Modelling climate change effects on a marine commodity:
Small pelagic fish, fisheries and fishmeal in a globalized market
Gorka Merino, Manuel Barange, Christian Mullon et al… (Blanchard ?) (2008)
Journals
1) Global Environmental Change
2) Journal of Marine Systems
Abstract
Introduction
Wild fisheries production has remain stable at approximately 100 M t y-1 over the last years (FAO
2006), but the contribution of farmed fish to global fish production has increased from 13 Mt in
1990 to nearly 50 Mt in 2006 (FAO, 2007). The demand for fish continues to increase, growing
from 9.0 kg per person and year in 1961 to an estimated 16.5 in 2003, much of which has come
from of low-value freshwater fish in East Asia (Delgado et al., 2003). There has been increasing
discussion about the potential for aquaculture expansion to respond to the increasing protein
demand worldwide and how it may affect marine fish stocks and their changing environment
(Asche and Tveterås, 2004; Briones et al., 2006; Delgado et al., 2003; Deutsch et al., 2007;
Failler, 2007; Jackson, 2008; Kristofersson and Anderson, 2006; Liu and Sumaila, 2008; Naylor
et al., 2000; New, 2003; Pike and Barlow, 2003; Tacon and Forster, 2003).
Aquaculture expansion, particularly of carnivorous fish, is linked to the use of fishmeal, a global
commodity traded at international markets and the result of the reduction of small pelagic fish to a
powder of high protein content (Delgado et al., 2003). Fishmeal is an excellent source of protein,
lipids (oils), minerals, and vitamins. Addition of fishmeal to animal diets increases feed efficiency
and growth of farmed fish (Miles and Chapman, 2006). Fishmeal is also used in the poultry and
pork farming industries.. It is now increasingly recognised that an increase in aquaculture
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production, unless compensated by fishmeal substitution or change of species farmed, will drive
the demand side for fishmeal over a supply-demand balance.
The aquaculture industry’s role in contributing to, rather than alleviating, the overexploitation of
fish stocks worldwide has been debated recently (Alder and Pauly, 2006; Deutsch et al., 2007;
Kristofersson and Anderson, 2006; Naylor et al., 2000).. Fishmeal utilization in fish farming has
shifted from 17% [OF WHAT] in 1996 to 35% in 2000 and 60% in 2007 (Hardy, 2006; Jackson,
2008) and it is predicted to reach 70% in 2010 (Deutsch et al., 2007; Miles and Chapman, 2006;
New and Wijkström, 2002; Pike and Barlow, 2003). The recent increasing price of fishmeal has
forced to reduce its consumption and replace its use in animal feeds by cheaper vegetable
protein sources. Substitution thus, could contribute to reducing fishing pressure on small pelagic
fish , although soya and cereal meals have shown limitations in substituting fishmeal in salmon
and trout feeding (Drakeford and Pascoe, 2007) compared to successes in the pork and poultry
industries (Hardy, 2006). Furthermore, recent observations indicate that fishmeal consumption
has not been linearly related to aquaculture production (Kristofersson and Anderson, 2006). On
the contrary, the non substitutability for carnivorous fish species and the introduction of fishmeal
in herbivorous farming to increase growth might increase and not alleviate pressure on wild
stocks (Deutsch et al., 2007).
Global aquaculture production allows some distinction between herbivorous and carnivorous
species. Herbivorous farming is mainly composed by different carp species, representing 55% of
worlds aquaculture production, 60% of it produces by China. Although fishmeal has not always
been part of the diet of herbivorous species, it is gaining interest in the recent years due to
improved growth rate raises farmers profits (Deutsch et al., 2007). Carnivorous aquaculture
production is more varied. Chilean and Norwegian salmon and trout species represent 4% of total
production and 8% of total value in 2006, while Chinese and Thai shrimps and prawns account
for 3.4 % of total production and 8% of total value in 2006. Both shrimp and salmon (carnivorous)
industries have increased at a higher rate than the total aquaculture increase due to economic
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incentives (Deutsch et al., 2007; Lebel et al., 2002). Salmon, trout, shrimp and prawn aquaculture
industries in Norway, Chile, China and Thailand represented 6% of the world aquaculture
production but 16.5% of the total value in 2006 (FAO YEAR). Norwegian and Chilean diadromous
species production has increased at a rate of 8.2% year-1.
Since 1994 Thailand has produced 1.5 M t of shrimp and prawn being the largest grass shrimp
or giant tiger prawn (Penaeus monodon) exporter until 2001. However, China, which produced
prawn but not shrimp until 2001, triplicates in 2006 Thai production. Shrimp production has
expanded at a rate of 19.2% year-1. Panaeus monodon, the most valuable farmed species, needs
35-50% of fishmeal in its feed (Deutsch et al., 2007; Hardy, 2006; Tacon, 2002).
Fishmeal is a global commodity which price formation is, and it is expected to be in the near
future, the result of global supply and demand equilibria. Recently, fishmeal price as well as other
global commodities (soya meal, rice, grains…) has increased causing food security problems in
developing countries (FAO, 2007). Fishmeal supply setting limits to growth of aquaculture, world
protein supply and food security have been cause of concern in recent research (Delgado et al.,
2003; Deutsch et al., 2007; Naylor et al., 2000; Pauly et al., 2003).
