International Environmental Modelling and Software Society (iEMSs)

7th International Congress on Env. Modelling and Software, San Diego, California, USA,

Daniel P. Ames, Nigel W. T. Quinn, and Andrea Rizzoli (Eds.) http://www.iemss.org/society/index.php/iemss-2014-proceedings

A modeling framework for oceanic basins under double exposure

Christian Mullona, Gorka Merinob, Jose Fernandezc, William Cheungd and Manuel Barangec,

aUMR212, IRD, 32 avenue Jean Monnet, 34200, Se te, France, (Christian.Mullon@ird.fr)

bAZTI-Tecnalia, Pasaia, Spain, (gmerino@azti.es)

cPlymouth Marine Laboratory, U.K. (jfs@pml.ac.uk, MABA@pml.ac.uk)

dUniversity of British Columbia, Fisheries Centre, Canada, (wwlcheung@gmail.com)

Abstract: Oceans stand under double exposure to global warming and of economic globalization. This

generates anxieties and contributes to the need for specific governance. A first expression lies in the needs

for integrated representations allowing a collaborative development of scenarios. Main challenge here lies in

finding, in these representations, a balance between realism and abstraction admissible by all participants.

In this talk, we present modeling framework that has been designed to build scenarios about the possible

futures of an oceanic basin. The principle lies in using a network model in which nodes correspond to fish

stocks, specialized fleets, farming systems, trade systems, markets, and links correspond to the exchange

of fish and fish commodities. It allows the integration of biological processes, such as the effects of changing

habitats upon fish stocks, and economic processes, such as the increase of demand for fish.

Contrasted scenarios for the next forty years the North Atlantic basin have been developed in this modeling

framework.

Keywords: Integrated modeling; Scenarios; Network economics; Oceanic basin.

1 INTRODUCTION

The simultaneous consequences of climate change and economic globalization have been summarized in

the concept of double exposure [Leichenko and O’Brien, 2008]. The conclusion of their study lies in the

recognition that there will be winners and losers of both, climate change and economic globalization, at

different scales (local, national and global). Harmonizing the hopes and aspirations of different societal

groups is thus the ultimate purpose of an overarching policy. There is a requirement to create governance

solutions that incorporate assessment of natural and social processes on a global scale, and allow flexibility

for local policies.

In order to create fair policies, the deliberative system in which decisions are made must be based on

shared representations [Dryzek and Stevenson, 2011], i.e. representations of what is at stake in formats

that are digestible by all the participants, from citizens to specialists and policy makers. There are needs

for integrated models that facilitate the development of future scenarios of social-ecological systems such

as oceanic basins. This should enable policy makers to be informed about possible futures, rather than

reacting to them as they occur.

C. Mullon et al. / A modeling framework for oceanic basins under double exposure

Figure 1. A modeling framework for ecosystems under double exposure. Network economics should allow

deliberating about processes that are diverse and described by heterogeneous data sets

All these features, the ecosystems under double exposure and the needs for deliberative systems based on

integrated models define a specific methodological problem. How can we build integrated models of inter-

mediate complexity, that are realistic, provide synthesized result and allows the development of scenarios

at the same time? Our goal is thus to define a modeling framework for ecosystems under double exposure.

To that purpose we develop a case study about the building of scenarios concerning the North East Atlantic

basin. Then, in the discussion part, we summarize the key features of this modeling framework.

2 MODELING THE NORTH ATLANTIC BASIN UNDER DOUBLE EXPOSURE

2.1 The North Atlantic basin under double exposure

The North Atlantic basin is a dynamic environment for physical [Marshall et al., 2001] as well as biological

processes [Parsons and Lear, 2001]. Past dynamics, such as the well-studied regime shift in the 1920s

and 1930s [Drinkwater, 2006] can be used to identify the potential effects of global warming on the North

Atlantic basin now and in near future. However, projecting the future state of the North Atlantic system over

the next century requires a broader perspective, in which the effects of human activity must be considered,

especially regarding the impact of fisheries [Lange, 2008]. The economic system related to North Atlantic

fisheries is not isolated: it is impacted by economic globalization and the opening of national fish markets

[Anderson et al., 2010]; for many traded fish commodities, the emergence of a global market becomes

evident [Asche, 2006].

