Bayesian methods have been proposed to estimate optimal escapement
goals, using both knowledge about physical determinants of salmon
productivity and stock-recruitment data. The Bayesian approach has
several advantages over many traditional methods for estimating stock
productivity: it allows integration of information from diverse
sources and provides a framework for decision-making that takes into
account uncertainty reflected in the data. However, results can be
critically dependent on details of implementation of this approach.
For instance, unintended and unwarranted confidence about
stock-recruitment relationships can arise if the range of relationships
examined is too narrow, if too few discrete alternatives are
considered, or if data are contradictory. This unfounded confidence
can result in a suboptimal choice of a spawning escapement goal.