Climatic signals are perceived in the markets: Fluctuations in Peruvian Fishmeal supply and price
have been closely related to the El Niño-Southern Oscillation (ENSO), and soya meal markets
have also been affected for environmental events such as the draught in the U.S. in 1988
(Deutsch et al., 2007; Kristofersson and Anderson, 2004). Future fishmeal price estimation and its
correspondence to other agricultural commodities’ is expected to be linked to two main forces: 1)
environmental factors such as climate change induced events and 2) market events such as the
caused by the potential replacement of fishmeal by less expensive protein sources.
Evidences on the effect of environmental alterations affecting the production of small pelagic
fisheries and thus fishmeal have been exposed for the main small pelagic fisheries, such as
Peruvian anchoveta (Egraulis ringens) in the Humboldt current, Japanese anchovy (Engraulis
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japonicus) in the South and East China sea and north sea sandeel (Ammodytes marinus)
exploited by the Scandinavian countries (Chavez et al., 2003; Lewy et al., 2004; Takasuka et al.,
2008).
Peruvian anchovy (Engraulis ringens) is the top reduction fishery with more than 50% of total
production. Peru and Chile land nearly 9 M t of Peruvian anchovy which is almost solely
commercialized as fishmeal in a 2.5 M tones production (Chilean production includes also meal
from South American pilchard, Sardinops sagax) during the last years (2002-2006).
Environmental events such as ENSO have affected Peruvian anchovy production provoking
significant fluctuations and occasionally, associated to longer term events as El Viejo (Chavez et
al., 2003), dramatic collapses e.g. 1998. Scandinavian countries (Denmark, Iceland and Norway)
supply ~15% of fishmeal production mainly exploiting North Sea sandeel (Ammodytes marinus),
fished by Danish and Norwegian fleets. The recruitment of North Sea sandeel is negatively
correlated with the North Atlantic Oscillation (NAO) winter index, possibly because the latter
results in warmer sea surface temperatures, which negatively affect sandeel recruitment. It is
predicted that the NAO will continue its recent positive phase over the 21st century, suggesting
that climate change may also impact upon sandeel populations in the North Sea. The uncertainty
on future production simulations is caused by the temperature dependent recruitment variability
(Lewy et al., 2004). The last fishmeal producer considered here is China. Chinese small pelagic
fisheries include 60% of Japanese anchovy (Engraulis japonicus), but also chub mackerel
(Scomber japonicus) 28% and South American pilchard (Sardinops sagax) 12% in 2006.
Japanese anchovy has shown production fluctuations and recruitment dependency on
temperature (Takasuka et al., 2008; Takasuka et al., 2005). Chinese small pelagic fishes are
commercialized fresh and salted, and processed into fishmeal and oil. China produced an
average of 348 th tones of meal during 2002-2006. The impacts of Global Environmental Change
(GEC) on the intensity of ENSO events and NAO patterns suggest that climate change may
impact fishmeal production in the future. Changes in small pelagic fish production are expected to
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alter global fishmeal markets and consequently the manner in which supply-demand, prices,
access and benefits produced by small pelagic fisheries, are balanced at global scale.
GORKA: I think that this introduction needs to be simplified and focused a bit more. All the
information is here (too much actually - one struggles to see the wood for the trees). I suggest
that you follow a skeleton of the main issues leading to the point of the paper, such as:
- Fisheries production, protein demand and the rise of aquaculture
- Trends in aquaculture production: herbivorous and carnivorous, players and trends
- Dependency of the above on fishmeal/oil – worries due to the globalised nature of FM
- Herb/ carn / Substitution issues
- Concerns over global and local environmental fluctuations and global fishmeal production and
thus aquaculture production and fishmeal prices.
- Goal (which actually is the next paragraph)
The sections on
- The demand/ supply system of fishmeal production/ consumption: price trends, players, etc.
- The role of climate and climate change on the above
To me should be in the Material and Methods, to streamline the Introduction
In this paper we investigate the link between environmental-driven fluctuations of regional
resources and the response of a globalized market. We used both short- (10-year) and long-term
(75 year) estimations of small pelagic fisheries and fishmeal production systems forecasted with
dynamic and equilibrium solutions of a global surplus model (Schaefer, 1954). There are some
common features about the structure and hypotheses on the modeling approach for both the long
and short runs, but also some differences. In the short-term environmental fluctuation is driven by
interannual patterns encapsulated in indices such as the El Niño-Southern Oscillation (ENSO),
and the North Atlantic Oscillation (NAO), while in the long term small pelagic stocks are expected
to be affected by the response of ocean ecosystems to climate warming (Jennings and
Blanchard, 2004; Jennings et al., 2008; Sarmiento et al., 2004). Under both simulations small
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pelagic fish population dynamics, fishing industries tactics and fishmeal supply are faced with
alternative hypotheses regarding aquaculture expansion and fishmeal replacement in the short
term, and market responses to climate-driven ecosystems alterations in the long-term. In the
latter case we investigate both a world led by market rules (Global Markets), and another led by
international organizations (Global Commons) (IPCC, 2007a; Pinnegar et al., 2006).
