‘Drivin' with your eyes closed’: Results from an international, blinded simulation experiment to evaluate spatial stock assessments

• Published in: Fish and fisheries (Oxford, England) Vol. 25; no. 3; pp. 471 - 490
• Main Authors: Goethel, Daniel R., Berger, Aaron M., Hoyle, Simon D., Lynch, Patrick D., Barceló, Caren, Deroba, Jonathan, Ducharme‐Barth, Nicholas D., Dunn, Alistair, Fu, Dan, Izquierdo, Francisco, Marsh, Craig, Xu, Haikun, Correa, Giancarlo M., Langseth, Brian J., Maunder, Mark N., McKenzie, Jeremy, Methot, Richard D., Vincent, Matthew T., A'mar, Teresa, Cardinale, Massimiliano, Cousido‐Rocha, Marta, Davies, Nick, Hampton, John, Minte‐Vera, Carolina, Urtizberea, Agurtzane
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.05.2024
• Subjects: Assessments; Biomass; Climate change; Complexity; Fish and Aquacultural Science; Fishing; Fishing effort; Fishing pressure; Fisk- och akvakulturforskning; Marine ecosystems; Marine resources; Platforms; Scaling; Simulation; Spatial data; Stock assessment; Thunnus albacares; Trends
• ISSN: 1467-2960; 1467-2979; 1467-2979
• DOI: 10.1111/faf.12819

Abstract Spatial models enable understanding potential redistribution of marine resources associated with ecosystem drivers and climate change. Stock assessment platforms can incorporate spatial processes, but have not been widely implemented or simulation tested. To address this research gap, an international simulation experiment was organized. The study design was blinded to replicate uncertainty similar to a real‐world stock assessment process, and a data‐conditioned, high‐resolution operating model (OM) was used to emulate the spatial dynamics and data for Indian Ocean yellowfin tuna ( Thunnus albacares ). Six analyst groups developed both single‐region and spatial stock assessment models using an assessment platform of their choice, and then applied each model to the simulated data. Results indicated that across all spatial structures and platforms, assessments were able to adequately recreate the population trends from the OM. Additionally, spatial models were able to estimate regional population trends that generally reflected the true dynamics from the OM, particularly for the regions with higher biomass and fishing pressure. However, a consistent population biomass scaling pattern emerged, where spatial models estimated higher population scale than single‐region models within a given assessment platform. Balancing parsimony and complexity trade‐offs were difficult, but adequate complexity in spatial parametrizations (e.g., allowing time‐ and age‐variation in movement and appropriate tag mixing periods) was critical to model performance. We recommend expanded use of high‐resolution OMs and blinded studies, given their ability to portray realistic performance of assessment models. Moreover, increased support for international simulation experiments is warranted to facilitate dissemination of methodology across organizations.