Using an individual-based model to simulate the Gulf of Maine American lobster (Homarus americanus) fishery and evaluate the robustness of current management regulations

Abstract

Individual-based models (IBMs) can capture complex processes with a flexible probabilistic approach, which makes them useful for studying organisms with complex life history and fishery processes such as the American lobster (Homarus americanus). This research aims to modify and parameterize an individual-based lobster simulator (IBLS) to simulate the American lobster fishery in the Gulf of Maine. To simulate the fishery, the IBLS was tuned to match the seasonal catch and size composition from the 2015 American lobster stock assessment by adjusting the values of coefficients for select parameters. With appropriate coefficients for the initial abundance, recruitment, and seasonal encounter probability levels, the tuned IBLS accurately simulated the historical landings. Given the uncertainty in future American lobster recruitment, the tuned IBLS was then used to evaluate the effectiveness of current management regulations under different levels of recruitment.