A comparative study of observation-error estimators and state-space production models in fisheries assessment and management

Abstract

State-space production models are increasingly being used in fisheries stock assessment as they provide the ability to account for observation and process errors. However, model performance when the population dynamics specified differs from the true biological process requires evaluation. We compared the estimation performance of a standard observation-error approach with a state-space production model for various simulated levels of model, process, and observation errors. We found that the state-space production model was generally superior to the observation-error estimator. However, the advantage of the state-space production model in parameter estimation diminished with increased model errors. The observation-error estimator outperformed the state-space production model when model error exceeded a certain level. A significant number of small process and observation error estimates (<0.0001) from the state-space model were observed. The process and observation error estimates were biased, with the bias direction influenced by the ratio of process error to observation error. Our results highlight precautions in applying different types of production model estimators in fisheries stock assessment and management.