What makes fisheries data informative?
Version of Record online: 16 NOV 2007
Fish and Fisheries
Volume 8, Issue 4, pages 337–358, December 2007
How to Cite
Magnusson, A. and Hilborn, R. (2007), What makes fisheries data informative?. Fish and Fisheries, 8: 337–358. doi: 10.1111/j.1467-2979.2007.00258.x
- Issue online: 16 NOV 2007
- Version of Record online: 16 NOV 2007
- Received 28 Nov 2006 Accepted 29 Aug 2007
Vol. 13, Issue 4, 479–480, Version of Record online: 4 MAY 2012
- abundance index;
- catch at age;
- informative data;
- reference points;
- stock assessment
Informative data in fisheries stock assessment are those that lead to accurate estimates of abundance and reference points. In practice, the accuracy of estimated abundance is unknown and it is often unclear which features of the data make them informative or uninformative. Neither is it obvious which model assumptions will improve estimation performance, given a particular data set. In this simulation study, 10 hypotheses are addressed using multiple scenarios, estimation models, and reference points. The simulated data scenarios all share the same biological and fleet characteristics, but vary in terms of the fishing history. The estimation models are based on a common statistical catch-at-age framework, but estimate different parameters and have different parts of the data available to them. Among the findings is that a ‘one-way trip’ scenario, where harvest rate gradually increases while abundance decreases, proved no less informative than a contrasted catch history. Models that excluded either abundance index or catch at age performed surprisingly well, compared to models that included both data types. Natural mortality rate, M, was estimated with some reliability when age-composition data were available from before major catches were removed. Stock-recruitment steepness, h, was estimated with some reliability when abundance-index or age-composition data were available from years of very low abundance. Understanding what makes fisheries data informative or uninformative enables scientists to identify fisheries for which stock assessment models are likely to be biased or imprecise. Managers can also benefit from guidelines on how to distribute funding and manpower among different data collection programmes to gather the most information.