The robustness of our assumptions about recruitment: A re-examination of marine recruitment dynamics with additional data and novel methods

Abstract

Recruitment is the number of young fish entering a population and is an essential process in age-structured stock assessment models. Many of the models on which stock assessments are based assume some level of influence of spawning biomass on recruitment, and estimation of this functional relationship has been a focus of substantial research. However, recruitment is also influenced by environmental fluctuations that induce autocorrelated patterns, trends, and shifts in deviations from the stock-recruitment relationship. Consequently, previous meta-analyses of stock-recruitment relationships have had trouble calibrating the relationship. We revisited the findings of Szuwalski et al. (2015) to determine if their results are robust to the addition of data to the RAM Legacy Stock Assessment database and choice of analysis method (the PELT algorithm, MARSS models, and Bayesian change point detection). Since the publication of Szuwalski et al. (2015), the number of stocks in the database has doubled. We determined the primary influence of spawning biomass on recruitment and examined the recruitment time series for regime shifts for 432 stocks. Our results indicated that 57% of stocks did not have a significant correlation between spawning biomass and recruitment over the observed biomasses. Environmental conditions played a larger role in recruitment variation than spawning biomass. The presence, location, and number of regime shifts in recruitment time series was highly dependent on the detection method, with Bayesian change point detection identifying the fewest regime shifts. Despite the sensitivity to the detection method, 46% of stocks without a significant correlation between spawning biomass and recruitment are estimated to have experienced at least one regime shift as determined by the PELT algorithm Our analyses suggest that effective methods for modeling and forecasting large variations in recruitment over time are needed, particularly given that climate change is predicted to impact the frequency and magnitude of regime shifts.