A consensus-based simulation model for management in the ...

Ecological Modelling 110 (1998) 79 – 103

A consensus-based simulation model for management in the Patagonia coastal zone ˚ sa Jansson c Marjan van den Belt a,*, Lisa Deutsch b, A a

Ecological Economics Research and Applications, Box 1589, Solomons, MD 20688, USA b Department of Systems Ecology, Stockholm Uni6ersity, S-106 91 Stockholm, Sweden c The Beijer International Institute of Ecological Economics, Box 50005, S-104 05 Stockholm, Sweden

Abstract We applied computer modeling as a consensus building tool as part of the development of the Patagonia Coastal Zone Management Plan (PCZMP). The objective was to build a ‘scoping model’ to assess some of the important ecological and economic interlinkages of the coastal zone of Patagonia. The main purposes were to build consensus, integrate across several parts of the system. and to educate stakeholders about other sectors, not to create a detailed research model. Nevertheless, the model provides some interesting preliminary conclusions. This model indicates that the total net present value (NPV) of the fisheries sector over a period of 40 years may be increased by 13% compared with current income, with a decrease in hake fishing levels by : 50%. The natural capital on which the fishery sector depends would be used in a more sustainable way, both ecologically and economically. The model also simulates possible impacts of oil spills and dumping of tanker ballast water on the penguin population. which can have a significant negative impact on tourist industry incomes. The model implies that the importance of the tourist sector in Patagonia could in the future greatly exceed the value of the fishing industry (by 29%). The results argue for more emphasis on integrating the currently disparate components of coastal zone management. © 1998 Elsevier Science B.V. All rights reserved. Keywords: Ecological economics; Modeling; Consensus building; Integrated coastal ecosystem management; Patagonia; Fishery; Tourism; Oil pollution

1. Introduction The Fundacion Patagonia Natural (FPN), in cooperation with other local organizations, have * Corresponding author. Tel.: + 1 410 5861517; fax: +1 410 5861517; e-mail: [email protected]

generated a vast amount of ecological and economic data about the three provinces in the south of Argentina which make up the main part of Patagonia: Rio Negro, Chubut and Santa Cruz. The specialized research data gathered by FPN needed to be interpreted and placed in a systems perspective, as part of the development of the

0304-3800/98/$19.00 © 1998 Elsevier Science B.V. All rights reserved. PII S0304-3800(98)00043-X

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M. 6an den Belt et al. / Ecological Modelling 110 (1998) 79–103

Patagonia Coastal Zone Management Plan (PCZMP) funded by UNDP/GEF. A preliminary or ‘scoping’ model was constructed to assist discussions about the integration of some important ecological and economic considerations. Some affected stakeholders were involved in the process of developing this model. The modeling process took place over a period of a few weeks. This scoping model gives a simplified but integrated picture to help understand complex systems. The objective was not to describe the system and all trophic relations in great detail, but rather to ‘scope-out’ important linkages between different parts of the system. The goal was to create a tool to illustrate the need for an integrative management approach to a broader public. It was an attempt to incorporate both ecological and economic aspects of the coastal system. The learning experience of the participants during the construction process of this integrative model represents a large part of the value of the modeling process. It required synthesis rather than analysis. The data for the model were mainly provided by FPN and some local stakeholders. Therefore, the results are heavily dependent on these inputs and our interpretation of them. The approach should be reproducible, but these specific results might be different even if we continued this work in the same area. The challenge of linking ecological and economic data is not restricted to Patagonia. All over the world the integration of ecology and economy is posing major challenges, and several studies with these goals have been carried out or are under development (Baker et al., 1991; Bockstael et al., 1995; Wilgren et al., 1996; Costanza et al., in press).

1.1. Study area Patagonia is situated in the southern tip of Argentina and has a coastline which stretches over almost 3000 km from Rio Negro to the Straits of Magellan. It hosts a rich and diverse marine life. Elephant seals, penguins, killer whales, dolphins and endangered right whales are some of the vulnerable natural assets. This shore is one of the world’s most biologically productive

coastal ecosystems according to the agencies funding the PCZMP. Although Patagonia is still sparsely inhabited by people the coastal towns are growing rapidly. The fisheries industries in the southwest Atlantic are the fastest growing on earth. A fast growing ecotourism market is also making use of the natural assets. In addition, there exists an economically significant oil industry (van den Belt, 1996a).

2. Approach and methodology

2.1. Modeling tools for scoping and consensus building Computer modeling can be used in building consensus between disciplines and between science, policy and the public (Costanza and Ruth, 1997). The process of modeling can help build mutual understanding, solicit input from a broad range of stakeholder groups and maintain a substantive dialogue between members of these groups. The modeling process must involve ecological, sociological and economic aspects in order to clarify the interlinkages about the way in which the system works. The aim is to generate more effective, equitable and resilient management options. Once preliminary versions of a model have been constructed, it can be run to develop understanding of its dynamics and sensitivity, to compare the results of its behavior with existing data, and to help decide where best to put additional effort in improving the model. This can be thought of as an initial scoping model that facilitates broadbased input and consensus (Costanza and Tognetti, 1996)

2.2. Methods and data Micro- and macro-economic data were gathered through official agencies (Banco Central de la Repu´blica Argentina, 1993; INDEC 1994) for the fisheries, tourism and oil industry sectors. Initial ecological and economic data (about fisheries, tourism and oil pollution) were available

M. 6an den Belt et al. / Ecological Modelling 110 (1998) 79–103

through the PCZMP administered by FPN in Puerto Madryn, Argentina. Also, the values of economic benefits generated by ecological services (tourism and fisheries) and the ecological costs of human activities (overfishing, net deaths, oil pollution) which we use in the model were determined using data previously generated by PCZMP Further data gathering was undertaken by interviewing some stakeholders in the Argentine government, non-governmental organizations and the commercial sectors in Patagonia. A small scale contingent valuation survey was also carried out in order to reveal the preferences of the local population in monetary terms1. Without claiming conclusive answers, there are several indications that an additional willingness to pay for ecosystem services exists (van den Belt, 1996b). There is no universally agreed upon way to assess the value of ecosystems in monetary units that includes both their market value and their non-market value. An integrated valuation process is considered more appropriate in this project (Gregory et al., 1993). Integration requires stakeholders to examine all parts of as well as the inter-linkages between the ecological and economic systems. This is integral to the valuation process. The model simulates economic results in the shape of the present values of the income generated by by tourism (ecotourists and other tourists) and fisheries (coastal and offshore) and an estimation of the potential costs of clean up of oil spills.

