Collaborative Research Team Project 2014-2017
The goals of this project are two-fold: first, to develop general-purpose, statistically rigorous state-space model (SSM) methodologies for fisheries science, and second, to demonstrate the utility of these new approaches for addressing pressing fisheries issues in Canada through carefully chosen case-studies. By so doing, the team aims to facilitate the adoption of these novel approaches by Fisheries and Oceans Canada (DFO) and others to enhance science-based fisheries management. For the first goal, the team will:
- Build robust statistical methodologies for SSMs used in fisheries science and management that allow for both errors and misspecification in the observation process and in the state/dynamic process.
- Develop a formal model selection mechanism to choose reliably among candidate SSMs by necessarily accounting for the variability associated with the model criterion measure itself.
- Develop relevant model diagnostics that can be used to evaluate model goodness-of-fit for both the observation and state process. In practice, stock assessment scientists and fisheries managers need to know if their advice is sensitive to reasonable alternative assumptions.
- Evaluate the reliability of the SSM approach, in terms of accuracy and precision for measuring model outputs which are key for fisheries, with a view to developing a better understanding of the data requirements and limitations of the models.
The team leader is Joanna Mills Flemming of Dalhousie University, and collaborators are Noel Cadigan of Memorial University, David Campbell and Rick Routledge of Simon Fraser University, Eva Cantoni of the University of Geneva, Steven Cooke of Carleton University, Daniel Duplisea of Fisheries and Oceans Canada, Chris Field and Boris Worm of Dalhousie University, Scott Hinch of the University of British Columbia, Aaron MacNeil of the Australian Institute of Marine Science, Anders Nielsen of the Technical University of Denmark, and Håvard Rue of the Norwegian University of Science and Technology.