Abstract
This project explored how the Stock Assessment Continuum (SAC) tool can support evaluating spiny lobster stocks in the US Virgin Islands under conditions of limited data availability. The goal was to determine whether the SAC tool could provide informative outputs that aid management when conventional data-rich methods are not feasible. Using catch and length data for St. Croix, St. Thomas, and St. John spiny lobster populations,a series of models were developed to reflect increasing data and parameter complexity, from scenarios relying solely on length data to those incorporating catch records and recruitment estimation. Through these applications, the SAC tool consistently produced clear trends in relative spawning biomass across all configurations.
The findings from this project underscore the SAC tool’s value as a practical addition to fisheries managers’ toolkit in data-limited conditions. It can serve as a diagnostic and exploratory tool that helps managers understand possible stock conditions and informs discussions around data collection priorities and management decisions. Integrating this tool into routine assessment processes could enhance adaptive management approaches, ensuring that management decisions remain grounded in science even when information is limited. Ultimately, this work demonstrates that flexible, data-efficient tools like the SAC tool can contribute to the sustainable management of spiny lobster fisheries in the US Virgin Islands.