The bio-economic approach proposed here aims to link global environmental change (expressed
as a ecosystem-human scale feedback) induced alterations to the use of ecosystem commodities
services at global scale. The link between ecosystems response to human development, small
pelagic fish, fishmeal and aquaculture expansion is proposed as a paradigmatic case study of the
ecosystem response to anthropogenic activities and the limits of the ecosystem services.
Material and Methods
The fishmeal supply/ demand system
Aqui parte de lo que saques de la Introduction
A simplified production/ consumption system
you need to explain why you have simplified the system to 3 sub-systems, and list them. Maybe a
table with their characteristics would help] Three ecosystems, small pelagic fisheries and
associated fishmeal industries are considered on the fishmeal supply side.
Peru and Chilean production is obtained reducing anchovy (Engraulis ringens). Both countries
combined produced an average of 1.7 and 0.8 M tones of fishmeal per year between 2002 to
2006. Peru supplies world fishmeal markets with its entire national production while Chile directs
part of its production (25%) to national aquaculture. Peruvian and Chilean fishmeal production is
imported mainly by China, Thailand and Norway. Scandinavian fishmeal production is
approximately 0.67 M tones (15% of total production), mostly from North Sea sandeel. Denmark
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and Iceland export most of its production, while Norway supplied local demand with 60% of its
national production in 2001. Chinese production is almost entirely consumed locally (Figure 1).
{Insert Figure 1}
China is the top fishmeal consumer, importing 18% of the total fishmeal available in international
markets, excluding national?? in 2001. Norway and Chilean trout and salmon industries are
supplied by Peruvian anchovy fishmeal and regional origin, Denmark in Norway and own
production in Chile. Thailand uses local fishmeal production (removing fresh fish for local
consumption, (FAO, 2007)) and importing mostly from Peru (Deutsch et al., 2007).
Within this framework, distant small pelagic fish stocks are connected in a globalized market.
Fishmeal economic indicators, supply and price are assumed global and based on World Bank
data.
{Insert Table 1}
On the year 2000, the 64% of the fishmeal consumption in the aquaculture industry was used in
marine shrimp, salmon and trout and carp (Table 1). In 2007, worlds salmon and trout industries
represented 28% of the total fishmeal use in aquaculture and crustaceans used 27% (Jackson,
2008). Chinese and Thai shrimp production (carnivorous) has expanded a 19.2% per year while
Chilean and Norwegian salmon and trout industry has expanded a 8.2%. Fishmeal represents the
23 and 30% respectively of the aquafeed of these species (Deutsch et al., 2007).
Hypotheses
Two hypotheses are considered for the short term bioeconomic modelling. First, based on Naylor
et al. (2000) linearly relates aquaculture expansion to fishmeal demand and second, based on
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Kristofersson (2006) considers that there is incentive to innovate and to advance in the direct
substitution of fishmeal by soybean meal or other alternative protein sources. The direct
consequence is that fishmeal will not limit the expansion of aquaculture.
A modified version of the bioeconomic model of Mullon et al (2008) is used to characterize fishing
and fishmeal producing industries on the supply side and a global market with an exogenous
demand function. The model simulates producers’ stimulus to exploit small pelagic stocks and
supply meal markets based on a cost and revenue balance. Each production system seeks to
maximize its profits from the exploitation and processing of its fish stocks attending to a global
demand. Production systems thus, are assumed to share a common market and therefore
strategic interactions among them will drive their exploitation strategies (Merino, 2007). The
cause of these interactions is the known market externality and is implemented in the model by
means of a game theoretic algorithm. Fish stocks are affected by these interactions, i.e. regional
stocks exploitation levels will evidence globalization shortcomings. The model does not only allow
estimating supplier’s production behaviour but explicitly models stocks dynamics in response to
these exploitation tactics (Schaefer, 1954; Schaefer, 1984) and climate induced fluctuations
(Mullon et al., 2008). Fish stocks dynamics, suppliers economic outcome and fishmeal market
indicators are estimated for the two alternative demand scenarios.
THE MODELS
The short term model
Short term projections rely on a multi-species, multi-production (country) and multi-market model
(Mullon et al., 2008). Fish stocks are exploited by each producer country which strives to
maximize its profits. For each stock, population dynamics depend on biomass dynamics
parameters (intrinsic growth rate r, carrying capacity K) and yield (Y) (Schaefer, 1954) (eq. 1).
Recruitment variability is implemented with a random coefficient (-1<ξ<1). For the short term
analysis, environmental factors are simulated with a 20% recruitment variability (ξ=[-0.2, 0.2]).
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Yield is a function of the countries catchability coefficient (q) and fishing tactics (Effort=E) but
limited by quotas imposed from biological criteria (eq. 2).
