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A Dynamic Mass-Balance Model for Marine Protected Areas

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A Dynamic Mass-Balance Model for Marine Protected Areas Paper 1 Disc FISH and FISHERIES, 2000, 1, 94±98 Short Communication A dynamic mass-balance model for marine protected areas R Watson, J Alder1 & C Walters Fisheries Centre, University of British Columbia, 2204 Main Mall, Vancouver, B.C. Canada V6T 1Z4. 1Centre of Ecosystem Management, Edith Cowan University, Joondalup, Australia Abstract Correspondence: R. Watson, A modified ECOSIM model was used to investigate the impact of establishing marine Fisheries Centre, protected areas (MPAs) in ecosystems defined by existing models. The impact ECOPATH University of British of MPAs of various sizes was simulated, and changes in biomass and catch over a Columbia, 2204 Main range of years observed. The response of biomass and catch to MPA size depended on Mall, Vancouver, B.C. Ahed the time period examined. For some ecosystem groups, the initial response was Canada V6T 1Z4 Bhed negative, but for all groups there were increases after 10 years. The greater the E-mail: regwatson@ Ched fisheries.com biomass exchange rate across the MPA boundary, the larger the MPA required to Dhed increase biomass levels. Within the range of exchange rates simulated, the maximum Ref marker increases in catch and overall biomass levels were reached when 20% of the system Received 15 July 1999 Fig marker was protected. Accepted 23 Nov 1999 Table marker Ref end ecological models, ECOPATH, ECOSIM, fisheries, marine reserves; protected Keywords Ref start areas. systems is the ECOPATH mass-balance approach Introduction introduced by Polovina (1984) and expanded by One approach used widely to manage complex Christensen and Pauly (1992). ECOPATH uses as- marine systems is the establishment of no-take sumed mass-balance (production/consumption) re- marine reserves or marine protected areas (MPAs), lationships to describe equilibrium trophic flux which over the last 30 years have been established patterns. Such models were of limited interest to throughout the world. McNeill (1994) examined the most fisheries managers until recently when Wal- selection and design of MPAs in Australia and found ters et al. (1997) introduced ECOSIM, which provided that few studies had tested hypotheses concerning a means of using ECOPATH assessments in a dynamic the effects of location, size or shape. These are way. ECOSIM allows scientists and managers to vary especially important when proposed MPAs are previously static variables such as fishing mortality expected to contribute to sustainable fisheries rates, and to predict transient and equilibrium management, and when their establishment will changes in catch rates as well as biomass. This tool reduce access by traditional resource users. provides managers with the means to investigate Many scientists and managers now accept that the potential effects of various management strate- an ecosystem approach is needed to evaluate the gies on groups described in ECOPATH models, includ- establishment of marine or fish reserves (Done and ing those of commercial significance. Reichelt 1998). Management at the ecosystem level While ECOSIM offers some facilities to mangers, it requires information on the resources and their does not provide a means of describing the spatial interdependencies, including potential trophic inter- relationships of biomass and fishing mortalities, actions. Several tools are available to create which are required to examine the potential impacts ecosystem models; the most widely used for marine of MPAs. This paper describes a simple modification 94 #2000 Blackwell Science Ltd Paper 1 Disc Dynamic model for MPAs R Watson et al. to ECOSIM that allows the biomass of ECOPATH groups mortality rates were held constant, simulating a to be partitioned into two portions with exchange displacement of the total fishing effort, not a processes operating between them. One portion is reduction as the MPA size was increased. Catches assumed to be within an MPA, and is subject to reported are in biomass units per unit area. lower levels of fishing mortality (at least for some At equilibrium the change in biomass outside an i groups) than the other portion. Our modification to MPA, dBout /dt, of any ECOPATH group i (regardless of ECOSIM allowed us to observe the transient impact of the MPA's size) is balanced by the change in i MPA `size' and of biomass exchange rates on the biomass within the MPA, dBin /dt, therefore biomass and catches of ECOPATH groups. i i i i i i dBout /dt=Rout Bout =dBin /dt=Rin Bin (2) Methods and materials where Rout and Rin are the proportion of biomass Simulations were performed using a modified transferred from outside and inside the MPA, respectively. Because the movement of biomass version of ECOSIM (Walters et al. 1997), which is based on the equation: out of the MPA is proportional to its perimeter (roughly the square root of its area), Xn dB =dt f B ÀMo B À F B À Q 1 T i i i i i i ij Ri pi : 3 j1 out S where Bi represents the biomass of ECOPATH group i, Here, S is the proportion of the modelled area that is Mo is the natural mortality rate of group i, F is the i i within an MPA, and Ti is a user-supplied exchange average fishing mortality rate of group i, and Qij is rate for group i with units time71. The biomass of the consumption of group i by group j predators; group i within the MPA Pn f(Bi)=gij Qij represents the growth rate as a j1 i i B in=S B total and that outside the MPA function of Bi, where gij is the growth efficiency of group i consuming group j. ECOSIM used the mass- i i Bout =(1-S) Btotal , therefore at equilibrium balanced ecosystem models developed using ECOPATH i i i i 2.0 (Christensen and Pauly 1992). Rout SBtotal =Rin (1±S) B total, which means Modifications were made to the program ECOSIM S described above to allow all ECOPATH biomass pools R R : 4 in out 1 À S (Bi) to be split in a quasi-spatial way. The biomass of each ECOPATH group was divided into two subpools ECOPATH models representing a variety of marine that were treated as if they were spatially separated environments have now been published. We were and subject to different fishing patterns. This most interested in heavily exploited systems where allowed for one subpool to be treated as if it were the impact of protecting part of the biomass from in an MPA and not subject to fishing, while the fishing through the use of an MPA would be most biomass of the other subpool was subject to fishing obvious. We chose an ECOPATH model (Thai10) that mortality. The rate of exchange or transfer of represents shallow water areas (0±10 m) of the Gulf biomass between the two subpools could be set of Thailand (Model A, Pauly and Christensen 1993). independently. At the start of each simulation, the Although all model groups were included in the biomass of each group was distributed between the simulations, for simplicity the results from only four areas in proportion to the relative size of the representative fished groups are presented. protected area; for example, when 5% of the modelled area was an MPA, then 5% of the biomass described in the ECOPATH model for each pool, such as Results `Large Demersal Fishes,' was initially placed in the MPA size and simulated period MPA subpool, and the remaining biomass was placed in the non-MPA subpool and subject to The response of the Thai10 ECOPATH model to fishing mortality. differing MPA sizes for a 10-year period was All ECOPATH groups were protected from fishing simulated using a moderate biomass exchange rate within the MPA. The biomass reported is the total (Fig. 1). After one year, biomass for three of the four from the MPA and non-MPA areas. Fishing ECOPATH groups followed showed slow increases with #2000 Blackwell Science Ltd, FISH and FISHERIES, 1, 94±98 95 Paper 1 Disc Dynamic model for MPAs R Watson et al. MPA size (the crustacean group responded nega- rapid exchange rate the response of biomass was less tively; Fig. 1a). By year three, the biomass of peaked, and occurred at larger MPA sizes (10±15%; another group (molluscs/jellyfish) was responding Fig. 2b). This trend continued, and a rapid biomass negatively. The response of biomass ECOPATH groups exchange rate required even larger MPAs to reach to MPA size, however, differed depending on the the biomass maxima of slower rates, after which time period examined, and after 10 years the biomass continued to increase slowly (Fig. 2c). simulations predicted that there would be a marked Catches generally followed the biomass pattern of increase in biomass of most groups. This was response (Fig. 2d±f). Catches of molluscs/jellyfish particularly evident for crustacean and mollusc/ continued to peak at smaller MPA sizes than did jellyfish, two groups that responded negatively in other groups regardless of the exchange rate used. simulations of shorter time periods (Fig. 1c). With The peaked responses of slow exchange rates MPA sizes exceeding 15% there were no further became more gradual and occurred at larger MPA increases in predicted biomass. sizes. Unlike biomass, the catches of most groups Changes in catch rates with the proportion of remained peaked for all exchange rates examined, MPA size largely paralleled those predicted for predicting that even at rapid exchange rates larger biomass (Fig. 1d±f). Initially (after one year), the MPAs would not allow fishers sufficient access to catch of all groups (except intermediate predators) enhanced biomass levels to maintain catches. was restricted by MPA size. By year 10, catches of all groups increased with MPA size, reaching a maximum with MPAs of 10±15% in size (Fig.
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