2.3. Time frame The model is set to run from 1980 to 2020. Calibration of available data with the results of the model is presented in Appendix A. The model uses random number generators to mimic some natural variability and unpredictable human impacts such as oil spills. 1 Contingent valuation as a technique has been criticized on the point that values are often not clear in the mind of the individual. Interviewing adds value to the process by allowing the interviewees to ‘construct’ preferences rather than attempting to get them to ‘reveal’ preferences that are ill formed (Gregory et al., 1993).

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3. Structure of the model The choice of sectors for the scoping model resulted from discussion with researchers in FPN. The linkages (positive and negative feedback) between the coastal ecosystem and the economic sectors, specifically those sectors which directly depend on or affect the services of the marine ecosystem were analyzed. The model attempts to estimate an economic value for the fishery and tourist industries, while making these sectors dependent on some possible, but not exclusive, environmental indicators. The overall structure of the model is shown in Fig. 1. A complete list of the equations in the model is given in Appendix B. The model was designed to simulate different scenarios. The user interface shows an explanation of the operation of the model, a summary graph of each run, and graphic displays for different sectors. Slide bars provide the opportunity to quickly change parameters and run new scenarios. It is a user-friendly model and can be operated without the need for manuals. We examined possible trade-offs between the income generated by fisheries and tourism by maximizing the total net present value of the area over the time span of the model (1980–2020).

3.1. Penguins Penguins are one of the most important tourist attractions in Patagonia (together with other wildlife such as whales and other marine mammals). Some of the relations existing between penguins and oil and also the fisheries and tourist industries could be quantified and the required data were available. Fig. 2 shows the relations affecting the penguin population, that make up the ‘penguin sector’ of the model. A small percentage of penguins die in fish nets each year and penguins depend on the availability of fish for food. The oil industry is a major polluter in Patagonia. It creates pollution from coastal oil wells, oil tankers’ loading facilities and illegal ballast washings. Over 10% of all penguin carcasses recovered at Punta Tombo (a main ecotourism site) are fouled by petroleum (Boersma et al., 1990). For other significant marine mammals, these relations are less clear or data is unavailable.

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Fig. 1. Overall structure of the PCZMP model, showing the linkages of information flows (the thin lines) and the physical flows (the thick line) between the individual sectors.

The Magellanic penguin (Spheniscus magellanicus) population is most sensitive to the death of breeding adults, considering the variability in reproductive success. Because information is lacking about the recruitment rate, we estimate this rate from fledgling data (Boersma et al., 1990). Penguin recruitment was estimated with a Michaelis-Menten function (Eq. (1)) with halfsaturation constant of 30 and a maximum recruitment of (.14*Penguin – chicks). A random influences function simulates some of the environmental uncertainty: .14*Penguin – chicks* (Fish – Cons – per – P/(30+Fish – Cons – per – P)) P – Random – in fluences (1) Ten percent of the adult breeding population dies each year of natural causes. Six percent of returned banded penguins (0.71% of total population), die in fishing nets (Pablo Yorio, personal communication). According to Gandini et al. (1994), 20000 adult penguins die every year along the coast of Chubut and St Guz due to oil spills. This corre-

sponds with a FPN estimate of 1.14% that die due to chronic oil pollution (Eq. (2)). P – Oil – regular – deaths =(IF Ballast – Dump \350 AND Ballast – DumpB 2300 THEN (Adult (2) – Penguins*.0114) ELSE 0) The population in 1980 is estimated at 2000000 individuals (Pablo Yorio, personal communication) as stated in Eq. (3). Since then some large colonies have declined and some small colonies have been created (Eq. (4)). The only complete census, carried out 1993–1995, estimated the number of breeding pairs along the coast of Patagonia at 875000 or 1750000 individuals. INIT Adult – Penguins=2000000

(3)

Adult – Penguins(t) =Adult – Penguins(t−dt) +(P – Recruitement –P – Natural – Mortality−P – Fishnet – Deaths−P – Oil – regular – deaths−P – Oil (4) – spill – deaths)* dt Generalized for the coast of Patagonia, a pen-

M. 6an den Belt et al. / Ecological Modelling 110 (1998) 79–103

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Fig. 2. The penguin sector.

guin diet consists of anchovy (70%), squid (20%) and hake (10%) (Dee Boersma, personal communications). In our model the consumption per penguin is at most 0.6 kg hake and anchovy per day, depending on the availability of food (Eq. (5)). P – Net – Births=P – Birth – rate* (Adult – Penguins* (Fish – Cons – per – P/(30+Fish – Cons – per – P))) (5)

ture reserves and other tourists that come to the area for different purposes. The value of tourism in this model consists of the market values for ecotourists (Eq. (6)) and other tourists (Eq. (7)), and the non-market value of ecotourists (Eq. (8)). The market value depends on the number of eco- and other tourists, the number of days spent and the spending per tourist, as shown in Fig. 3.

3.2. Tourism

Annual – Eco−Tourist – Value=

The tourism sector of the model is composed of ‘ecotourists’ and ‘other tourists’, making a distinction between tourists that are motivated primarily by nature’s attractions who visit na-

Ecotourists* Days – spent – ET* Spending – per – ET* Wildlife (6) – consv – index

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Fig. 3. The tourism sector.