X (t  1)  rX (t )  (1  X (t ) / K )  Y (t ))  ( )
(eq. 1)
Y (t )  Max (qE (t ) X (t ), Quota (t ))
(eq. 2)
Investments (i) on fishing units (E) at price (v) that can make increase countries fishing capacity
will be the result of previous income (I) from their production at price pmeal (following a yield/meal
observed proportionality λ), and production costs (including fishing (c f), reducing (cr)) (eq. 3),
capital (KC) and amortisement costs at a rate (j) (eqs. 3 and 4).
I (t )    pmeal  Y (t )  c f E (t )  crY (t )
 I (t )  KC (t ) 

E (t  1)  E (t )  i  
 v  jE(t ) 
(eq. 3)
(eq. 4)
Fishmeal markets are supplied by regional production systems. These settings are simplified into
a global market and three production systems (ps) paths towards it. The total fishmeal quantity
placed in a global market (Q) is the sum of the reduced fish catch traded from geographically
distant fish stocks (eq. 5). The price for the product follows a linear function, α is the asymptotic
price of the product in a static market and β is the price flexibility to changes on supply (eq. 6):
Q(t ) 
3

ps 1
Y (t )
(eq. 5)
ps ps
pmeal (t )      Q(t )
(eq. 6)
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The model allows expanding the fishmeal market, i.e. increasing the supply traded maintaining
the price of the product. In an expanding market, if the supply is constant the price of the product
is expected to increase. In our case, the market expansion is proposed to be proportional to the
demand of fishmeal in aquafeeds. Setting the expansion at the same rate of aquaculture
expansion is the manner to mimic the demand increase hypothesis (Asche and Tveterås, 2004;
Naylor et al., 2000). On the contrary, the market is not expanded for the substitution scenario
(Kristofersson and Anderson, 2006).
The bioeconomic simulation is run with a set of parameters describing the three production
systems and the global market (Table 2).
{Insert Table 2}
Long term model
For the long period simulation, the estimations for 2080 are mainly based on the expected
response of the ecosystems to the Alternative Future Scenarios for Marine Ecosystems.
Ecosystems carrying capacity to sustain fish stocks is estimated to be affected by climate change
in different ways in the Global Markets and Global Commons scenarios (Jennings et al., 2008;
Pinnegar et al., 2006). Open access and maximum sustainable yield (Gordon, 1954; Smith, 1969)
solutions are associated to Global Commons and Global Markets scenarios of the Alternative
Future Scenarios for Marine Ecosystems for the UK (Pinnegar et al., 2006).
Carrying capacity is the asymptotic population biomass supported by an ecosystem (Kashiwai,
1995), and is synonymous with the general productivity, or ‘productive capacity’, of an ecosystem.
In marine fish populations carrying capacity has typically been considered in terms of sustainable
catch, with catch often the aggregate of many species e.g.(Mueter and Megrey, 2006). The linear
relationship observed between chlorophyll standing stock and fish yield, both at the species
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(Perry and Schweigert, 2008) and multispecies (Ware and Thomson, 2005) level, suggests that
primary production can be used to estimate ecosystem carrying capacity. Recently analysis have
reported climate change impacts on primary production and chlorophyll standing stock through
changes in ecosystem habitat volume (Behrenfeld et al., 2006; Polovina et al., 2008; Sarmiento et
al., 2004) indicating that carrying capacity is likely to be affected by climate change.
Ecosystems are expected to respond to global environmental change through variations in their
carrying capacity. This assumption relies on references linking sea surface temperature
alterations, primary production and carrying capacity (Brown et al., 2004; Jennings and
Blanchard, 2004; Jennings et al., 2008). The Schafer’s equilibrium solutions (eq. 7) relate a stable
stock level (X) with a fishing mortality rate (F) composed by a fishing effort term (E) and a
catchability parameter (q) and species intrinsic growth rate (r) and carrying capacity (K)
(Schaefer, 1954).
X
(r  F )
(r  qE )
K
K  f ( r , K , q, E )
r
r
(eq. 7)
The equilibrium equation was used for the sake of simplicity and to illustrate the link between
climate uncertainty and ecosystems productivity.
Two AFMEC scenarios are compared. First, Global Markets scenario represents a world where
market rules and no other international or regional organizations will manage natural resources.
Fisheries are approached to the open access (OA) solution where the rent from fishmeal is
dissipated (with a constant price equation). The fishing effort and mortality in the OA solution
depend on the fish price (p = pmeal/4), fishing costs (c), fleets catchability parameter (q) and
species population dynamics parameters, r and K (eqs. 8 and 9):
Y  F  X  q  E  X  f ( r , K , q, E )
(eq. 8)
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0
  p  q  E  X  c  E 
 EOA 
OA
r 
c 
  f ( q , p , c, r , K )
 1 
q 
pqK 
(eq. 9)
Second, the Global Commons scenario where international agreements will seek to maximize the
fisheries production, i.e. reach the maximum sustainable yield (MSY). The fishing mortality will be
that to maximize total yield (eq. 10).
Y
r
MS Y
 0 Y
 FMSY   f (r )
F
2
(eq. 10)
Biomass at OA and MSY levels is estimated with equations 11 and 12). Increasing the price of
fish it is straightforward that will decrease the stocks level in the open access solution (eq. 11).
