝑞𝑞𝑞𝑡𝑡Χ𝑥𝑥 𝐸𝐸𝑥𝑥𝑥𝑥
𝐻𝐻𝑡𝑡negative.
Ht and Lt denote harvest levels for predator and prey
biomass
is why the
prey’s interaction
term 𝑏𝑏𝑥𝑥𝑡𝑡 𝑦𝑦functions,
𝑡𝑡 is
or
minus
the
interaction
term.
Χ𝑥𝑥
We
assume
generalised
Schaefer
harvest
production
functions,
𝐻𝐻
𝑞𝑞𝑞𝑞𝑡𝑡Χ𝑦𝑦
=
𝐸𝐸𝑦𝑦𝑦𝑦
𝐿𝐿
species
respectively.
Thus,
the
change
in
biomass
is
determined
𝑡𝑡 by
𝑥𝑥𝑥𝑥the biological growth of the stock, minus catches, plus
𝑡𝑡𝑡𝑡 = 𝑑𝑑𝑑𝑑
We assume generalised Schaefer harvest production functions,
Χ𝑥𝑥
= 𝑑𝑑𝑑𝑑
𝑞𝑞𝑞𝑞𝑡𝑡Χ𝑦𝑦
𝐻𝐻
𝐿𝐿𝑡𝑡𝑡𝑡 =
or minusthe interaction term.
𝑡𝑡 𝐸𝐸𝑦𝑦𝑦𝑦
𝑥𝑥𝑥𝑥
Χ𝑥𝑥
= 𝑑𝑑𝑑𝑑
𝑞𝑞𝑞𝑞𝑡𝑡Χ𝑦𝑦
𝐻𝐻
=
𝐸𝐸𝑦𝑦𝑦𝑦
𝐿𝐿
𝑡𝑡denote
𝑡𝑡Χ𝑥𝑥catchability
𝑥𝑥𝑥𝑥
We
assumeand
generalised
Schaefer
harvest production
for
predator
prey species
respectively.
Here, 𝑐𝑐 andfunctions,
𝑑𝑑
coefficients and  x and y denote the stock
𝑡𝑡
𝐻𝐻𝑡𝑡 = 𝑞𝑞𝑞𝑞Χ𝑦𝑦
𝑡𝑡 𝐸𝐸𝑥𝑥𝑥𝑥
=xt𝑑𝑑𝑑𝑑
𝐸𝐸E𝑦𝑦𝑦𝑦
𝐿𝐿𝑡𝑡denote
and
and y and
denote
for predatorof
preywhich
species
Here,from
𝑐𝑐 and
𝑑𝑑
coefficients
and at
and
levels directed
preythe
fishstock
elasticities
output,
arerespectively.
allowed to differ
one.
𝑡𝑡Χ𝑦𝑦catchability
 xpredatory
yt are effort
= 𝑑𝑑𝑑𝑑
𝐸𝐸
𝐿𝐿𝑡𝑡 E
𝑡𝑡Χ𝑥𝑥 𝑦𝑦𝑦𝑦
and y and
denote
for predatorofand
preywhich
species
Here,from
𝑐𝑐 and
𝑑𝑑𝐻𝐻𝑡𝑡denote
coefficients
and at
𝐸𝐸
and
E𝑥𝑥𝑥𝑥
levels directed
preythe
fishstock
elasticities
output,
arerespectively.
allowed to differ
one.
E=xt 𝑞𝑞𝑞𝑞
 xpredatory
respectively.
yt are effort
𝑡𝑡 catchability
Χ𝑦𝑦catchability coefficients and
and y and
denote
the
for predatorthat
and
prey
species
respectively.
Here,
𝑐𝑐 and
𝑑𝑑𝐿𝐿both
denote

and
E𝑦𝑦𝑦𝑦
effort
levelsfor
directed
predatory
prey
fish
elasticities
output,
which
are
allowed
from
one.
E=xt 𝑑𝑑𝑑𝑑
𝐸𝐸
respectively.
Assuming
the
marginal
effort
costs to
arediffer
constant
for
fisheries,
and
allowing
a and
trendat
of
declining
costs
due
tostock
yt are
x
𝑡𝑡 catchability
and
denote
the
for predatorofand
prey
species
respectively.
Here,
𝑐𝑐 and
𝑑𝑑 𝑡𝑡denote
coefficients

y and prey fishstock
and Eas
effort
levelsfor
directed
atofxpredatory
elasticitiesprogress
of output,
which
are
allowed
to
differ
fromcan
one.
Extwritten
yt are
respectively.
Assuming
that
the
marginal
effort
costs
are
constant
for
both
fisheries,
and
allowing
a
trend
declining
costs
due
to
and
v
)
,
fishing
costs
be
technical
(at
rates
v
x
y
elasticities of output, which are allowed to differ from one. Ext and Eyt are effort levels directed at predatory and prey fish
respectively.
and y denote
the to
stock
for predator
species
respectively.
Here,
𝑐𝑐 and
denote
catchability
coefficients
Assuming
theprey
marginal
costs
are constant
for 𝑑𝑑both
fisheries,
for a and
trendof
declining
costs due
vy) , fishing
costs
can
be
written
as and allowing
technical
progress
(at rates
veffort
thatand
x and
x
respectively.
−Χ𝑥𝑥allowing for a trend of declining costs due to
Assuming
that
the marginal
effort
costs
are
constant
for
both
fisheries,
and
and
E
are
effort
levels
directed
at
predatory
and
prey
fish
elasticities
of output,
which
are
allowed
to differ
from
one.
E
technical
progress
(at
rates
v
v
costs
can
be
written
as
(𝐻𝐻
)
xt
yt
,
𝑥𝑥
−
𝑣𝑣
𝑡𝑡)𝑥𝑥
𝐻𝐻
=
𝑒𝑒𝑒𝑒𝑒𝑒(𝑐𝑐
𝐶𝐶
x and
y) , fishing
𝑥𝑥
𝑡𝑡 for
𝑡𝑡 both fisheries,
𝑥𝑥
𝑥𝑥 and
𝑡𝑡
𝑡𝑡 allowing
Assuming that the marginal effort costs are constant
for a trend of declining costs due to
−Χ
−Χ
can
be𝑒𝑒𝑒𝑒𝑒𝑒(𝑐𝑐
written
as
technical
progress
(at rates vx and vy) , fishing 𝐶𝐶costs
respectively.
(𝐿𝐿𝑡𝑡 , 𝑦𝑦
𝑥𝑥
𝑣𝑣𝑥𝑥𝑦𝑦 𝑡𝑡)𝑥𝑥
𝑡𝑡)𝑦𝑦𝑡𝑡 𝑥𝑥𝑥𝑥𝐻𝐻
𝐿𝐿𝑡𝑡
𝑥𝑥
𝑥𝑥 − as
𝑦𝑦(𝐻𝐻
𝑡𝑡 ) =
𝑦𝑦
can
be written
technical progress
(at rates vx and vy) , fishing costs
−Χ
−Χ
𝑥𝑥
𝑥𝑥
that the marginal effort costs are constant
Assuming
both
fisheries,
and
allowing
for a trend of declining costs due to
𝑥𝑥for
𝐻𝐻
𝐶𝐶𝑥𝑥𝑦𝑦(𝐻𝐻
(𝐿𝐿 𝑡𝑡, 𝑦𝑦
𝑡𝑡)𝑦𝑦
𝑒𝑒𝑒𝑒𝑒𝑒(𝑐𝑐
𝑡𝑡) =
𝑥𝑥 − 𝑣𝑣𝑥𝑥
𝑦𝑦
𝑦𝑦 𝑡𝑡)𝑥𝑥
𝑡𝑡−Χ 𝐿𝐿 𝑡𝑡
−Χ𝑥𝑥𝑥𝑥
(𝐻𝐻
𝑣𝑣𝑥𝑥𝑦𝑦 𝑡𝑡)𝑥𝑥
𝑒𝑒𝑒𝑒𝑒𝑒(𝑐𝑐
𝐶𝐶costs
andbiological
vy) , fishing
be
written
technical
progress
rates
(𝐿𝐿 , 𝑥𝑥can
)=
𝑡𝑡)𝑦𝑦
𝐿𝐿the
𝑥𝑥 − as
As
discussed
in the (at
main
text,vxthe
parameters
are
estimated
using
Catch-MSY
method developed by Martell
𝑡𝑡𝑡𝑡−Χ𝑥𝑥 𝐻𝐻
𝑦𝑦
𝑡𝑡𝑡𝑡
𝑥𝑥𝑡𝑡𝑡𝑡𝑡𝑡 ) =
−
𝑣𝑣
𝑡𝑡)𝑥𝑥
𝐻𝐻
𝑒𝑒𝑒𝑒𝑒𝑒(𝑐𝑐
𝐶𝐶𝑥𝑥𝑥𝑥𝑦𝑦(𝐻𝐻𝑡𝑡𝑡𝑡𝑡𝑡 , 𝑦𝑦
𝑥𝑥
𝑥𝑥
𝑡𝑡−Χ𝑥𝑥 𝐿𝐿 𝑡𝑡
) = 𝑒𝑒𝑒𝑒𝑒𝑒(𝑐𝑐
𝑡𝑡)𝑦𝑦
𝐶𝐶𝑦𝑦 (𝐿𝐿𝑡𝑡 , 𝑦𝑦allows
𝑦𝑦 − 𝑣𝑣𝑦𝑦parameters
𝑡𝑡
As discussed
in the main
text, the
biological
parameters
estimated
using
Catch-MSY
method
by Martell
and
Froese (Martell
and Froese
2013).
This method
biological
based on catch
datadeveloped
to be estimated.
It
𝑡𝑡−Χ𝑥𝑥 𝐿𝐿the
− 𝑣𝑣𝑦𝑦 𝑡𝑡)𝑦𝑦
𝐶𝐶𝑦𝑦 (𝐿𝐿𝑡𝑡 , 𝑦𝑦𝑡𝑡𝑡𝑡 ) =are
𝑒𝑒𝑒𝑒𝑒𝑒(𝑐𝑐
−Χ
𝑦𝑦
𝑡𝑡
𝑥𝑥
𝑡𝑡
)=
𝑥𝑥𝑡𝑡the
𝑡𝑡)𝑥𝑥
𝐻𝐻
𝑒𝑒𝑒𝑒𝑒𝑒(𝑐𝑐
𝐶𝐶𝑥𝑥 (𝐻𝐻for
As discussed
in the main
text,data
the
biological
parameters
are
estimated
using
the
Catch-MSY
method
developed
by Martell
𝑡𝑡 ,allows
𝑥𝑥 − 𝑣𝑣𝑥𝑥values
𝑡𝑡 well
and
Froese
and
Froese
2013).
Thisranges
method
biological
parameters
based
on
catch
data
to
be
estimated.
It
𝑡𝑡
requires
time(Martell
series
of catch
and
prior
parameter
as
as
possible
ranges
of
stock
sizes
from
−Χ𝑥𝑥
(𝐿𝐿𝑡𝑡for
)=
, allows
𝑦𝑦𝑡𝑡sizes
− 𝑣𝑣𝑦𝑦values
𝑡𝑡)𝑦𝑦
𝐶𝐶𝑦𝑦stock
𝑒𝑒𝑒𝑒𝑒𝑒(𝑐𝑐
As discussed
in
theperiod.
main
text,
the
biological
parameters
are
estimated
using
the
Catch-MSY
method
by Martell
𝑦𝑦limits
𝑡𝑡 well
and
Froese
(Martell
and
Froese
2013).
