A generational randomized growth model for fall-spawning Atlantic herring: Insights from real-world data

The Southern Gulf of St. Lawrence fall-spawning Atlantic herring has seen rapid declines in biomass and is currently in the cautious zone of the Precautionary Approach Framework. Moreover, it is rapidly approaching its limit reference point. In this work, we build and calibrate a realistic model using random differential equations and age-length data for this species. The calibrated random model’s 95% probabilistic intervals encompass nearly all observed data points while maintaining narrow uncertainty bounds, exhibiting its capacity to capture data variability. Additionally, the Symmetric Mean Absolute Percentage Error between the model’s mean predictions and observed values remains below 2.86% across all calibrated years, indicating that the model effectively represents the species’ growth dynamics. Our results indicate that over time the population is experiencing a reduction in maximum size and is not achieving as advanced ages. Furthermore, we show that there is greater intra-annual variability over time, pointing towards less consistent growth patterns in recent years. These changes in the growth dynamics of the Southern Gulf of St. Lawrence fall-spawning Atlantic herring, suggested by our model, could significantly reduce reproductive output and hinder the species’ rebuilding capacity.