【摘要】：Various population structures or spatial heterogeneities in population distribution have been an important source of model misspecification and have had an impact on estimation performance in fisheries stock assessment. In this study, we simulated the Indian Ocean albacore spatial heterogeneity in age-structure using Stock Synthesis according to the stage-dependent migration rate and region-dependent fishing mortality rate and generated the stock assessment data. Based on these data, we investigated the performances of different spatial configurations,selectivity curves and selections of CPUE(catch per unit effort) indices of the assessment models which were used to account for spatial heterogeneity. The results showed:(1) although the spatially explicit configurations, which exactly matched the operating model, provided unbiased and accurate estimates of relative spawning biomass,relative fishing mortality rate and maximum sustainable yield in all simulation scenarios, their performance may be very poor if there were mismatches between them and the operating model due to gaps in knowledge and data;(2) for spatially explicit assessment configuration, the correct boundary was required, but for non-spatially explicit assessment configuration, it seemed more important for analysts to partition the area to properly reflect the transition in field data and to effectively account for the impacts of ignoring the spatial structure by using the additional spatially referenced parameters;(3) although the areas-as-fleets methods and flexible time-varying selectivity curves could be used as better alternative approaches to account for spatial structure, these configurations could not completely eliminate the impacts of model misspecification and the quality of estimates of different quantities from the same assessment model may be inconsistent or the performance of the same assessment configuration may fluctuate significantly between simulation scenarios;(4) although the worst estimates could generally be avoided by using multiple CPUE indices, there were no best solutions to select or regenerate the CPUE indices to account for the impacts of the ignored spatial structure to obviously improve the quality of stock assessment. Compared with the results of assessment model configurations which are used to account for the spatial structure by different modelers, the performances of the configurations are always casespecific except for spatially explicit configurations which exactly match the operating model. In this sense, our study will not only provide some insights into the current Indian Ocean albacore stock assessment but also enrich existing knowledge regarding the performance of assessment configurations to account for spatial structure.