2.2 Networks economics and double exposure

To model this system, we use the principles of network economics [Nagurney, 1998; Mullon, 2013]. We

consider the chain relating fish stocks (where occurs climate changes) to fish commodities trade systems

and markets (where economic globalization occurs). The first important step consists in defining the entities

that constitute the network. Choices must allow representing the system with the precision that is necessary

for substantial discussions between actors. At the same time they must be done according to existing

datasets1 and to computing requirements. Taking all these requirements into account, we obtain a simplified

representation of the North Atlantic basin as a network whose structure, nodes and links are summarized

in figure 2.

1 FAO Fishstat, ICES catch data, UN-COMTRADE for the trade data

Climate scenarios Trade data

Environmental data Production data

Climate change Economic globalisation

Environment Exploitation

Ecosystems

Assessment data

C. Mullon et al. / A modeling framework for oceanic basins under double exposure

The modeled network has 188 nodes2 and 492 links3. Some nodes are said to be constrained: this

corresponds to situations where there is a limitation on the quantity of fish that a node may process: for

example, due to a limited fishing capacity, there is maximal quantity of fish that a given fleet may catch.

Figure 2. Structure of the network. A simplified view. In the effective implementation, there are hundreds

of nodes and links. In the right part, the different kinds of nodes and links are specified ; for example red

arrows correspond to trade flow inside Europe.

2.3 Design of the bio economic model: flow chart

To represent the effects of climate changes on fish stocks, the bio economic model uses output (the quality

of habitat, a proxy of the carrying capacity of a group of species in an ICES area) of a dynamic bio-

climate envelope model [Cheung et al., 2009; Fernandes et al., 2013]. The latter model is based on the

output (temperature, biological production) of MEDUSA, an oceanic model [Yool et al., 2013] that represents

the future of oceans under IPCC scenarios (fig 3). The bio-economic model has two components: (1) a

market component which relates supply and demand on the network system including fleets, aquaculture

systems, trade systems, retailers, (2) an investment model relating profit and investment of both fleets and

2 There are 24 fish stocks (the pairing of an ICES area and a species family: Clupeiforms, Gadiforms, Percoidei, Salmoniforms,

Cephalopoda, Anguilliforms, Pleuronectiforms, Lophiiforms, Reptantia, Scorpaeniforms, Trachinoidei, Scombroidei), 23 fishing sys- tems (the pairing a species family and a group of countries: Ostrich-Germany-Benelux, Eastern-Northern Europe, France-Italy-Greece, Scandinavia, Spain-Portugal, UK-Ireland), 6 aquaculture systems (the pairing a species family and a group of countries), 78 trade sys- tems (the pairing of a group of countries and a fish commodity, that is a species family and a preparation: fresh, frozen, prepared, meal), 67 fish markets (the pairing a fish commodity and a group of countries). 3 There are 43 catching links (from a stock to a fishing system), 139 transformation links (from a fishing system to a trade system), 5

aquaculture consumption links (from a trade system for meal or oil to an aquaculture system), 125 trading links intra EU, 113 trading links between EU and other countries, 67 selling links (from a trade system to a market).

F

B VI

F

B

B F

F

B

B B B B

B

XII II F F I

F Fish stocks B

B M T Catches T

M

B F T

VII III Fleets

M IV

T

M

M F

Transformation B T

VIII

IX

T M

T

Trade

systems T

Trade T

M T M

Supply

F

V

T M

T M

Fish

markets M

C. Mullon et al. / A modeling framework for oceanic basins under double exposure

i. Figure 4 represents how to define the equilibrium on small parts of an economic

Figure 3. Integrating climate changes through an oceanic model (MEDUSA) and a dynamic bio climate

envelope model (DBCE). The input F of the DBCE model is the fishing mortality.

aquaculture systems. It works as follows. Firstly, the initial values of parameters (costs functions, prices

functions, fishing capacity, farming capacity, stocks biomass, habitat quality) are calibrated according to

datasets and bio-climate envelope model. Then, at every time step: (1) the economic equilibrium of the

network is computed, giving prices and flows all along the system; (2) several parameters are updated

according to the equilibrium characteristics (prices and flows) and scenario: the profit of fishing systems

and of aquaculture systems is a simple function of flows and prices; fishing capacities and aquaculture

capacities are obtained using simple investment functions; habitat quality is updated according to the bio-

climate envelope model; fish stocks are updated according to yield and habitat quality.

