Carl Walters, James H. Prescott, Richard McGarvey, and Jeremy Prince. Introduction

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1 Management options for the South Australian rok lobster (Jasus edwardsii) fishery: a ase study of o-operative assessment and poliy design by fishers and biologists Carl Walters, James H. Presott, Rihard MGarvey, and Jeremy Prine Abstrat: A modelling workshop proess was used to bring biologists and ommerial fishers together to develop a spatial model for population dynamis and harvest regulation of the South Australian rok lobster (Jasus edwardsii) fishery. The resulting model provided a redible reonstrution of how the spae, time, and size strutures of the stok have hanged over the history of the fishery, and offers a rih variety of regulatory poliy options for exploration of how the stok might have behaved (and might behave in the future) if managed differently.lnitial use of the model has been to test options for reduing risk of reruitment overfishing by inreasing spawning stok and egg prodution. A number of regulations ranging from inreased size limits to large spatial refuges ould aomplish this risk redution aim. One option is to simply redue the fishing season length dramatially. The model predits that short-term yield loss under this strategy would eventually be regained through inreased survival and higher ath rates of larger lobsters, and offers the eonomi advantage of greatly redued fishing osts. This poliy hypothesis an be tested in the field by a management experiment allowing fishers to see for themselves whether an area with a short season does indeed result in ath rates high enough to ompensate for fishing time loss. Resume : La formule de!'atelier de modelisation a ete utilisee pour reunir des biologistes et des peheurs ommeriaux pour elaborer un modele spatial dans le but d'examiner Ia dynamique des populations et les reglements regissant Ia reolte dans Ia peherie de langouste (Janus edwardsiz) du sud del' Australie. Le modele qui a resulte a fourni une reonstrution redible de Ia faon dont le stok a evolue du point de vue du temps, de l'espae et de sa struture de taille au ours de l'histoire de Ia peherie et offre une gamme tres rihe d'options du point de vue de Ia politique de reglementation permettant de se faire une idee du omportement passe et futur du stok s'il etait gere differemment. Le modele a ete utilise initialement pour verifier des options visant a reduire les risques de surpehe au niveau du rerutement en augmentant le stok de geniteurs et Ia prodution d'oeufs. Unertain nombre de mesures reglementaires variant de!'augmentation de Ia limite de taille a Ia reation de grandes reserves pourraient permettre d'atteindre et objetif de redution du risque. Une des options onsiste simplement a eourter de maniere substantielle Ia saison de pehe. Le modele prevoit que Ia perte de rendement a ourt terme deoulant de ette strategie finirait par etre reuperee griie a une survie arue et a des taux de apture plus eleves de langoustes de plus grande taille, sans ompter que ette strategie presente l'avantage eonomique de reduire onsiderablement les outs de Ia pehe. Cette hypothese de politique peut etre verifiee sur le terrain par une experiene de gestion permettant aux peheurs de onstater par eux-memes si une zone soumise a une ourte saison de pehe entralne effetivement des taux de apture suffisamment eleves pour ompenser Ia redution du temps de pehe. [Traduit par Ia Redation] Introdution Traditional approahes to fishery management have frequently reated deep divisions between fishers and regulatory agenies. C. Walters. Fisheries Centre, University of British Columbia, Vanouver, BC V6T lz4, Canada. J.H. Presott and R. MGarvey. South Australian Researh and Development Institute, South Australian Aquati Sienes Centre, PO Box 12, Henley Beah, SA 522, Australia. J. Prine. Biospheris Pty. Ltd., Perth, Australia. Corret itation: Walters, C., Presott, J.H., MGarvey, R., and Prine, J Management options for the South Australian rok lobster (Jasus edwardsii) fishery: a ase study of o-operative assessment and poliy design by fishers and biologists. In Proeedings of the North Paifi Symposium on Invertebrate Stok Assessment and Management. Edited by G.S. Jamieson and A. Campbell. Can. Spe. Publ. Fish. Aquat. Si pp Fishers take as muh as they an, show remarkable inventiveness at fmding ways around regulatory measures, and often provide only as muh information as absolutely required. Biologists try to make sense of this information for assessment, then regulators often adopt openly paternalisti attitudes when fored to disuss assessments and regulatory options with fishers. No one wins in these situations; assessments are dangerously unreliable; opportunities for better information gathering and o-operative experiments are missed; and regulatory systems are usually ineffetive due to both immediate enforement problems and tehnologial innovations to irumvent them. There has been an opportunity in the South Australian (SA) lobster fishery to begin moving toward a more o-operative approah to management, building upon the shared goal of both fishers and regulators to ensure a sustainable future for the fishery. The SA lobster fishery is relatively small, with liensing and regulation split into two zones (northern, 377

