Leveraging what we do not know to quantify uncertainty in fisheries management

Abstract

Stock assessment plays an important role in the broad field of risk management by providing a means to link scientific uncertainty to consequences of management actions, such as overfishing. The first chapter of this thesis reviews the methods and tools used to quantify scientific uncertainty and their relationship to the presentation of uncertainty to fisheries managers. I found that scientific uncertainty is being quantified and included in scientific advice across multiple fishery management systems. The second chapter relates to quantifying the scientific uncertainty used to set catch limits for US west coast groundfish and coastal pelagic species fisheries. This system serves as a good case study because the harvest control rule for reducing the overfishing limit to account for scientific uncertainty is based on the Pacific Fishery Management Council’s risk tolerance (i.e., the probability of overfishing) and a measure of scientific uncertainty (i.e., model specification based on among assessment variation). My approach bases the calculation of this uncertainty on projected overfishing limits, accounting for uncertainty in future recruitment as well as among-assessment variation. Methods for quantifying uncertainty and their incorporation into management advice are quickly advancing and approaches for reviewing progress towards clearly and explicitly communicating the sources, treatment, and impacts of uncertainty in our management processes must keep pace.