2.4 Design of the bio economic model: equilibrium

To compute the economic equilibrium of this network, we use an extension of convex programming called

complementarity problems [Facchinei and Pang, 2003; Mullon, 2013]. The idea of mathematical comple- mentarity is very simple : given two numbers, x and y, we put x⊥y if both are positive, one is zero, that is: x ≥ 0, y ≥ 0 and x × y = 0. Given two vectors X = (xi), Y = (yi), we put X⊥Y if xi⊥yi for all

network in terms of

Figure 4. Complementarity equations on nodes, links, constrained nodes. The whole network can be split

in elementary sub-networks such as these three ones. On a node a there is a price denoted Pa. On a link

ab, there is a flow denoted Xab, and a transportation cost denoted Cab.

complementarity. On a node c, we define excess as inflow minus outflow: (Xac + Xbc) − (Xcd + Xce); in any

way, excess is non negative; if price Pc is positive, then excess is zero; there is complementarity between price and excess: Pc⊥(Xac + Xbc) − (Xcd + Xce). On a link ab not ending in a constrained node, we define

excess as destination price minus origin price and transport costs: Pb − Pa − Cab; in any way excess is

non negative; if there is a flow Xab, then excess is zero; there is complementarity between flow and excess

C. Mullon et al. / A modeling framework for oceanic basins under double exposure

Xab⊥(Pb − Pa + Cab). On a constrained node c, for example with a constraint Xac + Pbc ≤ Mc, we define

excess as Mc − (Xac + Xbc); in any way excess is non negative; there is a non negative shadow price λc;

if it is positive, then excess is zero; constraint is saturated; there is complementarity between the constraint and the shadow price: λc⊥Mc − (Xac + Xbc). On a link ending to this node, for the same reason as before, we still have a complementarity relationship: Xac⊥(Pc − (Pa + λc + Cac)).

We get a complementarity problem [Facchinei and Pang, 2003] by putting all these conditions together. We denote it V ⊥ F (V ). State vector V is made of prices on intermediate nodes, flows on links, shadow prices on constrained nodes. Function V → F (V ) is related to the excess on the corresponding entity according

to the aforementioned definitions. The resulting complementarity problem is quite large. The length of a

state vector is about 500 (the number of nodes, plus the number of links, plus the number of constraints

in the network). We have implemented and adapted the necessary algorithms for computing the network

equilibrium when it exists.

2.5 Model calibration and testing

Above excess functions that constitute functional F (V ) are expressed in a simple way according to pa-

rameters corresponding to network entities: transportation costs on links, production functions on nodes corresponding to fleets, price functions (inverse demand functions) on nodes corresponding to markets. To

calibrate the model, we have used data sets concerning catches and trade4. They provide values character- izing states of the modeled network (on nodes and links). We have assumed that the values corresponding to the 1996-2006 period were defining a reference state of the system. We have then used a technique of in-

verse modeling: knowing the values characterizing the reference state V , find the values of the parameters

of the function F that ensure the complementary equation V ⊥ F (V ).

To test the model and its realism, we built a reference simulation (without any variation of parameters)

and checked that the results corresponded to the above reference state. Then we proceeded to systematic

sensitivity analysis on all input parameters, checking that they were having, one by one, the expected effect.

Recall that our goal is not to predict exactly the future of the system but to provide a tool for envisaging it in all

its components (biology, economics, policy) and that all conclusions we draw from a simulation experiment

are hypothetical.

