In recent years, there have been increasing demands on data with documented quality to support stock assessments, advice, and the ecosystem-approach to management.
An increase in the volume and complexity of data collection alongside the need to meet quality standards within logistical and economic constraints requires prioritization and optimization of the national and regional sampling programs through: better regional coordination; improved survey sampling designs and analytical methods; development of new technology; quality documentation; cost-benefit analysis; and uncertainty in assessments.
The main focus of this theme session is the wide range of data, including new datasets, that ICES uses to support its stock assessment and advisory process. It is essential that the quality of the data, and how this impacts the accuracy of key parameter estimates that are the basis for advice, is understood before they are used to support fisheries stock assessments and advice, as well as other end users.
Stock assessments are based on data from fisheries-independent and fisheries-dependent sampling surveys with inherent uncertainty due to sampling errors and various sources of systematic errors (bias). It is important to quantify how errors in input data propagate through assessments to help identify the most cost-effective data collections and sampling efforts that adequately support assessments and advice or other management processes. In recent years, statistical assessment models (such as SAM) have been developed which can account for sampling errors and the high degree of complexity in the input data.
This session aims to bring together fisheries scientists and statisticians with expertise in survey sampling design and analysis, practical experience with data collections, stock assessment modelling, harvest control rules, simulation studies, and statistical analysis to assess our current ability to quantify uncertainty in input data, and to track how uncertainty in input data propagates through stock assessment models to affect harvest rules.
Papers are welcomed in the following areas: