Symposium overview

Stock assessment minimally requires historical data of catch, fishing intensity (e.g., fishing effort, or alternatively, age or length compositions) and/or relative stock abundance.  Any two of these three quantities will usually support an assessment.  If data on all three quantities are utilized, the assessment contains redundant information, and may be termed “data rich.”  If two or less of these quantities is utilized, perhaps involving incomplete or unreliable data, the assessment may be termed “data-poor.”  The tactics of data-poor stock assessment often involve: 1) Simplifying or re-casting the model to accommodate the available data; 2) Modifying the statistical basis to accommodate unusual data; and 3) Borrowing data or parameters from comparable but better-known cases (e.g., Bayesian approaches).  Aside from the problems associated with limited or inadequate data, some additional challenges in data-poor stock assessment include model validation and capturing the full extent of imprecision or uncertainty.  Testing data-poor assessment methods against data-rich cases can help with validation, but the data-rich assessments themselves may not be as reliable as we assert them to be.