2 Proeedings of the North Paifi Symposium on Invertebrate Stok Assessment and Management Fig. 1. South Australian rok lobster marine fishing areas (as 1 a square bloks) and the two management zones. Southern zone is urrently managed through individual transferable quota (ITQ) system, northern zone through traditional effort ontrols (pot number, season length). South Australia Marine Fishing Areas 78 vessels; and southern, 187 vessels). Most fishers are relatively prosperous, well-eduated, and keenly aware of the biology and population dynamis of the lobster. Many have been in the fishery sine its major development in the early 196's, and have seen very substantial hanges in the stok sine then. They have also been part of a relatively dynami management system that has adjusted season length, redued pot numbers, and in the southern zone atively redued the number of vessels liensed. The liense buy-bak was paid for by the remaining fishers with the help of a state government loan, a poliy that has sine been widely ommended by fishers. Reently southern zone fishers opted to move to a quota management system, albeit one with the same effort regulations (pot limits, seasons, size limits) still in fore. As before, there has been muh onern about how this new regulatory measure will protet the spawning stok. Most fishers agree that fishing mortality rates are very high; perhaps dangerously high in the southern zone, as evidened by the sarity oflarger mature lobsters. Northern zone fishers have opted to remain with effort ontrols to manage their fishery. However, many northern zone fishers fear that the stok may be in danger of being overfished due to improved fishing tehnology. In 1994; fishers sought a means of ompensating for an antiipated inrease of 5% in effetive effort. A model was developed leading to the adoption of an inrease in minimum size and a series of8-9 day losures (MGarvey and Presott 1998). Conerned fishers in both zones helped initiate and have baked a researh program through finanial support and diret partiipation in a data olletion program in plae sine 1991 to provide data for a robust stok assessment. One aspet of the researh program that was speifially requested by fishers was a spatial model of the fishery with good visual output. Some fishers had seen models of an abalone fishery, AbaSim (Sluzanowski and Prine 1994), and the southern shark fishery, SharkSim (Sluzanowski 1994), and reognized their value for onveying omplex information in a way fishers ould understand. We intended to produe a model of the lobster fishery with suh an interfae but deided also to use the model developmentas a way to provide further motivation for o-operative information gathering. At the same time we tried to apitalize on fishers' knowledge of fators suh as distribution oflobster habitat, by involving both fishers and biologists o-operatively in a omputer model building proess. The expliit objetive ofbuilding the model was to provide a devie for synthesizing existing data into a useful format for poliy analysis. However, the more important objetives were to foster better ommuniation (trying to build a working simulation model requires preise definition of terms and use of information), to demonstrate to fishers exatly how data are used for biologial assessment, and hene why muh better data (and management experiments) are needed. We saw the model development as a level playing field for all stakeholders, with information and poliy analysis suggestions from fishers being potentially just as ritial as anything provided by professional biologists. We did not expet that the simulation model produed during this first o-operative effort would be partiularly useful for poliy analysis, but we did hope it would provide a onrete starting point for further ooperation and development. The ultimate aim would be to develop a poliy sreening tool that an deal not only with ob\lious poliies suh as pot redution and quotas, but also a wide variety of other regulatory tatis suh as size limits, fishing season pattern, et. Surprisingly, just a few days spent on model development led to both a very useful poliy sreening model, and to a possible win-win poliy option for both inreasing fishers' inomes and providing better protetion for the spawning stok. Here we desribe the model and poliy analysis results obtained to date, and speulate on how o-operative management will develop in the future of the fishery. We used an Adaptive Environmental Assessment (AEA) workshop proess (Holling 1978; Walters 1986) to struture involvement by biologists and fishers in the model development proess. AEA workshops proeed from definition of preisely what poliy options and performane indiators are to be evaluated, through a series of data analysis and submodel development sessions for developing the atual