Integrating ecosystem studies: A Bayesian comparison of hypotheses

Ecosystem studies are difficult to interpret because of the complexity and number of pathways that may affect a phenomenon of interest. It is not possible to study all aspects of a problem; thus subjective judgment is required to weigh what has been observed in the context of components that were not studied but may have been important. This subjective judgment is usually a poorly documented and ad hoc addendum to a statistical analysis of the data. We present a Bayesian methodology for documenting, quantifying, and incorporating these necessary subjective elements into an ecosystem study. The end product of this methodology is the probability of each of the competing hypotheses. As an example, this method is applied to an ecosystem study designed to discriminate among competing hypotheses for a low abundance of sea otters at a previously oiled site in Prince William Sound, Alaska.