Project summary:
Scientific uncertainty affects all parts of the fisheries management process. This study reviews methods for quantifying scientific uncertainty for presentation as part of the scientific advice to fisheries managers. We surveyed stock assessment scientists to a) identify the methods commonly used to quantify uncertainty, b) describe how method use has changed over time, c) investigate the factors that influence which methods are used, and d) characterize how scientific uncertainty is presented to fisheries managers. We found that scientific uncertainty is being quantified and included in scientific advice across multiple fishery management systems. Frequentist approaches for quantifying uncertainty are used more broadly than Bayesian approaches, and the survey did not detect this changing over time. Time restrictions and methodology requests during the scientific review process were commonly reported as factors influencing the use of uncertainty methods. Uncertainty in estimates of management targets (e.g., fishing mortality or biomass), projections, and catch limits were the quantities most frequently included in the scientific advice presented to fisheries managers. Methods for quantifying uncertainty and their incorporation into management advice are quickly advancing, and our approaches for reviewing progress towards clearly and explicitly communicating the sources, treatment, and impacts of uncertainty in management processes must keep pace.
Status: Published
GitHub: Not applicable.
Keywords: scientific uncertainty; stock assessment; scientific advice
Key definitions
Scientific uncertainty is an umbrella term for observation, process, model, and estimation uncertainties.
- Observation uncertainty: the uncertainty in measurement of observable quantities such as biomass from surveys, catch or sizes-at-age
- Process uncertainty: the uncertainty due to underlying stochasticity in stock dynamics such as recruitment or variation in the growth of a fish stock
- Model uncertainty: the misspecification of model parameters or structure (e.g., assuming the incorrect form for selectivity as a function of size)
- Estimation uncertainty: the inaccuracy and imprecision associated with estimated model parameters
