Application of Data Poor Methods in a Data Rich Environment: A Case Study Using the Atlantic Halibut

The Atlantic Halibut is not a data poor species. A fishery for this species has existed for centuries and there is an extensive catch history that dates to 1893. Two broad scale annual surveys have provided population abundance indices since 1963. It is however, an information poor species. The surveys have very low encounter rates (usually < 10 fish yr^-1) and the fishery has removed little catch since its peak in the years before World War I. The United States “stock” may in fact be a part of a larger stock that includes fish caught in Canadian waters. Given the potential for immigration and emigration, low encounter rates in the survey, and a lack of information regarding the early period of the fishery, traditional, data rich assessment methods are difficult to apply. This study describes the application of simple mortality estimators to determine status relative to biological reference points and a decision interval cumulative sum harvest control rule to set annual catch. Information poor methods were compared to the existing current status quo assessment model and tested in a simulation based management strategy evaluation. Initial results indicate that Atlantic halibut management would benefit from the application of information poor methods.