A Bayesian state-space model for mixed-stock migrations, with application to Northeast Atlantic mackerel Scomber scombrus

Abstract

Management of fisheries that exploit mixed-stock populations relies on assumptions made concerning stock structure and mixing in different areas. To address the problems of accounting for uncertainty when formulating scientific advice for the management of highly migratory fish stocks, management decisions need to be based upon assessment models that represent plausible alternative hypotheses for stock structure and migration patterns of the exploited populations. We present a multi-stock, multi-fleet, multi-area, seasonally structured Bayesian state-space model in which different stocks spawn in spatially different areas and the mixing of these stocks is explicitly accounted for in the absence of sufficient tagging data with which to estimate migration rates. The model is applied to the Northeast Atlantic mackerel Scomber scombrus population, accounting for the annual spawning-feeding-overwintering migration patterns of the three spawning components, together with uncertainty in the extent to which the southern component migrates north to feed and overwinter, and consequently the extent to which it mixes with the other components and is subject to exploitation. The model allows the effect of exploitation on the individual components to be assessed, and the results suggest that the fishing mortality of southern spawning adults was insensitive to the extent to which they migrated north.