simulation ode, to gaming sessions where workshop partiipants "tesf' the model and its predited poliy options and suggest ways to improve it. In this ase, the workshop inluded twenty biologists with a range of experiene in lobster fisheries and population dynamis from aross Australia and New Zealand, and twenty fishers from various fishing ports and the two South Australian management zones. The model reviewed in the following setion thus represents the experiene (and onsensus) of a remarkably diverse partiipant/development group. Spatial model desription The same model aounting struture for spatial population and fishing effort dynamis was hosen as had been developed in a previous analysis of the Western Australian rok lobster fishery (Walters et al. 1993). Here we review only the main features of that struture, whih represents population and harvest proesses on a spatial grid of ells (1 o bloks in this ase) laid over the fishing grounds orresponding to statistial reporting bloks for the fishery (Fig. 1). Various poliy 378

3 Walters et al.: Management options for the South Australian rok lobster fishery parameters (liense aess, refuge losures) and biologial parameters (growth patterns, proportions of annual total reruitment, proportions of oean bottom of suitable habitat for lobsters) are allowed to vary aross ells. The ells are linked through three main proesses: {i) alloation of total fishing effort among ells, {iz) larval settlement pattern (alloation of total reruitment over spae), and (iii) movement oflobsters. A key initial part of the model development was to have experiened fishers provide rough maps ofbenthi habitat type within eah model spatial ell based on their past fishing suess, using the simple lassifiation: suitable for lobsters at all pot setting sites, sparse with small suitable pathes requiring areful pot loation, and not suitable for lobsters. This lassifiation allowed us to apture some known differenes among spatial ells in the effetive area for lobsters (and fishing); in partiular, muh of the northern zone is either unsuitable or sparse habitat, while most of the southern zone ells have very high proportions of suitable habitat. All reruitment and fishing effort alulations for model ells were made relative to the estimated suitable habitat rather than ell size; thus in some ells with relatively little habitat, even a low total fishing effort an generate high simulated fishing mortality rate, while muh higher efforts are needed to generate similar fishing rates in a ell with muh good habitat. The lobster subpopulation in eah spatial ell is represented in terms oflength (rather than age) struture, with the number of lobsters having 82 mm arapae length and larger divided into 8-mm (typial moult inrement) ategories.ln eah model ell, growth is represented by size-speifi tables of moult frequenies (the proportion of animals moulting in eah season/size ategory is then moved to the next larger size ategory). Reruitment to the smallest ategory (82-9 mm) is alulated from simulated puerulus settlement 3-4 years earlier (using a Beverton-Holt stok-reruit relationship between egg prodution and total settlement). The Beverton-Holt reruitment relationship was inluded in the simulation to represent the possibility of reruitment overfishing should the population egg prodution be redued suffiiently. The phyllosoma larvae spend a year or more in a pelagi phase in the open oean, potentially traversing distanes of perhaps 1 km based on typial urrent speeds in these waters of the Southern Oean south of the Great Australian Bight. We presume that this preludes development of loal subpopulation struture, so that total reruitment for eah simulated year is alulated as a grand pool of settling puerulus dependent on anteedent egg prodution throughout South Australia. But we soon found that no hypothesis or model involving delining reruitment as the fishery developed would predit the observed pattern of athes and relative abundane (as indiated by ath per unit effort, CPUE); this suggests that reruitment has been relatively stable sine the early 197's. The reruitment relationship is left in the model as a funtional form with parameter values set so that simulated reruitment is impaired only if egg prodution is redued substantially from urrent (early 199's) levels. This is not a serious limitation of the model, sine there was a very lear onsensus among biologists and fishers that they were only interested in exploring "safer" poliy options involving regulations to inrease egg prodution and hene move away from that unertain point on the reruitment relationship where reruitment begins to fail. For survival, growth, and harvest alulations, eah simulated year is divided into 2-week time steps. Two fortnights in the middle of the moulting periods, summer and winter, are designated as the seasonal moulting times. Using a 2-week time step is of ourse not really neessary for the survivavgrowth alulation; its value is to allow model users to vary fishing season patterns and season length widely, and to allow more