Annual – Other−Tourist – Value = Other – Tourists* Spending – T – per – day* Days – spent – per – T* DELAY(Wildlife – consv (7) – index,5) Annual – Non−Market – Eco-Tourist = Days – spent – ET* Ecotourists* WTP* Wildlife (8) – consv – index A GEF survey coordinated by FPN shows that the incentive for 65% of the tourists was the

wildlife. In the model, ecotourism depends on a wildlife conservation index (Eq. (9)) to which penguins contribute 40% (Alicia Tagliorette, personal communication). Wildlife – consv – index =((Adult – Penguins/1750000)

*.4)+(.6)

(9)

A decline in the penguin population reduces the income from ecotourism. Income from other tourists will also decline with a time lag and to a lesser degree. If all penguins disappear, eco-

M. 6an den Belt et al. / Ecological Modelling 110 (1998) 79–103

tourists in the model are attracted by the remaining 60% of the wildlife, unless the slide bar for the wildlife conservation index is placed on a specific value. The estimation of the growth rates for tourism (5%) and ecotourism (25%) are based on a GEF survey (Piedad Losano, personal communication) in Pto Piramide, Pto Madryn, Trelew, Pto Deseado Rio Gallego and St Julian. (Chubut and St Cruz.). These are the main tourist areas. The data were extrapolated to other coastal tourist areas. The model simulates a levering off of growth rates in the future. The non-market value is the value of tourism which currently remains uncaptured by the economic market as revenues. Moreover, this potential income is influenced by the level of information in the community. We reflected this idea by an ‘information’ slide bar. If 100% of the community is informed about ecosystem services, this will result in an increased willingness to pay (WTP) (Eq. (10)). Increased ability to monitor WTP eventually increases market ecotourist revenues. We use a moderate 20 pesos per tourist as a potential non-marketed WTP (van den Belt, 1996b). WTP =information* 20

(10)

The value of the ecotourist market is estimated as income minus the management costs for the nature reserves. These costs are based on the reserves in Chubut, the most commercially important province for tourism. Management costs are not available for other tourists. Present Value – Market – Ecotourists(t) =PV – Market – Ecotourists(t − dt) +(Annual – ET – value − Disctng – ET − Management (11) – costs)* dt

3.3. Hake, ancho6y and the fish market Hake (Merluccius hubbsi ), the most commercially important species, is overfished to the

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verge of collapse of some of the populations. Anchovy is an important common food source for penguins and hake. Although squid is an important food source for penguins as well, the lack of information prevented us from including this species in this model. Fig. 4 and Eq. (12) illustrate how hake and anchovy populations are influenced by landings and discard (catch which is too small for commercial purposes and is, therefore, disposed of), predation, and natural deaths. Hake(t)=Hake(t−dt) +(H – Net – recruitement +H – Growth-hake – landings−discard−H – Pred – by – Penguins−H – Natural (12) – deaths)* dt

INIT Hake=2950000

(13)

In 1991, total hake stock biomass in Patagonia was estimated at 2.0–2.2 million metric tons. The initial value of Eq. (13) reflects the situation in 1980. In 1994, this figure was 1.5 million metric tons. The model estimates total catch at 500000 tons (400000 tons landed, 100000 tons discard) (SAGyP, 1994). The capacity of landing fish is determined by a capacity index, which depends on the capital available in the sector. The level of capital is assumed to be influenced by the profits made in the sector. Fisheries landings of hake are then divided into offshore and coastal according to their fraction of total fish landings. OS – Capacity – Index=Offshore – Fishery – Capital/INIT(Offshore – Fishery (14) – Capital) OS – H – Income=Harbor – to – Ind* (1−Coastal – Fraction)* Hake (15) – processed – price* Waste Waste=.5 (16) Hake – processed – price=2200

(17)

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M. 6an den Belt et al. / Ecological Modelling 110 (1998) 79–103

Fig. 4. The hake, anchovy and fish market sectors.

***Coastal – Fishermen – Income = hake – landings* Coastal – Fraction* Hake – price – at (18) – harbor

Hake – price – at – harbor=250

(19)

3.4. Coastal and offshore fisheries Fig. 5 shows that the accumulation of capital is

M. 6an den Belt et al. / Ecological Modelling 110 (1998) 79–103

different for these groups of fisheries. For example, the price received by coastal fisherman for hake at the harbor is much lower than the price received by the fisheries industry after the fish is processed. However, the details and the dynamics of this situation went beyond the scope of this model. Price changes are not generated by the model (Eq. (19)), although a user can change the price at any time during a run with the slide bar to see the impact of such a change. The incentive to a fisherman or vessel to increase its landings depends on the profits that are made in the sector (Eq. (25)). The ability to fish effectively depends on the rate of reinvestment in vessels. This ability is determined in the model by the sector’s capacity index. The savings rate is the percentage of the profits reinvested in new or more efficient vessels. The variable costs consist of estimates of labour and fuel costs per ship per year. Offshore – Fishery – Capital(t) =Offshore – Fishery – Capital(t − dt) +(OS – Invesnnents − OS – Depreciation)* dt

(20)

INIT Offshore – Fishery – Capital = OS – Price – of – Ships* 239 (21)

OS – Price – of – Ships = 4000000

(22)

OS – Investments = OS – Profits* OS – Savings – rate

(23)

OS – Depreciation = Offshore – Fishery – Capital* OS – Depr – rate

(24)

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OS – Profits=(OS – H – Income+OS – A – income)−OS – Fixed – costs−OS – Variable – costs

(25)

OS – Savings – rate=.25

(26)

OS – Variable – costs=OS – Number – of (27) – ships* 50000 Capital is calculated in the coastal sector with similar equations. The coastal sector includes fishermen as well as fish processing companies.

3.5. Oil pollution The oil industry is not included in this model as an income generating sector. However, it is a source of pollution which has been documented to have significant and negative effects on penguins (Boersma et al., 1990). The oil pollution sector as shown in Fig. 6 simulates the first known major oil spill in Patagonia in 1991. Thereafter oil pollution is caused by sporadic randomly generated major oil spills. The releases of ballast water are frequently and randomly causing oil pollution problems at a low but chronic level. If there is a willingness to clean oil spills, the impact on penguins of oil spills will be reduced by an estimate of 20% and costs for clean up will be calculated based on the clean up costs of the Exxon Valdez disaster. If WTC= 1 then the polluter pays according to clean up cost. If ballast treatment is not equal to zero there will be no negative impacts on the penguin population and the annual cost will be USD 14785934.

4. Results and discussion The result of the modeling process was a userfriendly model. The user can easily change the parameters and thereby simulate a broad range of scenarios. A parameter can be turned off (zero) or the equation can be turned on (eq.on). Alterna-

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M. 6an den Belt et al. / Ecological Modelling 110 (1998) 79–103

Fig. 5. The coastal and offshore fisheries sectors.

eq.on eq.on

15

16

100000

eq.on

14

19

eq.on

13

60000

eq.on

12

18

eq.on

11

30000

eq.on

10

17

eq.on

0

6

9

0

5

eq.on

0

4

8

0

3

eq.on

0

2

7

0

Anchovy

1

Scenario

eq.on

eq.on

eq.on

0,3

0,01

0,02

0,03

0,06

0,08

0,13

0,13

0,13

eq.on

0

0

0

0

0

0

Hake

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

0

0

0

0

0

Fish net

Table 1 Summary resulls of 25 scenarios

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

0

0

eq.on

eq.on

eq.on

eq.on

0

Oil spill

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

0

0

eq.on

eq.on

eq.on

eq.on

0

Ballast

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

0

0

0

0

0

eq.on

eq.on

0

0

Clean up

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

0

0

0

eq.on

0

eq.on

0

0

Treatment

7981 171 6119 236 7591 176 6640 194 6926 479 7321 170 6648 94 5303 240 5987 311 6525 105 6576 90 6654 166 6748 97 6814 152 6651 139 6435 239 6468 138 6468 247 6375 114

Tourism 0 0 0 0 0 0 0 0 0 0 0 0 47 0 48 0 47 0 47 0 26 0 23 0 24 0 23 0 23 0 87 0 46 0 43 0 41 0

Coastal

Model results

0 0 0 0 0 0 0 0 0 0 0 0 2215 5 2249 11 2223 8 2218 6 3003 10 3394 30 3120 4 2262 23 1294 6 1957 4 2278 6 2 393 11 2568 5

Offshore 0 0 0 0 1837 785 2306 1166 0 0 0 0 0 0 0 0 0 0 1868 442 1466 636 1906 1241 1916 645 1956 1145 2445 1429 2190 883 1917 577 2183 752 2180 265

Clean up 0 0 0 0 15 0 0 0 15 0 0 0 0 0 0 0 15 0 15 0 15 0 15 0 15 0 15 0 15 0 15 0 15 0 15 0 15 0

Treatment

7981 171 6119 236 7591 176 6640 194 6926 479 7321 170 8911 92 7600 230 8258 308 8790 101 9605 81 10071 137 9892 96 9100 155 7968 143 8480 235 8791 133 8904 239 8984 110

Total

M. 6an den Belt et al. / Ecological Modelling 110 (1998) 79–103 89

Anchovy

250000 400000 250000 250000 250000 25000

Scenario

20

21

22

23

24

25

Table 1 (Continued)

0,08

0,08

0,06

0,06

eq.on

eq.on

Hake

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

Fish net

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

Oil spill

eq.on

eq.on

eq.on

eq.on

eq.on

eq.on

Ballast

0

eq.on

0

eq.on

eq.on

eq.on

Clean up

0

eq.on

0

eq.on

eq.on

eq.on

Treatment

6324 284 5140 929 6438 223 5042 565 6392 109 5599 120

Tourism

32 0 28 2 19 0 20 0 20 0 20 0

Coastal

Model results

3325 11 3041 984 4557 21 3957 630 4079 12 4131 5

Offshore

2056 698 2528 1533 1795 597 0 0 2024 1355 0 0

Clean up

15 0 15 0 15 0 0 0 15 0 0 0

Treatment

9681 274 8208 1910 11015 203 9018 1126 10491 98 9751 117

Total

90 M. 6an den Belt et al. / Ecological Modelling 110 (1998) 79–103

M. 6an den Belt et al. / Ecological Modelling 110 (1998) 79–103

91

Fig. 6. The oil pollution sector.

tively, a specific numerical value can be inserted by a slide bar. We have selected a few scenarios produced by the model for this discussion. Table 1 lists the results from 25 scenarios. There are random number generators in several places in the model. Therefore, the outcome of run with identical parameters can differ somewhat. The analysis of the results are based on the average of five runs for every scenario. The mean is followed by the S.D. of the five runs for each scenario in Table 1. These scenario’s mainly evaluate changes in economic values. By presenting and analyzing these figures we do not want to raise a false sense of security. They are merely illustrative of the kind of results produced by the model and the possible range of outcomes. We compared changes in the NPV of tourism with other sectors. The model is based on the assumption that tourism currently has no negative impacts on the ecosystem, which could be a subject for a future discussion. In a scenario with no

negative human impacts to penguins or even fish, the NPV of tourism is maximized (scenario 1: no fishing, no fish net deaths, no oil spills, no chronic oil pollution). When oil pollution is introduced without clean up of spills or prevention of chronic oil pollution (scenario 2) tourist income is decreased by 23%. However, if pollution is cleaned and treated (scenario 3) then income is raised by 19%. Yet, it remains below the level of scenario 1 by 5%, or a NPV (over a 40 year period) of USD 390 million because all of the impacts from a spill cannot be removed once it has taken place. The treatment ‘ballast water’ is a preventative measure which results in no impact from oil. The point illustrated by oil spills and chronic oil pollution is that the cost of prevention could be much less than the cost of the cure. Results from our runs indicate that the costs of cleaning up spills are substantial, with a mean of USD 2 billion per run (Table 1). In fact, clean up and treatment costs exceed the gains in income by USD 380 million

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Fig. 7. Graph of penguin population without human impacts.

comparing scenarios 2 and 3, while breaking even if comparing scenarios 1 and 3. The model only quantifies costs of oil spills and ballast dumping after they have taken place. The oil pollution costs could not be verified for the coast of Patagonia and are based on estimates from the Exxon Valdez case. Due to the remoteness of the area a major disaster could be more expensive to clean up or do greater damage than simulated here. While one can see that clean up costs are substantial, one can also see that the impact is substantial as well. Certainly the cost of preventing oil spills is much lower than cleaning them up once they have taken place. If we examine the

removal of oil pollution by the prevention measure ‘ballast treatment’ in scenario 4 we see a reduction in tourist incomes by 13% from scenario 3. The cost to clean ballast water is almost USD 15 million per run, but when examined alone it reduces tourist NPV income by USD 950. It would definitely be a worthwhile investment in this scenario from a regional point of view. A subject for discussion is the accurateness of the numerical values and the accountable party for these investments. When excluding all human impacts but fish net deaths (scenario 6) the NPV of tourism decreases by $660 million or 8% compared with the poten-

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93

Fig. 8. Trends in the tourist sector.

