‘Building the (Im)perfect Beast’: Strategies for Identifying Appropriate Spatial Stock Assessment Model Complexity From an International, Blinded High‐Resolution Simulation Experiment
Year: 2025
Type: article
Abstract: ABSTRACT Despite their potential to inform sustainable regional harvest and climate‐resilient fisheries management, spatial stock assessment models remain underused for management advice. To identify barriers that inhibit broader use of these methods, we conducted a blinded international simulation experiment mimicking real‐world stock assessment development when confronting spatial complexity. Seven analyst teams built spatially aggregated and spatially explicit assessment models using data simulated from high‐resolution operating models based on Indian Ocean yellowfin tuna and Ross Sea Antarctic toothfish dynamics. Each team documented how assessment software platform, data analyses, model building approach, and diagnostics influenced model complexity and realism. A consensus emerged on key assessment building approaches: (1) conduct high‐resolution data analyses to identify appropriate spatial structure; (2) start with simplified models and incrementally add complexity; (3) iteratively evaluate diagnostics to determine necessary spatial complexity; and (4) maintain models with different spatial structures to aid interpretation. The experiment also revealed several valuable insights for parameterising assessments, including consideration of data pre‐processing with spatiotemporal models to better inform data‐sparse regions; regression trees to identify fleet and spatial structure; trade‐offs in complexity between productivity and movement dynamics to achieve tractable and stable model structures; and ensemble modelling approaches to address structural uncertainty. Our findings demonstrate that international collaborations and simulation experiments are crucial for addressing challenges in implementing spatial stock assessments and for evaluating whether their added complexity is justified given management objectives. Broader collaborations are encouraged to foster innovation in fisheries management and to help recognise the practical trade‐offs between model parsimony and complexity.
Source: Fish and Fisheries
DOI: 10.1111/faf.70048
Authors: Aaron M. Berger, Daniel R. Goethel, Simon D. Hoyle, Patrick Lynch, Caren Barceló, Alistar Dunn, Brian J. Langseth, Carolina Minte‐Vera, Jemery Day, Haikun Xu, Francisco Izquierdo, Dan Fu, Nicholas D. Ducharme‐Barth, Mathew Vincent, Arnaud Grüss, Jonathan J. Deroba, Giancarlo M. Correa, Jeremy McKenzie, Will Butler, Jie Cao, Craig Marsh, Teresa A'mar, Valerio Bartolino, Massimiliano Cardinale, Claudio Castillo‐Jordan, Bjarki Þór Elvarsson, John Hampton, Andrea Havron, Pamela Mace, Arni Magnusson, Mark Maunder, Richard Methot, Sophie Mormede, Maria Grazia Pennino, Alfonso Perez‐Rodriguez, Marta Cousido‐Rocha, Thomas Teears, Agurtzane Urtizberea
Institutions: NOAA National Marine Fisheries Service, National Oceanic and Atmospheric Administration, NOAA National Marine Fisheries Service Northwest Fisheries Science Center, NOAA National Marine Fisheries Service Alaska Fisheries Science Center, University of Auckland, National Institute of Water and Atmospheric Research, University of Washington Tacoma, Inter-American Tropical Tuna Commission, Pacific Community, Instituto Español de Oceanografía, Centro Oceanográfico de Vigo, Indian Ocean Tuna Commission, NOAA National Marine Fisheries Service Pacific Islands Fisheries Science Center, NOAA National Marine Fisheries Service Southeast Fisheries Science Center, NOAA National Marine Fisheries Service Northeast Fisheries Science Center, University of Washington, Marine and Freshwater Research Institute, North Carolina State University, Swedish University of Agricultural Sciences, Ministry for Primary Industries, New Zealand Association of Counsellors, Wageningen Marine Research, AZTI
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Life below water
Open Access status: hybrid
Estimated APC paid: $5,000
OpenAlex ID: W7111131307