ANALYSIS
Simulation of ecological and economic impacts of distant
water fleets on Namibian fisheries
Ussif Rashid Sumaila
a,b,*, Marcelo Vasconcellos
aaFisheries Centre,Uni6ersity of British Columbia,2204Main Mall,Vancou6er,BC V6T1Z4,Canada bChr.Michelsen Institute,Fantoft6egen38,5036Bergen,Norway
Received 5 May 1999; received in revised form 24 August 1999; accepted 25 August 1999
Abstract
This paper analyses the impact of the activities of distant water fleets on Namibia’s marine ecosystem through the use of simulation modeling. Mass-balance trophic models and economic valuation techniques are combined to simulate the ‘with’ and ‘without’ distant water fleet (DWF) scenarios for two decades prior to Namibia’s indepen-dence in 1990. Our modeling effort and assumptions lead to two key results. First, because of the activities of the DWFs in this period, Namibia inherited an ecosystem well below its productive capacity. In addition, the ecosystem seems to have been altered by heavy fishing on the three main exploited species (hake, horse mackerel and sardine). Second, fishing by DWFs appears to have led to a loss of 50% of the economic rent that could have been obtained
by Namibia from the three key species from 1970 to 1990. The reason for the latter result is that,ceteris paribus, in the absence of the DWFs, the domestic fleet would have benefited from exploiting a much healthier stock at lower costs. © 2000 Elsevier Science B.V. All rights reserved.
Keywords:Namibia; Fisheries; Ecosystem; Distant water fleet; Hake; Horse mackerel; Sardine
www.elsevier.com/locate/ecolecon
1. Introduction
The objective of this paper is to assess the impact of fishing by distant water fleets (DWFs) on the Namibian marine ecosystem and the eco-nomics of Namibia’s fisheries using an ecosystem simulation model. Currently, DWFs are defined
as those operating outside the exclusive economic zones (EEZs) of their countries of origin. The impact of fishing activities of DWFs on ecosys-tems around the world is just beginning to be comprehended. For instance, packs of eastern block factory trawlers caught tons of fish in what was then international waters. Through their ac-tivities, they destroyed long-lived sponge forests that harbored juvenile snapper and groupers off Australia, and shifted the ecological balance to-* Corresponding author.
wards more volatile pelagic fish in marine ecosys-tems off the west coasts of North and Latin America (Bonfil et al., 1998). This paper is an-other effort at helping understand the impact of DWFs on the Benguela upwelling ecosystem, one of the world’s most productive ecosystems.
Namibia’s coastline borders the highly produc-tive northern Benguela current system. The Benguela system is dominated by pelagic fishes,
mainly sardine (Sardinops ocellatus), anchovy
(Engraulis capensis) and horse mackerel (Trachu -rus capensis). The demersal ecosystem is
domi-nated by the valuable stocks of hakes (Merluccius
capensis, M. paradoxus and M. pollis). The food web off the Namibian coast is represented mainly
by seals as the top predators. Hakes, squid (Om
-mastrephidae), snoek (Thysites atun), and chub
mackerel (Scomber japonicus) are the piscivorous
species. Pelagic preys include horse mackerel,
sar-dine and anchovy, while lightfish (Maurolicus
spp), lanternfish (Lampanyctodes hectoris) and
goby (Gobiidae) are the main demersal prey
(Shel-ton, 1992).
Distant water fleets started operating in Namib-ian waters in the early 1960s. Fleets from the former USSR and Spain arrived in 1964; followed by Japan, Bulgaria and Israel in 1965; Belgium, and Germany in 1966; France in 1967; Cuba in 1969; Romania and Portugal in 1970; Poland in 1972; Italy in 1974; Iraq in 1979; Taiwan in 1981; and South Korea in 1982 (Anon., 1994). With the announcement of the EEZ regime by the indepen-dent government in 1990, there was a drop of more than 90% in the number of unlicensed for-eign vessels fishing in the area (WWF, 1998).
Dynamic simulations were performed to cap-ture the ecological impacts of the activities of DWFs in the Namibian Exclusive Economic Zone between 1970 and 1990. These impacts were then valued to give an indication of their economic effects. Scenarios of the Namibian ecosystem were developed using information on the catches of hake, horse mackerel and sardine during this pe-riod. Based on these scenarios, the following ques-tions were addressed: Given the harvest of hake, horse mackerel, and sardine in the years prior to independence, what were the impacts of DWFs on the biomass of the major species in the ecosystem?