On the other side, fisheries optimally managed attending only to biological criteria do not perceive
the effect of price changes (Deutsch et al., 2007).
X OA 
(r  FOA )
c
K
 f ( q, p, c )
r
qp
X MSY 
(eq. 11)
(r  FMSY )
K
K   f (K )
r
2
(eq. 12)
The yield obtained with the two fishing mortality solutions (eqs. 13 and 14) is reduced to fishmeal
with the observed fish/fishmeal proportionality (λ) (eq. 15).
YOA  FOA  X OA 
rc  ( Kqp  c)
 f (q, p, c, r , K )
Kq 2 p 2
YMSY  FMSY  X MSY 
rK
 f (r , K )
4
(eq. 13)
(eq. 14)
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Q   Y
(eq. 15)
Stocks available biomass in both cases (eqs. 11 and 12), and therefore yield and fishmeal
production, relies on ecosystems carrying capacity (eqs. 13, 14 and 15). Carrying capacity is
introduced as a non constant parameter and proposed to reflect ecosystems response to GEC in
terms of changes on primary production (Jennings and Blanchard, 2004; Jennings et al., 2008;
Sarmiento et al., 2004). Sarmiento et al 2004 predict integrated primary production responses for
low latitude upwelling on the two Hemispheres in the Pacific ocean (China and Peru), and
subpolar regions of the Northern Atlantic (Scandinavia). The estimated primary production
changes are used as estimations of carrying capacity changes on the three ecosystems.
Sarmiento et al. estimated primary production changes by means of two different models
(Behrenfeld and Falkowski, 1997; Marra et al., 2003) and the two of them are used as upper and
lower limits of biomass, yield and fishmeal projections here.
The literature based (Sarmiento et al., 2004) response of carrying capacity to climate change are
shown in Table 3.
{Insert Table 3}
On the demand side, Global Markets scenarios are predicted with a rapid expansion of marine
fish-farming industry, followed by the increase of industrial fishing pressure on fish stocks.
The fish farms predicted to grow more are diadromous species’ (Failler, 2007). The sustainability
of the aquaculture expansion will again be linked to the increasing technological research on
fishmeal substitution (Hardy, 2006). The same two substitution hypotheses tested for the short
term analysis are contrasted here for the Global Markets world scenario (Asche and Tveterås,
2004; Deutsch et al., 2007; Kristofersson and Anderson, 2006; Naylor et al., 2000). The price
trends estimated on the short term scenario are the basis of the prices of the used here.
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The Global Commons scenario describes a limited expansion of aquaculture and demography
and is presented as the most optimistic management scenario to face the effects of GEC on
ecosystem services.
The Global Commons scenario describes a limited expansion of aquaculture and demography
and is presented as the most optimistic management scenarios to face the effects of GEC on
ecosystem services
Results
Short term simulation
Two demand scenarios were contrasted using the same bioeconomic model (Mullon et al., 2008).
The first assumes that fishmeal use can be successfully substituted by alternative protein sources
(Kristofersson and Anderson, 2006), the second assumes that substitution is not significantly
succeful and thus that aquaculture expansion is proportional to and limited by fishmeal production
(Naylor et al., 2000). The results are presented in the form of four bioeconomic indicators (Figure
2): a global exploitation index calculated adding the estimated three species biomass and
normalized to their carrying capacity parameter; an estimate of global small pelagic fish
production, following the proportionality with the three species’ observed in recent years; a
measure of traded fishmeal to globalized markets and preditions of price of fishmeal in
international markets.
In the first scenario (successful substitution) fish stocks vary randomly as a result of climatic
variability that also slightly perturbs yield and fishmeal production. However, despite these
perturbations, fishmeal supply remains constant by adjusting fishing effort, thus keeping stability
in the industry. As the fishmeal market is fully utilised and not expanding (any growth in demand
is absorbed by alternative protein production), prices stay constant and fishmeal production is
sustained nearby 5.2 M t.
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In the second scenario (no substitution), strong economic-ecosystem feedbacks can be
observed. Fish stocks are affected by natural environmental variability while at the same time the
fishmeal market is expanding. Producers are encouraged to increase or at least sustain
production, increasing fishing mortality, i.e. removing a larger part of the living population from
their ecosystem. Prices in the international markets rises steadily, allowing balancing the
increasing fishing costs under declining resources. The combination of higher fishing mortality
with environmental perturbation adds risk of stocks collapse. The feedbacks between the
economics of the market and climate variability can cause dramatic stock reductions in
approximately five years time after a climatic perturbation. It is important to note that fishmeal
supply and fish catches remain constant and might be sustainable at least in a ten year horizon,
while fishing mortality increases constantly, potentially collapsing the fisheries and the fishmeal
production system.
Long term simulation (year 2080)
The results of the 2080 year estimations are shown in Figure 3.