This
method
biological
parameters
based
on catch
datadeveloped
toofbe
It
𝑡𝑡the𝐿𝐿
requires
time
series
of catch
data
and
prior
ranges
the
parameter
as
as possible
ranges
stock
from
the
initial
and
final
After
specifying
initial
and
for
final
stock
size,
a parameter
setestimated.
is sizes
randomly
As discussed
in
the main
text,
the
biological
parameters
are
estimated
using
the
Catch-MSY
method
developed
by Martell
and
Froese
(Martell
and
Froese
2013).
This
method
allows
biological
parameters
based
on to
catch
data tothe
be
estimated.
It
requires
time
series
of
catch
data
and prior
ranges
for
the parameter
well
asused
possible
ranges
stock
from
the
initial
and
final
period.
After
specifying
initial
stock
sizes
and limits
for theas
final
stock
size,
acalculate
parameter
set
is sizes
randomly
drawn
from
the
prior
parameter
distribution.
Then,
the
underlying
fish values
supply
model
is
biomass
and
Froese
(Martell
and
Froese
2013).
This
method
allows
biological
parameters
based
on catch
data toofbe
estimated.
It
Asinitial
discussed
in
the
main
text,
the
biological
parameters
estimated
using
the
Catch-MSY
method
developed
by
Martell
requires
time
series
of
catch
data
and
priorinitial
ranges
for sizes
theare
parameter
values
as
well
asused
possible
ranges
of stock
sizes
fromis
the
and
final
period.
After
specifying
stock
and
limits
for
the
final
stock
size,
a
parameter
set
is
randomly
drawn
from
the
prior
parameter
distribution.
Then,
the
underlying
fish
supply
model
is
to
calculate
the
biomass
corresponding
to
the
level
of
harvest
given
the
parameter
set.
If
this
biomass
is
in
a
reasonable
range,
the
parameter
set
requires
time
series
of
catch
data
and
prior
ranges
for
the
parameter
values
as
well
as
possible
ranges
of
stock
sizes
from
and
Froese
(Martell
and
2013).
This
method
biological
parameters
onsamples
catch
data
tothe
beset
estimated.
It is
the
initial
final
period.
After
specifying
initial
stock
sizes
and
limits
for
theLME.
final
stock
size,
parameter
is randomly
drawn
from
the
prior
parameter
distribution.
Then,
theallows
underlying
fish
supply
model
used
toa
the
biomass
corresponding
to
the
level
of
harvest
the
parameter
set.
If this
biomass
is inbased
a is
reasonable
range,
parameter
set
stored.
In and
our
analysis,
weFroese
repeat
thisgiven
procedure
10,000,000
times
for
each
We
use
of 1,000
randomly
picked
the
initial
and
final
period.
After
specifying
initial
stock
and
limits
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theas
final
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acalculate
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issizes
randomly
requires
time
series
of we
catch
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and
prior
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forsizes
the
parameter
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well
as
possible
ranges
of
stock
from
drawn
from
the
prior
parameter
distribution.
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the
underlying
fish
supply
model
is
used
to calculate
the
biomass
corresponding
to
the
level
of
harvest
given
the
parameter
set.
If
this
biomass
is
in
a
reasonable
range,
the
parameter
set
is
stored.
In
our
analysis,
repeat
this
procedure
10,000,000
times
for
each
LME.
We
use
samples
of
1,000
randomly
picked
accepted
parameter
values
in
our
model
computations
to
compute
mean
estimates
and
confidence
intervals.
Thus,
all
drawn
fromand
thefinal
priorperiod.
parameter
distribution.
Then,
the underlying
fish supply
model
is used
toacalculate
the biomass
the initial
After
specifying
initial
stock
sizes
and
limits
for
the
final
stock
size,
parameter
is
randomly
corresponding
tobelow
the values
level
given
the and
parameter
set.
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biomass
is infrom
a and
reasonable
range,
theset
parameter
stored.
In parameter
our analysis,
we of
repeat
this
procedure
10,000,000
times
for
each
LME.
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use
samples
of
1,000
randomly
picked
accepted
inharvest
our
model
computations
to compute
mean
estimates
confidence
intervals.
Thus,
allset
results
reported
are
based
on
averages
standard
deviations
obtained
1,000
separate
model
runs
each.
Inis
corresponding
to
the
level
of
harvest
given
the
parameter
set.
If
this
biomass
is
in
a
reasonable
range,
the
parameter
set
is
drawn
from
the
priorvalues
parameter
distribution.
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the
underlying
supply
model
is
used
to
calculate
the biomass
stored.
In
our
analysis,
we
repeat
this
procedure
10,000,000
timesfish
for
each
LME.
We
use
samples
of
1,000
randomly
accepted
parameter
in our
model
computations
toby
compute
mean
estimates
and
confidence
intervals.
Thus,
allpicked
results
reported
below
are
based
on
averages
and
standard
deviations
obtained
from
1,000
separate
model
runs
each.
In
the
global
predator-prey
model,
we
extend
the
approach
Martell
and
Froese
(2013)
and
determine
parameter
values
for
stored.
In
our
analysis,
we
repeat
this
procedure
10,000,000
times
for
each
LME.
We
use
samples
of
1,000
randomly
picked
corresponding
to
thevalues
level
of
given
theand
parameter
set.
If thismean
biomass
in
a reasonable
range,
the
parameter
set In
is
accepted
parameter
in harvest
our
model
toby
compute
estimates
and
confidence
intervals.
Thus,
results
reported
below
are
based
on
averages
standard
deviations
obtained
from
1,000
separate
model
runs
each.
the
global
predator-prey
model,
we
extend
the
approach
Martell
and
Froese
(2013)
and
parameter
values
for
Lotka-Volterra
predator-prey
model.
Incomputations
each
case,
initial
parameter
sets
toisbe
tested
aredetermine
randomly
drawn
from
a all
uniform
accepted
values
in our
model
computations
to compute
estimates
and
confidence
Thus,
all
stored.
Inparameter
our analysis,
we
repeat
this
procedure
10,000,000
times mean
for
each
LME.
We
use
samples
ofintervals.
1,000
randomly
picked
results
reported
below
are
based
on
averages
and
standard
deviations
obtained
from
1,000
separate
model
runs
each.
In
global
predator-prey
model,
we
extend
the
approach
by
Martell
and
Froese
(2013)
and
determine
parameter
values
for
the
Lotka-Volterra
predator-prey
model.
In
each
case,
initial
parameter
sets
to
be
tested
are
randomly
drawn
from
a
uniform
distribution.
Biological
parameters
are
accepted
if
final
biomasses
fall
between
a
minimum
and
two-thirds
of
their
equilibrium
results
reported
belowvalues
are based
on
averages
and standard
deviations
obtained
from
1,000
separate
model runs
each.
In
accepted
parameter
in our
model
toby
compute
mean
estimates
and
confidence
intervals.
Thus,
the
global
predator-prey
model,
we
extend
the approach
Martell
and
Froese
and
determine
parameter
for
Lotka-Volterra
predator-prey
model.
Incomputations
each
case,
parameter
sets
to be
tested
areand
randomly
drawn
fromvalues
aalluniform
distribution.
Biological
parameters
are
accepted
if finalinitial
biomasses
fall
between
a(2013)
minimum
two-thirds
of their
equilibrium
value
without
fishing.
the
global
predator-prey
model,
we
extend
the approach
by Martell
and
Froese
(2013)
and
determine
parameter
values
for
results
reported
below
are
based
on
averages
and
standard
deviations
obtained
from
1,000
separate
model
runs
each.
In
the Lotka-Volterra
predator-prey
model.
In each case,
initial
parameter
sets toand
be
tested are
randomly
drawn
from
a uniform
distribution.
Biological
parameters
are
accepted
if
final
biomasses
fall
between
a
minimum
and
two-thirds
of
their
equilibrium
value
without
fishing.
Economic
theory
predicts
a
positive
relationship
between
fish
stock
biomass
market
supply
of
fish
(or
no
relationship
at
the
Lotka-Volterra
predator-prey
model.
In
each
case,
initial
parameter
sets
to
be
tested
are
randomly
drawn
from
a
uniform
the global
predator-prey
model, we
extend
the approach
by Martellfall
and
Froese a(2013)
and determine
parameter
values
for
distribution.
Biological
are
accepted
ifbetween
final
biomasses
between
minimum
and two-thirds
of
their
equilibrium
value
without
fishing.
Economic
theory
a positive
relationship
fish stock
biomass
and
market
supply
of fish
nofish
relationship
all
in the
case
of apredicts
pureparameters
schooling
fishery),
and thus
a negative
relationship
between
stock
biomass
and(or
the
price.
Weat
distribution.
Biological
parameters
are
accepted
if
final
biomasses
fall
between
a
minimum
and
two-thirds
of
their
equilibrium
the Lotka-Volterra
predator-prey model. In each case, initial parameter sets to be tested are randomly drawn from a uniform
value
without
fishing.
Economic
theory
predicts
afor
positive
relationship
between
fish stock
biomass
and market
of fish
nowe
relationship
at
all
inprice
the
case
of
purerun
schooling
fishery),
and thus
relationship
between
stock
biomass
and(or
the
fish
price. We
use
data
in aeach
a tested
parameter
setatonegative
check
whether
this requirement
issupply
met.
Specifically,
assume
that
value
without
fishing.
distribution.
Biological
parameters
are
accepted between
if final biomasses
fall
between
a minimum
and two-thirds
ofnotheir
equilibrium
Economic
theory
predicts
afor
positive
relationship
fish
stock
biomass
and
market
of
fish
(or
relationship
all inopen
the case
of
purerun
schooling
and pthus
ato
relationship
between
stock
biomass
and
the
fish
price. We
use
price
data
in a
each
set
whether
this
issupply
met.
Specifically,
we
assume
that
Cfishery),
xt) and
=C
(Lcheck
yt) should
hold
forrequirement
the
period
between
1976
and
2000
(Quaas
etat
the
access
conditions
pHta=tested
x(H
t,parameter
Lt
ynegative
t, fish
Economic
theory
predicts
a
positive
relationship
between
stock
biomass
and
market
supply
of
fish
(or
no
relationship
at
value
fishing.
all
in
thewithout
case
of
a
pure schooling
fishery),
and
thus
ato
negative
relationship
between
stock
biomass
and
the
fishassume
price. We
usein
price
data
in a
each
for
tested
whether
this
istest
met.
Specifically,
we
that
Cfishery),
(Ht,parameter
xSea
pthus
=C
(Lcheck
should
hold
forrequirement
the period
between
1976
and
2000
(Quaas
et
the
open
access
conditions
pprices
al.
2012).
We use
observed
Around
and
stock
estimates
fromstock
the
runs
inand
the
Martell/Froese
Hta =
xfrom
t) and
Lt set
ynegative
t, yt) the
all
the case
of
purerun
schooling
and
aUs
relationship
between
biomass
fish
price. We
Economic
theory
predicts
a positive
relationship
between
fish stock
biomass
and market
of fish
(orthe
nowe
relationship
at
use
price
data
in
each
run
for
tested
parameter
set
to
check
whether
this
requirement
issupply
met.
Specifically,
assume
that
Cxfrom
(Ht,OLS
xSea
pLt set
= CUs
yt)the
should
hold
for
the period
between
1976
andMartell/Froese
2000
(Quaas
et
the2012).
open
access
conditions
pprices
al.
We
use
observed
Around
and
stock
estimates
from using
the
test
infunctions
the
procedure
to estimate
arun
log-linearised
regression
ofcheck
open
access
conditions
theruns
cost
and
including
Hta =
t) and
y(L
t,the
use
price
data
in
each
for
a
tested
parameter
to
whether
this
requirement
is
met.
