What do fishermen think about the SLOSS debate*

Transcription

1 What do fishermen think about the SLOSS debate* Abstract: Mette Termansen a Alberto Ansuategi b Recently marine biologists and economists have shown increased interest in promoting marine protected areas as a possible insurance policy to help sustain marine ecosystems and possibly even over the long run increase harvest levels. Some interesting work has come out of expanding the spatial representation of traditional bioeconomic models by introducing a two-patch environment with one patch representing the fishing ground and the other the reserve. A particularly interesting paper following this approach is Sanchirico and Wilen (2001) (SW hereafter). SW analyze whether setting aside areas is likely to produce fishery benefits as well as conservation benefits, and under which conditions this would hold. In this paper we use an agent based model to replicate part of the analysis carried out by SW and extend it to analyze the effect of the spatial structure of MPAs in the results. Key words: fisheries, marine reserves, agent based models. JEL codes: Q22, Q28 (a) (b) Sustainability Research Institute, University of Leeds, Leeds LS2 9JT (UK). E- mail: m.termansen@see.leeds.ac.uk Departamento de Fundamentos del Análisis Económico I, Facultad de Ciencias Económicas y Empresariales, Bilbao (SPAIN). alberto.ansuategi@ehu.es * Preliminary version, not to be quoted 1

2 1. Introduction Marine protected areas (MPAs) have existed for centuries 1 but it is only in the last two decades that they have really emerged as innovative fisheries management tools. This is probably due to the fact that the World s oceans are under more pressure than ever before. According to a report to the Food and Agriculture Organization of the United Nations on the state of the World fisheries (FAO, 2003), 10 per cent of the major marine fish stocks or species groups for which information is available have become significantly exploited, 18 per cent are reported as overexploited and 47 per cent are fully exploited. Yet, only 0.5 per cent of the oceans are protected with almost all MPAs open to tourism and recreation and 90 per cent open to fishing (WWF, 2005). This reluctance to using marine reserves for fisheries management responds to the fact that policymakers worry that reducing fishing grounds will reduce catches, increase fishers variable costs associated with the choice of fishing location or even generate lower payoffs than if traditional input or output controls are used (Holland and Brazee, 1996). However, using MPAs are also predicted to produce a wide range of benefits such as spillover effects (Gell and Roberts, 2003), protection of ecosystems structure, function and integrity (Bohnsack, 1998), increase of aesthetic and recreational values (Bhat, 2003) and reduction of the probability of extinction (Grafton et al., 2005). Recently marine biologists and economists have shown increased interest in promoting marine protected areas (MPAs hereafter) as a possible insurance policy to help sustain marine ecosystems and possibly even over the long run increase harvest levels. Some interesting work has come out of expanding the spatial representation of traditional bioeconomic models by introducing a two-patch environment with one patch representing the fishing ground and the other the reserve. This is the approach taken, for example, in Conrad (1999), Tuck and Possingham (2000), Pezzey et al. (2000), Sanchirico and Wilen (2001) and Armstrong and Skonhoft (forthcoming). A particularly 1 Johannes (1978) describes how permanent reserves existed in Oceania generations before the arrival of Europeans. 2

