As commercial fishing activity shifts to target different grounds over time, spatial gaps can be created in catch rate data and lead to biases in derived indices of fish abundance. Imputation has been shown to reduce such biases. In this study, the relative performance of several imputation methods was assessed using simulated catch rate datasets. Simulations were carried out for three fish stocks targeted by a commercial hook and line fishery off the south-western coast of Australia: Snapper (Chrysophrys auratus), West Australian Dhufish (Glaucosoma hebraicum), and Baldchin Groper (Choerodon rubescens). For High Growth scenarios, the mean squared errors (MSEs) of Geometric and Linear imputations were lower, indicating higher accuracy and precision, than Base method (constant value) imputations. For Low Growth scenarios, the lowest MSEs were achieved for Base method imputations. However, for the final standardised and imputed abundance indices, the Base method index consistently demonstrated the largest biases. Results demonstrate the importance of selecting an appropriate imputation method when standardising catch rates from a commercial fishery that changed its spatial pattern of fishing over time
