MEPS 483:245-256 (2013)  -  DOI: https://doi.org/10.3354/meps10295

ABSTRACT: Recruitment is highly variable in marine fishes, and is often estimated using stock–recruit relationships that explain little of the observed variability in recruitment. Researchers have sought for decades to identify environmental indices that are associated with cohort strength, and often use stock assessment estimates of recruitment within secondary regressions to test hypothesized drivers of recruitment variability. This practice is statistically questionable because it fails to acknowledge differences in the precision of recruitment estimates among species and years, as well as covariance between recruitment estimates within a given species. We developed an alternative, statistically rigorous method to estimate an index of cohort strength that is shared among several species while accounting for each single-species stock–recruit relationship. This method simultaneously optimizes multiple stock assessment models with shared cohort strength parameters, while using observation-level fishery data for each species to propagate the precision and covariance of recruitment estimates. The method is demonstrated using data for 8 groundfish species off the US West Coast for which recruitment is relatively well estimated: our model estimated high recruitment during 1990–1991 and 1999–2000, followed by anomalously low recruitment during 2002–2007. The impact of a shared index of cohort strength is demonstrated for 2 additional species with little information about recruitment, yelloweye Sebastes ruberrimus and blackgill Sebastes melanostomus rockfishes, where it decreases the coefficient of variation for recruitment estimates in the most recent modeled year by 40%. The method can be applied to other fishery management regions in the USA and elsewhere, and represents a rigorous method to estimate associations in cohort strength among species within a region.

KEY WORDS: Recruitment · Stock–recruit relationship · Meta-analysis · Dynamic factor analysis · Stock assessment