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FISH and , 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. V6T 1Z4. 1Centre of Ecosystem Management, Edith Cowan University, Joondalup,

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 University of British of MPAs of various sizes was simulated, and changes in 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 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 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

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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 ˆ f B †ÀMo B À F B À Q 1† T i i i i i i ij Ri ˆ pi : 3† jˆ1 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 jˆ1 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 ˆ 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. ; 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 ,' 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

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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. 1f). Discussion There have been several approaches used to determine the optimum MPA size for exploited MPA size and exchange rates species; however, most have ignored trophic inter- As expected, the rate of biomass exchange assumed actions. Not surprisingly, optimal MPA size in this in simulations had an impact on biomass and catch context is related to intrinsic stock production rates, predictions with differing MPA size after a 10-year the prevailing harvest rates for the species involved period. The biomass of most groups showed a very (Mangel 1998) and the interchange of fishes peaked response at MPA sizes of 5±10% when a slow between the fished and unfished areas for all the exchange rate was assumed (Fig. 2a). With a more life history stages (Russ et al. 1992; Man et al. 1995;

Figure 1 Effect of proportion of biomass from ECOPATH model Thai10 included in a marine protected area (MPA) on total biomass (tonnes km72) values after (a) 1 year, (b) 3years and (c) 10 years and catch (tonnes km 72) after (d) 1 year, (e) 3 years and (f) 10 years using a biomass mobility T = 1. Selected ECOPATH model groups shown include: molluscs/jellyfish (solid line), crustaceans (dashed), small demersal fishes (dotted), and intermediate predators (dash-dot).

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Dynamic model for MPAs R Watson et al.

Figure 2 Effect of proportion of biomass from ECOPATH model Thai10 included in a marine protected area (MPA) on biomass (tonnes km72) values after 10 years using a biomass mobility, T of (a) 0.1, (b) 0.5 and (c) 2.0, and on catch (tonnes km72) using a T of (d) 0.1 (e), 0.5 and (f) 2.0. Selected ECOPATH model groups shown include: molluscs/jellyfish (solid line), crustaceans (dashed), small demersal fishes (dotted), and intermediate predators (dash-dot).

Gue nette and Pitcher 1999). The impact of MPAs It is worth noting that the movement of larvae or on whole community structures, not just single prerecruitment individuals can occur at different species, has now been demonstrated in natural rates than that of older individuals. In such systems (Russ and Alcala 1989) and should not be circumstances it would seem warranted to use the ignored. split-pool option of ECOSIM that allows different Models using an ecosystem approach to manage- developmental stages to be split but linked through ment need both temporal and spatial components. a delay-difference model structure (Walters et al. Most models have a temporal component but few 1997). Although this was originally conceived to have the facilities to partition and manipulate space. allow for developmental changes in diet it could also Indeed, Done and Reichelt (1998) pointed out that allow for changes in movement rates. defining appropriate spatial scales was a major Managers are often concerned about detecting problem in adapting ecosystem approaches to fish- changes caused by the introduction of an MPA, and eries' management problems. The approach used whether the initial indications will be favourable. here applies in a range of reserves as long as the This is especially so if they have to justify manage- management system includes a component that ment changes that have denied local fishers access excludes fishing. to traditional stocks. We have shown that although The case study presented here suggests that an the initial changes in biomass and catches of some MPA that protected 10±15% of the biomass of the groups (such as the mollusc/jellyfish of model Thai10 system from fishing would increase both Thai10) can be negative, this trend can continue biomass and catches of the groups followed over a for three years or more. By 10 years, there was an 10-year period. If the exchange rate was slowed, the increase for all groups after which there was no reserve area could be reduced, but if the exchange further change in biomass or catch. rate was faster then the reserve size should be Size is only one consideration in the selection of increased to protect 20% or more of the biomass. In an appropriate MPA. Edge effects can become practice, there is little information on rates of significant with small MPA sizes. The rapid ex- movement or biomass exchange rates for marine change of biomass between the MPA and the systems; the required information about immigra- can reduce biomass levels within the MPA by tion and emigration rates is often lacking. creating a gradient, with higher biomass levels

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Dynamic model for MPAs R Watson et al. found toward the centre of the MPA (Rakitin and Done, T.J. and Reichelt, R.E. (1998) Integrated coastal Kramer 1996). The strength of the biomass gradient zone and fisheries ecosystem management: generic goals is related to the exchange rate. At low exchange and performance indices. Ecological Applications rates it is possible to create a strong gradient 8(Suppl.), S110±S118. Gue nette, S. and Pitcher, T.J. (1999) An age-structured between biomass levels in the fishery and those in model showing the benefits of marine reserves in the centre of the MPA. If, however, the MPA is small controlling overexploitation. Fisheries Research 39, enough and/or the biomass exchange rate ( = mo- 295±303. bility) of species is great enough, then there can be Man, A., Law, R. and Polunin, N.V.C. (1995) Role of no protection from fishing. MPA shape can also marine reserves in recruitment to reef fisheries: a have a significant impact, if the shape of the MPA is metapopulation model. Biological Conservation 71, narrow and the net movement of animals is 197±204. perpendicular to its long axis then little protection Mangel, M. (1998) No-take areas for sustainability of will be afforded. In contrast, in situations where the harvested species and a conservation invariant for animals tend to move along the primary axis of the marine reserves. Ecology Letters 1, 87±90. MPA, staying within its boundaries for more of McNeill, S.E. (1994) The selection and design of marine protected areas: Australia as a case study. the time, then even narrow reserves would yield and Conservation 3, 586±605. results. This may describe the majority of coastal Pauly, D. and Christensen, V. (1993) Stratified models of reef reserves. large marine ecosystems: a general approach, and an Investigation of further aspects of establishing application to the South China . Large marine MPAs, such as their location and shape, will require ecosystems: stress, mitigation and sustainability (eds K. a true spatial model. Such a model could include Sherman, L.M. Alexander & B.D. Gold), pp. 148±174. data on richness, currents and other Am. Assoc. Advan. Sci., Washington DC, USA. factors affecting species distribution and inter- Polovina, J.J. (1984) Model of a ecosystem. I. The actions. In the mean while, our approach can be ECOPATH model and its application to French Frigate used to represent MPAs within the context of an Shoals. Coral Reefs 3, 1±11. ecological model, and to investigate the effects Rakitin, A. and Kramer, D.L. (1996) Effect of a on the distribution of coral reef fishes in of MPA size on the biomass and catch of the Barbados. Marine Ecology Progress Series 131, 97±113. groups represented. Russ, G.R. and Alcala, A.C. (1989) Effects of intense fishing pressure on an assemblage of coral reef fishes. Marine Acknowledgements Ecology Progress Series 56, 13±27. Russ, G.R., Alcala, A.C. and Cabanban, A.S. (1992) The authors are grateful for the support provided by Marine reserves and on coral D. Pauly, M. Vasconcellos and the UBC Fisheries reefs with preliminary modeling of the effects on yield Centre, and for the assistance of V. Christensen and per recruit. Proceedings of the Seventh International Coral S. Martell. Two referees provided valuable sugges- Reef Symposium, , 1992 (2). R.H. Richmond, ed. tions for improvement. University of Guam, Marine Laboratory, Mangilao, Guam. 978±985. Walters, C., Christensen, V. and Pauly, D. (1997) References Structuring dynamic models of exploited ecosystems

Christensen, V. and Pauly, D. (1992) ECOPATH II±a software from trophic mass-balance assessments. Reviews in Fish for balancing steady-state models and calculating net- Biology and Fisheries 7, 139±172. work characteristics. Ecological Modeling 61, 169±185.

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