realisti representation of the annual fishery depletion and spatial effort movement proess. Disard mortality from undersized lobsters and females bearing spawn whih are returned to the water, as well as losses from illegal fishing and a small rereational setor, are also inorporated in the harvest submodel. Spatial variation in reruitment rate (proportions of total reruits settling in different spatial ells) appears to be ritial to the struture of the fishery. We notied that athes in most spatial ells have been stable for the past deade. This implies that annual reruitment rate per ell (or per unit suitable habitat within eah ell) an be estimated from the average ath and estimated yield per reruit (average reruitment in a near-equilibrium situation must be yield divided by yield per reruit). We used fishing effort, natural mortality, and growth estimates to estimate yield per reruit for eah ell. The athability oeffiient (fishing mortality rate per unit effort) needed for the fishing mortality part of the yield per reruit alulation was estimated by running the overall simulation model while varying the athability parameter and historial fishing effort, to fmd athability and total fishing mortality that would math hanges from early in the fishery to the present in observed length frequenies. The resulting reruitment (yield/yield per reruit) alulation is admittedly rude, but it provides at least a more realisti estimate of spatial variation in reruitment rate than would rude ath or ath-per-effort statistis alone. Cath-per-effort does not in fat vary muh over the whole fishing area, indiating that effort is attrated to areas of high lobster density and reruitment quikly enough to ause strong exploitation ompetition among fishers. We found a very lose relationship between reruitment rate estimated as above and annual fishing effort (averaged for ), apparently indiating that effort is strongly responsive to spatial variations in reruitment rate (Fig. 2). Unfortunately we annot be ertain that the strong relationship in Fig. 2 does in fat represent attration of fishing effort to areas where reruitment is onentrated. The observed pattern ould be produed in at least two other ways. First, reruitment ould be the same in all ells, but effort ould be distributed in some way related to fators like aess from port. Then if fishing mortality rate were in fat low in all ells, our reruitment alulation (ath divided by yield per reruit) would be dominated by ath variation due only to effort variation, with some spurious orretion in yield per reruit from inorretly assuming high fishing rates in some areas. The main evidene against this explanation is that length frequenies in areas with high effort indiate that fishing mortality rates are defmitely not low in suh areas. Seond, reruitment ould again be the same in all areas, but athability ould vary greatly so as to make the apparent or vulnerable stok look muh larger in some areas (and attrat more fishing to those areas). We see no way to rejet this explanation using data from the fishery; there ould indeed be substantial abundanes oflobsters that are for some reason "invisible" to the fishery, but it would be plainly unwise 379

4 Proeedings of the North Paifi Symposium on Invertebrate Stok Assessment and Management Fig. 2. Estimated relationship between reruitment rate per unit usable habitat and fishing effort for statistial subareas within the South Australian lobster fishery. Reruitment rate is estimated as observed average ath for eah statistial subarea divided by estimated yield per reruit for the subarea...:::- Q) "' <J) :5 t5 3 "' "" w , l' Southern zone IJ Northern zone l' ULJ :J ntjo o iooooo p.,-ij\"t 2 4 lj 6 8 Reruitment index (lobsters/km') 1 12 to ount on suh invisible animals as a soure of protetion against overfishing (egg and reruitment soure). The model uses the approah of Walters et al. (1993) and Allen and MGlade (1986), simulating the spatial redistribution of fishing effort eah biweekly time step aording to the desirability for vessels to fish in eah ell. This variable, the effort "attrativity" of eah ell, is diretly proportional to the expeted profitability from fishing there and is hypothesized in the model to be a funtion of spatial patterns in expeted fishing suess as measured by CPUE. For eah simulation fortnight, the model alulates expeted attrativity for eah ell as a weighted average of the CPUE experiened the previous fortnight that same year and the historial value of the previous year's CPUE for the ell that fortnight of the season, with eah preditor weighted equally. Effort is then alloated to eah ell aording to the attrativity proportion, the attrativity for the ell divided by the sum of expeted attrativities over all ells. Over a fishing season of several months, repeating this redistribution alulation results in the pattern shown in Fig. 2; high effort is attrated to areas with high reruitment early in the season where it drives CPUE down. Later, effort spreads out to ells with lower initial reruitment as the more attrative ells are