How did DWF activities impact the biomasses and potential catches of the major commercial stocks off Namibia? How might these impacts on catch levels translate into economic values?
2. Material and methods
Simulations were carried out using the dynamic ecosystem model, Ecosim (Walters et al., 1997). This modeling framework is chosen because it has a number of advantages over other existing ecosystem or multispecies modeling approaches, such as the multispecies VPA (Sparre, 1991), biomass dynamics models (Larkin and Gazey, 1982), and bio-energetic models (Kitchell et al., 1996). First, it includes all trophic levels in the analysis (from primary producers to top preda-tors) as opposed to focusing only on the commer-cially important fish species. Second, its emphasis on ecological relationships makes it intuitively simple and transparent, without requiring a high degree of expertise from the modeler. Finally, as a dynamic version of the Ecopath mass-balance models (Christensen and Pauly, 1992), Ecosim is capable of answering ‘what-if’ questions about policy and ecosystem changes that would cause shifts in the balance of trophic interactions (Wal-ters et al., 1997).
Data obtained from an Ecopath model off the Namibian marine ecosystem (Jarre-Teichman and Christensen, 1998) were used to obtain the parameters for the simulation. This model cap-tures the ecosystem condition from 1971 to 1977, when sardine was the dominant pelagic species and both sardine and hakes were under heavy fishing pressure from DWFs. Other exploited groups included in the model are anchovy, mack-erel, horse mackmack-erel, tunas, and other pelagic and demersal fish species. For a full description of the model the reader is referred to Jarre-Teichman and Christensen (1998).
i as affected directly by fishing and predation on
i, changes in food available to i, and indirectly
by fishing or predation on other groups with
which i interacts (Walters et al., 1997). Ecosim
relies on a system of differential equations for
each component i defined by
dBi
tion, which is dependent on biomass, B; M is
the natural mortality from causes other than predation; F is the fishing mortality and cij(BiBj) is the function used to predict consumption
rates from Bi to predators, Bj. Different
hy-potheses about ‘top-down’ and ‘bottom-up’ con-trol of trophic interactions can be tested by setting the maximum instantaneous mortality
rate that consumer j could ever exert on food
resource i (see Walters et al., 1997). Under
‘bot-tom-up’ control, prey-availability governs the productivity of predators, while under ‘top-down’ control the increase in the biomass of predators leads to cascade effects in the food web. It should be noted that the bottom-up control version of the model is the base case for the simulations. However, we tested the sensitiv-ity of our results to the type of control of trophic interactions by simulating fishing scenar-ios under both a ‘bottom-up’ control (maximum mortality four times the baseline value) and a ‘top-down’ control (maximum mortality 20 times the baseline value).
For the economic analysis, we took the catches and fishing efforts generated by Ecosim under different fishing scenarios, and applied unit prices for fish landed, the cost incurred in landing the fish, and the appropriate dis-count rate (see Angelsen and Sumaila, 1997). Price and cost data were obtained from infor-mation complied by the Ministry of Fisheries
and Marine Resources (MFMR, personal
communication). The net discounted economic rent that is achievable under the different scenarios was then computed. This in turn al-lowed us to determine the economic impacts of DWFs.
2.1. Background information to scenarios
During the period under study, there was little or no surveillance of most fishing operations in Namibian waters, hence there was a massive as-sault on the fishery resources of Namibia during this period (Anon., 1994). For example, the offi-cial statistics, which are suspected of being on the low side, show that 1.4 million tons of sardine were caught in 1968. Before this massive catch, pre-1968 catches are reported to have been be-tween 100 000 and 600 000 tons, most of it taken by DWFs.
The massive, almost uncontrolled exploitation of Namibian hake started in 1964 and reached a peak in 1972, when 800 000 tons of hake were reported to have been harvested. It is believed by many that the catch was considerably higher. The reported catches were lower between 1972 and
1980 at 150 000 tons. Catches improved again
to around 400 000 tons in 1985, and then declined again until 1991 when Namibia took full control of its resources for the first time. Again, most of these catches were taken by DWFs. In fact, it is reported that up until 1985, 99% of hake catch
was landed by DWFs. From 1985 to 1990, 90%
was still landed by DWFs (Anon., 1994).
Horse mackerel was also heavily targeted by DWFs active in Namibia’s EEZ before indepen-dence. Annual catches were seldom below 300 000 tons, with the peak of 570 000 tons landed in 1982, according to the statistics.
