Bias and Precision of Estimates from an Age-Structured Stock Assessment Program in Relation to Stock and Data Characteristics

Assessments for many U.S. Pacific coast groundfish stocks have been developed using the statistical catch-at-age method known as Stock Synthesis. This study used Monte Carlo simulation and a fractional factorial experiment to evaluate the effects of input data errors and stock characteristics on bias and precision in estimates of ending exploitable biomass, rate of fishing mortality, depletion, and other output variables. Nine factors were examined: length of the data series, rate of natural mortality, shape of the fishery selectivity curve, trend in fishing mortality, recruitment pattern, and level of sampling error in the data for catch, fishing effort, a survey biomass index, and sample size for fishery and survey age compositions. Length of the data series, age composition sample size, survey biomass variability, and fishing effort variability were the most influential factors for most of the output variables. The estimates of depletion had the least bias and the estimates of starting biomass the smallest variability; the estimates of ending recruitment had the greatest bias and largest variability. For all the output variables examined the estimates appeared to be median- unbiased. For the conditions considered in the experiment, it appears that the accuracy of assessment estimates for ending exploitable biomass and projected catch would be more readily improved by increased age composition sampling than by comparable (but much more expensive) improvements in survey estimates of stock biomass