3 interesting paper following this approach is Sanchirico and Wilen (2001) (SW hereafter). Most of the other deterministic bioeconomic models have not assumed fixed effort but analysed open access regimes. Bearing in mind that in open access equilibrium rents are fully dissipated, assessment of the economic success of establishing MPAs has not been based on rents but on harvest levels 2. Pezzey et al. (2000) and Sanchirico and Wilen (2001) show that, with density-dependent population dynamics, MPAs may generate win-win outcomes: increase biomass and increase harvest. In Pezzey et al. (2000) it is found that the win-win outcome associated to establishment of an MPA requires that the ratio of the stock to its carrying capacity is less than a half before the creation of the MPA. They also show that harvest maximising portion of MPA rises towards 50% as this ratio falls towards zero. Sanchirico and Wilen (2001) also centre their attention in identifying parameter configurations and ecological dispersion processes that give rise to win-win outcomes. They show that (i) MPAs in closed systems will not generate harvest benefits, (ii) in sink-source systems win-win outcomes may arise if we close the source, the dispersal rate for the source is not either too high or too low, and the source is relatively profitable, and (iii) in density dependent systems when the cost/price ratio in the reserve is low and the intrinsic growth rate is not too high, win-win outcomes will arise. Another important contribution of Sanchirico and Wilen (2001) is that their model incorporates a spatial dispersal component to the traditional open access model introduced by Gordon (1954) and Smith (1968). This spatial dispersal component is similar to the metapopulation depiction of biological dispersal and considers that fishermen choose location in a manner that eliminates spatial arbitrage opportunities. There are however important aspects of the design of MPAs that are not well represented in a two-patch system. Networks of MPAs are often advocated by marine biologists (Sala et al., 2002). This is not to say that the issues surrounding spatial optimal design of conservation areas have been resolved, as shown by the heated debate on the SLOSS (single large or several small) principles, which evolved around the attempt to answer the question whether one big reserve was better than many small, given an equal area of the total conservation area (Whittaker, 1998; Roberts et al., 2001). Nevertheless, the spatial layout of reserves is still believed to be an important 2 Sanchirico and Wilen (1996) also consider transitional rents. 3

4 determinant of conservation success and the two-patch models offer a poor representation of alternative spatial patterns of MPAs, as the connectivity of the protected areas is left out of the analysis. Sanchirico (2004) extend the two-patch analysis to a nine-patch analysis, using a spatially explicit bioeconomic model that allows analysis of connectivity of individual patches. It is concluded that the linkages between the patches are important not only for predicting the biological responses of area closures but also for predicting the response in fishing effort. The analysis also points out the importance of spatial heterogeneity for optimal design of MPAs. This has been emphasised by marine biologist as a very important issue for the success of MPAs in meeting conservation goals (Garcia-Charton and Perez-Ruzafa, 1999; Crowder et al., 2000). Relying on two-patch models precludes economist from contributing entirely successfully to the analysis as their models exclude one of the most important aspects by assumption. In addition, the bioeconomic models assume that spatial arbitrage is eliminated. Once analysing alternative spatial structures it may be far from obvious that it is an appropriate assumption to make. Attractiveness of individual fishing grounds may be dependent on the spatial layout of the protected areas and the relative distance from the harbour. It may also be a very strong assumption, that fishermen have complete information of the spatial pattern of profits for the entire sea. This paper aims to address the issue of optimal spatial structure of MPAs from the perspective of fishermen as well as conservationist. Therefore we aim to identify winwin solutions in line with the analysis of S and W. However, we go further in the analysis of alternative spatial layouts to answer the SLOSS debate from the perspective of the fishermen. We do this by modelling individual fishermen behaviour in an agent based model. Agent based computational economics is a newly developing field that tries to study economies modelled as evolving systems of autonomous interacting agents. In this context this modelling framework is particularly useful as it enable us to model agent interactions mediated by space. The paper is structured in four sections. After this brief introduction, section 2 describes the model. Section 3 describes the methodology used in the analysis. Finally, section 4 presents our main results. 4

5 2. The model 2.1. Environment The marine ecosystem is represented as a lattice of N sites, modelled as an n n grid Resource The marine resource is the spatially distributed stock of fish, x ij, where i and j refer to the location on the grid, i, j {1,..,n}. It is assumed that the growth and the dispersal of the fish stock is density dependent. The growth of the resource is logistic, with metapopulation depiction of biological dispersal Economic agents There are M economic agents and there is no demographic growth. At time 0 a certain proportion of economic agents are compulsorily enrolled as fishers and randomly distributed on the lattice; the rest are enrolled other economic activities. Fishing involves an opportunity cost: the rent obtained in other economic activities. At each time step fishers act as price-taking agents and choose the level of effort that maximises profits. If there are other fishers in the same location they assume that the total level of effort determines the total size of output, and individual fishers outputs are proportional to their effort. At each time step fishers evaluate the list of sites within their range of vision. The site selected for moving to is that for which expected profits are greatest Technology Catch is a function of biomass and effort exhibits decreasing marginal returns to both input factors (gear saturation and congestion are considered). 5

