Catch-Age Analysis with Auxiliary Information

Abstract

We examined the use of catch-at-age data for estimating population abundance, productivity, and year-class abundance. A review section is included where various published models and our new models are shown to form a cohesive theory of catch-at-age analysis linked by level of model complexity. We developed three new models with different error structures: a log-normal measurement error model, a multinomial measurement error model, and a log-normal process error model. By application to data on Pacific halibut (Hippoglossus stenolepis), we show that moderate amounts of auxiliary information, such as fishing effort data or the assumption of a spawner–recruit relationship, are needed to stabilize estimates. The models performed very similarly with moderate amounts of auxiliary information, suggesting a degree of robustness to the underlying error structure. We also developed an extension to classic catch-curve analysis that estimates relative year-class strength reasonably well.