Towards Sustainable Fisheries: State Space Assessment Models for Complex Fisheries and Biological Data

Collaborative Research Team Project: 2018-2021

Fisheries scientists collect biological and fisheries data to perform stock assessments. These provide resource managers with information required to regulate fish stocks. Stock assessment models typically combine biological and fisheries data to estimate either the total population or the total biomass of a given stock. These models make it possible to assess the status and condition of the stock, as well as predict how it will respond to varying levels of fishing pressure in the future. If fisheries scientists can reliably estimate the biomass of the stock and understand its biology, then they can additionally estimate how many fish can be safely removed from the stock, while ensuring a sustainable resource.

Over-fishing, or the taking of fish beyond sustainable levels, is a global problem that threatens fish stocks and employment with many serious social, economic, and environmental consequences. As a result, the key goals of fisheries management are to eliminate over-fishing and restore stocks that have been previously over-fished.

Recently, State space stock assessment models (SSAMs) have received renewed attention, because new software has made it possible to use the Laplace approximation to implement and estimate these models efficiently. These recent implementations have finally made SSAMs fully operational such that they are now routinely used to manage important fish stocks around the world. More than 20 official fish stock assessments in the International Council for the Exploration of the Seas (www.ices.dk) are now conducted with SSAMs. The availability of software to easily express and efficiently estimate these models has made all the difference.

The complex SSAMs required in the field of fish stock assessment make it an interesting and fruitful field for applied statisticians. There is a clear need for further development of both the underlying theory and supporting statistical software. Overall, this project will not only improve tools for accurate stock assessment, it will also address the void of highly qualified personnel in Canada. As a result, it will help to develop best practice for the conservation of fish.

The team leader is Joanna Mills Flemming from Dalhousie University. 

Statistical Collaborators:
William Aeberhard and Chris Field – Dalhousie University; Marie Auger-Méthé  – University of British Columbia; Eva Cantoni – University of Geneva; Anders Nielsen  – Technical University of Denmark; and Louis-Paul Rivest– Université Laval.

Fisheries Collaborators:
Hugues Benoit and Daniel Duplisea – Maurice Lamontagne Institute, Fisheries and Oceans Canada (DFO); Noel Cadigan – Fisheries and Marine Institute of Memorial University of Newfoundland; Andrew Edwards – Pacific Biological Station DFO; David Keith – Bedford Institute of Oceanography, DFO; Aaron MacNeil and Boris Worm – Dalhousie University; and Cóilín Minto – Galway-Mayo Institute of Technology.

Partner organizations include Fisheries and Oceans Canada, Canada; DTU AQUA, Denmark; Institute of Marine Research, Norway; Marine and Freshwater Research Centre, Galway-Mayo Institute of Technology, Ireland; The Ocean Frontiers Institute (OFI), Canada; The Ocean Tracking Network (OTN), Canada.

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