Improving stock assessments through data prioritization

Abstract

The need for “better data” is a common response of stakeholders and managers when confronted with the uncertainty of advice resulting from quantitative stock assessments. Most contemporary stock assessments are based on an integrated analysis of multiple data types, each with their associated cost to collect. Data collection resources are inevitably limited; therefore, it is important to quantify the relative value of increased sampling for alternative data types in terms of improving stock assessments. We approached this universal problem using a simulation study of a hypothetical, amalgam species developed from eight separate stock assessments conducted for species found in southeastern US Atlantic waters. We simulated a population and a stock assessment from the amalgam species and then individually improved alternative data types (indices, age compositions, landings, and discards) by increasing either precision or sample size. We also simulated the effects of increased sampling for alternative groupings of data that might be collected in concert (e.g., commercial, recreational, or survey). Our results show that for the snapper–grouper complex we modeled, age composition data have the largest effect on the accuracy of assessments, with commercial age compositions being the most influential. This is due in part to the relative paucity of age composition data for many southeast US marine stocks, so that modest increases in collection efforts have relatively high benefits for age-based assessment models currently in use for the region. Though this study used data from a particular region of the US, our investigative framework is broadly applicable for quantitatively evaluating the benefits of improved data collection in terms of the precision of stock assessments in any region.