depleted. This effort redistribution submodel is ritial for evaluating impats of a variety of poliy options, inluding spatial refuges that onentrate effort into remaining open areas and redutions in fishing season length that may redue the tendeny for effort to move into less attrative areas later. In addition to abundane, model effort re sponds to variations in prie, both through the season, and as it varies with the supply, taken as the ath in South Australia overall. Frations of larger lobsters migrating between the spatial ells will be obtained diretly from the results of a large markreapture study now being ompleted. Rates of migration in the model are assumed to be proportional to the density of animals in eah ell. The proportion of lobsters leaving a ell is also assumed to derease with inreasing lobster size, so that lobsters with 12+ mm arapae length are not moved in the model. Preliminary tagging data suggest that movements are prinipally offshore and in distane are generally less than the width of a model ell, so only movement to adjaent ells is simulated. As it turns out, varying the small proportions of lobsters moving between ells does not alter model poliy preditions, sine inreasing simulated movement simply auses simulated fishing effort to move as well. We were onerned about the movement parameters in initial workshop disussions, sine we had found in the West Australian model (W alters et al. 1993) that offshore movement rate is ritial to assessments of population egg prodution. Historially, offshore areas in Western Australia reeive muh less effort than inshore, providing a partial refuge for the spawning population. However, this is not the ase in South Australia, where fishers are apparently muh more willing to fish offshore than their Western Australian ounterparts. Model user interfae and gaming proedures The model is programmed to provide a series of spreadsheetlike interfaes for hanging model parameters and poliies, and a omplex visual display of referene data and simulation results as eah simulation or gaming trial proeeds. When we first presented this interfae, biologists were onerned that it would be too omplex for fishers to understand. In fat, fishers learned very quikly how to read the display sreen that resembles the instrument panel of a modem fishing vessel, where several display bloks eah show some relatively simple part of the results. The upper left area of the display shows oloroded maps of overall density hanges from year to year, for juvenile and adult (egg produing sizes) lobsters. The yearly simulated size distributions are ompared with reent data in two panels in the left enter of the sreen. The right side of the sreen has four panels showing time series plots of long-term hange in egg prodution, effort as pot lifts per year, ath, and CPUE outputs for both zones showing historial data and simulation time series. Simulation time begins in 195 (when the fishery started to develop rapidly), so that umulative effets over time of any biologial parameter hanges made by the model user are immediately evident in terms of how well the model mathes 45-year historial ath/effort and size distribution trends. This protool helps to avoid the risk of onfusing biologial parameter hanges made in one simulation run with hanges made during subsequent runs in a poliy evaluation session. The model an be stopped in any simulated year to introdue parameter hanges. Normally only poliy hanges would be made, then the model would be restarted so the effets of hange an be evaluated by omparing predited outomes to atual experiene and to previous model outputs simulating historial poliy. This method of omparing poliy options is easier to understand (and far more redible) for both biologists and fishers than the usual modelling approah of just simulating alternative futures. The model was deliberately run with a onstant athability hosen to math reent ath, CPUE estimates, and fishing mortality rate (as evidened by length frequeny) sine 198. Using this parameter value for early simulation years results in predited athes that are muh higher than reported for the 38

5 Walters et al.: Management options for the South Australian rok lobster fishery Fig. 3a. Predited ath under four alternative management senarios in the southern zone (SZ). Senarios plotted inlude the baseline model (i.e., the historial management simulation), immediate redution in fishing season length from seven to two months, gradual redution in season over eight years, inreased minimum size limit from 98 to 144 mm, and a refuge area of 1 square, i.e., one model ell in the zone , +Base SZ.._Redued season SZ *Gradual season redution SZ 4 g 3.<: 2 -Inreased size SZ -&Refuge SZ Fig. 3b. Predited ath under four alternative management senarios in the northern zone (NZ). Senarios plotted inlude the baseline model (i.e., the historial management simulation), immediate redution in fishing season length from seven to two months, gradual redution in season over eight years, inreased minimum size limit from 98 to 144 mm, and a refuge area of 1 square, i.e., one model ell in the zone , +Base NZ.._Redued season NZ +Gradual season redution NZ :;::;-.8.