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Complex stock assessment methods are data- and expertise-hungry, with the annual updates of catch-at-age data and models typically seen as an essential requirement for sound management. But are the heavy commitments of resources required for this level of annual intervention really necessary to achieve efficient long-term fishery management? This question is addressed through a retrospective analysis of management performance over the last 20 years for four North Atlantic fish stocks. The assessments for two of these stocks have exhibited fairly strong retrospective patterns. The actual assessment advice for these stocks was provided based on complex assessment methods making use of age data. The outcomes are compared with what could have been achieved with much simpler catch control rules based upon age-aggregated survey indices alone. Even for the stocks whose assessments exhibit retrospective patterns, these simple rules can achieve virtually equivalent catch and risk performance, with much less interannual TAC variability, compared with what actually occurred over the past 20 years. [ABSTRACT FROM PUBLISHER]

The majority of fish stocks worldwide are not managed quantitatively as they lack sufficient data, particularly a direct index of abundance, on which to base an assessment. Often these stocks are relatively “low value”, which renders dedicated scientific management too costly, and a generic solution is therefore desirable. A management procedure (MP) approach is suggested where simple harvest control rules are simulation tested to check robustness to uncertainties. The aim of this analysis is to test some very simple “off-the-shelf” MPs that could be applied to groups of data-poor stocks which share similar key characteristics in terms of status and demographic parameters. For this initial investigation, a selection of empirical MPs is simulation tested over a wide range of operating models (OMs) representing resources of medium productivity classified as severely depleted, to ascertain how well these different MPs perform. While the data-moderate MPs (based on an index of abundance) perform somewhat better than the data-limited ones (which lack such input) as would be expected, the latter nevertheless perform surprisingly well across wide ranges of uncertainty. These simple MPs could well provide the basis to develop candidate MPs to manage data-limited stocks, ensuring if not optimal, at least relatively stable sustainable future catches. [ABSTRACT FROM PUBLISHER]