tial NPV of tourism in the scenario without any human impact (scenario 1). The decrease in NPV of tourism when adding fish net deaths is due to an increased mortality of adult penguins. The adult penguin stock decreases by : 3 – 13% from 1980 to 2020. The user interface of the model shows these results in a graphical way, from which the trend between 1980 and 2020 becomes clear. For example scenario 1, where penguins would not be affected by human impacts, is shown in Fig. 7. The trends in the economic revenues relating to scenario 1 in the tourist sector are shown in Fig. 8. The units of: (1) ecotourists; (2) other tourists are number of persons; (3) present value market ecotourists; (4) present value market other tourists; (5) present value non-market ecotourists are in USD. A similar graph can be displayed by the model for the fishery sector. Scenario 23 is shown in Fig. 9. The penguin population in this scenario is affected by all modeled influences (deaths by entanglement in fishing nets. regular oil dumping, a major oil spill and overfishing). This has an impact on the NPV of tourism. When hake landing rates are varied, total NPV

is maximized with a rate of : 6% per year (scenario 12). When the landings rate is increased from 3–30% per year (scenario 16) the total NPV decreases by $1.591 billion (16%). Total NPV increases by 13% if hake landings rate is decreased from 13% (scenario 10) to 6%. These estimates suggest that today’s landings rate of 13%, or increased landings rates, may result in a lower total NPV than can be achieved with a rate of 6%. An explanation could be that the hake stock will decrease 72% by 2020 with a landings rate of 8% and by 97% with a rate of 13%. With a landing rate of 30% the hake stock will be reduced from almost 3 million metric tons to : 30 metric tons by 2020. This projection does not take a systemic collapse of the hake population into account. The correlation between the behavior of the model and the real data from 1980 to 1995 is reasonable. After 1995 the rates are merely extrapolated. This relationship is true for both off shore and coastal fisheries. The NPV of coastal fisheries would indeed benefit from a substantial increase in hake landing rate. The sector’s NPV would increase 46% by increasing the landings rate from 13% to 30%. This difference between the fisheries sectors may be explained by differences in the

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M. 6an den Belt et al. / Ecological Modelling 110 (1998) 79–103

Fig. 9. Graph of the penguin population with all human impacts (scenario 23).

structure of the two industries. The offshore sector has strong roots of ownership abroad, particularly in Spain, Japan and South Korea, and is dominated by relatively few large corporations. The sector has strong ties to the world fish market, access to capital, and apparently in some cases significant subsidies from their home government. Typically, these companies have reinvested the profits made on the world market in their offshore vessels and in new technology. Coastal fisheries are exclusively locally-owned and are generally smaller in size and greater in number. The coastal fisheries are price takers. Their fish is sold at the harbor to another company

and they are not able to capture the value added to the same degree as the large offshore firms. They do not have a large national market, as Argentines strongly prefer beef or pork to fish and their ties to the international market are weak. The only possibility for them to increase their income was to increase landings. This strategy worked only until the end of the 1980’s. Before 1990 (when the offshore sector began to dominate the industry), coastal fisheries could increase fishing effort to increase revenues, and this would show up as an increase in landings. In our model fishermen have an incentive to increase the time fishing if their income level is lower than

M. 6an den Belt et al. / Ecological Modelling 110 (1998) 79–103

subsistence. Today coastal fisheries are fishing at their maximum capacity. Further compounding their problems is the recent phenomena that hake has begun migrating to the offshore waters, putting it out of reach for the coastal vessels during a part of the year. So, coastal fisheries are losing even more ground to the offshore fisheries. Our scoping model does not include this relation. The PV of tourism does not exhibit more than a 6% difference across these seven scenarios. In our model a drastic reduction of the hake stock does not greatly affect the penguin population, partly because not enough is known about this relation. Anchovy is currently fished during winter and spring, at a time when penguins have migrated from the area. However, it is unclear how the food dynamics precisely affect the penguins. In scenarios 15–19 we increase anchovy landings from a maximum of 17400 (model equation mode) to 400000 metric tons, leaving hake landings rate in equation mode. As anchovy landings increase up to 250000 total revenues also increase as a result of increasing off shore revenues. At some point between 250000 and 400000 (scenarios 18 – 19) total revenues decline 15% as a result of decreases in the coastal (13%), offshore (9%) and tourist (19%) sectors. Since total NPV is greatest at the 250000 metric tons level, we chose to leave this level constant and set the hake landings rate at 0.06, its maximum revenue yielding level as shown in Table 1 (scenario 20).This run resulted in an even higher final total NPV. This is a 9% increase compared with scenario 10 (table 2) where anchovy landing are much lower (equation mode maximum= 17400). These results suggest that an increase in present anchovy fishing levels could yield greater total revenues. However, the anchovy population crashes in several runs at the 250000 level suggesting that this level is too high and does not support a sustainable fishery as the risk of the anchovy population crashing has been greatly increased. According to the model, one can maximize total NPV by increasing present anchovy landings substantially while the hake landings rate should be lower than at present. However,

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in practice the market for anchovy is considered to be saturated. 5. Conclusions This version of the model helped to: (1) develop understanding of the dynamics and sensitivity of the forces at work in the Patagonia coastal zone; (2) place the specialized research data in a larger perspective; (3) compare the model’s behavior with the available historical data for the system; (4) decide where best to put additional effort in improving the model for more detailed assessments. The discussions between the stakeholders which occurred during the process of building the model have contributed to a better understanding of the complexity of the Patagonia coastal zone and hopefully will contribute to improved management of this region. Modeling has helped to clarify interlinkages between the different sectors as well as indicating that some are more complicated than first anticipated. Data gaps exist and the purpose of the model is to assess different management practices on a large scale (the Patagonia coastal zone) thus it is unsuitable for providing quantitative data and answering precise questions. The model provides low resolution data for interpretations of general trends. The model can be used as a background for development of policies and a forum for discussion. For future improvements the potential impacts of other ecological services could be added and if they are found to be relevant, integrated into the model. For example, the red seaweed Nothogenia fastigiata, which we know to be commercially interesting to the pharmaceutical industry (Damonte et al., 1994), might be added to the model. Also, other species that are important for tourism and fisheries could be integrated. The model could be further improved by acquiring additional information on the foraging behavior of penguins, in order to better understand the effects of changes in availability of food sources. The model could also develop towards a higher spatial resolution, allowing differences between different parts of the coast to be modeled.