Specifically,
we
assume
that
all
in
the
case
of
a
pure
schooling
fishery),
and
thus
a
negative
relationship
between
stock
biomass
and
the
fish
price.
We
(H
xSea
pLt =if C
(L
yt)non-negative
should
hold
for
the period
between
1976
2000
(Quaas
et
the2012).
opentrends
access
conditions
pHt = Caxfrom
t,OLS
t) and
ygives
t,the
al.
We
use
observed
Around
the
stock
estimates
fromfor
the
test
inXfunctions
Martell/Froese
procedure
to estimate
a
log-linearised
regression
ofand
open
access
conditions
using
the
and
including
and
.and
Parameter
sets that
the
time
below.
We
accept
parameter
set
itUs
estimates
both
Xruns
x cost
ythe
=tested
Cx(H
xt) and
pLt set
=C
hold
for
the period
between
1976
and
2000
(Quaas
et
the
open
pprices
Ht a
t, parameter
y(Lcheck
t, yt) should
use
priceaccess
data
inconditions
each
run for
to
whether
this
requirement
is test
met.runs
Specifically,
we assume
that
al.
2012).
We
use
observed
prices
from
Searegression
Around
the
stock
estimates
from
the
inXfunctions
the
Martell/Froese
procedure
tothis
estimate
a
log-linearised
OLS
ofand
thenon-negative
open
access
conditions
using
the
and
including
and
. Parameter
sets
that
the
timepass
trends
below.
We
accept
a
parameter
setuse
if itUs
gives
estimates
for
both
Xruns
dothe
not
test
are
rejected.
Otherwise,
we
the
resulting
information
on
the
relationship
between
price
and
stock
x cost
ythe
al.
2012).
We
use
observed
prices
from
Sea
Around
Us
and
the
stock
estimates
from
the
test
in
Martell/Froese
open access conditions pHt = Cx(Ht, xt) and pLt = Cy(Lt, yt) should hold for the period between 1976 and 2000 (Quaas et
procedure
estimate
a
log-linearised
OLS regression
of
thenon-negative
openinformation
accessestimates
conditions
using
and
including
and
Xfunctions
sets
that
the
timepass
trends
below.
We
accept
parameter
setuse
if itthe
gives
boththe
Xx cost
do
this
test
are
rejected.
Otherwise,
we
resulting
onfrom
thefor
relationship
between
price
and
stock
biomass
toto
obtain
an
estimate
for a
economic
parameter
values.
y. Parameter
procedure
to
estimate
a
log-linearised
OLS
thethe
open
access
conditions
using
the
cost
and
including
al.not
2012).
We
use
observed
prices
from
Searegression
Around
Usof
and
stock
estimates
the
test
runs
infunctions
the
Martell/Froese
Xy. Parameter
sets
that
the
timepass
trends
We
accept
a
parameter
setuse
if itthe
gives
non-negative
estimates
both Xx and
do not
thisbelow.
test
are
rejected.
Otherwise,
we
resulting
information
on thefor
relationship
between
price and
stock
biomass
to
obtain
an
estimate
for
economic
parameter
values.
and
X
.
Parameter
sets
that
the
time
trends
below.
We
accept
a
parameter
set
if
it
gives
non-negative
estimates
for
both
X
procedure to estimate a log-linearised OLS regression of the open access conditions using thex cost functions
and including
y
do
not
pass
this standard
test
are
rejected.
Otherwise,
we use
thevalues.
resulting
information
on the relationship
between
price
and
stock
biomass
to
obtain
an
estimate
for
economic
parameter
The
means
and
deviations
for
the
1,000
parameter
sets
used
in
the
computations
are
given
in
the
table
below
dothe
not
pass
this test
areWe
rejected.
weset
use
resulting
information
on thefor
relationship
between
price and
stock
Xy. Parameter
sets
that
time
trends
below.
acceptOtherwise,
a parameter
if itthe
gives
non-negative
estimates
both Xx and
biomass
to
an
economic
parameter
values.
The
and
deviations
forkythe 1,000
parameter
table
below
rx obtain
ry estimate
kx for
b sets used
cx in the computations
cy the relationship
vx are given
vy in the
 xand
biomass
to
obtain
an
estimate
for economic
parameter
values.
do means
not pass
thisstandard
test
are rejected.
Otherwise,
weause
the
resulting
information
on
between
price
stocky
The
means
and
standard
deviations
for
the
1,000
parameter
sets used
r
r
k
k
a
b
c49.14
c24.27
v0.024
v0.011
Mean
1.44
2.24
0.044
0.0096
0.0046
0.014
0.24
 x below0.32
x obtain an
y estimate
x for economic
y
x in the computations
y
x are given
y in the table
y
biomass to
parameter values.
The
means
and standard
deviations
for0.0096
the 1,000a
parameter
sets used
in the computations
are given
in the table
r0.56
r0.74
k
b
c10.45
c7.82
v0.005
v0.003
 x below
x and standard
y
x
ythe 1,0000.0046
x in the computations
y
x are given
y in the table
Mean
1.44
2.24
0.044
0.014
49.14
24.27
0.024
0.011
0.24
0.32
Std
0.023
0.0062
0.0020
0.0077
0.20
0.20
y
The means
deviations
fork
parameter
sets used
below
r1.44
r2.24
k
k
a
b
c49.14
c24.27
v0.024
v0.011

x
y
x
y
x
y
x
y
Mean
0.044
0.0096
0.014
0.24
y
Std
0.56
0.74
0.023
0.0062
0.0020
0.0077
10.45
7.82
0.005
0.003
0.20
0.20
x below0.32
The means
deviations
for
rx and standard
ry
kx
ky the 1,0000.0046
a parameter
b sets used
cx in the computations
cy
vx are given
vy in the table

y
x
Mean
1.44
2.24
0.044
0.0096
0.0046
0.014
49.14
24.27
0.024
0.011
0.24
0.32
Std
0.56
0.74
0.023
0.0062
0.0020
0.0077
10.45
7.82
0.005
0.003
0.20
0.20
b.
Fish
Supply
Model
at
LME-Level
r
r
k
k
a
b
c
c
v
v

x
y
x
y
x
y
x
y
Mean
1.44
2.24
0.044
0.0096
0.0046 0.014
49.14
24.27
0.024
0.011
0.24
0.32
y
x
Std
0.56
0.74
0.023
0.0062
0.0020
0.0077
10.45
7.82
0.005
0.003
0.20
0.20
b.Mean
Fishtotal
Supply
Model
at LME-Level
In
the
fish supply
model,
the change
of biomass
over time
is defined
1.44
2.24
0.044
0.0096
0.0046
0.014
49.14as 24.27
0.024
0.011
0.24
0.32
Std
0.56
0.74
0.023
0.0062
0.0020
0.0077
10.45
7.82
0.005
0.003
0.20
0.20
b.Std
Fishtotal
Supply
Model
at LME-Level
In
the
fish supply
model,
the change
of biomass
over time
is defined
0.56
0.74
0.023
0.0062
0.0020
0.0077
10.45as 7.82
0.005
0.003
0.20
0.20
2
b. the
Fish Supply
Modelmodel,
at LME-Level
In
fish supply
the change of biomass
is defined
− 𝑘𝑘𝑙𝑙𝑙𝑙𝑙𝑙
𝑥𝑥𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
−as
𝐻𝐻𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑥𝑥̇ 𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 = 𝑟𝑟over
𝑙𝑙𝑙𝑙𝑙𝑙 𝑥𝑥𝑡𝑡time
b. Fishtotal
Supply
Model at LME-Level
2
Inb.the
total
fish
supply
model,
the
change
of
biomass
over
time
is
defined
as
=
𝑟𝑟
𝑥𝑥
−
𝑘𝑘
𝑥𝑥
𝐻𝐻𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑥𝑥̇
𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑙𝑙𝑙𝑙𝑙𝑙 𝑡𝑡time 𝑙𝑙𝑙𝑙𝑙𝑙
Fish
Supply
Model
at LME-Level
𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 −as
In the
total
fish supply
model,
the change of biomass
over
is defined
2
= 𝑟𝑟over
−lme
𝑘𝑘𝑙𝑙𝑙𝑙𝑙𝑙
𝑥𝑥year
𝐻𝐻
describes
stockmodel,
size inthe
thechange
large marine
ecosystem
𝑡𝑡,as
𝑟𝑟lme
describes the intrinsic growth rate of the
xlme,t
𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑙𝑙𝑙𝑙𝑙𝑙 𝑥𝑥𝑡𝑡time
𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 −
In the
total fishthe
supply
of 𝑥𝑥̇biomass
isindefined
2
=H𝑟𝑟lmt,t
𝑥𝑥describes
𝑘𝑘𝑙𝑙𝑙𝑙𝑙𝑙
−
𝑥𝑥̇ 𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑡𝑡 −lme
𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
the stockof
size
in thedependence
large marine
ecosystem
in𝑥𝑥year
𝑡𝑡, 𝐻𝐻
𝑟𝑟lme
describes
the intrinsic
of the
xstock,
2
density
and
the
catches
from the LME
in year growth
t (Clarkrate
1991).
Thus,
klme is a measure
lme,t describes
= 𝑟𝑟𝑙𝑙𝑙𝑙𝑙𝑙
𝑥𝑥̇ 𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑙𝑙𝑙𝑙𝑙𝑙 𝑥𝑥𝑡𝑡 − 𝑘𝑘𝑙𝑙𝑙𝑙𝑙𝑙 𝑥𝑥𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 − 𝐻𝐻𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
2
describes
the stockisof
size
in thedependence
largegrowth
marine
ecosystem
lmethe
in𝑥𝑥year
𝑡𝑡, 𝑟𝑟 𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
describes
the
intrinsic
growth
rate
of the
xstock,
measure
density
and
H𝑟𝑟lmt,t
describes
the
catches
from
in year
t (Clark
1991).
Thus,
klme isofa biomass
the
the
biological
of
a=
stock
minus
catches
taken
bythe
theLME
fishing
industry.
In a
similar
way as
lme,tchange
𝑥𝑥̇ 𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑙𝑙𝑙𝑙𝑙𝑙 𝑥𝑥
𝑡𝑡 − 𝑘𝑘𝑙𝑙𝑙𝑙𝑙𝑙
𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 − 𝐻𝐻lme
describes
the stockisof
size
in thedependence
large
marine
ecosystem
lme
in year
𝑡𝑡, 𝑟𝑟lme
describes
the
intrinsic
growth
rate
of the
xstock,
lme,t
apredator-prey
measure
density
Hlmt,t cost
describes
the
catches
from
in year
t (Clark
1991).
Thus,
kglobal
change
biomass
the
biological
growth
of
a stock
minus
the
catches
taken
bythe
theLME
fishing
industry.
In a
similar
way as
for
the
model,
we
assume
aand
fishing
function
lme isof
xthe
lme,t describes the stock size in the large marine ecosystem lme in year 𝑡𝑡, 𝑟𝑟lme describes the intrinsic growth rate of the
Fishing
for Proteins
| 51
measure
density
dependence
Hlmt,t cost
describes
the
catches
from
LME
in year
t (Clark
Thus,
stock,
kglobal
lme is a biomass
the
change
isof
biological
growth
of
a stock
minus
the
catches
taken
bythe
thethe
fishing
industry.
Inrate
a1991).
similar
way as
for
the
model,
we
assume
aand
fishing
function
describes
the stock
size
in the
large
marine
ecosystem
lme
in year
𝑡𝑡, 𝑟𝑟lme
describes
intrinsic
growth
of the
xlme,t
measure
ofthe
density
dependence
and
Hlmt,t describes
the
catches
from
the
LME
in
year
t
(Clark
1991).
