Building next-generation diagnostic and forecasting capacity to achieve management objectives by increasing stock assessment accuracy and throughput
Management decisions about overfishing limits and acceptable biological catch estimates must be made for all harvested species subject to a fisheries management plan and are typically informed by stock assessments that model fishery dynamics and forecast stock trends. These complex stock assessments depend on many diverse data sources which limit model interpretability, and require extensive development effort, which limits the rate of assessment throughput. The project team will address these existing limitations and uncertainties in the stock assessment process by developing novel model diagnostics and interim assessment methods. These new methods, along with associated implementation tools, will be incorporated into active stock assessments to reduce the error, uncertainty, and throughput delays associated with overfishing limits and acceptable biological catch estimates.
Lead Investigator: Nathan Vaughan, Vaughan Analytics (firstname.lastname@example.org)
Natural Resource Managers: John Walter, NOAA Southeast Fisheries Science Center; Katie Siegfried, NOAA Southeast Fisheries Science Center; Skyler Sagarese, NOAA Southeast Fisheries Science Center; Luiz Barbieri, Florida Fish and Wildlife Conservation Commission Fish and Wildlife Research Institute
Project Team: Ryan Rindone, Gulf of Mexico Fishery Management Council; Nick Farmer, NOAA Southeast Regional Office
Federal Program Officer/Point of Contact: Frank Parker (email@example.com)
Award Amount: $1,151,562
Award Period: October 2023 – September 2028
Why it matters: Successful fishery management in the Gulf of Mexico is dependent upon stock assessment advice providing accurate and timely overfishing limits and annual biological catch estimates. Increasingly frequent environmental and regulatory changes require stock assessment models to capture more complex details of the fishery and population dynamics relevant to management at a faster pace. However, this increasing model complexity has not been matched with increasing model interpretability causing reduced throughput of management advice and increased sensitivity of advice to model assumptions.
What the team is doing: The project team will develop new methods and technological capacity to increase the accuracy of projection assumptions, improve transparency in the impact of structural assumptions made in model development, and increase the throughput of stock assessment models to improve the accuracy of management advice under changing environmental conditions.The team will develop, test, and implement capacity to 1) better incorporate known uncertainties and regulatory impacts into stock assessment model projections and management reference points; 2) facilitate multi-model ensemble inference approaches, though improved model diagnostics and interpretation techniques; and 3) utilize scalable model complexity between current full and interim assessment methods to iteratively update data sources and parameter estimates as available and appropriate to maximize the accuracy and timeliness of management advice.
Expected outcome: This project will reduce bias and uncertainty in the estimation of overfishing limits and acceptable biological catch limits critical to the management of fisheries in the Gulf. This will be achieved by providing practical and actionable improvements to existing stock assessment modeling software in conjunction with focused training for stock assessment analysts integrated directly into current stock assessment workflows.