Predicting year class strength for climate-stressed gadid stocks in the Gulf of Alaska

Abstract

Climate change makes fish stocks more vulnerable to recruitment failure, and early detection of these events is important for an effective management response. Here, we evaluate the value of larval and juvenile surveys, and a thermal spawning habitat index, for predicting recruitment in two economically important gadids, walleye pollock (Gadus chalcogrammus) and Pacific cod (G. macrocephalus), in the Gulf of Alaska. These stocks have been exposed to rapid human-induced ocean warming since 2014, which has apparently contributed to anomalies in age structure, size at age, and other population variables (for pollock) and stock collapse (for cod). We found that warming results in recruitment that falls short of predictions from historical spawner-recruit relationships for both stocks, highlighting that climate change makes recruitment expectations based on historical experience less reliable. However, we also found that recruitment could be successfully predicted with surveys of early-life stages. Using Bayesian regression, we found that juvenile trawl survey data for pollock predicts recruitment to age-1 (as estimated by a stock assessment model), while prediction from larval surveys was less successful. Beach seine estimates of juvenile abundance also predicted pollock recruitment, a surprising result for a species that is typically sampled in offshore habitats. The spawning habitat index and beach seine survey both predicted cod recruitment to age-3 as estimated by the stock assessment model. We did not find a predictive relationship between cod larval abundance and recruitment. However, residuals from the larval model showed low-frequency variability, suggesting nonstationarity (time-dependence) in the predictive relationship. Dynamic Factor Analysis (DFA) models summarizing information across multiple data sets showed reasonable predictive value for both species (Bayesian R2 ≈ 0.4 for log recruitment), and they also allowed recruitment prediction for years with missing observations in some data sets. We conclude that surveying multiple early life stages may be the most useful approach for predicting gadid recruitment.