Improving the robustness of fisheries stock assessment models to outliers in input data

Outliers caused by atypical observation error often occur in fishery data. These outliers have an adverse effect on the parameter estimation for fishery stock assessment models. We tested a robust distribution for identifying and removing outliers from fishery data. We conducted a simulation study in which a surplus production model was used to mimic fishery population dynamics and outliers caused by atypical observation error were imposed in the biomass index data. The method performed well by effectively identifying the real outliers and avoiding defining other data points as outliers. By removing the detected outliers and fitting the model with the remaining data points, the accuracy of the parameter estimation was improved. We discussed the precautions of applying this method and its potential applicability in other fishery stock assessment models.