2.6 An example of model output

A modeling experiment consists in setting a value to input parameters, to run the previous algorithm for 30

years (from 2010 to 2040) and then to observe the effects on fish stocks, on catches, on fishing capacity, on

farm production, on fish consumption, on import and export. As an example, we have modeled the effects

of changing the catch limitations. The definition of total allowable catches (TAC) in the North Atlantic is

the key point of the European Unions Common Policy for Fisheries. How to take into account a possible

change of fish distribution and abundance due to climate changes when determining TAC? A simulation experiment has been built in which the parameter related to TAC5 is set to −2% per year and +2% per year.

Figures 5 and 6 show how these changes affect yields, stocks, and fishing capacities. We observe that the

model predicts that catch limitation is positively related to health stocks, and more surprisingly negatively to

fishing capacity; this is obviously due to the fact that a less abundant stock implies more fishing capacity for

similar catches. A general feature appears that is related to the way of modeling: profits are highly variable.

This variability is the kind of situations that is experimented by fishing entities and that is often neglected by

other participants to negotiations about the level of TAC. This variability is currently evoked in the scientific

literature about fisheries but rarely identified in bio-economic modeling.

They are issued from databases COMTRADE (United Nations), FAO, EUROSTAT and ICES

Every year, according to the biomass of a fish stock, a quantity called Maximum Sustainable Yield (MSY) is computed by scientists

and agreed by other stakeholders; then catch limitation are expressed in terms of MSY. The model mimics this process.

C. Mullon et al. / A modeling framework for oceanic basins under double exposure

STOCKS CATCHES CATCHES LIMITATION 200 000 40 000 40 000

100 000 20 000 20 000

2020 2030 2040 2020 2030 2040 2020 2030 2040

FARM PRODUCTION FISH CONSUMPTION TRADE INTRA EU 2000 80 000 8. x 106

1000 40 000 4. x 106

2020 2030 2040 2020 2030 2040 2020

FISHERIES PROFIT FISHING CAPACITY

2030

IMPORT

2040

8. x 109 200 000. 2 000 000

4. x 109 100 000. 1 000 000

2020 2030

EXPORT

2040 2020 2030 2040 2020 2030 2040

FARMING CAPACITY FISH CONSUMPTION PRICE 2 000 000 2000 20 000

15 000

1 000 000 1000 10 000

5000

2020 2030 2040 2020 2030 2040 2020 2030 2040

Figure 5. States variable of the system in case of decreasing TAC. Every year, the ratio TAC / MSY

decreases by 2% Colors correspond to ICES areas for catches plots, to groups of countries for other plots.

2.7 An example of scenario building

The model has been used to build and analyze contrasted scenarios about the futures of the North Atlantic

basin. These scenarios have been defined during a dedicated workshop. They are named: Baseline,

Fortress (countries protect their fleet, support their aquaculture, pay no attention to the environment), Free

trade (countries open their markets, do not support their fleets or their aquaculture, pay no attention to

the environment) and Global commons (countries open their markets, do not support their fleets or their

aquaculture, pay attention to environment)6 and have been coupled with three climate scenarios7.

To any of these scenarios is associated an output of the model synthesized with maps and figures such as

the ones of figures 5 and 6. From the analyses of these synthesized results we conclude that concerning

climate scenarios, few significant changes are to be expected in the considered period (this is due to the

fact that in IPCC scenarios, effective changes occur after 2050), and that concerning governance scenar-

ios, the following narratives can be formulated. (1) Baseline. Consumption is maintained; overexploitation

lasts: stocks decrease, fishing capacity increases; aquaculture develops. (2) Fortress. Consumption de-

creases; overexploitation increases; aquaculture develops greatly; fish trade decreases; export increases

at the end of period. (3) Free trade. Consumption is maintained; overexploitation lasts: stocks decrease,

fishing capacity increases; fish trade increases. (4) Global Commons. Consumption decreases greatly;

overexploitation disappears: stocks increase, fishing capacity slightly decreases; import decreases; export

increases a lot.