Thus,
stock,
klme isofapredator-prey
−Χ𝑙𝑙𝑙𝑙𝑙𝑙 by the fishing industry. In a similar way as
the
change
of
biomass
is
the
biological
growth
of
a
stock
minus
the
catches
taken
for
the
global
predator-prey
model,
we
assume
a
fishing
cost
function
=
𝑒𝑒𝑒𝑒𝑒𝑒(𝑐𝑐
,
𝑥𝑥
−
𝑣𝑣
𝑡𝑡)𝑥𝑥
𝐻𝐻
𝐶𝐶
(𝐻𝐻
)
is
a
measure
of
density
dependence
and
H
describes
the
catches
from
the
LME
in
year
t
(Clark
1991).
Thus,
stock,
k
lme of biomass is the biological𝑙𝑙𝑙𝑙𝑙𝑙
lmt,t minus 𝑙𝑙𝑙𝑙𝑙𝑙
𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑙𝑙𝑙𝑙𝑙𝑙
𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 by
the change
growth
of a𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
stock
the catches
taken
the fishing industry. In a similar way as
−Χ𝑙𝑙𝑙𝑙𝑙𝑙
for
the
globalof
predator-prey
model,
we𝐶𝐶𝑙𝑙𝑙𝑙𝑙𝑙
assume
a, 𝑥𝑥fishing
function
𝑒𝑒𝑒𝑒𝑒𝑒(𝑐𝑐
𝑣𝑣𝑙𝑙𝑙𝑙𝑙𝑙 𝑡𝑡)𝑥𝑥taken
(𝐻𝐻𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡of
) =cost
the
change
biomass is the
biological
growth
a𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
stock
minus
the−catches
by
the fishing industry. In a similar way as
𝑙𝑙𝑙𝑙𝑙𝑙
𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 𝐻𝐻
for
the
global predator-prey
model,
we assume
a fishing
cost
function
−Χ𝑙𝑙𝑙𝑙𝑙𝑙
(
)










use price data in each run for a tested parameter set to check whether this requirement is met. Specifically, we assume that
the open access conditions pHt = Cx(Ht, xt) and pLt = Cy(Lt, yt) should hold for the period between 1976 and 2000 (Quaas et
al. 2012). We use observed prices from Sea Around Us and the stock estimates from the test runs in the Martell/Froese
procedure to estimate a log-linearised OLS regression of the open access conditions using the cost functions and including
the time trends below. We accept a parameter set if it gives non-negative estimates for both Xx and Xy. Parameter sets that
do not pass this test are rejected. Otherwise, we use the resulting information on the relationship between price and stock
biomass to obtain an estimate for economic parameter values.
The means and standard deviations for the 1,000 parameter sets used in the computations are given in the table below
rx
ry
kx
ky
a
b
cx
cy
vx
vy
x
y
Mean
1.44
2.24
0.044
0.0096 0.0046 0.014
49.14
24.27
0.024
0.011
0.24
0.32
Std
0.56
0.74
0.023
0.0062 0.0020 0.0077 10.45
7.82
0.005
0.003
0.20
0.20

b. Fish Supply Model at LME-Level
In the total fish supply model, the change of biomass over time is defined as
2
− 𝐻𝐻𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑥𝑥̇ 𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 = 𝑟𝑟𝑙𝑙𝑙𝑙𝑙𝑙 𝑥𝑥𝑡𝑡 − 𝑘𝑘𝑙𝑙𝑙𝑙𝑙𝑙 𝑥𝑥𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
xlme,t describes the stock size in the large marine ecosystem lme in year 𝑡𝑡, 𝑟𝑟lme describes the intrinsic growth rate of the
stock, klme is a measure of density dependence and Hlmt,t describes the catches from the LME in year t (Clark 1991). Thus,
the change of biomass is the biological growth of a stock minus the catches taken by the fishing industry. In a similar way as
for the global predator-prey model, we assume a fishing cost function
−Χ
𝑙𝑙𝑙𝑙𝑙𝑙
𝐻𝐻𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝐶𝐶𝑙𝑙𝑙𝑙𝑙𝑙 (𝐻𝐻𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 , 𝑥𝑥𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 ) = 𝑒𝑒𝑒𝑒𝑒𝑒(𝑐𝑐𝑙𝑙𝑙𝑙𝑙𝑙 − 𝑣𝑣𝑙𝑙𝑙𝑙𝑙𝑙 𝑡𝑡)𝑥𝑥𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
All parameter values differ for the 64 LMEs. We use the same approach as for the global predator-prey model for the
regionalised model, which yields 64,000 parameter sets for rlme, klme, clme, vlme, and Xlme.
Demand Model
Demand Model
Demand Model
a.
Global Demand Model
Demand
a. GlobalModel
Demand Model
In
our
global
demand
model, we consider one representative consumer who has preferences over consumption of three
a. our
Global
Demand
Model
In
global
demand
model, we consider one representative consumer who has preferences over consumption of three
fish of
(quantity
types
of
protein-rich
food,
namely
non-fish,one
protein-rich
food items
(quantity
t ), high-trophic-level
In
our
global
demand
model,
we consider
representative
consumer
whoC
preferences overpredatory
consumption
three
types of protein-rich food,
namely
non-fish, protein-rich
food items
(quantity
Chas
t ), high-trophic-level predatory fish (quantity
),
and
low-trophic-level
forage
fish
(quantity
L
).
H
t
t
types
of
protein-rich
food,
namely
non-fish,
protein-rich
food
items
(quantity
C
t ), high-trophic-level predatory fish (quantity
),
and
low-trophic-level
forage
fish
(quantity
L
).
H
t
t
Preferences
over these goods
well(quantity
as numeraire
Xt is described by the utility function:
forageas
Lt). consumption
H
t), and low-trophic-level
Preferences
over these goods
asfish
well as numeraire
consumption Xt is described by the utility function:
Preferences over these goods as well as numeraire consumption Xt is described by the utility function:
𝐸𝐸𝑡𝑡 𝜎𝜎
𝐸𝐸𝑡𝑡 𝜎𝜎 𝑙𝑙𝑙𝑙(𝑉𝑉 )
𝑈𝑈𝑡𝑡 = 𝑁𝑁𝑡𝑡 +𝜎𝜎−1
𝑡𝑡
𝐸𝐸𝑡𝑡 𝜎𝜎 𝑙𝑙𝑙𝑙(𝑉𝑉𝑡𝑡 )
𝑈𝑈𝑡𝑡 = 𝑁𝑁𝑡𝑡 +𝜎𝜎−1
𝜎𝜎−1
𝑈𝑈𝑡𝑡 = 𝑁𝑁𝑡𝑡 +
𝑙𝑙𝑙𝑙(𝑉𝑉𝑡𝑡 )
with Et being total expenditures of w for protein-rich food in year t, Nt being numeraire consumption, and Vt being a subwith Et being total expenditures of w for protein-rich food in year t, Nt being numeraire consumption, and Vt being a subutility
fortotal
protein
food consumption,
given by (Quaas
2013;
Quaas consumption,
et al. 2016).
with Eindex
expenditures
of w for protein-rich
food in and
yearRequate
t, Nt being
numeraire
t being
utility
index
for protein
food consumption,
given by (Quaas
and
Requate
2013;
Quaas et al. 2016). and Vt being a subutility index for protein food consumption, given by (Quaas and Requate 2013; Quaas
et al. 2016).
𝜎𝜎
𝜎𝜎−1
𝜎𝜎−1
𝜎𝜎−1 𝜎𝜎−1
𝜎𝜎
𝜎𝜎−1
𝜎𝜎
𝜎𝜎
𝜎𝜎 𝜎𝜎−1
𝜎𝜎
+ 𝜂𝜂 𝐿𝐿𝜎𝜎−1
𝑉𝑉𝑡𝑡 = [(1 − 𝜂𝜂𝐻𝐻 −𝜂𝜂𝐿𝐿 )𝐶𝐶𝑡𝑡𝜎𝜎−1
𝜎𝜎 + 𝜂𝜂𝐻𝐻 𝐻𝐻𝑡𝑡𝜎𝜎−1
𝑡𝑡 𝜎𝜎 ] 𝜎𝜎−1
𝑉𝑉𝑡𝑡 = [(1 − 𝜂𝜂𝐻𝐻 −𝜂𝜂𝐿𝐿 )𝐶𝐶𝑡𝑡𝜎𝜎−1
+ 𝜂𝜂𝐻𝐻 𝐻𝐻𝑡𝑡 𝜎𝜎𝜎𝜎 + 𝜂𝜂𝐿𝐿𝐿𝐿 𝐿𝐿𝜎𝜎−1
𝑡𝑡 𝜎𝜎 ]
𝜎𝜎
𝑉𝑉𝑡𝑡 = [(1 − 𝜂𝜂𝐻𝐻 −𝜂𝜂𝐿𝐿 )𝐶𝐶𝑡𝑡 + 𝜂𝜂𝐻𝐻 𝐻𝐻𝑡𝑡 + 𝜂𝜂𝐿𝐿 𝐿𝐿𝑡𝑡 ]
Here, σ reflects the elasticity of substitution between different types of food. Following Quaas et al. (2016), we assume σ =
Here, σ reflects the elasticity of substitution between different types of food. Following Quaas et al. (2016), we assume σ =
η L are different
estimated
using
quantity
data from
Around
Us and the
1.7.
The
further preference
ηH and
Here,
σ reflects
the elasticityparameters
of substitution
between
types
ofprice
food.and
Following
Quaas
et al.Sea
(2016),
we assume
σ=
ηL are estimated
using
price
and
quantity
data from
Sea
Around
Us and the
1.7. The
further preference
parameters
ηH and
fish,
P
and
forage
fish,
P
of the
FAO.
Using
the yearly
pricesparameters
of non-fishηprotein-rich
food,
P
Ct, predatory
Htquantity
Lt, maximisation
and
η
are
estimated
using
price
and
data
from
Sea
Around
Us and
the
1.7.
The
further
preference
H
L
of the
FAO. Using the yearly prices of non-fish protein-rich
food, PCt, predatory fish, PHt and forage fish, PLt, maximisation
utility
with
respect
to consumption
of protein-rich
foodfood,
leads
to, predatory
the following
demand
functions
fish,inverse
PHt and
forage fish,
PLt, maximisation of the
FAO.