M D

Hake landings

Metric tons

Number of

D, data; M, model.

shore ships

Number of off-

M

data 239

146 239

Freezer

93

349

349

236000

277350

2950000

1980

Year

Total

trawler

Ice

M

D

Metric tons

coastal ships

D M

Hake stock

Appendix A

245

242

147

95

352

349

239633

228729

2921916

1981

254

246

148

98

363

353

245306

281862

2887488

1982

263

254

149

105

374

354

249930

257100

2844555

2594000

1983

272

260

150

110

384

356

253653

183233

2793640

2775000

1984

281

265

152

113

393

358

256344

259334

2715030

2967000

1985

290

276

156

120

401

360

257945

270558

2669582

2719000

1986

299

296

171

125

409

369

258443

304078

2598181

2519000

1987

308

322

192

130

414

386

257804

296026

2521620

2225000

1988

316

339

203

136

419

397

256024

294333

2440640

2324000

1989

324

346

206

140

421

417

253062

341042

2355479

2160000

1990

332

347

206

141

422

428

404645

409250

2267407

2529000

1991

339

355

209

146

420

435

368034

368998

2021437

1992

358

356

210

146

452

441

345039

422195

1795061

1993

373

473

1575870

1994

0.971

0.863

0.806

0.669

coefficient

Correlation

96 M. 6an den Belt et al. / Ecological Modelling 110 (1998) 79–103

M. 6an den Belt et al. / Ecological Modelling 110 (1998) 79–103

Acknowledgements The consensus building modeling approach presented in this paper was novel for the participants and funding agencies and we would like to acknowledge their courage for experimenting with it. The views presented in this paper are the views of the authors, not necessarily those of the participants or the funding agencies. This project was funded by UNDP/GEF and the Wildlife Conservation Society (New York). Special thanks to Dr William Conway (WCS). The project was developed together with and for the Fundacion Patagonia Natural. Without the data and information inputs from the following peo-

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ple, this project would not have been possible. Thanks to: Alicia Tagliorette, Pablo Yorio, Guillermo Caille, Jose Luiz Esteves, Juan Palla, Enrique Crespo, Patricia Gandini, Dee Boersma, Raul Gonzalez, Luis Curtolo, Ricardo Fondacaro, Gerardo Dittrich, Piedad Losano, Marianna Martinez Rivarola, Antonio Torrejon, Nestor Garcia, and Laura Piriz. Special assistance was provided by Horracio Ocariz, Jose Maria Musmeci, Guillermo Harris, Sara del Rio, Maria del Carmen Falcon, Claudio Campagna, Adrian Contreras Vasco, and Juan Carlos Aguerrebere. Thanks to Robert Costanza for assistance in developing the model and for helpful comments on earlier drafts of the manuscript.

Appendix B PV – Market – Ecotourists(t) = PV – Market – Ecotourists(t−dt)+(Annual – ET – value−Disctng ET−Management – costs)*dt INIT PV – Market – Ecotourists = 0 Annual – ET – value = Ecotourists*Days – spend – ET*Spending – per – ET*(Wildlife – consv – index) Disctng – ET=PV – Market – Ecotourists*Discount – rate Management – costs = 584372*2 PV – Market – Other – Tourists(t) = PV – Market – Other – Tourists(t−dt)+(Annual – OT – Value−Mngt – Costs−Disctn – T)*dt INIT PV – Market – Other – Tourists = 0 Annual – OT – Value = Other – Tourists*Spending – T – per – day*Days – spend – per – T*DELAY(Wildlife – consv – index,5) Mngt – Costs=0 Disctng – T=PV – Market – Other – Tourists*Discount – rate PV – NonMarket – ET(t) = PV – NonMarket – ET(t−dt)+(Annual – NM – ET−Disctng – opp – ET)*dt INIT PV – NonMarket – ET = 0 Annual – NM – ET=Days – spend – ET*Ecotourists*WTP*Wildlife – consv – index Disctng – opp – ET= PV – NonMarket – ET*Discount – rate Days – spend – ET= 3 Days – spend – per – T = 4 Ecotourists =Potential – growth – ecotourists*Wildlife – consv – index information =1 Other – Tourists = Wildlife – consv – index*Potential – Growths – Other – Tourists Spending – per – ET = 50

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M. 6an den Belt et al. / Ecological Modelling 110 (1998) 79–103

Spending – T – per – day = 35 Wildlife – consv – index = ((Adult – Penguins/1750000)*4)+(.6) WTP =information*20 Potential growth – ecotourists = GRAPH(TIME) (1990, 165000), (1992, 195000), (1994, 253000), (1996, 395000), (1998, 550000), (2000, 720000), (2002, 880000), (2004, 1e+06), (2006, 1.3e+06), (2008, 1.5e+06), (2010, 1.7e+06), (2012, 1.9e+06), (2014, 2.2e+06), (2016, 2.4e+06), (2018, 2.6e+06), (2020, 2.7e+06) Potential – Growths – Other – Tourists = GRAPH(TIME) (1990, 110000), (1992, 120000), (1994, 137000), (1996, 150000), (1998, 165000), (2000, 180000), (2002, 190000), (2004, 220000), (2006, 235000), (2008, 265000), (2010, 285000), (2012, 310000), (2014, 340000), (2016, 380000), (2018, 415000), (2020, 470000) Anchovy(t)=Anchovy(t−dt) +(Net – recruitement – A−Anchovy – landings−discard – P−A – Natural – deaths−Predation)*dt INIT Anchovy=2900000 Net – recruitement – A = if AnchovyBCarrying – capacity – A then A – recruitement – rate*Anchovy*Anchovy – random else Anchovy*A – recruitement – rate*Anchovy – random*.4 Anchovy – landings = if (Anchovy*A – landing – rate)B14500 then (Anchovy*A – landing – rate) else 14500*RANDOM(.8,1.2) discard – F=Anchovy*A – Discard A – Natural – deaths = Anchovy*Death – rate – A Predation=(.00000004*Hake*Anchovy)+(Adult – Penguins*Anchovy*1E−8) A – Discard=0 A – landing – rate = 0.005 A – recruitement – rate = 7 Anchovy – random = RANDOM (.5.1.5) Carrying – capacity – A = 5000000 Death – rate – A=.4 Anchovy – landing – data = GRAPH(TIME) (1979, 19797), (1980, 10702), (1981, 12387), (1982, 10098), (1983, 10577), (1984, 9700), (1985, 12047), (1986, 15712), (1987, 19370), (1988, 19468), (1989, 20730), (1990, 13100), (1991, 20615), (1992, 19289), (1993, 19149), (1994, 19100), (1995, 25000), (1996, 0.00) Coastal – Fishery – Capital(t) = Coastal – Fishery – Capital(t−dt)+(C – lnvestments−C-Depreciation)*dt INIT Coastal – Fishery – Capital = 349*C – Price – ships C – Investments = C – Profits*C – Savings – rate C – Depreciation = Coastal – Fishery – Capital*.03 C – Fisher – wage – rate = 10000 C – Fixed – costs= C – Depreciation

