Using acoustic technology to improve the modelling of the transportation and distribution of juvenile gadoids in the Barents Sea

• Published in: ICES Journal of Marine Science
• Main Authors: Johansen, Geir O., Godø, Olav R., Skogen, Morten D., Torkelsen, Terje
• Format: Article in Journal/Newspaper
• Language: English
• Published: Oxford University Press (OUP) 2009
• Subjects: Ecology; Aquatic Science; Ecology, Evolution, Behavior and Systematics; Oceanography; Barents Sea; Johansen
• Online Access: http://dx.doi.org/10.1093/icesjms/fsp081; http://academic.oup.com/icesjms/article-pdf/66/6/1048/29133252/fsp081.pdf

Abstract Johansen, G. O., Godø, O. R., Skogen, M. D., and Torkelsen, T. 2009. Using acoustic technology to improve the modelling of the transportation and distribution of juvenile gadoids in the Barents Sea. – ICES Journal of Marine Science, 66: 1048–1054. Transport of the juvenile stages of gadoids by oceanic currents in the Barents Sea is governed by interactions between the vertical positioning of the fish and the horizontal movement of the water masses. The resulting geographical distribution is important for growth and survival. There is need for observations at proper temporal and spatial scales to improve the representation of vertical distribution in models of the transport process. Stationary acoustic systems are suitable for this purpose. We use such a system to quantify the vertical dynamics of 0-group gadoids with reference to a conceptual model of the temporal variation. The vertical distribution from the conceptual model is applied within a Lagrangian, particle-tracking model. This approach performs better in predicting the geographic distribution of the 0-group during the first 10 months after hatching than a model with random, vertical distribution. The potential of stationary acoustic systems to provide high-quality vertical distributions that improve the predictive power of the transport model is demonstrated. Extensive sampling programmes based on the principles presented here can provide the observations needed to obtain more realistic recruitment–prediction models.