Estimation of historical stock sizes and recruitment anomalies from relative abundance time-series

Often only simple relative abundance time series and basic growth and (or) survival estimates are available for assessing impacts of fishing and environmental factors. Assessment then involves fitting production models to the series, while forcing the model with observed catch or effort series. A key uncertainty in this approach is how to deal with recuitment variations due to factors other than stock size. A dynamic programming algorithm can be used to compute maximum likelihood estimates of the recruitment anomaly sequence, given prior knowledge of growth parameters, the natural survival rate, and proportion of the variation in the relative abundance index that is due to abundance measurement errors. The temporal pattern of anoamly estimates from the dynamic programming procedure is quite robust to uncertainties about the absolute stock size and average historical recruitment rate, ao it can at least provide information for studies of factors affecting recruitment in cases where the abundance index measurement error is small compared with recruitment process errors (¸<10% of total error). Further, the procedure can easily be embedded within a Bayesian or maximum likelihood estimation of stock size and surplus production.