2.2. The scenarios
2.2.1. The ‘with’ DWF scenario
Based on the background information outlined above, we assumed that on average 425 000 tons of sardine was harvested annually in the 1970s and 1980s. The equivalent numbers for hake and horse mackerel are 400 000 and 500 000 tons, re-spectively. Furthermore, we assumed that during
this period 90% of these catches were taken by
simulations were carried out using fishing efforts that give the DWFs 90% of the assumed catch of hake, horse mackerel, and sardine. Similarly, fishing efforts that will ensure the domestic fleet 10% of the catch are applied.
2.2.2. The ‘without’ DWF Scenario
The model was re-run, but this time with only the domestic fleet operating with the same fishing effort as in the ‘with’ scenario. That is, we elimi-nated the DWF completely. From the runs we determined the state of the stock, the catch poten-tial of the species in the habitat, and the potenpoten-tial
discounted economic rents that might be
obtained.
Simulations were run for twenty years, i.e. until 1990 when Namibia attained independence. Our aim in simulating the ‘with’ and ‘without’ DWF fishing scenarios is to isolate the impact of the high harvest levels in the ‘with’ case on the health of the major species in the ecosystem. In addition, this helped to determine the impacts on the poten-tial catches and discounted economic rents.
3. Results
3.1. Ecological impacts
Tables 1 and 2 show the results of simulations under a bottom-up control of trophic interactions (the base case). The average standing biomass of hake in the ‘with’ scenario is only 51% of that in the ‘without’ scenario. For sardine and horse
mackerel, this is 68 and 61%, respectively. For
the ecosystem as a whole, the effect of DWF activities was to reduce the potential standing
biomass of fish by 16%. The equivalent
per-centages for the top-down control are 54, 86 and 12% for hake, sardine and horse mackerel, respec-tively. In this case the potential standing biomass for the whole ecosystem is reduced by 29%. In general, the qualitative results from the two trophic control hypotheses are in agreement.
There are two inter-related reasons for the lower impact of DWF activities on the whole ecosystem. First, our analysis assumes that only hake, sardine and horse mackerel are targeted by
Table 1
Ecological and economic results in the ‘with’ DWF scenarioa Economic Rent
Anchovy 0.232 2.49 22.45
189.69 Sardines 1.646 0.473 21.07
12.29 Mackerel 0.107 0.026 1.37
13.55 121.91 Horse mack- 0.806 0.258
erel
Other demer- 0.210 0.004 0.63 5.67 sals
112.66
5.799 1.372 1013.93
Total
aBiomass and catch in million tons, economic rent in mil-lion Namibia dollars (N$ approx. US$0.15 as of December 1998).
the DWF, so the decreases in biomass of other commercially targeted species are not accounted for. Second, Ecosim accounts for the indirect trophic effects of release from predation and in-crease in competition for resources in the food web following fishing impact. Hence, the deple-tion of hake, horse mackerel and sardine is bal-anced in the food web by an increase in the biomass of preys and competitors (e.g. anchovy,
Table 2
Ecological and economic results in the ‘without’ DWF sce-narioa
Economic rent Catch
Biomass
Domestic DWF Anchovy 0.380 0.125 13.43 0
2.409 0.069
Sardines 30.95 0
0.039 0.010
Mackerel 5.06 0
1.320 0.410
Horse mackerel 21.64 0
0
Other pelagics 0.439 0.54 0
Hake 2.094 0.069 137.53 0
0.191 0.004 5.87
Other demersals 0
6.878
Total 0.329 233.02 0
mackerel, other pelagic and small demersal fish). The converse is predicted with the reduction in fishing pressure on these three species in the ‘with-out’ DWF scenario. In fact, after the heavy ex-ploitation by DWFs during the 1960s and 1970s, the sardine fishery off Namibia declined and was partially replaced by a fishery for anchovy, which is now one of the most abundant pelagic species in the ecosystem.
