ON A STRUCTURAL TIME-SERIES METHOD FOR ESTIMATING STOCK BIOMASS AND RECRUITMENT FROM CATCH AND EFFORT DATA

Structural time series or state-space methods are well suited to stock assessment, where catch and effort are readily available observations , and population size is an unobserved component that one would like t o describe. We discuss the use of such a model in estimating absolute abundance and recruitment from catch data and an index of abundance. W e discuss the model in the context of the time series literature and t he earlier fisheries models for such data. Model fitting is via maximu m likelihood and the Kalman Filter; the approach has advantages over t raditional models in that it allows for both stochastic population dyn amics and sampling error in index measurement. Further, it makes no as sumptions about density-dependent growth or any functional relationshi p between recruitment and spawning stock size. We illustrate the metho d with an investigation of performance using simulated data and two se ts of data pertaining to cod (Gadus morhua) in the North Sea. In the r eal examples the age-composition of the catch is available for part of the duration of the series. Virtual population analysis can therefore also be used; the results are found to be compatible with those from a time series approach.