M. 6an den Belt et al. / Ecological Modelling 110 (1998) 79–103

99

C – Fuel – Cost – per – ship = C – Number – of – ships*10000 C – Number – of – ships = Coastal – Fishery – Capital/C – Price – ships C – Price – ships= 150000 C – Profits=(C – H – Income + C – A – Income) − C – Fixed – costs−C – Variable – costs C – Savings – rate = .35 C – subsistance – incr = if (C – Profits/Number – of – Coastal – Fishermen) B Subsistence – Coastal then ((Subsistence – Coastal−(C – Profits/Number – of – Coastal – Fishermen))/Subsistence – Coastal)/100 else 0 C – Variable – costs = (Number – of – Coastal – Fshermen*C – Fisher – wage – rate)+C – Fuel – Cost – per – ship Number – of – Coastal – Fishermen = C – Number – of – ships*4 Subsistence – Coastal = 10000 C – ships – data=GRAPH(TIME) (1980, 349), (1981, 349), (1982, 353), (1983, 354), (1984, 356), (1985, 358), (1986, 360), (1987, 369), (1988, 386), (1989, 397), (1990, 417), (1991, 428), (1992, 435), (1993, 441), (1994, 0.00) H – at – harbor(t) = H – at harbor(t − dt) + (hake – landings−Harbor – to – Ind)*dt INIT H – at – harbor = 200000 hake – landings (IN SECTOR: Hake) Harbor – to – Ind=H – at – harbor A – price – at – harbor = 300 A – Income – on – world – market = Anchovy – landings*Anchovy – waste*A – processed – price A – processed – price = 1400 Anchovy – waste= .5 C – A – Income=Anchovy – landings*A – price – at – harbor*0 C – Capacity – Index = Coastal – Fishery – Capital/INIT(Coastal – Fishery – Capital) C – H – Income=Coastal – Fraction*Harbor – to – Ind*Hake – processed – price*Waste Coastal – Fishermen – Income = hake – landings*Coastal – Fraction*Hake – price – at – harbor Coastal – Fraction = 2*(Coastal – Fishery – Capital/(Coastal – Fishery – Capital+Offshore – Fishery – Capital)) Hake – price – at – harbor = 250 Hake – processed – pnce = 2200 International – Demand = 1 OS – A – income=Anchovy – landings*A – price – at – harbor OS – Capacitv – Index = Offshore – Fishery – Capital/lNIT(Offshore – Fishery – Capital) OS – H – Income=Harbor – to – Ind*(1 − Coastal – Fraction)*Hake – processed – price*Waste Total – Fisheries – Income = C – H – Income +OS – H – Income+A – Income – on – world – market Waste=.5 Hake(t)=Hake(t − dt) +(H – Net – recruitement + H – Growth−hake – landings−discard−H – Pred – by – Penguins − H – Natural – deaths)*dt INIT Hake=2950000

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M. 6an den Belt et al. / Ecological Modelling 110 (1998) 79–103

H – Net – recruitement = If HakeBHake – CC then Hake*H – recruitment – rate else Hake*H – recruitment – rate*.1 H – Growth=If HakeBHake – CC then Hake*H – Growth – rate else Hake*H – Growth – rate*.1 hake – landings= (Hake*H – landrate – pre91)* ((C – Capacity – Index*Coastal – Fraction) + (OS – Capacity – Index*(1−Coastal – Fraction))) + (H – Illegal – fishing*Hake) + + (C – subsistance – incr*H – landrate – pre91*Hake) discard=Hake*H – discard – rate H – Pred – by – Penguins = Adult – Penguins*Hake*5.44e −9 H – Natural – deaths = Hake*H – Mortality – rate H – discard – rate = .05 H – Growth – rate= .2 H – Illegal – fishing = 0 H – landrate – post91 = .13*International – Demand H – landrate – pre91 = If TIMEB 1991 then .08*International – Demand else H – landrate – post91 H – Mortality – rate = .3 H – recruitment – rate = .23 Hake – CC=7e6 Hake – landing – data = GRAPH(TIME) (1979, 370860), (1980, 277350), (1981, 228729), (1982, 281862), (1983, 251100), (1984, 183233), (1985, 2S9334), (1986, 270558), (1987, 304078), (1988, 296026), (1989, 294333), (1990, 341042), (1991, 409250), (1992, 368998), (1993, 422195), (1994, 0.00) Hake – stock – data = GRAPH(TIME) (1983, 2.6e+06), (1984, 2.8e + 06), (1985, 3e+06), (1986, 2.7e+06), (1987, 2.5e+ 06), (1988, 2.2e + 06), (1989, 2.3e + 06), (1990, 2.2e+06), (1991, 2.5e+06), (1992, 0.00) Offshore – Fishery – Capital(t) = Offshore – Fishery –Capital(t − dt) + (OS – Investrnents −OS – Depreciation)*dt INIT Offshore – Fishery – Capital = OS – Price – of – Ships*239 OS – Investments = OS – Profits*OS – Savings – rate OS – Depreciation = Offshore – Fishery – Capital*OS – Depr – rate OS – Depr – rate=.025 OS – Fixed – costs = OS – Depreciation OS – Number – of – ships = Offshore – Fishery – Capital/OS – Price – of – Ships OS – Price – of – Ships = 4000000 OS – Profits=(OS – H – Income + OS – A – income)−OS – Fixed – costs−OS – Variable – costs OS – Savings – rate = .25 OS – Variable – costs = OS – Number – of – ships*50000 OS – ships – data= GRAPH(TIME) (1980, 239), (1981, 242), (1982, 246), (1983, 254), (1984, 260), (1985, 265), (1986, 276), (1987, 296), (1988, 322), (1989, 339), (1990, 346), (1991, 347), (1992, 355), (1993, 356), (1994, 0.00) .

