Provision of catch advice taking account of non-stationarity in productivity of Atlantic salmon (Salmo salar L.) in the Northwest Atlantic

• Published in: ICES Journal of Marine Science
• Main Authors: Chaput, G., Legault, C.M., Reddin, D.G., Caron, F., Amiro, P.G.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Oxford University Press (OUP) 2005
• Subjects: Atlantic salmon; Northwest Atlantic; Salmo salar
• Online Access: http://dx.doi.org/10.1016/j.icesjms.2004.10.006; http://academic.oup.com/icesjms/article-pdf/62/1/131/29150052/62-1-131.pdf

Abstract The paper presents the data, the models, and the approach for the provision of management advice for a high seas mixed stock fishery on Atlantic salmon (Salmo salar L.). The approach incorporates observation errors, model uncertainty, and considers a possible shift in the productivity of Atlantic salmon. The risk analysis framework further incorporates uncertainty in the fishery harvest characteristics and presents the catch advice as probabilities of meeting or exceeding the conservation objectives relative to catch options. There is very strong evidence from the analyses that there has been a phase shift in productivity of Atlantic salmon of North American origin in the Northwest Atlantic. The change in productivity likely resulted from a change in marine survival which occurred in the early 1990s and has persisted to date. When the uncertainties in the input data are considered, the most parsimonious models suggest that there has been a shift in absolute abundance independent of variations in the spawner index contributing to the recruitment. There continues to be a large amount of uncertainty in the measures of abundance and population dynamics of Atlantic salmon. Uncertainty in the understanding of population dynamics does not necessarily equate to uncertainty in management advice. If model results suggest that spawning objectives are unattainable even when harvest rates are zero, then any harvest level will either accelerate the rate of decline if the model prediction is correct or diminish the probability of recovery if the model prediction is wrong.