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ICES Journal of Marine Science: Journal du Conseil 2005 62(4):708-722; doi:10.1016/j.icesjms.2005.02.005
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© 2005 International Council for the Exploration of the Sea

Estimation of Atlantic salmon smolt carrying capacity of rivers using expert knowledge

Laura Uusitaloa,*, Sakari Kuikkaa and Atso Romakkaniemib

a Department of Biological and Environmental Sciences, University of Helsinki PO Box 65, FI-00014 University of Helsinki, Finland
b Finnish Game and Fisheries Research Institute, Oulu Game and Fisheries Research Tutkijantie 2 A, FI-90570 Oulu, Finland

*Correspondence to L. Uusitalo: tel: +358 9 191 58992; fax: +358 9 191 58257. e-mail: laura.uusitalo{at}iki.fi.

A mixed salmon fishery with both natural wild salmon stocks and reared salmon exists in the Baltic Sea. The agreed-upon goal of management is to safeguard the wild stocks, and the practical management objectives have been agreed to attain at least a 50% maximum salmon production capacity in each river. This natural production capacity is, however, largely unknown. Here, a new approach has been used to estimate the salmon maximum production capacity of northern Baltic Sea rivers. A probabilistic salmon production capacity model was built entirely upon expert knowledge. The model describes the external physical and biological factors of the rivers and the juvenile salmon stocks' response to these factors. We found that the experts estimated the carrying capacity to be considerably higher than former estimates. A very high uncertainty was, however, connected with these estimates. We also found considerable disagreement over the general carrying capacity level among the experts; the major uncertainty emerged from the conflicting views of the experts. The result implies that perhaps operational management objectives other than those based on maximal smolt production levels should be considered to decrease the uncertainty connected with evaluation of management success.

Keywords: Baltic salmon, Bayesian belief network, carrying capacity, expert knowledge, probabilistic modelling, smolt production, smolt production model, wild salmon

Received 18 June 2003; accepted 22 October 2004.


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