To further illustrate the effect of DWFs on the future potential of the stocks, Fig. 1 shows the stock profiles for sardine, hake and horse mack-erel in the ‘with’ and ‘without’ scenarios and under two contrasting trophic control hypotheses, i.e. bottom-up and top-down. Clearly, the model predicts that much healthier stocks of sardine and hake would have been left over at independence in the ‘without’ than in the ‘with’ scenarios. On the
other hand, the stock of horse mackerel does not benefit from the release in fishing pressure, but also declines in the absence of DWFs. This de-crease in biomass is closely related to the inde-crease in the biomass of hake, which is an important predator of horse mackerel in the model (Jarre-Teichman and Christensen, 1998). Simulation of fishing scenarios under a top-down control pro-duces more radical changes in biomass. A top-down control generates a larger impact of predators on preys resulting in a decrease in the exploitation level supported by the stocks (e.g. sardine collapse after 10 years with DWFs), and an intensification of cascade effects such as the hake-horse mackerel interaction described above. However, the choice of which type of trophic control to apply does not change the overall qualitative conclusions of the simulations.
Fig. 2. Plots of the annual discounted economic rent to the DWF (labeled ow DWF) and Namibia (labeled ow Nam) in the ‘with’ case, and to Namibia in the ‘without’ case (labeled No DWF) from 1970 to 1989.
The above results on the biomass are reflected in the harvest levels (see Tables 1 and 2), where substantially higher catches of hake, sardine and horse mackerel are taken in the ‘with’ than in the ‘without’ DWF scenarios.
3.2. Economic impacts
Tables 1 and 2 report the discounted economic rent obtained from harvesting the resources in the Namibian ecosystem in the ‘with’ and ‘without’ scenarios, for the bottom-up model. In the ‘with’ scenario, the DWFs make on average 1014 mil-lion Namibian dollars (N$) annually while the domestic fleet makes only about 113 million N$. In the ‘without’ DWF scenario, the domestic fleet makes 233 million N$. This part of the analysis reveals that the presence of DWFs in Namibian
waters led to a loss of 50% of what Namibia
would have earned in their absence. This gain is derived from the fact that in the absence of the DWFs, the domestic fleet benefits from exploiting much more abundant stocks of fish at lower costs.
Figs. 2 and 3 plot the discounted annual eco-nomic rents to the DWF and Namibia in the ‘with’ and ‘without’ DWF scenarios. Fig. 2 shows that the DWFs take the lion’s share of the eco-nomic benefits throughout the period. In Fig. 3, only the yearly payoffs to Namibia in the ‘with’ and ‘without’ scenarios are plotted to highlight the impact of DWFs. This figure shows that Namibia does consistently better in the ‘without’ scenario, even with the same amount of fishing effort as in the ‘with’ scenario. It is worth men-tioning here that the decline in the annual eco-nomic rent seen in the figures from 1970 to 1990 is due to discounting.
4. Discussion and conclusion
We carried out a simple ex post analysis that shows that, (i) because of the over-exploitation by DWFs before 1990, Namibia inherited an altered
ecosystem whose production potential was
Fig. 3. Plots of the annual discounted economic rent to Namibia in the ‘with’ (labeled ow Nam), and ‘without’ (labeled No DWF) scenarios against years (1970 – 1989).
sardine stocks the reduction ranged between 32 and 49% of the biomass potential; (ii) intense fishing by DWFs may have accounted for a sig-nificant shift in species dominance in the pelagic environment between sardine and anchovy. With the demise of the sardine stock, anchovy catches in the Benguela Current system grew, reaching
600 000 tons by 1974 and peaking at nearly 1
million tons in 1987 (Bakun, 1996). However, the extent to which those fisheries have contributed to the observed shift in species dominance is still a matter for debate (Lluch-Belda et al., 1992; Steele, 1996); (iii) had Namibia employed the same fishing effort it used in the ‘with’ DWF scenario in their absence, the economic rent to the country would have doubled.
Distant water fleet activities have been cited as one of the reasons for the buildup of excessive effort and increasing stock exploitation in the years leading up to the extended fishery jurisdic-tion (OECD, 1997). This work shows that ex-ploitation of Namibian fisheries during the two
decades to 1990 by the DWFs is a good example of the exertion of excess effort, shifts in species
dominance, and consequent stock
over-exploitation.
Acknowledgements
We wish to thank the World Wildlife Fund International (WWF) for commissioning the pro-ject from which this paper has developed. Also, we thank the UBC WWF project team, including Daniel Pauly, Tony Pitcher, Nigel Haggan, Gor-don Munro, Ramon Bonfil, and Dave Preikshot for reading and commenting on earlier drafts of the paper. Sumaila acknowledges the support of the Research Council of Norway, the Developing Countries Fisheries Research Programme.
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