Incorporating Uncertainty into Fishery Models

Mixed Monte Carlo/Bootstrap Approach to Assessing King and Spanish Mackerel in the Atlantic and Gulf of Mexico: Its Evolution and Impact

Christopher M. Legault, Joseph E. Powers, and Victor R. Restrepo

doi: https://doi.org/10.47886/9781888569315.ch4

Abstract.— The stock assessment analyses of king and Spanish mackerel fisheries of the southeastern United States have a long history of incorporating uncertainty. The development of this philosophy resulted from a number of unique circumstances, both biological and historical, that encouraged the incorporation of stochastic approaches and risk evaluation to the assessment and management process. The progression from simple discrete decision tree analysis to delta methods to Monte Carlo/bootstrap methods was due not only to advances in assessment technology but also to changing requirements for management. The current method for mackerel stock assessment is a tuned virtual population analysis with uncertainty incorporated via a mixed Monte Carlo/bootstrap algorithm. Through this procedure, uncertainty in the tuning indices, catch-at-age and natural mortality rate are directly incorporated into the advice provided to management. The management advice is given in terms of probability statements, as opposed to point estimates, to reflect this uncertainty in the stock assessments. This approach is a result of the evolution of the assessment and management and provides a pragmatic alternative in the “frequentist versus Bayesian” debate.