6 2.5. Institutional setting Fishers operate under open access. The policy instrument we will be considering is creation of marine protected areas (MPAs). The MPAs are excluded from fishing operations and it is assumed that fishermen comply fully with the regulations. We analyse a set of sizes and spatial distributions of MPAs. The spatial layouts are illustrated in Figure 1. 3 Methodology We analyse the harvest dynamics through simulations of behaviour of the fishermen and the interactions with the marine environment. This allows us to assess the impact of parameter values on our conclusions and determine under which conditions MPA may offer gains to fishermen. 3.1 Simulations The fishing ground is assumed to consist of 10,000 individual sites, located as grid. We test three different sizes of MPAs, covering 1%, 4% and 16% of the total fishing ground respectively, and three different spatial layouts. This gives the nine spatial layouts illustrated in Figure 1. The initial endowments of the resource are homogeneous across the lattice. The growth is density dependent and the function, F, is assumed concave, F(0) = F( K ) = 0, where K max is the maximum carrying capacity of the site. It is assumed that there are no biological differences in terms of growth across the marine system. The dispersal function, G, is assumed to be linear in the stock. The growth in fish biomass in individual locations can therefore be formulated as: max 6

7 4 x = F( x ) dx + dx + dx + dx + dx (1) ij ij ij i 1j i+ 1j ij 1 ij+ 1 n= 1 where the growth is calculated as: x F( xij ) = rxij 1 K ij max (2) The biological parameters used in the simulations are given in Table 1. The agents are assumed to have identical economic characteristics in terms costs and benefits of the fishing activities and alternative employment opportunities. The net benefits accruing to the m th agent from harvesting are modelled as: π = ph we (3) m m m where h m is the harvested fish biomass and p is the price of fish. The effort level is denoted e m and the unit cost of effort is w. The agents are also assumed to have equal fishing ability modelled through identical catch functions, dependent only on the stock of the fish on the location and the total effort employed. m β ij h = qx e α (4) The parameter α accounts for the effect of congestion, whereas β is the gear saturation coefficient. The economic parameters used in the simulations are given in Table 2. When a fisherman is alone in a location the effort level is assumed chosen to maximise short-term net benefits, π m. 7

8 e m β α pqx ij = w 1 1 α (5) When fishermen are competing for fish on the same location and b ij denotes the number of boats fishing at the location referenced by i and j, then the effort levels are derived as: e ij = b ( b + α 1) β 1 ij pqxij ij 1 1 α bij w (6) which are assumed divided between the individual fishermen fishing on the location so that each fisherman employs e / b units of effort. ij ij All simulations are performed over 15 years, simulating daily fishing activities. To avoid dependence of initial conditions in terms of number of agents and the resource stock, the outputs are given as the average values over the last year of the simulation. Simulations using varying initial conditions showed that levels of economic activity after initial fluctuations were independent of initial conditions. Each simulation is repeated 10 times to avoid sensitivity to specific random number generations. Figure 1 gives and example of the fluctuations in number of fishermen over a 15 year simulation Dependence on biological parameters Simulations were performed varying the biological parameters of the growth function. The intrinsic growth rate, r, was varied from to 0.007, and the dispersal parameter, d, was varied from 0.01 to 0.1. These simulations were carried out for all the MPA scenarios shown in Figure 1. In these simulations it is assumed that the fishermen have full information of the fish stock and can move with out costs to any location on the lattice. Fishermen move to the location with the highest expected profits. The number of fishermen is updated daily based on the daily economic performance. A new 8

