In the Quantitative Fisheries Research Group, our research objective is to improve the management of fish populations and fisheries. Our research covers marine and freshwater systems, including interactions among fishes, marine mammals, invertebrates, and their habitats. We are pursuing research on three broad topics:
- Key processes that affect the dynamics of aquatic populations.
- Methods for estimating key parameters and quantifying uncertainties for use in conservation and management of aquatic populations.
- Approaches to reducing uncertainties or their consequences.
1. Key Processes
- Environmental causes of variation in survival and growth rates in fishes;
- Dynamic responses of commercial and recreational fishing fleets;
- Ecosystem effects of fishing, both direct and indirect;
- Fisheries management from an ecosystem perspective;
- Effects of uncertainties on management regulations;
- Effects of land use on fish habitat.
For example, to improve understanding of environmental causes of variation in survival rates, growth rates, and age at maturity of Pacific salmon, we are using large multi-stock data sets. Such work is important because changes in these key processes affect conservation risks associated with fishing, climatic change, and other disturbances, as well as fish biomass available for harvesting.
2. Development of methods for conservation and management of aquatic populations
- Assessment methods for new and data-poor fisheries;
- Combining multiple sources of information;
- Maximum likelihood methods for estimating recruitment from survey data;
- Bayesian hierarchical models of multiple populations;
- Statistical methods for tracking time-varying fish productivity arising from changes in climate and the environment;
- Pre-season forecasting of abundance of Pacific salmon;
- Effects of regulations, bycatch, and discarding behaviour on marine biodiversity;
We use these methods to estimate key parameters and quantify uncertainties. We are also exploring the management implications of uncertainties arising from natural variability and measurement error by developing and applying empirically based simulation models to various fisheries.
We also apply decision analysis, Bayesian statistics, and classical statistical analysis of data to a wide range of fisheries management issues. Some examples include:
- Improved models for managing low-abundance salmon stocks to help identify appropriate “limit reference points” and the associated decision rules;
- Biological and economic analysis of uncertainties about lake fertilization;
- Harvest strategies for marine invertebrates;
- Appropriate “safety margins” for harvesting fish, given uncertainties and conservation concerns;
- Discarding behaviour in groundfish fisheries and regulations to reduce discarding;
- A decision-support model for deciding when to open or close a fishery;
- Optimal stocking rates for rainbow trout in lakes.
3. Approaches to reducing uncertainties or their consequences
We are developing and applying approaches to reduce the magnitude of uncertainties that are inherent in fisheries. For instance:
- Design of monitoring programs for determining effects of climate change on Pacific salmon;
- Application of Marine Protected Areas in fisheries management;
- Simulation studies to develop robust methods for fisheries management that incorporate all aspects of the system, from data collection and stock assessment methods through to the setting of regulations and allowing for imperfect compliance with those regulations.