No abstract

The World Conference on Stock Assessment Methods (July 2013) included a workshop on testing assessment methods through simulations. The exercise was made up of two steps applied to datasets from 14 representative fish stocks from around the world.Step 1 involved applying stock assessments to datasets with varying degrees of effort dedicated to optimizing fit.Step 2 was applied to a subset of the stocks and involved characteristics of given model fits being used to generate pseudo-data with error. These pseudo-data were then provided to assessment modellers and fits to the pseudo-data provided consistency checks within (self-tests) and among (cross-tests) assessment models. Although trends in biomass were often similar across models, the scaling of absolute biomass was not consistent across models. Similar types of models tended to perform similarly (e.g. age based or production models). Self-testing and cross-testing of models are a useful diagnostic approach, and suggested that estimates in the most recent years of time-series were the least robust. Results from the simulation exercise provide a basis for guidance on future large-scale simulation experiments and demonstrate the need for strategic investments in the evaluation and development of stock assessment methods.

An "exponential–logistic" selectivity function is presented in which a single parameter (γ) determines whether gear selectivity is asymptotic (γ = 0) or reaches a maximum at finite age (γ > 0). The function is used to develop a model in which both γ and the natural mortality rate M are formally indeterminate and in which the coming year's catch limit can be viewed as a response function of either estimated γ or estimated M. Decision theory is then used to derive the optimal catch. The optimal catch is shown to increase with the degree of uncertainty surrounding M, although this conclusion may depend on the short managerial time frame assumed. Three "suboptimal" strategies are also considered: (1) setting catch at the level corresponding to the expected value of M, (2) setting catch at the minimum of the response function, and (3) setting catch at the level corresponding to γ = 0. The first suboptimal strategy never results in a catch greater than the optimum and always results in a lower expected loss than the second. The performance of the third strategy (relative to the others) depends on parameter values.

Lack of guidance for interpreting the definitions of endangered and threatened in the U.S. Endangered Species Act (ESA) has resulted in case-by-case decision making leaving the process vulnerable to being considered arbitrary or capricious. Adopting quantitative decision rules would remedy this but requires the agency to specify the relative urgency concerning extinction events over time, cutoff risk values corresponding