{Insert Figure 3}
Again, the three production units are aggregated into global indicators (DESCRIBE THEM).. Two
scenarios are considered no encapsulate the dynamics of the production system up to 2080:
World Markets and Global Commons (ASSUMED THESE WILL BE DESCRIBED IN THE
MAT&METH). There is little variability in parameter values under the Global Commons scenario
(grey). Stocks are at the half of their estimated carrying capacity. Changes on carrying capacity
parameter do not cause any perturbation at the exploitation level, always ½ of the carrying
capacity but they do on the total available biomass. The two primary production estimations for
the regions inhabited by the stocks considered are used here as proxies to the uncertainty on
long term climate estimations. The width of the trajectories thus, reflects a range of realistic
climate perturbations on the fish stocks in a given socioeconomic scenario.
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Climate induced perturbations are less significant (NOT SIGNIFICANT UNDER ANY
SCENARIO…) for the World Markets socioeconomic scenarios, because primary production
estimations do no vary significantly and thus the biomass remains stable (IS THIS CORRECT?).
The biomass for the two carrying capacity variations by Sarmiento et al. for each demand
scenario (p1 is the price of fishmeal if it has been successfully substituted by alternative protein
sources and p2, the price if aquaculture expansion relies on an increasing demand of fishmeal)
are similar. Biomass at open access reference point depends on two economic parameters, price
of the product and costs of fishing (eq. 11). Increasing the value of the produced commodity,
higher pressure will be applied on the stocks increasing the risk of collapse (p1 (1000 $ t-1) < p2
(1200 $ t-1)). Stocks at Global Markets are stabilized at 20-25% of their carrying capacity, i.e.
overexploited and under risk of collapse. Fish and fishmeal production (c and d) appear to be
sustainable for the next decades on the with the climatic uncertainty used here on the Global
Commons scenario, i.e. in fisheries managed based on biological criteria, climatic events should
not represent a major threat to aquaculture and fish stocks. Increasing the demand on a marine
product, makes stocks endangered in a fishery driven by market rules, as well as increase the
risk of collapse. The increased pressure (fishing mortality) on the Global Markets scenarios does
not bring higher yield or fishmeal production. The uncertainty on environmental conditions is
negligible here compared to the socioeconomic management shortcomings.
It is suggested that that fish and fishmeal production being driven more by human factors as a
response to climate variability than by environmental perturbations themselves. The feedback
climate perturbation-ecosystem-production systems(fisheries)-market (human scale) feedback
express this conclusion .
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Discussion
Climate change potential to affect ecosystem services is explored by means of a fisheries
bioeconomic model. Fisheries outcome depends critically in the forces of nature (Hannesson et
al., 2006). Natural resources are affected by environmental factors and these alterations are
ultimately perceived in the markets. Markets are described here as a pool of trades of marine
products, determined by quantities and prices. The last ones limit the access to marine
commodities and are the result of consumer preferences. In the present case, aquaculture
industry is proposed as the main consumer and our results suggest that whether recent
aquaculture expansion continues and whether this expansion relies on the use of fishmeal in
aquafeed or substitution successes will be critical for the immediate and far futures of the worlds
small pelagic fisheries and the overall available fish protein for human consumption.
The three fisheries explicitly modeled here represent the 70% of the annual small pelagic
fisheries and 75% of total fishmeal production. The three exploited species are sensitive to
environmental alterations. ENSO effects on Peruvian anchovy fisheries have produced
fluctuations of fishmeal price at international markets. Future climatic alterations are expected to
affect not only Peruvian anchovy but global small pelagic stocks and managing these climate
signals at market level is suggested to be crucial.
Consumers preferences and production systems are converged in a global market in the 10 year
simulation shown in Figure 2. Regional exploitation products are placed in a single market
illustrating the economic globalization process and its interaction with geographically distant
industries. Is at this lower, regional scale where climate perturbations alter natural resources. The
synergies between climate alterations and economic globalization are formulated and the impacts
of this “double exposure” (Hannesson et al., 2006; O'Brien and Leichenko, 2000) forecasted. The
ecological impacts of globalization requires ways to match the growth of marine commodities
demand with appropriate management systems and institutions to regulate harvesting (Berkes et
al., 2006; Young, 2002). The regional climatic signal is responded at markets and re-bounced to
- 17 -
regional level. Climate change thus, has potential to add pressures in some regions (Arnell,
1999). Different regions vulnerability to this feedback and to the climatic perturbation itself
depends on countries capacity (Adger, 2006; Andrew et al., 2007; FAO, 2008b).
In the 10 years simulation, when no replacement is considered in a expanding aquaculture,
climatic alterations at regional levels are responded with an increase of fishing mortality to supply
the increasing demand until the whole ecosystem-market system is critically altered. The
incoming collapse of the system is suggested in spite of the fish and fishmeal production
remaining constant at the 10 years projection, because constants yield is only maintained by
constantly increasing fishing mortality. The system is suggested to be unsustainable in the near
future. The need to respond to climate alterations in an optimal manner is suggested, even before
happen (Arnason, 2006). In fisheries such as small pelagic, where the schooling is accepted to
be related to environmental conditions, the appropriate response to an increasing risk of collapse
implies more conservative fisheries policy (Arnason, 2006).