Using
the yearly
prices of non-fish
protein-rich
PCt
utility with
respect
to consumption
of protein-rich
food leads
to the following
inverse
demand
functions
utility with respect to consumption of protein-rich food leads to the following1 inverse demand functions
𝐸𝐸𝑡𝑡
−
𝑃𝑃𝐶𝐶𝐶𝐶 = 𝐸𝐸𝑡𝑡 (1 − 𝜂𝜂𝐻𝐻 − 𝜂𝜂𝐿𝐿 ) 𝐶𝐶𝑡𝑡−𝜎𝜎𝜎𝜎11
𝑉𝑉𝑡𝑡 (1 − 𝜂𝜂𝐻𝐻 − 𝜂𝜂𝐿𝐿 ) 𝐶𝐶𝑡𝑡−
𝑃𝑃𝐶𝐶𝐶𝐶 = 𝐸𝐸
𝑃𝑃𝐶𝐶𝐶𝐶 = 𝑉𝑉𝑡𝑡 (1 − 𝜂𝜂𝐻𝐻 − 𝜂𝜂𝐿𝐿 ) 𝐶𝐶𝑡𝑡 𝜎𝜎
1
𝑉𝑉𝑡𝑡 𝐸𝐸
−
𝑡𝑡
𝑃𝑃𝐻𝐻𝐻𝐻 = 𝐸𝐸𝑡𝑡 𝜂𝜂𝐻𝐻 𝐻𝐻𝑡𝑡−𝜎𝜎𝜎𝜎11
𝑃𝑃𝐻𝐻𝐻𝐻 = 𝑉𝑉
𝐸𝐸𝑡𝑡𝑡𝑡 𝜂𝜂𝐻𝐻 𝐻𝐻𝑡𝑡−
𝑃𝑃𝐻𝐻𝐻𝐻 = 𝑉𝑉𝑡𝑡 𝜂𝜂𝐻𝐻 𝐻𝐻𝑡𝑡 𝜎𝜎
1
𝑉𝑉
𝐸𝐸𝑡𝑡𝑡𝑡
−
𝑃𝑃𝐿𝐿𝐿𝐿 = 𝐸𝐸𝑡𝑡 𝜂𝜂𝐿𝐿 𝐿𝐿−𝑡𝑡 𝜎𝜎𝜎𝜎11
𝑉𝑉𝑡𝑡 𝜂𝜂𝐿𝐿 𝐿𝐿−𝑡𝑡
𝑃𝑃𝐿𝐿𝐿𝐿 = 𝐸𝐸
𝑃𝑃𝐿𝐿𝐿𝐿 = 𝑉𝑉𝑡𝑡 𝜂𝜂𝐿𝐿 𝐿𝐿𝑡𝑡 𝜎𝜎
𝑉𝑉𝑡𝑡
from which we estimate the demand parameters ηHt, ηLt, using data on Ht, Lt, Ct, PCt, PHt, and PLt for the period 1976 to 2010.
from which we estimate the demand parameters ηHt, ηLt, using data on Ht, Lt, Ct, PCt, PHt, and PLt for the period 1976 to 2010.
This
leads
from
whichto
we estimate the demand parameters ηHt, ηLt, using data on Ht, Lt, Ct, PCt, PHt, and PLt for the period 1976 to 2010.
This leads
to
ηH
0.1554
This leads to
ηH
0.1554
η
0.3675
L
η
0.1554
H
ηL
0.3675
ηL
0.3675
For expenditures Et we use the scenarios described in section 2.3. For the consumption of non-fish protein-rich food, we
For expenditures Et we use the scenarios described in section 2.3. For the consumption of non-fish protein-rich food, we
assume
that the past
trend
the perioddescribed
1976 to 2010
will continue,
an exponential
of 2.09% food,
per year.
useover
the scenarios
in section
2.3. Forwith
the consumption
of growth
non-fishrate
protein-rich
we
For expenditures
Et we
assume
that the past
trend
over
the period 1976 to 2010
will continue,
with
an exponential
growth
rate
of 2.09% per year.
assume that the past trend over the period 1976 to 2010 will continue, with an exponential growth rate of 2.09% per year.
b. Demand Model at LME Level
b. Demand Model at LME Level
For
the modelling
regional
demand we grouped countries at large marine ecosystem (LME) level. We assume that there
b. Demand
Modelof
LME Level
For
the modelling
ofatregional
demand we grouped countries at large marine ecosystem (LME) level. We assume that there
is
a the
representative
for each we
large
marinecountries
ecosystem
who consumes
protein-rich
foodlevel.
in each
t, which
For
modelling ofconsumer
regional demand
grouped
at large
marine ecosystem
(LME)
We year
assume
that is
there
is
a representative
consumer
for each large
marine ecosystem
who consumes
protein-rich
food in each
year
t, which
is
non-fish,
protein-rich
food items,who
quantity
Flme,t ofprotein-rich
fish food-items.
Preferences
are
described
composed
of a quantity
Clme,t of
is
a
representative
consumer
for
each
large
marine
ecosystem
consumes
food
in
each
year
t,
which
is
composed of a quantity Clme,t of non-fish, protein-rich food items, quantity Flme,t of fish food-items. Preferences are described
by
the utilityoffunction
of
non-fish,
protein-rich
food
items,
quantity
F
of
fish
food-items.
Preferences
are
described
composed
a
quantity
C
lme,t
lme,t
by the utility function
𝐸𝐸𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 𝜎𝜎
by the utility function
𝑈𝑈𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 = 𝑁𝑁𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 + 𝐸𝐸𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 𝜎𝜎 ln(𝑉𝑉𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 )
− 1𝜎𝜎 ln(𝑉𝑉𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 )
𝑈𝑈𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 = 𝑁𝑁𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 + 𝐸𝐸𝜎𝜎𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑈𝑈𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 = 𝑁𝑁𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 + 𝜎𝜎 − 1 ln(𝑉𝑉𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 )
𝜎𝜎 − 1
As in the global model, Elme,t describes the total expenditures for protein-rich food in year t, Nlme,t is numeraire consumption,
As in the global model, Elme,t describes the total expenditures for protein-rich food in year t, Nlme,t is numeraire consumption,
is a sub-utility
index
for protein food consumption:
and
52 Vlme,t global
As
model, E
lme,t describes the total expenditures for protein-rich food in year t, Nlme,t is numeraire consumption,
andinVthe
lme,t is a sub-utility index for protein food consumption:
and Vlme,t is a sub-utility index for protein food consumption:
𝜎𝜎
𝜎𝜎−1
𝜎𝜎−1
𝜎𝜎−1 𝜎𝜎−1
𝜎𝜎
𝜎𝜎−1
𝑑𝑑
𝑖𝑖
𝑑𝑑
𝑑𝑑
𝑖𝑖
𝜎𝜎
𝜎𝜎 + 𝜂𝜂 𝑖𝑖
𝜎𝜎 ] 𝜎𝜎−1
𝜎𝜎
− 𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑉𝑉𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 = [(1 − 𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
) 𝐶𝐶𝑙𝑙𝑙𝑙𝑙𝑙
) 𝜎𝜎−1
) 𝜎𝜎−1
(𝐹𝐹𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
(𝐹𝐹𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑑𝑑
𝑖𝑖
𝑑𝑑
𝑑𝑑
𝑖𝑖
𝑖𝑖
𝜎𝜎 + 𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝜎𝜎
𝜎𝜎
𝜎𝜎−1
ηHL
0.3675
0.1554
types of protein-rich food, namely non-fish, protein-rich
food
items (quantity Ct ), high-trophic-level predatory fish (quantity
η
0.3675
L
Ht), and low-trophic-level forage fish (quantity Lt).
theas
scenarios
described consumption
in section 2.3.XFor
the consumption of non-fish protein-rich food, we
For expenditures
t we use
Preferences
over E
these
goods
well as numeraire
t is described by the utility function:
assume
that the past
trend
the perioddescribed
1976 to 2010
will continue,
an exponential
of 2.09%food,
per year.
use over
the scenarios
in section
2.3. For with
the consumption
of growth
non-fishrate
protein-rich
we
For
expenditures
Et we
assume that the past trend over the period 1976 to 2010 will continue, with an exponential growth rate of 2.09% per year.
𝐸𝐸𝑡𝑡 𝜎𝜎
b. Demand Model at LME Level
𝑈𝑈𝑡𝑡 = 𝑁𝑁𝑡𝑡 +𝜎𝜎−1 𝑙𝑙𝑙𝑙(𝑉𝑉𝑡𝑡 )
For
the
modelling
of
regional
demand
we
grouped
countries
at large marine ecosystem (LME) level. We assume that there
b. Demand Model at LME Level
is a the
representative
for each we
large
marinecountries
ecosystem
who consumes
protein-rich
foodlevel.
in each
t, which
is
For
modelling ofconsumer
regional demand
grouped
at large
marine ecosystem
(LME)
We year
assume
that there
protein-rich
foodinitems,
of
fish food-items.
composed
of total
a quantity
Clme,t of
with
Et being
expenditures
ofeach
w forlarge
protein-rich
food
yearwho
t,quantity
Ntconsumes
beingFlme,t
numeraire
consumption,
and year
Vt being
adescribed
subis
a representative
consumer
fornon-fish,
marine ecosystem
protein-rich
food inPreferences
each
t,are
which
is
by theindex
utilityoffor
function
utility
given by (Quaas
and Requate
Quaas
et al. 2016).
of non-fish, protein-rich
food items,
quantity2013;
Flme,t of
fish food-items.
Preferences are described
composed
a protein
quantityfood
Clme,tconsumption,
𝐸𝐸𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 𝜎𝜎
by the utility function
ln(𝑉𝑉𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 )
𝑈𝑈𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 = 𝑁𝑁𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 +
𝜎𝜎
𝐸𝐸𝜎𝜎𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
− 1𝜎𝜎𝜎𝜎−1
𝜎𝜎−1 𝜎𝜎−1
ln(𝑉𝑉
+
𝑈𝑈𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 = 𝑁𝑁𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡𝜎𝜎−1
𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 ) 𝜎𝜎
𝜎𝜎
𝜎𝜎
𝑉𝑉𝑡𝑡 = [(1 − 𝜂𝜂𝐻𝐻 −𝜂𝜂𝐿𝐿 )𝐶𝐶𝑡𝑡 +𝜎𝜎 𝜂𝜂−𝐻𝐻 𝐻𝐻
1 𝑡𝑡 + 𝜂𝜂𝐿𝐿 𝐿𝐿𝑡𝑡 ]
As in the global model, Elme,t describes the total expenditures for protein-rich food in year t, Nlme,t is numeraire consumption,
is a sub-utility
for protein
food
andinVthe
lme,t global
the
totalconsumption:
expenditures for protein-rich food in year t, Nlme,t is numeraire consumption,
As
model, Eindex
lme,t describes
Here,Vlme,t
σ reflects
the elasticity
between
different types of food. Following Quaas et al. (2016), we assume σ =
is a sub-utility
indexofforsubstitution
protein food
consumption:
and
𝜎𝜎
using price𝜎𝜎−1
and quantity data𝜎𝜎−1
from𝜎𝜎−1
Sea Around Us and the
1.7. The further preference parameters ηH and ηL are estimated
𝜎𝜎−1
𝑑𝑑
𝑖𝑖
𝑑𝑑
𝑑𝑑
𝑖𝑖
𝑖𝑖
𝜎𝜎
𝜎𝜎
𝜎𝜎
𝜎𝜎
FAO. Using the yearly prices
protein-rich
P , predatory fish, P and forage fish, PLt, maximisation of the
𝐶𝐶𝑙𝑙𝑙𝑙𝑙𝑙
− 𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 )food,
𝑉𝑉𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡of=non-fish
[(1 − 𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝜎𝜎−1 +Ct𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 (𝐹𝐹𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 )𝜎𝜎−1 +Ht𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 (𝐹𝐹𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 )𝜎𝜎−1 ]𝜎𝜎−1
𝑑𝑑
𝑖𝑖 food leads
𝑑𝑑the following
𝑑𝑑
𝑖𝑖
𝑖𝑖
𝜎𝜎 ]
utility with respect to consumption
of −protein-rich
demand
𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
− 𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
+ 𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑉𝑉𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 = [(1
) 𝐶𝐶 𝜎𝜎 +to
) 𝜎𝜎 inverse
) functions
(𝐹𝐹𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
(𝐹𝐹𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑙𝑙𝑙𝑙𝑙𝑙
1
𝐸𝐸𝑡𝑡 and non-fish protein-rich
−
Here, σ reflects the elasticity of substitution between
fish
food. Using the Armington (1969)
(1 − 𝜂𝜂𝐻𝐻 − 𝜂𝜂𝐿𝐿 ) 𝐶𝐶𝑡𝑡 𝜎𝜎
𝑃𝑃𝐶𝐶𝐶𝐶 =
1
𝑉𝑉𝑡𝑡 and non-fish
allows
a distinction
to be madebetween
betweenfish
domestically
produced
and imported
fish.the
Again
we assume
that the
assumption
Here,
σ reflects
the elasticity
of substitution
protein-rich
food. Using
Armington
(1969)
d
elasticity of 1demand
1.7 according
Asche
et al. (1996)
and
Quaas
(2013).
