A state-space approach for detecting growth variation and application to North Pacific groundfish

Understanding demographic variation in recruitment and somatic growth is key to improving our understanding of population dynamics and forecasting ability. Although recruitment variability has been extensively studied, somatic growth variation has received less attention, in part because of difficulties in modeling growth from individual size-at-age estimates. Here we develop a Bayesian state-space approach to test for the prevalence of alternative forms of growth rate variability (e.g., annual, cohort-level, or in the first year recruited to the fishery) in size-at-age data. We apply this technique to 29 Pacific groundfish species across the California Current, Gulf of Alaska, and Bering Sea - Aleutian Islands marine ecosystems. About 40% of modeled stocks were estimated to exhibit temporal growth variation. In the majority of stocks, growth trends fluctuated annually across ages in a single year, suggesting that either there are shared environmental features that dictate growth across multiple ages or the presence of some systematic (within-year) observation errors. This method represents a novel way to use size-at-age data from fishery or other sources to test hypotheses about growth dynamics variability.