Theme session O

“When is enough, enough?” Methods for optimising, evaluating, and prioritising of marine data collection

Co-sponsored by PICES
​​​​​Tuesday 20 September 17:00—18:00 in Alfa
Wednesday 21 September 08:30—13:00 in Alfa
Thursday 22 September 10:00—13:00 in Alfa​
J.H. Vølstad (Norway)
Mike Armstrong (UK)
Marie Storr-Paulsen (Denmark)
Robyn Forrest (Canada)​

​​​​​​​​​​​​​​​Contact conveners​​

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:

  • Approaches to evaluate the quality of datasets, how to use them effectively, and objective methods to identify and prioritize data needs
  • Data collections to support the ecosystem approach with cost effective designs and documented quality. Simulation studies to test and develop sampling designs, particularly in a regional context
  • Sampling and analysis methods for fisheries-dependent and fisheries-independent surveys that follow best scientific practice to provide data for stock assessments
  • Methods that reflect assessment uncertainty by allowing for uncertainty in the historical catch estimates and other key population parameters typically assumed as fixed or measured without error
  • Incorporation of sampling errors in input data in the assessment model, evaluation of model fit to observation data, and how this can be integrated in a stock assessment outputs
  • Demonstration of how management decisions are 
    affected by uncertainty in survey data and stock assessment - central to this will be papers that demonstrate a feedback loop showing how improved survey design can lead to reduced variance, improved stock assessment model prediction, and a subsequent reduction in uncertainty associated with implementing a specific harvest rule, such as one based on MSY
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Theme session O

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