Simulation Analyses Testing the Robustness of Productivity Determinations from West Coast Pacific Ocean Perch Stock Assessment Data

Abstract

The estimated stock size and recruitment data from assessment models provide a way to evaluate the history of a fishery relative to the expected response given a target fishing mortality rate. For example, if recruitment estimates are below the fishing mortality replacement line, then further reductions in spawning biomass can be expected. However, recruitment variability, serial correlation, and variable historical harvest rates can create misleading perceptions of sustainable harvest rates when they are derived from these simple replacement-line curves. Careful analysis of the stock–recruitment relationship given the available data is required in order to evaluate stock productivity in age-structured modeling. In this study, simulation–estimation procedures were developed to evaluate stock productivity estimates given the level of information and historical fishing mortality currently reported for the Pacific ocean perch Sebastes alutus stock off the coast of Washington, Oregon, and California. Two scenarios were designed with significantly different optimum yield calculations, one with moderate productivity and one with relatively low productivity. Within each productivity scenario, two types of simulations were performed, one with recruitment values identical to the original estimates (the observation-error-only case) and one with recruitment varying about the stock–recruitment curve (the observation-and-process-error case). For each of the two scenarios and two cases, 100 simulated stock assessment data sets were generated. Each was then analyzed using the stock assessment model used in 1998. The results suggest that if the actual stock–recruitment productivity of this stock is low, the assessment model estimates will have a slight positive bias (estimated productivity will be higher than actual productivity). Conversely, if the actual stock–recruitment productivity is moderate, estimates are likely to have a negative bias given the observed sequence of recruitments and fishing mortality history. Including time series effects (simple autocorrelation) is shown to be a reasonable step toward remedying this problem.