<: (.).6 -Inreased size NZ -&Refuge NZ southern zone and lower than reported for the northern zone. We believe that two fators ontributed to this result. Confliting results between the two zones are thought to be the result of the way that total effort was asribed to eah zone prior to their reation in However, we attribute most ofthe differene between reported and predited ath rates in the southern zone to the inrease in fishing power that has ourred over time. Basially, the model results with onstant athability indiate that the modem fishing fleet would have ahieved roughly three times the ath (and population impat) of its early ( ) ounterpart. We ould obviously vary the athability parameter by inluding an arbitrary time effet to improve the fit to historial data, but in this instane the predition-data disrepany was very easily understood by fishers and in fat appeared to make the whole model more redible to them. Options for reduing risk of reruitment overfishing Fishers partiipating in the AEA workshop were given an opportunity to ''play" with the model in a session where no biologists were present. They quikly tested a variety of surprisingly intrusive and potentially effetive options for reduing risk of reruitment overfishing by inreasing average annual egg prodution. Among these options were: (i) very large and immediate redution in fishing season lengths, (ii) very large but more gradually imposed redution in fishing season lengths, (iii) large inreases in legal minimum size limits, and (iv) establishment of permanent losed areas (egg prodution "refuges"). Many simpler and more modest options were also examined by fishers, and the history-referene simulation approah desribed above appeared to help them very quikly grasp why suh modest options would not likely have a signifiant effet. The most interesting option examined to date is the very simple one of drastially. reduing the season length, from seven to two or even one month. Fishers first tried this option as a perturbation test on the southern zone, to see what the model would do with an extreme poliy hange. They then brought this to the attention of the model development biologists and said that they had found a mistake in the ode. The model predited a dramati derease in ath in the years immediately after reduing the season, as expeted. However, ath slowly built bak to historial levels (Fig. 3a) over a period of eight years. We agreed that there must be an error, and had a franti workshop session trying to find it. We finally realized that there was no mistake, and we were looking at the transient dynamis for a very lassial predition in the yieldper-reruit theory of fishing: starting at zero, equilibrium yield inreases rapidly with fishing rate (F), then the relationship beomes very flat or slightly dereasing (provided reruitment overfishing is not a fator) over a wide range of higher F values. This undiminished ath rate is due primarily to inrease in yield-per-reruit sine the stok-reruitment relationship is effetively flat through the range of model egg prodution. One qualifier on this predition of a reovery in ath to near preseason redution levels with a large redution in effort is the assumption of no density dependene in growth or adult mortality, or of reruitment. The history-referene display interfae beame ritial in helping to explain this predition to fishers; we simply pointed out how the model CPUE and length-frequeny struture would reover under redued fishing time to values near what the fishery produed in early years when total fishing effort was muh lower. One unertainty that fishers raised was whether lobsters an survive to larger sizes today as they did historially when F was lower. To test this ritial assumption fishers suggested a planned experimental redution in fishing mortality in a few spatial areas. They also quikly found a poliy involving progressive redution in season length over about 1 years that would result in substantial inrease in population egg prodution with minor ath 381

6 Proeedings of the North Paifi Symposium on Invertebrate Stok Assessment and Management Fig. 4a. Effet of alternative southern zone poliies shown in Fig. 3a on total population egg prodution: omparison of the baseline output with immediate and gradual fishing season redution, inreased minimum size, and refuge area. 6 5 u :::J e 4 ". > > "' :;.. "' 3 2 Qj 1 : +Base SZ +Redued season SZ +Gradual season redution SZ -Inreased size SZ -&Refuge SZ Fig. 4b. Effet of alternative northern zone poliies shown in Fig. 3b on total population egg prodution: omparison of the baseline output with immediate and gradual fishing season redution, inreased minimum size, and refuge area. u :::J e " > 3 > Q) :;.. Q) Qj : 2 1 +Base, NZ Redued season NZ *-Gradual season redution NZ -Inreased size _NZ -&RefugeNZ redutions(of the order of 1-2% per year) along the way (Fig. 3a, gradual redution ase). Note in Fig. 3b that thereovery effet is not predited to