Performance of length-based data-limited methods in a multifleet context: application to small tunas, mackerels, and bonitos in the Atlantic Ocean. (6th February 2019)

• Record Type: Journal Article
• Authors: Pons, Maite; Kell, Laurence; Rudd, Merrill B; Cope, Jason M; Lucena Frédou, Flávia
• Editors: Anderson, Emory
• Is Part Of: ICES journal of marine science. Volume 76:Number 4(2019)
• Page Start: 960
• Page End: 973
• Publication Date: 2019-02-06
• Subjects: length-based assessments -- management -- scombrids -- spawning potential ratio; Ocean -- Periodicals; Fisheries -- Periodicals; Fishes -- Periodicals; Marine biology -- Bibliography -- Periodicals; 551.4605
• DOI: 10.1093/icesjms/fsz004
• Languages: English
• ISSNs: 1054-3139

Abstract

Abstract: Large scombrids, commercial tuna species, are regularly assessed and managed. However, most of the small scombrids, many mackerels and bonitos, lack accurate catch data to implement traditional stock assessments despite their economic importance in many small-scale fisheries. In this study, we analysed different approaches using length composition data from multiple fleets with different gear selectivity to assess small scombrids in the Atlantic Ocean. Using simulated populations, we compared two length-based methods (length-based spawning potential ratio and length-based integrated mixed effects ), under different length data grouping scenarios. We found that using length data from the fleet targeting the broadest range of sizes resulted in the lowest bias in spawning potential ratio of all options tested. Based on these results, we used biological and length data to estimate a quantitative proxy of current stock status for ten small scombrid stocks in the Atlantic Ocean. We found that some stocks are likely to be overfished, such as little tunny ( Euthynnus alletteratus ) in the Southeast Atlantic and wahoo ( Acanthocybium solandri ) in the Northwest Atlantic. This is a starting point in the estimation of stock status for these species, but should not be thought of as a replacement for other more data-intensive assessments.