Maite Pons1
1SAFS
November 07, 2017 9:00 (PST): FSH 203
Evaluation of data-poor stock assessments for tuna fisheries
For the “principal market tunas”, like bluefin, bigeye, yellowfin, albacore and skipjack, stock assessments are performed regularly and a variety of management measures are in place to protect these stocks from overfishing. However, there are also other Scombrid species like mackerels and bonitos (commonly referred to as “small tunas”) that account for a notable proportion of the total tuna and tuna-like species catch. These species usually are not assessed and are currently not managed. Small tunas sustain important regional commercial fisheries in many coastal communities throughout their spatial distribution and their assessment is essential to be able to provide management advice which can help to ensure long-term sustainability. For most of these species there is a shortage of data that inhibits the performance of a “full” stock assessment and this is probably the most important reason why they have not been assessed so far. In general, catch time series from official tuna Regional Fisheries Management Organizations (tRFMOs) are incomplete or pooled by group of species. But, length composition data is usually available for a high proportion of these stocks thanks to numerous sampling programs implemented by different member countries of each tRFMO. In particular, the International Commission for the Conservation of Atlantic tunas (ICCAT) have suggested that different “data-limited” approaches that use life-history characteristics and length compositions of the catch should be considered in order to provide scientific information on the status of these stocks and guide management actions. The objective of the present study is to evaluate appropriate assessments and management options for small tunas in the Atlantic Ocean. First we took a “data-rich” stock (i.e. North Atlantic Albacore) as a case study, and we applied a set of different length-based and catch-based “data-poor” assessments in order to compare model outputs. Next, we will use a Management Strategy Evaluation (MSE) to determine the best combination of data, assessment and control measures to meet management objectives. Preliminary results related to the simulation modeling and data-poor assessment performance will be presented.