An Evaluation of Harvest Control Rules for Data-Poor Fisheries

For federally managed fisheries in the USA, National Standard 1 requires that an acceptable biological catch be set for all fisheries and that this catch avoid overfishing. Achieving this goal for data-poor stocks, for which stock assessments are not possible, is particularly challenging. A number of harvest control rules have very recently been developed to set sustainable catches in data-poor fisheries, but the ability of most of these rules to avoid overfishing has not been tested. We conducted a management strategy evaluation to assess several control rules proposed for data-poor situations. We examined three general life histories (“slow,” “medium,” and “fast”) and three exploitation histories (under-, fully, and overexploited) to identify control rules that balance the competing objectives of avoiding overfishing and maintaining high levels of harvest. Many of the control rules require information on species life history and relative abundance, so we explored a scenario in which unbiased knowledge was used in the control rule and one in which highly inflated estimates of stock biomass were used. Our analyses showed that no single control rule performed well across all scenarios, with those that performed well in the unbiased scenario performing poorly in the biased scenarios and vice versa. Only the most conservative data-poor control rules limited the probability of overfishing across most of the life history and exploitation scenarios explored, but these rules typically required very conservative catches under the unbiased scenarios. In many fisheries, management actions are based on estimates of stock biomass and management targets (biological reference points [BRPs]) produced from stock assessment models. Such models typically require long time series of catch and relative abundance by age and often life history information, and stocks for which there is such information are considered “data rich.” For many stocks, however, this information is lacking, preventing the use of a data-driven assessment model. Such stocks are considered “data poor,” and they pose a challenge to fisheries managers. In the USA, fisheries managers are now confronting this challenge due to the Magnuson–Stevens Fishery Conservation and Management Reauthorization Act (MSFCMRA). The act requires that the Statistical and Scientific Committees of each of the eight regional fisheries management councils recom*Corresponding author: [email protected] 1Present address: Institute of Marine and Coastal Sciences, Rutgers, State University of New Jersey, 71 Dudley Road, New Brunswick, New Jersey 08901, USA. Received June 22, 2012; accepted May 22, 2013 Published online August 8, 2013 mend acceptable biological catch (ABC) levels for all stocks under a fisheries management plan. National Standard 1 of the MSFCMRA further requires that the ABC prevent overfishing (i.e., when the fishing mortality rate exceeds that which produces the maximum sustainable yield, or FMSY), while still attempting to achieve optimum yield for the fishery. To prevent overfishing, the ABC must have a probability of overfishing (POF) that does not exceed 50%. Scientific uncertainty must also be considered in the selection of an ABC, with the goal of achieving a specific, acceptable probability of overfishing. Importantly, the ABCs constrain the council’s annual catch limits, which may not exceed the ABC. For data-rich stocks, approaches have been developed for selecting a catch level that is expected to achieve a specified probability of overfishing, or P* (Shertzer et al. 2008). Although 845 D ow nl oa de d by [ R ut ge rs U ni ve rs ity ] at 1 0: 20 2 2 A ug us t 2 01 3 846 WIEDENMANN ET AL. National Standard 1 does not mandate the use of the P* approach, many councils have adopted some variant of this technique for setting ABCs (e.g., Prager and Shertzer 2010; Ralston et al. 2011). The challenge in setting an ABC with the P* approach lies in determining whether scientific uncertainty has been adequately accounted for in estimating stock biomass and BRPs. For data-poor stocks, however, implementation of the P* approach is impossible, and setting ABCs that prevent overfishing for these stocks is challenging (Wetzel and Punt 2011). Recently, a number of approaches for setting ABCs for data-poor stocks have been developed. These approaches are called harvest control rules, as they specify a rule or set of rules for setting harvests in response to various factors, such as stock abundance (Deroba and Bence 2008). Data-poor harvest control rules were reviewed and ranked by Berkson et al. (2011), who recommend using a depletion-based stock reduction analysis (DB-SRA; Dick and MacCall 2011) when a catch series spanning the entire history of the fishery is available. If such catch data are not available, Berkson et al. (2011) recommend using a depletion-corrected average catch analysis (DCAC; MacCall 2009). MacCall (2009) advises that DCAC only be used for stocks with low natural mortality rates (M) and values of FMSY at or below M. In cases in which DCAC is not appropriate, Berkson et al. (2011) recommend using a general framework they developed called the only reliable catch series (ORCS) approach. The rankings described above were not based on a formal evaluation of how these control rules performed with respect to preventing overfishing. Wetzel and Punt (2011) conducted a simulation analysis to explore how well DB-SRA and DCAC estimated the catch that achieves FMSY (called the overfishing limit, or OFL) for species with life histories typical of groundfishes, principally flatfishes (order Pleuronectiformes) and members of the genus Sebastes, found off the western USA. They found that both DB-SRA and DCAC generally produced estimates of the OFL at or below the true values. However, Wetzel and Punt (2011) did not look at the long-term effects of applying each control rule to the population. Although Wetzel and Punt (2011) showed that DCAC and DB-SRA can be effective at limiting overfishing, these control rules cannot be applied in all situations due to the limitations described above. Therefore, a broader examination of data-poor control rules is needed. In this study, we used simulation testing (also called management strategy evaluation) to explore the performance of a suite of data-poor harvest control rules over a 20-year period for a range of fishing pressures and species’ life histories. We calculated different performance measures associated with each control rule but focused on identifying control rules that were robust at preventing overfishing across the range of scenarios we explored. Our analysis included the control rules recommended by Berkson et al. (2011) as well as other rules because we wanted to evaluate a broad spectrum of potential data-poor approaches to provide quantitative advice in managing fisheries. To our knowledge no formal approach for updating the control rules has been proposed, but we reapplied control rules sequentially over the 20-year period, as this allowed us to evaluate how they perform when updated with new information.