M. 6an den Belt et al. / Ecological Modelling 110 (1998) 79–103

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PV – clean – up – costs(t) = PV – clean – up – costs(t−dt) + (clean – up – event − D – clean – up)*dt INIT PV – clean – up – costs = 0clean – up – event=Clean – up – costs D – clean – up=PV – clean – up – costs*Discount – rate PV – oil – income – lost(t) = PV – oil – income – lost(t−dt)+(Lost – oil−D – Oil)*dt INIT PV – oil – income – lost = 0 Lost – oil=Lost – oil – Dump D – Oil=PV – oil – income – lost*Discount – rate PV – treatment – costs(t) = PV – treatment – costs(t−dt)+(Treatment – cost−D – Atc)*dt INIT PV – treatment – costs = 0 Treatment – cost= IF Ballast – Treatment \ 0 THEN Lost – oil – Dump ELSE 0 D – Atc=PV – treatment – costs*Discount – rate Ballast – Dump=IF (Ballast – Treatment \0) THEN 0 ELSE (Dump – Event) Ballast – Treatment = 0 Dump – Event=PULSE (RANDOM(0.2300), RANDOM(.08,.25), RANDOM(.08,.25)) Lost – oil – Dump= 629825 Oil – Spill=If time = 1991 then 3e8 else (IF Willingness – to – Clean=0 THEN (PULSE (RANDOM(2300,600000000),Random(1996, 2020),RANDOM(1,15))) ELSE .2*(PULSE (RANDOM(2300,600000000),Random(1996, 2020),RANDOM(1,15)))) Willingness – to – Clean = 0 Clean – up – costs = GRAPH(Willingness – to – Clean*Oil – Spill) (0.00, 0.00), (6e + 07, 5.8e + 08), (1.2e+08, 1.2e+09), (1.8e+08, 1.4e+09), (2.4e+08, 1.6e+ 09), (3e + 08, 1.7e + 09), (3.6e+08, 1.8e+09), (4.2e+08, 1.9e+09), (4.8e+08, 2e+09), (5.4e + 08, 2e + 09), (6e+08, 2e+09) Adult – Penguins(t) = Adult – Penguins(t − dt) +(P – Recruitement − P – Natural – Mortality−P – Fishnet – Deaths−P – Oil – regular – deaths − P – Oil – spill – deaths)*dt INIT Adult – Penguins = 2000000 P – Recruitement = .14*Penguin – chicks*(Fish – Cons – per – P/(30 + Fish – Cons – per – P))*P – Random – influences P – Natural – Mortality = P – Mortality – rate*Adult – Penguins P – Fishnet – Deaths = Adult – Penguins*P – Fishnet – death – rate P – Oil – regular – deaths = (IF Ballast – Dump\ 350 AND Ballast – DumpB 2300 THEN (Adult – Penguins*.0114) ELSE 0) P – Oil – spill – deaths = Percent – oil – deaths*Adult – Penguins*P – Random – influences Penguin – chicks(t) = Penguin – chicks(t − dt) + (P – Net – Births−P – Recruitement −Chick – death)*dt INIT Penguin – chicks = 1500000 P – Net – Births=P – Birth – rate*(Adult

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M. 6an den Belt et al. / Ecological Modelling 110 (1998) 79–103

– Penguins*(Fish – Cons – per – P/(30 + Fish – Cons – per – P))) P – Recruitement = .14*Penguin – chicks*(Fish – Cons – per – P/(30 + Fish – Cons – per – P))*P – Random – influences Chick – death=.8*Penguin – chicks P – Birth – rate=.9 P – Fishnet – death – rate = .0071 P – Mortality – rate = .1 P – Random – influences = RANDOM(.8,1.2) Total – fish – stock = Anchovy + Hake Fish – Cons – per – P = GRAPH(Total – fish – stock) (0.00, 1.25), (1e + 06, 50.0), (2e + 06, 91.2), (3e+06, 116), (4e+06, 140), (5e+06, 158), (6e+06, 175), (7e + 06, 189), (8e+06, 201), (9e+06, 211), (1e+07, 218) Percent – oil – deaths = GRAPH(Oil – Spill) (0.00, 0.00), (6e + 07, 0.0068), (1.2e +08, 0.0153), (1.8e+08, 0.0272), (2.4e+08, 0.0425), (3e+08, 0.0612), (3.6e + 08, 0.0918), (4.2e+08, 0.134), (4.8e+08, 0.173), (5.4e+08, 0.227), (6e + 08, 0.34) Net – PV – Coastal – Fish(t) = Net – PV – Coastal – Fish(t−dt)+(Annual – P – C−Disc – C)*dt INIT Net – PV – Coastal – Fish = 0 Annual – P – C=C – Profits Disc – C=Net – PV – Coastal – Fish*Discount – rate Net – PV – Offshore – Fish(t) = Net – PV – Offshore – Fish(t−dt) + (Annual – P – OS − Disc – OS)*dt INIT Net – PV – Offshore – Fish = 0 Annual – P – OS=OS – Profits Disc – OS=Net – PV – Offshore – Fish*Discount – rate Net – PV – Tourism = PV – Market – Ecotourists +PV – Market – Other – Tourists+PV – NonMarket – ET Tot – Annual – T – value = Annual – ET – value+Annual – NM – ET+Annual – OT – Value Total – Net – PV=Net – PV – Tourism + Net – PV – Coastal – Fish+Net – PV – Offshore – Fish Discount – rate= 0.03 Squid – landing – data = GRAPH(TIME) (1979, 83579), (1980, 9111), (1981, 10622), (1982, 38885), (1983, 28687), (1984, 28969), (1985, 21541), (1986, 12555), (1987, 29610), (1988, 20777), (1989, 23106), (1990, 27603), (1991, 46313), (1992, 77468), (1993, 193690), (1994, 0.00) Total – Fish – Landing – data = GRAPH(TIME) (1979, 549385), (1980, 376865), (1981, 351856), (1982, 459602), (1983, 401771), (1984, 305494), (1985, 369874), (1986, 411766), (1987, 450657), (1988, 482609), (1989, 475529), (1990, 544941), (1991, 630006), (1992, 692110), (1993, 919503), (1994, 0.00)

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