Reconciling stock assessment and management scales under conditions of spatially varying catch histories

Spatial homogeneity is the exception, not the rule, for many marine populations. Data limitations or biological knowledge gaps, though, often drive the assumption of limited stock structuring; the subsequent mismatch of model spatial scale and biological stock structure may compromise management goals. Spatial considerations thus remain a major challenge in providing managers with the best information for responsible and responsive management. This study uses simulation testing to offer a quantitative evaluation of spatial stock structure assumptions on the performance of stock assessments relative to management scales. Catch histories, not biological differences, are used to create stock structure. Simulation testing is based on an operating model of 'true' population states of nature that vary only in catch history across 9 regions. Stock assessments under various data scenarios are then performed on regions either as one aggregated assessment, or at finer area scales defined by zonal catch differences. The median absolute relative error of the terminal spawning biomass depletion is used as a performance statistic. One area stock assessments demonstrated low bias and high precision under all catch scenarios when stock structure is ignored, but perform poorly when applied to areas with differing regional catch histories. Separate area assessments grouped by zonal catch differences performed best under these circumstances, despite lower data quality, indicating the importance of identifying stock structure for management purposes. We suggest a focus on explicitly defining management units prior to conducting stock assessments with a concomitant resolve to increase data provisions at the resolution of management needs. Published by Elsevier B.V.