Marine and Coastal Fisheries

—The thrust of this paper is that decision rules for the management of data-poor fisheries cannot be based on expert judgment alone. Such rules need to specifically link management responses to the values of the indicators available for the fishery and their trends. Prior simulation testing is needed to confirm that the application of any rules suggested is likely to achieve the objectives sought for the fishery. The management procedure (MP) approach (also called management strategy evaluation), which provides a framework for such testing, is summarized briefly. How this approach could be used to develop a decision rule for a fishery for which the only indicator available is the mean length of the catch is presented as an example. The extent to which the ability to meet management objectives could be improved if an unbiased index of relative abundance were available, and an MP based on a fitted population model applied, is illustrated. An MP developed for the fishery for Patagonian toothfish Dissotichus eleginoides off the sub-Antarctic Prince Edward Islands is summarized. This illustrates how the MP testing framework can be used in circumstances in which the available indicators conflict, leading to considerable uncertainty about the present resource status. The information content of indicators is closely related to the extent to which they vary about trends in the underlying resource attributes (e.g., catch per unit effort and underlying abundance). The compilation of lists of the statistical properties (such as the coefficients of variation and autocorrelations) of the residuals about detrended time series of the indicators, together with their likely relationships to the underlying attribute, for fisheries worldwide is suggested. This would provide a sound basis for specifying error structure in the simulation tests advocated for both generic and case-specific decision rules for data-poor fisheries. A typical ideal for a fishery assessment scientist is a resource for which accurate data on total catch are available from the inception of the fishery, together with some reasonably precise fishery-independent index of abundance (e.g., from a research survey or tag recovery program conducted in a comparable manner over a considerable period of time that continues to the present). The addition of reliable annual age data for the catch (and desirably for any of the catches used to develop the abundance index) allows variations in recruitment to be taken into account. From this information, quantitative advice can be developed on the catch or effort (related to fishing mortality) levels appropriate to move resource abundance toward some target considered to correspond to optimal utilization (where this target is frequently related to the estimated abundance to provide the maximum sustainable yield level [MSYL]). Such ideals are seldom attained in practice. Line fisheries in which recreational fishers are the dominant user group are a ready example. For a start, total catches are usually very difficult to estimate with confidence. In South Africa, attempts have been made to estimate fishing mortality from length distribution data by means of the analysis of a catch curve obtained with the aid of information on somatic growth; management measures to raise or lower this fishing mortality have then been developed in the light of a comparison of this fishing mortality estimate with some reference point based on a spawner-biomass-perrecruit analysis (Griffiths 1997; Attwood 2003). However, such a comparison can be biased through error in the estimate used for natural mortality (never easy to determine) and the impact of nonequilibrium effects on the catch curve (see Butterworth and Punt 1990 for a fuller discussion)—aside from the frequent difficulty of calibrating the relationship between the desired change in fishing mortality and the magnitude of the management measure needed to achieve it (e.g., the daily bag limit or duration of the fishing season). The use of fishery indicators (e.g., the mean length of the catch) and their temporal trends as input into decision rules based primarily on expert judgment (perhaps within a ‘‘traffic light’’ framework; Caddy 2002) has at times been suggested in southern Africa and elsewhere as an attractively simple and appropriate approach to management in data-poor (and even some data-rich) situations (O’Boyle 2003; ICES 2007; Potts et al. 2008). Frequently, the direction of change in such Subject editor: Donald Noakes, Thompson Rivers University, British Columbia, Canada * Corresponding author: doug.butterworth@uct.ac. za Received November 10, 2008; accepted December 18, 2009 Published online April 19, 2010 131 Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science 2:131–145, 2010 Copyright by the American Fisheries Society 2010 DOI: 10.1577/C08-038.1 [Article] D ow nl oa de d by [ U ni ve rs ity o f C ap e T ow n L ib ra ri es ] at 0 0: 09 1 7 M ar ch 2 01 6 indicators relates to whether existing management measures need to be made more or less restrictive. However, even in the happy situation in which all such indicators point toward a common direction for a change in management measures, so that the difficult issue of how to weight different sources of information with contradictory implications does not arise, the fundamental question remains as to how to quantify the change required to achieve the objectives for the fishery. Thus, for example, it might be clear from the trends in an indicator that the total allowable catch or fishing effort needs to be decreased, but by how much? Too little a decrease runs the risk of undue reduction of the resource, thus threatening its future productivity; too great a decrease could cause unnecessary socioeconomic disruption. One also has to consider how much the trends in the resource monitoring data reflect random noise rather than the underlying resource dynamics. The thrust of this paper is that management decisions cannot be reliably based on qualitative ‘‘expert judgment. ’’ Even in data-poor situations, in which the standard stock assessment exercises are not possible, proposed decision rules need to be subjected to prior simulation testing to confirm that they are likely to achieve the objectives sought for the fishery. The basic framework for such simulation testing (known as the management procedure approach or management strategy evaluation) has already been developed and applied to a number of relatively datarich fisheries, as summarized in the following section. This paper illustrates how this might be done in a datapoor situation—one in which the mean length of the catch is the only index available—and contrasts its performance with that for the same situation in which there are enough data to take a standard approach. This is followed by a summary of the development of a management procedure for the fishery for Patagonian toothfish Dissotichus eleginoides off the sub-Antarctic Prince Edward Islands, for which the data quality is not only poor but two different indicators provide conflicting indications as to the status of the resource. Finally, some suggestions are made as to the areas in which further investigation of this approach is needed. The Management Procedure Approach The management procedure approach had its origins in the response of the Scientific Committee of the International Whaling Commission to the commission’s decision to impose zero catch limits (i.e., a moratorium) on commercial whaling in 1982. A major reason for this decision was uncertainty as to the determination of sustainable catch levels, together with the lack of a clear way to incorporate that uncertainty into a ‘‘best (stock) assessment’’ of the resource (Punt and Donovan 2007). A management procedure (MP) is defined as a set of prespecified methods for collecting and analyzing data on the status of a resource together with a simulationtested decision rule that furnishes a management recommendation (such as a total allowable catch) based on those data (Butterworth et al. 1997). Simulation testing is required to ensure that the decision rule provides the necessary feedback from the data to adjust management measures so as to achieve the objectives for the fishery. This necessitates that the pseudodata generated in the testing process incorporate the level of error (e.g., the sampling error associated with a survey) that would be anticipated in practice. Furthermore, continued simulation with the decision rule (typically for 10–20 years, though longer if very long-lived species are concerned) must show that it attains the objectives not only when the current best assessment of the resource reflects the true underlying dynamics but also when there are other plausible hypotheses consistent with the data. Figure 1 illustrates the calculation process. An operating model (OM) is developed to represent the possible underlying dynamics of the resource (the ‘‘best assessment’’ could be used for this purpose). This model generates resource monitoring data (e.g., abundance and catch per unit effort) of the type and with the same error structure that would be available in practice. These data are fed into a formula (the MP) that determines the TAC recommendation. The formula may be complex (e.g., replicating the standard annual assessment plus catch control law basis) or simple (such as making the change in TAC proportional to a recent trend in the catch rate). Importantly, the computer evaluations are structured so that the formula is not based on knowledge of or direct input from the actual underlying dynamics of the resource (the current values of the variables of the OM) except as reflected in the data available for assessment purposes, as would be the case in practice. FIGURE 1. —Schematic representation of the simulation testing of a management procedure (MP). 132 BUTTERWORTH ET AL. D ow nl oa de d by [ U ni ve rs ity o f C ap e T ow