our in the northern zone, where F is apparently muh lower in the first plae., The model predits large impats on egg prodution of losing even a few model grid ells (1 o square bloks) to fishing. Figures 4a and 4b show simulated egg prodution impats of losing one southern zone and one northern zone ell, with the northern zone losed ell in a high reruitment area west of Kangaroo Island. In the southern zone, this poliy has roughly the same simulated impat as drastially shortening the fishing season. However, this poliy disadvantages southern zone fishers by losing a larger perentage of their fishing area, and it intensifies ompetition within the remaining open areas thereby inreasing fishing mortality rate in these areas. Fishers suggested that instead of moving immediately to suh refuges, potential refuge areas ould instead be subjet initially to substantially redued fishing seasons. This staged approah to refuge area development would then also provide an experimental test of the preditions about season length redution. The deision to proeed to omplete losure of a refuge ould then be made on the basis of experimental evidene. Unfortunately, the model and data analysis of Fig. 2 predits that fishers will respond to any initial inrease in CPUE in the experimental areas by shifting effort to them, preventing stok size and CPUE from inreasing muh. Under regulation this shift ould presumably be restrained. Hene it appeared that the poliy was failing when, in fat, it would work if these effort shifts ould be restrained. Another poliy suggestion from fishers was to substantially shorten the fishing time allowed eah liense or quota holder, while allowing individual fishers to selet their own fishing "seasons." A similar poliy was adopted by northern zone fishers in the months following the workshop. Its effet is like a redued fishing season, but with less gear ompetition for available pot setting loations. It would also be muh like a poliy of greatly reduing the total number of liensed fishing pots, sine the number of pots fishing in any part of the overall season would be muh redued. The model does not keep trak of individual fishing patterns, but we did try simulations of major redutions in number of pots fishing for eah 2-week time step. A supplementary spreadsheet model was developed to test the onsequenes of fisher-hosen season redutions (MGarvey and Presott 1998) and was used to deide the lengths and months of losures in the 1994/1995 season. This option is very popular with fishers in the northern zone so it may be worth testing the onept further, if fishers are willing to ooperate in establishing and maintaining experimental areas where the annual number of pot lifts is drastially redued. With quota management established in the southern zone, it should be possible, in priniple, to establish a sequene of quotas over time that would ause the same ramping down in fishing mortality as would shortening fishing seasons. But implementation of this poliy has inherent risks when annual assessments of stok size are subjet to large errors. Quotas ould trigger a deline in stok abundane rather than an inrease if stok size were overestimated and quotas initially alloated were too large, beause an absolute level of removal is an inreasing fration as the stok delines. However, the southem zone is unusual in that the fishery has maintained the same effort ontrols, pot number limits, and seven-month season, that were in plae prior to quotas being introdued. Without effort ontrols, to be relatively sure of obtaining the same redution in risk of severe stok deline as a season redution poliy, quotas would have to be redued muh more, and for muh longer, than would athes under season redution. The dilemma of how to reonile quota management with need for diret and simple regulation of exploitation rate has not been resolved, and will likely be a matter of muh future disussion between biologists and fishers. Disussion We entered the modelling exerise thinking that a detailed and realisti spatial model would be needed to link long-term population dynamis onsiderations with an analysis of speifi regulatory options. Indeed, it was perhaps neessary to proeed with the analysis in detail so as to make it redible to biologists 382

7 Walters et al.: Management options for the South Australian rok lobster fishery and fishers alike. But in the end, the most important poliy findings, inluding redued season lengths and experimental tests of season length redution perhaps leading to refuge areas, involve only very simple preditions about the impat of redued fishing mortality rate on population size struture, CPUE, and feundity due to hanges in yield- and egg-perreruit. While it seems that we did more analysis than was really neessary, we view the overall findings as a very fortunate outome. Had the poliy exploration and testing unovered only a few speifi poliy alternatives whose effiay depended on partiular loal and highly unertain estimates of population parameters, the model would have been a muh less useful tool. We would have been