The
demand
parameters
ηthe
1 and Requate
allows aisdistinction
to betomade
between
domestically
produced
and
imported
fish.
Again
we
assume
that
assumption
lme,F
𝐸𝐸𝑡𝑡
−𝜎𝜎
i
d
𝑃𝑃
=
𝜂𝜂
𝐻𝐻
measure
relative
preference
for
domestic
and
imported
fish.
Using
the
yearly
prices
of
non-fish
protein-rich
food
and
η
𝐻𝐻𝐻𝐻 and Quaas
𝐻𝐻 𝑡𝑡 and Requate (2013). The demand parameters η lme,F
lme,Fof demand is 1.7 according to Asche et al. (1996)
elasticity
𝑉𝑉
𝑡𝑡
, domestically
produced
fish foodfor
items
PdF,lme,tand
andimported
imported
fish Using
food items
PiF,lme,tprices
, utilityof
maximisation
leads to the
PC,lme,t
measure relative
preference
domestic
fish.
the yearly
non-fish protein-rich
food
and
ηilme,F
1
d
𝐸𝐸
following
inverse
demand
functions:
−
fish𝜎𝜎 food items PiF,lme,t, utility maximisation leads to the
PC,lme,t, domestically produced fish food items P F,lme,t and imported
𝑡𝑡
𝑃𝑃𝐿𝐿𝐿𝐿 =
𝜂𝜂 𝐿𝐿
following inverse demand functions:
𝑉𝑉𝑡𝑡 𝐿𝐿 𝑡𝑡
1
𝐸𝐸𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
−𝜎𝜎
𝑑𝑑
𝑖𝑖
(1 − 𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
− 𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
) 𝐶𝐶𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑃𝑃𝐶𝐶,𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 =
1
𝐸𝐸
−𝜎𝜎
𝑉𝑉𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑑𝑑
𝑖𝑖
(1 − data
𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
− 𝜂𝜂H𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝐶𝐶𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑃𝑃𝐶𝐶,𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 η=Ht, ηLt, using
on
from which we estimate the demand parameters
t, Lt,) C
t, PCt, PHt, and PLt for the period 1976 to 2010.
𝑉𝑉𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
1
𝐸𝐸𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 𝑑𝑑
This leads to
−
𝑑𝑑
𝑑𝑑
𝑃𝑃𝐹𝐹,𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
=
(𝐹𝐹𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 ) 1𝜎𝜎 𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝐸𝐸
η𝑑𝑑H
0.1554
𝑉𝑉𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
−𝜎𝜎
𝑑𝑑
𝑑𝑑
𝑃𝑃𝐹𝐹,𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 =
𝜂𝜂
(𝐹𝐹
)
𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
ηL
0.3675
𝑉𝑉𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
1
𝐸𝐸𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
−
𝑖𝑖
𝑖𝑖
𝑖𝑖
𝑃𝑃𝐹𝐹,𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
=
) 1𝜎𝜎 𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
(𝐹𝐹𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝐸𝐸
𝑉𝑉𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 2.3.
−𝜎𝜎
𝑖𝑖
𝑖𝑖 For
𝑖𝑖 consumption of non-fish protein-rich food, we
in
section
the
For expenditures Et we use the scenarios described
𝑃𝑃𝐹𝐹,𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 =
(𝐹𝐹𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 ) 𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
assume that the past trend over the period 1976 to 2010𝑉𝑉𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
will continue, with an exponential growth rate of 2.09% per year.
With the given information on prices and quantities of domestically produced and imported fish and substitution goods for
d
i
b.
Model
at LME
Level
theDemand
period
1976
to 2011,
we
thequantities
preferenceofparameters
.
With
the given
information
onestimate
prices and
domesticallyη produced
fish and substitution goods for
lme,F and ηand
lme,F imported
For
the
of regional
demand weuse
grouped
countries
at large
marinegrowth
ecosystem (LME) level. We assume that there
For period
foodmodelling
consumption
expenditures,
the SSP1
scenario
on
income
and ηilme,F . and an income elasticity of food demand
the
1976 to 2011,
we estimatewe
the preference
parameters
ηdlme,F
is
representative
for each
largeconsumption
marine ecosystem
who protein-rich
consumes protein-rich
food in each
t, which
is
of a0.48
andconsumer
Masset
2010).
For
of non-fish
food,
linearyear
trends
fordemand
each
For
food(Cireira
consumption
expenditures,
wethe
use the SSP1 scenario
on income growth
andwe
andetermine
income elasticity
of food
protein-rich
items,
Fthat
fish food-items.
Preferences
are described
composed
ofon
a past
quantity
Clme,t of non-fish,
lme,t of
LME
based
observations
the
period
1976 food
to 2010
andquantity
assume
they
until
2050.
of
0.48
(Cireira
and Masset
2010).for
For
the
consumption
of non-fish
protein-rich
food,will
wecontinue
determine
linear
trends for each
by the utility function
LME based on past observations for the period 1976 to 2010 and
assume
that they will continue until 2050.
𝜎𝜎
𝐸𝐸𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
ln(𝑉𝑉𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 )
𝑈𝑈𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 = 𝑁𝑁𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 +
𝜎𝜎 − 1
1
The Armington assumption is a standard assumption of computable equilibrium models and implies that consumers are assumed to
between
goods based
origin, that
is whether
good is produced
domestically
or imported.
The Armington
assumption
is a on
standard
assumption
ofthe
computable
equilibrium
models and
implies that consumers are assumed to
differentiate
between
goodsEbased
on origin, the
that total
is whether
the good is
domestically
imported.
expenditures
forproduced
protein-rich
food inoryear
t, Nlme,t is numeraire consumption,
As in the global
model,
lme,t describes
1differentiate
and Vlme,t is a sub-utility index for protein food consumption:
𝑉𝑉𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 = [(1 −
𝑑𝑑
𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
−
𝑖𝑖
𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
)
𝜎𝜎−1
𝜎𝜎
𝐶𝐶𝑙𝑙𝑙𝑙𝑙𝑙
+
𝑑𝑑
𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝜎𝜎−1
𝑑𝑑
) 𝜎𝜎
(𝐹𝐹𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
+
𝑖𝑖
𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝜎𝜎
𝜎𝜎−1 𝜎𝜎−1
𝑖𝑖
𝜎𝜎
(𝐹𝐹𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 ) ]
Here, σ reflects the elasticity of substitution between fish and non-fish protein-rich food. Using the Armington (1969)
assumption1 allows a distinction to be made between domestically produced and imported fish. Again we assume that the
elasticity of demand is 1.7 according to Asche et al. (1996) and Quaas and Requate (2013). The demand parameters ηdlme,F
and ηilme,F measure relative preference for domestic and imported fish. Using the yearly prices of non-fish protein-rich food
PC,lme,t, domestically produced fish food items PdF,lme,t and imported fish food items PiF,lme,t, utility maximisation leads to the
following inverse demand functions:
𝑃𝑃𝐶𝐶,𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 =
1
𝐸𝐸𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
−𝜎𝜎
𝑑𝑑
𝑖𝑖
(1 − 𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
− 𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
) 𝐶𝐶𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑉𝑉𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑑𝑑
𝑃𝑃𝐹𝐹,𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
=
𝑖𝑖
𝑃𝑃𝐹𝐹,𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
=
1
𝐸𝐸𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 𝑑𝑑
−
𝑑𝑑
(𝐹𝐹𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 ) 𝜎𝜎 𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑉𝑉𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
1
𝐸𝐸𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
−
𝑖𝑖
(𝐹𝐹 𝑖𝑖 ) 𝜎𝜎 𝜂𝜂𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
𝑉𝑉𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡 𝑙𝑙𝑙𝑙𝑙𝑙,𝑡𝑡
With the given information on prices and quantities of domestically produced and imported fish and substitution goods for
the period 1976 to 2011, we estimate the preference parameters ηdlme,F and ηilme,F .
For food consumption expenditures, we use the SSP1 scenario on income growth and an income elasticity of food demand
of 0.48 (Cireira and Masset 2010). For the consumption of non-fish protein-rich food, we determine linear trends for each
LME based on past observations for the period 1976 to 2010 and assume that they will continue until 2050.
1
The Armington assumption is a standard assumption of computable equilibrium models and implies that consumers are assumed to
differentiate between goods based on origin, that is whether the good is produced domestically or imported.
Fishing for Proteins | 53
List of Large Marine Ecosystems (according to Sea Around Us)
Agulhas Current
Aleutian Islands
Antarctic
Arabian Sea
Baltic Sea
Barents Sea
Bay of Bengal
Beaufort Sea
Benguela Current
Black Sea
California Current
Canadian Eastern Arctic -West
Greenland
Canadian High Arctic - North Greenland
Canary Current
Caribbean Sea
Celtic-Biscay Shelf
Central Arctic Ocean (no data available)
East Bering Sea
East Brazil Shelf
East China Sea
East Siberian Sea
East-Central Australian Shelf
Faroe Plateau
Greenland Sea
Guinea Current
Gulf of Alaska
Gulf of California
Gulf of Mexico
Gulf of Thailand
Hudson Bay Complex
Humboldt Current
Iberian Coastal
Iceland Shelf and Sea
Indonesian Sea
Insular Pacific-Hawaiian
Kara Sea
Kuroshio Current
Laptev Sea
Mediterranean Sea
New Zealand Shelf
Newfoundland-Labrador Shelf
North Australian Shelf
North Brazil Shelf
North Sea
Northeast Australian Shelf-Great Barrier
Reef
Northeast U.S. Continental Shelf
Northern Bering - Chukchi Seas
Northwest Australian Shelf
Norwegian Sea
Oyashio Current
Pacific Central-American Coastal
Patagonian Shelf
Red Sea
Scotian Shelf
Sea of Japan / East Sea
Sea of Okhotsk
Somali Coastal Current
South Brazil Shelf
South China Sea
Southeast Australian Shelf
Southeast U.S. Continental Shelf
Southwest Australian Shelf
Sulu-Celebes Sea
West Bering Sea
West-Central Australian Shelf
Yellow Sea
List of Protein-Rich Non-Fish Food Substitute Goods (FAOstat Database 2016)
Almonds, shelled
Bambara beans
Beans, dry
Beans, green
Brazil nuts, shelled
Broad beans, horse beans, dry
Butter, cow’s milk
Cashew nuts, shelled
Cashew nuts, with shell
Cheese, sheep’s milk
Cheese, whole cow’s milk
Chestnuts
Chick peas
Coconuts
Cream, fresh
Eggs, hen, in shell
Eggs, other bird, in shell
Ghee, buffalo milk
54
Groundnuts, shelled
Hazelnuts, shelled
Kola nuts
Lard
Lentils
Maize
Maize, green
Meat, cattle
Meat, chicken
Meat, duck
Meat, game
Meat, goat
Meat, goose and guinea fowl
Meat, horse
Meat, not elsewhere included
Meat, pig
Meat, rabbit
Meat, sheep
Meat, turkey
Milk, skimmed, cow
Milk, skimmed, dried
Milk, whole, condensed
Milk, whole, dried
Milk, whole, evaporated
Milk, whole, fresh, cow
Nuts, not elsewhere included
Nuts, prepared (exc. groundnuts)
Peas, dry
Peas, green
Rice – total (rice milled equivalent)
Soybeans
Walnuts, shelled
Walnuts, with shell
Whey, condensed
Whey, dry
Yoghurt, concentrated or not
List of Figures and Tables
Fig. 1: World fish consumption.
