Data from: Accounting for movement in spatial surplus production models: A case study of redfish on the Eastern Grand Banks of Newfoundland

Spatial surplus production models (SSPMs) are a key alternative to spatial population dynamics models when reliable spatial aging data is unavailable. However, fish movements present computational challenges for SSPMs and can be confounded with process errors, hindering the identification of SSPM parameters. We propose leveraging a Gaussian Markov Random Field (GMRF) with a Matérn covariance structure to account for spatiotemporal variation in dynamics and population production, thereby circumventing the computational and confounding issues. Through simulation studies, wherein data is generated explicitly considering movements, our novel random field model outperforms the alternative methods in estimating fish spatial abundance, as evaluated using statistical metrics including Akaike information criterion, Bayesian information criteria, and correlation between simulated and estimated populations. We also applied our method to reveal the spatial distribution of redfish in NAFO 3LN divisions based on survey and commercial catch data. Model validation confirms a good fit. Our model's ability to fit relatively short time series data (i.e., eight years) demonstrates the benefits of using the random field approach in data-poor fisheries stock assessments.