In the contrary, the economic incentive to substitute fishmeal by cheaper protein sources, and its
possible success could make a threshold role in the bioeconomic system. However, the success
of substitution in high value carnivorous fish species farming (salmonids) is nowadays limited
(Drakeford and Pascoe, 2007), not that limited in other fish products uses (Deutsch et al., 2007;
Ganuza et al., 2008; Tacon and Forster, 2003). In the first case, the estimated price increase
could lead to limit the access to aquaculture and fish products and become a major food security
problem as has been pointed out recently (Failler, 2007).
The forthcoming balance between the utilization of worlds fishmeal production in aquafeed,
substitution in aquaculture and other farming industries, the potential of aquaculture production to
expand as a response to worlds increasing demand on fish products and protein on the one side;
and small pelagic wild stocks perturbed production will drive the price of fishmeal as a global
ecosystem commodity in the next years. It is important to remark that the expansion of
- 18 -
aquaculture has been estimated to have peaked (FAO, 2007) and recently being expanding at
lower rate than the last decade (Liu and Sumaila, 2008).
Economic incentives can vary exploitations bioeconomic indicators in different ways (Delgado et
al., 2003). Consumers preferences are modeled here exclusively as two hypotheses about
aquaculture’s dependency on fishmeal but small pelagic fish production might also shift from
fishmeal to direct human consumption (Jackson, 2008). In the top fishmeal producer country,
fresh fish gastronomy and canned fish potential markets are being explored (Drs. Badjeck, pers.
comm.). The observed recent price increase on fishmeal is also probably related to the general
price increase of worlds agricultural commodities and fuel (FAO, 2008b; Jackson, 2008).
Short term simulation (dynamic) results are enforced with long term (equilibrium) simulations.
Here, national industries are managed based on Alternative Future Scenarios for Marine
Ecosystems (Pinnegar et al., 2006). Within the model, the management scenarios explored
(Global Commons and Global Markets) are expressed by means of maximum sustainable yield
and open access solutions (Gordon, 1954; Schaefer, 1954). Regional primary production
estimations (Sarmiento et al., 2004) are used to proportionally modulate the three geographically
distant fish stocks carrying capacity parameters. The results are not quantitatively relevant, as the
relationship between primary production and carrying capacity might be too simplistic. In addition,
primary production estimations are based on two different models (Behrenfeld and Falkowski,
1997; Marra et al., 2003); Table 3). The width of the output trajectories responds to the
differences between these two primary production estimations. It is observed, however, that the
uncertainty over the effects of climate on fish stocks is not as significant as differences between
management scenarios. Here again, how economic drivers are managed or not managed is the
determinant factor to tackle the uncertainty on direct effects of climate change on fish stocks. The
real problem to avoid is the open access scenario (Global Markets), since increased demand for
a species might lead to serious depletion, and increase the risk of extinction (Asche and
Tveterås, 2004). Moreover, it is explored how the open access solution might vary if the fishmeal
- 19 -
demand and price increase. As it is observed, increasing the value of the commodity increases its
overexploitation in a non regulated fishery. On the contrary, resources managed exclusively
attending to biological criteria seem not to be endangered by changes on consumers
preferences, i.e. do not perceive the effect of price changes, nor the climatic uncertainty. Demand
hardly poses a threat to the stocks under enforced management, as it was demonstrated by
Asche and Tveterås referring to economic optimum equilibria (Asche and Tveterås, 2004).
Global management of fisheries (IPCC, 2007b) means solving the “The tragedy of the commons”
(Hardin, 1968), where common is a globalized market and the resources supplying it, but
particular the profits of their exploitation.
Regional fisheries and coastal ecosystems stability are shown as endangered by economic
incentives of fishers in the simulation. The climatic and economic synergies (O'Brien and
Leichenko, 2000) have been pointed out as the cause of cod and small pelagic fisheries collapse
in Newfoundland and Peru (Chavez et al., 2003; Choi et al., 2004) in ecosystem stability terms.
Moreover, a human scale approach to cod collapse refers to the socioeconomic forces and
drivers between managers as the cause of the collapse (Finlayson, 1994).
In the case of small pelagic fisheries, a long term management system should tend to
contemplate regime shifts between small pelagic species inhabiting common ecosystems
(Chavez et al., 2003; De Oliveira, 2006; Hannesson et al., 2006).
Forecasting growth patterns for fish demand, south-north flows, fish supply and fish trades are
necessary issues for fisheries policy makers and they should lead to clarify the aquaculturefisheries feedback, the implications on developing countries and food security issues, as well as
managing the sustainable use of marine resources (Delgado et al., 2003).
Mullon proposes to
remove
There is a great uncertainty concerning on when and how global warming or climate change will
affect natural resources, owing the complexity of physical and ecological relationships involved
- 20 -
(Arnason, 2006). Predicting fisheries yield under the climate uncertainty is difficult but the effort to
do so is worthy (Hannesson et al., 2006).
