Developing a management procedure robust to uncertainty for southern bluefin tuna: a somewhat frustrating struggle to bridge the gap between ideals and reality

Abstract

Abstract Fisheries management is conducted to achieve sustainable use of fishery resources, mainly through regulation of fishing activities. For almost a decade, the Commission for the Conservation of Southern Bluefin Tuna (CCSBT) struggled to reach agreement on a total allowable catch (TAC) for southern bluefin tuna (SBT) because of stock assessment uncertainties. To address this, in 2002 the CCSBT commenced development of a management procedure (MP), a pre‐agreed set of rules to determine how the TAC will be adjusted as new monitoring data become available. The CCSBT Scientific Committee tested various candidate MPs using operating models which simulate fish population and fishery dynamics as well as incorporate process, observation, and model uncertainties. Candidate MPs were evaluated using performance measures related to the following management objectives: maximize catches, avoid stock collapse, and minimize interannual catch variation. Of the MPs explored, some relied solely on empirical data [i.e., adjusted TAC based on catch per unit effort (CPUE) trends], whereas others were more complicated, based on population models. In 2005, the CCSBT adopted a model‐based MP that realized a moderate catch with low variability and avoided stock collapse. This MP struck a compromise between the risk‐prone and risk‐averse standpoints of the different stakeholders. However, despite this concerted scientific effort, the MP was not implemented because, shortly after its adoption, it became evident that historical catches may have been substantially underreported. This complication necessitates returning to near the beginning of the development process. MP approaches have various advantages and challenges to be explored further. However, it is essential to lessen human‐introduced uncertainty (such as catch misreporting) by enhanced enforcement, and to increase management robustness to biological uncertainties by implementing MPs.