Ensemble modelling for fisheries analyses is increasing and may improve on single-model approaches through better representation of uncertainty, reduced potential for bias, and greater stability in results. Stability, defined here as deviations from model estimates as each year of data are added, may be due to the use of multiple models (rather than periodic changes to a single base-case model) and from the buffering effect of characterizing the central tendency with a set of models. However, stability against the addition of new data, although logically appealing, has not been explored for fisheries stock assessment. We use the Pacific halibut (Hippoglossus stenolepis) ensemble as an example and provide a simple simulation to explore the general behavior of results from an ensemble of models. Counterintuitively, we found the models in the halibut example showed high temporal correlations among deviations. However, we found that a small number of models with low among-model deviation correlations could show a stability benefit. Among-model deviation correlations may be a useful diagnostic for analysts developing ensembles or for those performing sensitivity testing of single-model assessments.
The Pacific Halibut, Hippoglossus stenolepis, and Sablefish, Anoplopoma fimbria, Individual Fishing Quota Program: A Twenty-year Retrospective