To decide upon the most important stakes for the future of the system, these results have been discussed

by participants. The following points have been put forward: the variability of fleets’ profits, the strong

antagonism between aquaculture development and fleets development, the importance of demand for fish

outside Europe. More detailed analyses of the results have been obtained, taking account of the diversity

of fishing areas, of species, of countries. They are presented in the report of this modeling experiment.

These scenarios are defined in terms of model parameters as follows. (1) Baseline: TAC limited at MSY; no restriction of trade inside

European Union (EU); importation and exportation taxes outside EU at a low level defined by WTO. (2) Fortress: regular increase

of TAC starting at MSY; decrease of trade costs inside Europe; increase of external trade costs; support to aquaculture. (3) Free trade: regular increase of TAC starting at MSY; decrease of all trade costs, decrease of fishing costs, increase of efficiency of fisheries

and aquaculture. (4) Global commons: regular increase of TAC starting at MSY;; decrease of trade costs, decrease of fishing costs, decrease of efficiency of fisheries and aquaculture.

They are the three IPCC scenarios: A1B, RCP2, RCP8

C. Mullon et al. / A modeling framework for oceanic basins under double exposure

Figure 6. States variable of the system in case of increasing TAC. Every year, the ratio TAC / MSY increases

by 2%. Colors correspond to ICES areas for catches plots, to groups of countries for other plots.

3 DISCUSSION: TOWARDS A FRAMEWORK FOR THE MODELING OF OCEANIC BASIN UNDER DOUBLE EX-

POSURE

To anticipate in a collaborative way the futures of exploited oceanic basins, that is (1) to deal with ecosys-

tems under the double exposure, (2) to allow scenarios about the future of these ecosystems, (3) to provide

integrated views on the dynamics of these systems, including both biological and economic processes, we

suggest a modeling framework (figure 1), with the key features of using network economics principles, i.e.

(1) considering the system as a network, some parts being affected by climate changes, some other by

economic globalization, (2) representing the economics of this system as related to its network equilibrium,

(3) representing the dynamics of the system with conventional production and investment functions. This

allows representing the system with a network of intermediate size (several hundred of nodes and links), the

definition of which being based (1) on the objective of developing scenarios, (2) on the structure of existing

datasets. This allows to calibrate the system with standard inverse modeling techniques.

We have detailed here how to implement the model of a large oceanic basin in this framework, from the

design of the model to the scenarios’ interpretation. Let us recall that the methodological issue of building

scenarios about exploited ecosystems lies in the involvement of stakeholders. It appears from the experi-

ence of the modeling of the North Atlantic basin that the proposed approach provides specific tools to avoid

several well known pitfalls: to oversimplify versus to be too detailed (whence the idea of a model of inter-

mediate size), to draw a conclusion too rapidly versus to differ the conclusion (whence the implementation

of a normalized process of scenario building), neglect their knowledge versus be scared by their reactions

(whence the idea of a collaborative design of scenarios).

ACKNOWLEDGMENTS

This study was funded by the European Commission within the Seventh Framework Programme EURO-

BASIN (Grant Agreement #264933). Thanks to anonymous reviewers and to Rachel Mullon for helpful

comments.

STOCKS CATCHES CATCHES LIMITATION 200 000 40 000 40 000

100 000 20 000 20 000

2020 2030 2040 2020 2030 2040 2020 2030 2040

FARM PRODUCTION FISH CONSUMPTION TRADE INTRA EU 2000 80 000 4. x 107

1000 40 000 2. x 107

2020 2030 2040 2020 2030 2040 2020

FISHERIES PROFIT FISHING CAPACITY

2030

IMPORT

2040

1.6 x 1010 80 000. 2 000 000

8. x 109 40 000. 1 000 000

2020 2030

EXPORT

2040 2020 2030 2040 2020 2030 2040

FARMING CAPACITY FISH CONSUMPTION PRICE 2 000 000 2000 20 000

15 000

1 000 000 1000 10 000

5000

2020 2030 2040 2020 2030 2040 2020 2030 2040

C. Mullon et al. / A modeling framework for oceanic basins under double exposure

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