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.