The sustainability of the resources and the use of ecosystem services depends more on how
markets are driven, i.e. on how markets (human scale, management) respond to ecosystem
(climatic) alterations than purely on environmental uncertainty, at least with the parameters
explored here. The idea of managing dynamic ecosystems (Botsford et al., 1997), including
environmental alterations and designing flexible and adaptive management systems, i.e. rules
that can be modified depending on climatic signals, such as ENSO type events in upwelling
systems or cascading provoking deep sea stocks fluctuations (Company et al., 2008) is
suggested. Deducing optimal fisheries management of changing ecosystems might be desirable,
at least in the cases where empirical evidence of the phenomena affecting fish stocks is
available, sometimes adjusting policy to changes as they occur (Arnason, 2006; Chavez et al.,
2003; Company et al., 2008; Hannesson et al., 2006). This is not always possible due to the large
uncertainty on global warming and climate change processes, and is in those cases where long
term simulation approaches might be helpful.
- 21 -
Acknowledgements
Quest-Fish, for the funding.
Maria Jaume Martinez and Damien Eloire for the graphs.
Dr. Rashid Sumaila, Dr. Francesc Maynou, Dr. Joan Batista Company for their valuable
comments.
- 22 -
Figures
Figure 1
Figure 1. Three main small pelagic fisheries (Peruvian anchovy, North sea sandeel and Japanese
anchovy), fishmeal industries (Peru and Chile, Scandinavian countries and China) and traded
flows to five expanding aquaculture industries (Chinese herbivorous species, Chinese and Thai
shrimp farms, Chile and Norway salmon and trout producers). The three small pelagic fisheries
represent approximately 70% of the annual small pelagic landings in America, Europe and Asia
(FAO, 2008a).
- 23 -
Figure 2
Figure 2. Results of the bioeconomic simulation. Two trajectories represent two demand
scenarios. Continuous line is the simulation when aquaculture expansion is not dependant on an
increasing demand of fishmeal for aquafeed (Kristofersson and Anderson, 2006) while second,
dotted line, is the simulation considering that aquaculture expansion relies on increasing demand
on fishmeal.
- 24 -
Figure 3
Figure 3. Long-term approach. Trajectories width represents the uncertainty on primary
production estimations and differences within trajectories represent the shortcomings of two
different socioeconomic scenarios (grey, Global Commons and black, Global Markets; Dashed
trajectories are obtained with an average price for fishmeal of 1000 $ ton -1 and solid with a 15000
$ ton-1 price).
- 25 -
Tables
Table 1
Total world aquaculture expansion in the last decade (6.5% year -1); salmon and trout industries
expansion in Chile and Norway (8.2% year-1); shrimp and prawn production in China and Thailand (19.2%
year-1) and Chinese herbivorous species culture increase (5.2%).
Year
Aquaculture
Salmon/trout in Chile
Shrimp/prawn in China
Herbivorous spp in
production (M t)
and Norway (M t)
and Thailand (M t)
China (M t)
1997
28.52
0.61
0.33
12.01
1998
30.37
0.67
0.40
12.81
1999
33.24
0.70
0.45
13.73
2000
35.34
0.83
0.53
14.49
2001
37.79
1.01
0.58
15.17
2002
40.23
1.03
0.65
16.11
2003
42.34
1.07
1.12
15.93
2004
45.54
1.20
1.30
16.89
2005
48.05
1.24
1.43
17.91
2006
51.21
1.35
1.74
19.06
- 26 -
Table 2
Production systems (Humboldt, China and Scandinavia) parameters.
Humboldt
China
Scandinavia
Fishmeal production (M t)
2.62
0.79
0.79
Exploited small pelagic stock stock
Peruvian
Japanese anchovy
North Sea sandeel
anchoveta
(Engraulis
(Ammodytes
(Engraulis ringens)
japonicus)
marinus)
Fish production (M t)
11.4
1.75
2.82
Initial fish stock (M t)
18.5
9.47
3.2
1
0.5
0.991
Intrinsic growth rate
(year-1)
Species carrying capacity (M t)
49.99
18.95
6.4
Catchability coefficient (10-5 fishing units-1)
1.5
1.42
1.93
Used fishing units
120000
37902
49692
200000
40000
50000
(boats ·max volume in a boat)
Maximum fishing capacity
(boats ·max volume in a boat)
Fishing costs ($·t-1)
Price of a fishing units
50
50
79.6
($·boat-1)
2000
2200
4500
($·t-1)
100
200
226.7
200
200
200
13000
500
500
Meal transformation costs
Shipment costs
($·t-1
·km-1)
Distance to main consumer (km)
Global market
Asymptotic price parameter
Price flexibility
($·t-1)
1298
($·t-2)
0.0001
- 27 -
Table 3
Long term simulation parameters. Estimated changes on primary production in the selected
geographical regions inhabited by the three selected fish stocks (Sarmiento et al., 2004). [1]
(Behrenfeld and Falkowski, 1997) and [2] (Marra et al., 2003).
Fish stock
ΔPP[1] %
ΔPP[2] %
Southern Hemisphere, low latitude upwelling
Engraulis ringens
-10.2
1.9
Northern Hemisphere, low latitude upwelling
Engraulis japonicus
-2.3
15.7
Northern Hemisphere, subpolar region
Ammodytes marinus
22.7
22.2
Area
- 28 -
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