The Rosa Lee phenomenon and its consequences for fisheries advice on changes in fishing mortality or gear selectivity. (30th June 2019)

• Record Type: Journal Article
• Authors: Kraak, Sarah B M; Haase, Stefanie; Minto, Cóilín; Santos, Juan
• Editors: Anderson, Emory
• Is Part Of: ICES journal of marine science. Volume 76:Number 7(2019)
• Page Start: 2179
• Page End: 2192
• Publication Date: 2019-06-30
• Subjects: age–length-structured population model -- demographic consequences -- Rosa Lee phenomenon -- selectivity changes -- stock effects; Ocean -- Periodicals; Fisheries -- Periodicals; Fishes -- Periodicals; Marine biology -- Bibliography -- Periodicals; 551.4605
• DOI: 10.1093/icesjms/fsz107
• Languages: English
• ISSNs: 1054-3139

Abstract

Abstract: When size-selective fishing removes faster-growing individuals at higher rates than slower-growing fish, the surviving populations will become dominated by slower-growing individuals. When this "Rosa Lee phenomenon" is ignored, bias may occur in catch and stock projections. In a length-and-age-based model we quantified the effects through simulations of a simplified fishery on a stock that resembles Western Baltic cod. We compared outcomes of runs with and without taking account of the Rosa Lee phenomenon in scenarios of changes in fishing mortality. We found that, when only fishing rate was changed, the biases in predictions of spawning-stock biomass (SSB), yield and catches of undersized fish were relatively small (<10% in absolute values). When the selectivity parameters of the gear were increased, the bias in the prediction of the catches of undersized fish was very substantial (+120 to 160%). When the selectivity parameters were decreased, the biases in the predictions of SSB, yield and catches of undersized fish, were substantial (25–50% in absolute values). With slower mean growth the biases became more pronounced. We conclude that in short-term forecasts, medium-term projections, and MSE simulations featuring selectivity changes, the Rosa Lee phenomenon should be accounted for, ideally by using length-based models.