9 fisherman enters the fishing ground when aggregated profits are positive and the worst performing fisherman leaves the fishing ground if he/she is incurring a loss Dependence on spatial vision The simulations performed for this analysis are identical to the simulations described in section 3.1, apart from the assumption on full information about the entire spatial distribution of the fish stocks and costless spatial movements. To analyse the importance of this assumption we vary the spatial range for which the individual fishermen have full information on fish stocks and other fishermen. For the area outside this spatial range fishermen are assumed to have no information. Simulations are carried out for the following spatial ranges {5, 15, 25, 35, 45}, measured in terms of the number of locations from the location of the boat. 4. Results 4.1. Existence of win-win situation The simulations varying the biological parameters show that certain conditions generate both conservation gains and gains for fishermen in form of increased harvest levels (Figure 3). For an MPA of 1%, the dispersed spatial distribution of the area (scenarios (b) and (c)) generates fishing gains for relatively low growth rate and high level of dispersal. One MPA of 1% (scenario (a)) does not generate a gain in harvesting levels, according to the simulations. MPAs covering 4% of the fishing ground generate increased levels of fishing activities for fewer combinations of biological parameters. Only very low growth and very high dispersal generates increased fishing activities for the intermediate dispersal scenario (scenario (e)). For the fully dispersed MPA scenario (scenario (f)) the parameter space for which a double payoff exists is also reduced. MPAs covering 16% of the fishing ground (scenario (g), (h) and (i)) are not generating win-win situations, according to the performed simulations Restriction of spatial vision 9

10 Restriction of the spatial range for which fishermen have full information and can move without costs in one time step reduce the number of fishermen involved in fishing activities but increase the average harvest levels (Figure 4). These findings do not seem to be altered for alternative size and arrangements of MPAs. Discussion and Conclusion 10

11 Figures Connectedness of MPA (a) (b) (c) Size of MPA (d) (e) (f) (g) (h) (i) Figure 1: Spatial layout of the modelled MPAs. 11

12 no of boat days Figure 2: Number of fishing boats employed in fishing activities. Regulatory situation (e, Figure 1) r = 0.005, b =

13 r r Spatial distribution of MPAs r r d d d (a) (b) (c) Size of MPAs (d) (e) (f) (g) (h) (i) Figure 3: Gain in harvest levels from introduction of MPAs. The letters (a) to (i) refer to the spatial layout of MPAs illustrated in Figure 1. 13

14 no of boats harvest no of boats harvest spatial vision Figure 4: Dependence of spatial vision, measured as the number of sites in the grid, on the equilibrium number of fishermen participating in fishing activities and their average harvest per day. 14

15 Tables Table 1: Biological parameters used in the simulations Parameter Value Carrying Capacity K max Intrinsic growth rate r Dispersal rate d

16 Table 2: Economic parameters used in the simulations Parameter Value Unit cost of effort w price of fish p catchability coefficient q 1.0 congestion coefficient α 0.8 gear saturation coefficient β 0.5 no of economic agents M 600 initial enrolement 20% 16

17 References Armstrong, C. and A. Skonhoft (forthcoming), Marine Reserves: A Bio-Economic Model with Asymmetric Density Dependent Migration, Ecological Economics. Axtell, R. (2000), Why Agents? On the Varied Motivations for Agent Computing in the Social Sciences, Center on Social and Economic Dynamics Working Paper no 17, The Brooking Institution, Washington DC. Conrad, J. M. (1999), The Bioeconomics of Marine Sanctuaries, Journal of Bioeconomics 1 (2), Pezzey, J.C.V., C.M. Roberts and B.T. Urdal (2000), A Simple Bioeconomic Model of a Marine Reserve, Ecological Economics 33, Sanchirico, J.N. and J.E. Wilen (2001), A Bioeconomic Model of Marine Reserve Creation, Journal of Environmental Economics and Management 42, Tuck, G.N. and H.P. Possingham (2000), Marine Protected Areas for Spatially Structured Exploited Stocks, Marine Ecology Progress Series 192,