Evaluating the performance of data-limited methods for setting catch targets through application to data-rich stocks: A case study using Northeast U.S. fish stocks

Abstract

Use of data-limited methods for setting target catches is increasing in the Northeast U.S., but there remains considerable uncertainty over which methods may be suitable for stocks in the region. We retrospectively evaluated the ability of data-limited methods to set target catches close to the overfishing limit for data-rich stocks in the Northeast U.S. Methods explored include options that would be used in truly data-poor cases (i.e., catch-only methods), but we also evaluated methods with different data requirements for stocks that have information beyond a catch time series. The majority of options we explored that used average catches over some portion of the time period, or adjusted the recent catches based on trends in an index were sensitive to the level of historical exploitation. Such methods produced target catches above the overfishing limit for stocks that had a history of overfishing, or target catches that were overly conservative for stocks with a history of light exploitation. Careful consideration of the level of historical exploitation rates, if possible, is therefore needed if using such approaches are to be applied. Catch curve methods, which require catch-at-age information, were the only approaches not sensitive to the level of historical exploitation, and were largely effective at setting target catches close to the overfishing limit, even for stocks with intense historical exploitation rates. However, there were cases where catch curve methods produced unsustainable target catches, particularly for stocks with episodic recruitments, such that care is needed when implementing catch curve methods.