left with the tired old omplaint that the data are inadequate and more researh is needed. During the model development and testing, we repeatedly found the most ritial data to be length-frequeny patterns sampled from the fishery. Beyond the obvious use ofthese data for quantitative assessment of fishing mortality and athability, we found them neessary in "redibility heks" on poliy alternatives. Our only real justifiation for model preditions that lobsters would on average inrease in size under redued fishing mortality is that, in fat, lobsters were larger when effort was lower. However, it did not take fishers long to find the basi flaw in this argument, and to reognize the strong assumption of stationarity implied by arguing that historial data are good preditors of the effets of future poliy hange. They noted that the differene in size struture ould be due to reent growth rates being lower or unusually high reruitment some years before the early samples were taken. They further noted that we annot reliably use northern/southern zone size frequeny omparisons for mortality assessment, beause the higher frequeny of large lobsters in the northern area, where effort is lower, ould be due simply to large areas in the far western regions and offshore where fishing in the past was rare, leaving substantial numbers of near virgin stok to be harvested now. In the end, we annot rejet these ounter arguments and ritiisms of the model assumptions by using only available data, or by ontinued monitoring. The arguments are instead perhaps the best ase we an make for the need to immediately establish at least some small, experimental refuge areas to provide referene or baseline information on how population size struture should look under redued fishing mortality. Somewhat surprisingly, there was strong support in priniple by fishers for setting up management experiments to diretly test for suh effets as shifts in abundane and size distributions under redued fishing effort. The same experiments ould also reveal if there are signifiant inreases in gear effiieny when fewer pots are in ompetition. Usually there is a tait assumption by fishers, and many biologists, that just gathering more data will somehow permit analyses to resolve key unertainties. But in this ase, most stakeholders reognized immediately that the ritial unertainties involve irumstanes that no longer our naturally in the fishery, and must be deliberately reated if poliy evaluations are to be rigorously tested. Laying the groundwork for a o-operative program to design and ondut real adaptive management experiments is perhaps the most important single ahievement of our efforts to date. We believe that our AEA workshop will serve as a stimulus for other suh workshops in fisheries. Partiipants were quik to appreiate how the data they olleted were used to develop an understanding of suh a omplex and dynami system. Partiipants were also able to use the model to test poliies almost as soon as they had aess to it beause of the model's relatively simple graphial interfae. Models with suh easy-touse and understand graphial output are relatively rare. This may be beause most sientists are able to gain suffiient understanding of model outputs using methods familiar to them suh as tables of results and stati graphs. While the biologists may understand the results fishers often do not. The power of this type of graphial interfae for ommuniating results was learly obvious to every biologist in attendane. Aknowledgments Motivation and organization for the workshop proess was provided by Phillip Sluzanowski; he passed away before the proess was atually arried out, and was deeply missed. Key analyses and model development assistane were provided by Norm Hall. Rob Lewis ontributed institutional support, statistis, and perspetive on the fishery history. We gratefully aknowledge the patiene and support of all the fishers and biologists who partiipated in the AEA workshop proess. Referenes Allen, P.M., and MGlade, J.M Dynamis of disovery and exploitation: the ase of the Sotian Shelf groundfish fisheries. Can. J. Fish. Aquat Si. 43: Holling, C.S. (Editor) Adaptive environmental assessment and management Wiley-lntersiene, Chihester, England. MGarvey, R., and Presott, J.H A model for assessing losure season management options in the South Austnilian rok lobster (Jasus edwardsi1) fishery. In Proeedings of the North Paifi Symposium on Invertebrate Stok Assessment and Management Edited by G.S. Jamieson and A. Campbell. Can. Spe. Publ. Fish. Aquat Si pp Sluzanowski, P.R.W SharkSim: raising industry awareness. Agri. Syst lnf Tehnol. 6(1): Sluzanowski, P.R.W., and Prine, J.D User interfae adds value to fisheries model- ABASIM. Agri. Syst Inf. Tehnol. 6(1): Walters, C.J Adaptive management of renewable resoures. MMillan Pub. Co., New York, N.Y. Walters, C.J., Hall, N., Brown, R., and Chubb, C Spatial model for the population dynamis and exploitation of the Western Australian rok lobster, Panulirus ygnus. Can. J. Fish. Aquat Si. 5:

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