Analysis of Contradictory Data Sources in Fish Stock Assessment

Fisheries stock assessments sometimes prove, in retrospect, to be wrong. Errors may be due to poor model assumptions or to data that do not reflect the biological process of interest. We develop a method that formally admits the possibility of such errors. Likelihood functions derived from this method indicate greater uncertainty in parameter values than conventional likelihoods, whose derivations presume that models correctly describe the observed data. The problem of uncertainty is particularly acute when more than one data source is available and different data sets provide contradictory parameter estimates. Traditional methods of stock assessment involve weighted averages of the contradictory data, and these generally produce parameter estimates intermediate to those obtained from the data sets individually. We demonstrate that, when model or data errors are considered, the most likely parameter values are not intermediary to conflicting values; instead, they occur at one of the apparent extremes. We provide an example using contradictory trends in catch-per-u nit-effort data for the Canadian northern cod stock (1978–88).