Performance of time-varying catchability estimators in statistical catch-at-age analysis

Abstract

We used Monte Carlo simulations to evaluate how different methods of estimating fishery catchability within statistical catch-at-age analysis (SCA) performed when fishery catchability changed over time. Data-generating models included cases where catchability changed abruptly or gradually over time and where catchability was explicitly a function of population abundance, and we considered corresponding estimation models. In many cases, including fishery effort data in the estimation model and allowing catchability to follow a random walk provided the best (or nearly best) estimates of biomass in the last year as measured by the median of the absolute value of the relative error. Exceptions were cases where fishing mortality was low and catchability trended over time. The estimation model that ignored fishery effort data performed well in cases with a good survey, but performance degraded as survey precision decreased. The white noise estimation model performed poorly in situations where catchability trended over time. No estimation model was best for all underlying models of catchability, but the random walk estimation model performed well under most circumstances and should be used as a starting point for SCAs.