16
Fig. 2: Per capita world fish consumption.
16
Fig. 3: Per capita fish consumption.
17
Fig. 4: World fish consumption pattern.
17
Fig. 5: Fish consumption pattern of the case study continents.
18
Fig. 6: Fish consumption in the eight case study countries.
19
Fig. 7: Per capita fish consumption in the eight case study countries and the corresponding continent.
20
Fig. 8: Fish consumption pattern in the eight case study countries.
21
Fig. 9: Total fish consumption and fish contribution to total animal protein.
23
Fig. 10: Fish in the diets in relation to economic development of a country or population.
25
Fig. 11: Total protein supply and fish in total protein.
25
Fig. 12: Main drivers of fish dependency.
28
Fig. 13: Fish dependency scores worldwide.
29
Fig. 13a: Per capita catch worldwide.
29
Fig. 13b: Share of fish in total consumption of animal protein.
30
Fig. 13c: Per capita GDP in USD.
30
Fig. 13d: Share of undernourishment in population.
30
Fig. 14: P
opulation, catches and fraction of local consumption for population in 2010
that was covered by LME catches in 2010).
33
Fig. 15: Large Marine Ecosystems considered in this study.
35
Fig. 16: Development of global total catches, predatory and prey fish catches from 1950 to 2010 37
Fig. 17: Global ex-vessel price per year in 2005 USD per ton.
38
Fig. 18: Global Expenditure per Year in billion USD from 1976 to 2010.
38
Fig. 19: Global production per year in million tons from 1976 to 2010.
39
Fig. 20: Development of Global GDP from 2010 to 2050 for the SSP scenarios of the IPCC.
41
Fig. 21: Development of Global Population from 2010 to 2050 for the SSP scenarios of the IPCC.
42
Fig. 22: E
stimates of global maximum sustainable yield that the global fish stocks could supply from
three different model specifications.
43
Fig. 23: G
lobal fish catches according to global bio-economic predator-prey model for varying degrees
of management effectiveness for the reference scenario with income growth from SSP1.
44
Fig. 24: G
lobal fish catches according to global bio-economic predator-prey model for varying degrees
of management effectiveness for the high-pressure scenario with income growth from SSP5 and unit
income elasticity of food demand.
45
Fig. 25. E
xpected global fish catches according to a global bio-economic predator-prey model for varying
degrees of management effectiveness for the low-pressure scenario with income growth from SSP3 and
no technical progress in fishing.
45
Fig. 26: P
rojected MSY Catches, population size and fraction of local needs that could potentially be
covered by LME in 2050 under ideal conditions and population development according to SSP1 scenario.
46
Fig. 27: P
rojected MSY Catches, population size and fraction of local needs that could potentially be
covered by LME in 2050 under ideal conditions and population development according to SSP3 scenario.
46
Fig. 28: Net Import and Net Export of fish per LME in 2050 in million tons.
47
Fig. 29: LMEs with decreasing fish consumption between 2010 and 2050.
48
Fig. 30: LMEs with increasing fish consumption between 2010 and 2050.
49
Tab. 1: Dependence on fish in the diet in the eight case countries in this study. Source: FAO.
24
Tab. 2: (Multidimensional) Global Hunger Index 1995 to 2015 for selected target countries.
26
Tab. 3: F
ish dependence of the eight poorest countries and the eight countries with the highest
share of fish protein consumption (own results).
31
Tab. 4: Fish dependence in the eight target countries in this study.
31
Tab. 5: Mean catches in 2050 according to three model specifications.
44
Tab. 6: National recommended intakes for fish
44
Fishing for Proteins | 55
Footnotes
2) WHO technical report series 916: Diet, Nutrition and the Prevention of Chronic Diseases – Report of a Joint WHO/FAO
Expert Consultation 2002.
2) Recommendation issued by the German Nutrition Society (DGE): 1 to 2 portions of fish per week. Recommended
portion size is approx. 150 g = 225 g per week or 11.7 kg per capita per year. https://www.dge.de/ernaehrungspraxis/
vollwertige-ernaehrung/10-regeln-der-dge/
3) SSPs were developed by the climate change research community (e.g. IPCC) in order to simplify the integrated
analysis of the future effects of climate change. They lead to prognoses for population development and economic
development, especially for the following elements: 1. population by age, gender and educational status; 2.
urbanisation; and 3. Economic development (GDP). In addition to these basic elements, there are other hypothetical
scenarios, among others, for 4. energy supply and use; 5. land use; 6. greenhouse gas emissions and air pollution; 7.
average global radiative forcing and temperature changes; as well as 8. mitigation costs.
4) http://www.fao.org/fishery/cwp/search/en
5) See also section on fish consumption.
6) Standardised according to the limit values which, between 1988 and 2013, were slightly above the highest country
values of the relevant indicator measured worldwide.
7) Among the most common micronutrient deficits, fish has the greatest potential to help alleviate vitamin A, iron and
iodine deficits. This is particularly true for small species consumed whole with heads and bones, which can be an
excellent source of many essential minerals such as iodine, selenium, zinc, iron, calcium, phosphorus and potassium,
as well as vitamins such as A and D and several vitamins from the B group (Kawarazuka and Béné 2011). In addition,
fish is usually low in saturated fats, carbohydrates and cholesterol with a few exceptions for selected species
8) A similar conclusion, albeit with a different intention, was reached by Thurstan and Roberts (2014).
9) Measured in USD purchasing power equivalent, i.e. correcting for exchange rate fluctuations using a hypothetical
exchange rate to achieve equal purchasing power for a fixed basket of goods. This measure is often used in
international comparisons in order to minimise the effects of (short-term) fluctuations in exchange rates when comparing
the poverty status in several countries, for example.
10) As in the Allison et al. (2009a, 2009b) indicator.
11) For a full list of LMEs see appendix. The Antarctic and the Central Arctic Ocean are excluded due to a lack of data.
12) The full sets of parameter values, computation results and programming codes are available electronically as
supplementary material. For the numerical calculation we employ the interior-point algorithm of the Knitro (version 9.1)
optimisation software (Byrd et al. 1999; 2006). All programming codes were implemented in AMPL and are available as
supporting material.
13) Details on the resulting accepted parameter values for the global predator-prey model are given in the technical
appendix.
14) S
ee appendix for a detailed list of substitution goods. Note: ‘other’ refers to the FAOstat specification ‘not
elsewhere included’.
15) The following countries have been removed: Armenia, Austria, Azerbaijan, Belarus, Bermuda, Bhutan, Bolivia
(Plurinational State), Botswana, Burkina Faso, Burundi, Central African Republic, Chad, Cook Islands, Czech Republic,
Czechoslovakia, Ethiopia, Ethiopia PDR, Republic of Fiji, French Polynesia, Guam, Hungary, Kazakhstan, Kiribati,
Kyrgyzstan, Lao People’s Democratic Republic, Lesotho, Luxembourg, Former Yugoslav Republic of Macedonia,
Malawi, Mali, Marshall Islands, Mauritius, Federal States of Micronesia, Republic of Moldova, Mongolia, Nepal, Niger,
Northern Mariana Islands, Palau, Palestine (Occupied Territories), Paraguay, Rwanda, Samoa, Serbia, Serbia and
Montenegro, Slovakia, Swaziland, Switzerland, Tonga, Turkmenistan, Tuvalu, Uganda, Uzbekistan, Vanuatu, Yugoslavia
SFR, Zambia, Zimbabwe.
16) https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=welcome
17) Note that the data underlying this study only considers marine catches in LMEs. High seas catches, aquaculture
production and inland fisheries are not included.
56
References
Allison, E.H. et al. 2012. Rights-based fisheries governance: from fishing rights to human rights. Fish and Fisheries,
13: 14-29. DOI: 10.1111/j.1467-2979.2011.00405.x.
Allison, E.H. et al. 2009a. Vulnerability of national economies to the impacts of climate change on fisheries.
Fish and Fisheries. 10(2), 173-196 DOI: 10.1111/j.1467-2979.2008.00310.x.
Allison, E.H. et al. 2009b. Climate change and fisheries: a comparative analysis of the relative vulnerability of 132
countries. Fish and Fisheries, 10: 173-196.
Anderson, J.L. 1985. Market Interactions between Aquaculture and the Common-Property Commercial Fishery.
Marine Resource Economics 2(1): 1-24. Crane, Russak & Company, Inc.
Armington, P. 1969. A Theory of Demand for Products Distinguished by Place of Production. International Monetary
Fund Staff Papers, XVI, 159-78.
Asche, F. et al. 2015. Fair Enough? Food Security and the International Trade of Seafood. World Development,
67: 151-160. DOI: 10.1016/j.worlddev.2014.10.013.
Badjeck, M-C. et al. 2013. The vulnerability of fishing-dependent economies to disasters. FAO Fisheries and Aquaculture
Circular No. 1081 FIPI/C1081 (En)
Béné, C. et al. 2015. Feeding 9 billion by 2050 – Putting fish back on the menu. Food Security - The Science,
Sociology and Economics of Food Production and Access to Food, 7: 261-274.
Beveridge, M. 2013. Meeting the food and nutrition needs of the poor: the role of fish and the opportunities and challenges
emerging from the rise of aquaculture. Journal of Fish Biology, 83 (4): 1067-1084.
Beveridge, M., Phillips, M., Dugan, P. and Brummet, R. 2010. Barriers to aquaculture development as a pathway to poverty
alleviation and food security. In: Advancing the Aquaculture Agenda: Workshop Proceedings, OECD, 345-359.
Blanchard, J.L. et al. 2012. Potential consequences of climate change for primary production and fish production in large
marine ecosystems. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 367, 2979-2989.
BMEL (Bundesministerium für Ernährung und Landwirtschaft) 2015. Welternährung verstehen. Fakten und Hintergründe.
Branch, T.A. 2008. Not all fisheries will be collapsed in 2048. Marine Policy 32:38-39.
Branch, T.A., Jensen, O.P., Ricard, D., Ye, Y., Hilborn, R. 2010. Contrasting Global Trends in Marine Fishery Status
Obtained from Catches and from Stock Assessments, Conservation Biology, 25 (4), 777-786.
Britten, G.L. et al. 2014. Predator decline leads to decreased stability in a coastal fish community. Ecology Letters.
DOI: 10.1111/ele.12354.
Byrd, R., Hribar, M. and Nocedal, J. 1999. An interior point method for large scale nonlinear programming.
SIAM Journal of Optimization 9 (4), 877-900.
Byrd, R., Nocedal, J. and Waltz, R. 2006. Knitro: An integrated package for nonlinear optimization.
In: di Pillo, G., Roma, M. (eds.), Large-Scale Nonlinear Optimization. Springer, p. 35.
Cheung, W.W.L. et al. 2010. Large-scale redistribution of maximum fisheries catch potential in the global ocean under
climate change. Global Change Biology 16, 1: 24-35.
Christensen, V. et al. 2014. A century of fish biomass decline in the ocean. Marine Ecology Progress Series, 512: 155-166.
Cireira, X. and Masset, E. 2010. Income distribution trends and future food demand. Philosophical Transactions of the
Royal Society B 365: 2821-2834. DOI: 10.1098/rstb.2010.0164.
Clark, C.W. 1991. Mathematical Bioeconomics. 2nd Edition. John Wiley & Sons. New Jersey.
Clark, C.W., Munro, G.R. and Sumaila U.R. 2005. Subsidies, buybacks, and sustainable fisheries.
Journal of Environmental Economics and Management, 50(1), 47-58.
Costello, C. et al. 2016. Global fishery futures under contrasting management regimes. Proceedings of the National
Academy of Sciences of the United States of America. DOI: 10.1073/pnas.1520420113.
Costello, C. et al. 2012. Status and Solutions for the World’s Unassessed Fisheries. Science, 338: 517-520.
DOI: 10.1126/science.1223389.
Costello, C., Gaines, S.D. and Lynham, J. 2008. Can Catch Shares Prevent Fisheries Collapse?
Science, 321(5896): 1678-1681. DOI: 10.1126/science.1159478.
Essington, T.E. et al. 2015. Fishing amplifies forage fish population collapses.
Proceedings of the National Academy of Sciences of the United States of America 112(21), 6648-6652.
Evans, L.T. 1998. Feeding the ten billion – Plant and population growth. Cambridge University Press, 264 p.
FAO Committee on Fisheries 2014. Report of the fourteenth session of the Sub-Committee on Fish Trade.
Bergen, Norway, 24-28 February 2014.
FAO 2014. The State of World Fisheries and Aquaculture: Opportunities and Challenges.
Food and Agriculture Organization of the United Nations. Rome.
Fishing for Proteins | 57
FAO 2016. The State of World Fisheries and Aquaculture: Opportunities and Challenges.
Food and Agriculture Organization of the United Nations. Rome.
FAO Fisheries and Aquaculture Department. Fisheries and aquaculture software. FishStatJ - software for fishery statistical
time series. Rome. Updated 23 June 2015. [Cited 22 January 2016]. http://www.fao.org/fishery/statistics/software/fishstatj/en
FAO Statistical database, http://faostat3.fao.org/home/E
FAO 2016a. Species Fact Sheet Engraulis ringens. http://www.fao.org/fishery/species/2917/en
FAO 2016b. Fishery and Aquaculture Country Profiles Indonesia. http://www.fao.org/fishery/facp/IDN/en
FAO 2016c. Fishery and Aquaculture Country Profiles Peru. http://www.fao.org/fishery/facp/PER/en
Fenichel, E.P. et al. 2016. Measuring the value of groundwater and other forms of natural capital Proceedings of the
National Academy of Sciences of the United States of America, 113 (9) 2382-2387; published ahead of print 8 February
2016. DOI: 10.1073/pnas.1513779113
Frid, C.L.J. and Paramor, O.A.L. 2012. Food for Thought. Feeding the world: what role for fisheries?
ICES Journal of Marine Science, 69: 145-150. DOI: 10.1093/icesjms/fsr207.
Froese, R. and Proelss, A. 2010. Rebuilding fish stocks no later than 2015: will Europe meet the deadline?
Fish and Fisheries, 11: 194-202. DOI: 10.1111/j.1467-2979.2009.00349.x.
Grafton, R.Q. et al. 2005. Incentive-based approaches to sustainable fisheries.
Canadian Journal of Fisheries and Aquatic Sciences 63, 699-710.
Grafton, R.Q., Kompas, T., Hilborn, R. (2007): Economics of Overexploitation Revisited.In: Science 318, 1601.
Hannesson, R. 1983. Optimal Harvesting of Economically Interdependent Fish Species.
Journal of Environmental Economics and Management, 10: 329-345.
HLPE, 2014. Sustainable fisheries and aquaculture for food security and nutrition. A report by the High Level Panel of
Experts on Food Security and Nutrition of the Committee on World Food Security, Rome 2014.
Jennings, S. et al. 2016. Aquatic food security: insights into challenges and solutions from an analysis of interactions
between fisheries, aquaculture, food safety, human health, fish and human welfare, economy and environment. Fish and
Fisheries. DOI: 10.1111/faf.12152.
Kawarazuka, N. and Béné, C. 2011. The potential role of small fish species in improving micronutrient deficiencies in
developing countries: building evidence, 14: 1927-1938. DOI: 10.1017/S1368980011000814.
Kelleher, K. 2009. The Sunken Billions. The Economic Justification for Fisheries Reform. Sustainability Criteria for
Fisheries Subsides: The Latin American Context. 29-30 July 2009, Guayaquil, Ecuador. The World Bank.
Kjesbu O. S. et al. 2014. Synergies between climate and management for Atlantic cod fisheries at high latitudes.
Proceedings of the National Academy of Sciences of the United States of America 111 (9), 3478-3483.
Lam, V.W.Y., Cheung, W.W.L., Swartz, W. and Sumaila, U.R. 2012. Climate change impacts on fisheries in West Africa:
implications for economic, food and nutritional security. African Journal of Marine Science 34(1):103-117.
Martell, S. and Froese, R. 2013. A simple method for estimating MSY from catch and resilience. Fish and Fisheries,
14:504-514.
Merino, G. et al. 2012. Can marine fisheries and aquaculture meet fish demand from a growing human population in a
changing climate? Global Environmental Change, 22(4): 795-806. DOI: 10.1016/j.gloenvcha.2012.03.003.
McClanahan, T., Allison, E.H. and Cinner, J.E. 2015. Managing fisheries for human and food security. Fish and Fisheries,
16: 78-103. DOI: 10.1111/faf.12045.
Mora, C., Myers, R.A., Coll, M., Libralato, S., Pitcher, T.J., Sumaila, R.U., Zeller, D., Watson, R., Gaston, K.J. and Worm,
B. 2009. Management Effectiveness of the World’s Marine Fisheries. PLOS Biology, 7(6): 1-11.
MRI (Max Rubner-Institut) 2008. Nationale Verzehrsstudie II. Ergebnisbericht, Teil 2. Karlsruhe. https://www.mri.bund.de/
fileadmin/MRI/Institute/EV/NVSII_Abschlussbericht_Teil_2.pdf
O’Neill, B.C. et al. 2015. The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the
21st century, Global Environmental Change. DOI: 10.1016/j.gloenvcha.2015.01.004.
Pauly, D. et al. 2002. Towards sustainability in world fisheries. Nature, 418, 689-695.
Pauly, D., Watson, R., Alder, J. 2005. Global trends in world fisheries: impacts on marine ecosystems and food security.
Philosophical Transactions of the Royal Society B, 360: 5-12. DOI: 10.1098/rstb.2004.1574.
Pauly, D. and Zeller, D. 2015. Catch reconstruction: concepts, methods and data sources. Online Publication.
Sea Around Us (www.seaaroundus.org). University of British Columbia.
Pauly, D. and Zeller, D. 2016. Catch reconstructions reveal that global marine fisheries catches are higher than reported
and declining. Nature Communications, 10244. DOI: 10.1038/ncomms10244.
Quaas, M.F. et al. 2012. Fishing industry borrows from natural capital at high shadow interest rates.
Ecological Economics 82, 45-52.
58
Quaas, M.F. and Requate, T., 2013: Sushi or fish fingers? Seafood diversity, collapsing fish stocks and multi-species fishery
management. Scandinavian Journal of Economics, 115(2): 381-422.
Quaas, M.F. et al. 2016. It’s the economy, stupid. Projecting the fate of fish populations using ecological-economic
modeling. Global Change Biology, 22(1): 1365-2486. DOI: 10.1111/gcb.13060.
Sea Around Us, www.seaaroundus.org. University of British Columbia.
Sherman, K. and Hempel, G. (Editors) 2009. The UNEP Large Marine Ecosystem Report: A perspective on changing
conditions in LMEs of the world’s Regional Seas. UNEP Regional Seas Report and Studies No. 182. United Nations
Environment Programme. Nairobi, Kenya.
Smith, M.D. et al. 2010. Sustainability and Global Seafood. Science, 327: 784-786. DOI: 10.1126/science.1185345.
Squires, D. and Vestergaard, N. 2013. Technical Change and the Commons.
Review of Economics and Statistics, 95(5), 1769-1787.
Sumaila, U.R. et al. 2012. Benefits of Rebuilding Global Marine Fisheries Outweigh Costs.
PLOS one, 7(7). DOI: 10.1371/journal.pone.0040542.
Sumaila, U.R., Teh, L., Zeller, D. and Pauly, D. 2015. The global ex-vessel fish price database. In: D. Pauly and D. Zeller
(eds.). Catch reconstructions: concepts, methods and data sources. Online Publication. Sea Around Us (www.seaaroundus.
com). University of British Columbia.
Tacon, M. and Metian, M. 2013. Fish Matters: Importance of Aquatic Foods in Human Nutrition and Global Food Supply.
Reviews in Fisheries Science, 21(1):22-38. DOI: 10.1080/10641262.2012.753405.
Thiao, D. et al. 2012. Economic dimension of the collapse of the ‘false cod’ Epinephelus aeneus in a context of ineffective
management of the small-scale fisheries in Senegal. African Journal of Marine Science 34 (3): 305-311.
Thilstedt, S.H. et al. 2016. Sustaining healthy diets: The role of capture fisheries and aquaculture for improving nutrition in
the post-2015 era. Food Policy, Vol 61, 126–131.
Thurstan, R.H. and Roberts, C.M. 2014. The past and future of fish consumption: can supplies meet healthy eating
recommendations? Marine Pollution Bulletin, 89(1-2): 5-11. DOI: 10.1016/j.marpolbul.2014.09.016.
Tilman et al. 2002. Agricultural sustainability and intensive production practices. Nature 418, 671-677
United Nations, Department of Economic and Social Affairs, Population Division (2015). World Population Prospects:
The 2015 Revision, http://esa.un.org/unpd/wpp/
Voss, R., Quaas, M.F., Schmidt, J.O., Kapaun, U. 2015. Ocean acidification may aggravate social-ecological trade-offs in
coastal fisheries. PLoS ONE 10(3): e0120376.
Voss, R. et al. 2011. Temperature change and Baltic sprat: from observations to ecological-economic modelling. ICES
Journal of Marine Science, 68(6), 1244-1256.
Watson, R., Gaston, K. J., Worm, B. 2009. Management effectiveness of the world’s marine fisheries.
PLoS Biology 7 (6), e1000131.
World Bank 2008. The Sunken Billions. The Economic Justification for Fisheries Reform. Profish.
World Bank 2015. The Sunken Billions revisited.
World Bank: GDP. http://data.worldbank.org/indicator/NY.GDP.MKTP.CD
World Resources Institute, 2013. Creating a Sustainable Food Future.
A menu of solutions to sustainably feed more than 9 billion people by 2050. World Resources Report 2013-14:Interim
Findings, http://www.wri.org/sites/default/files/wri13_report_4c_wrr_online.pdf
Worm, B. et al. 2006. Impacts of biodiversity loss on ocean ecosystem services. Science 314, 787-790.
Worm, B. et al. 2009. Rebuilding Global Fisheries. Science 325 (5940), 578-585.
WWF 2015. Das große Wegschmeißen. Vom Acker bis zum Verbraucher: Ausmaß und Umwelteffekte der
Lebensmittelverschwendung in Deutschland.
Acknowledgement
WWF would like to thank the following people who were very helpful in providing critical
feedback and valuable information in the preparation of this study:
Rolf Willmann (former fisheries expert with the FAO)
Edward H. Allison (University of Washington, Seattle)
Birgit Meade (Agricultural Economist, USA)
Rashid Sumaila (Fishery Economics Research Unit, University of British Columbia, Vancouver)
Mark Prein and Anneli Ehlers (Deutsche Gesellschaft für Internationale Zusammenarbeit, GIZ)
Fishing for Proteins | 59
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