Use of multiple selectivity patterns as a proxy for spatial structure

• Simulation is used to evaluate the impact of spatial structure on the performance of non-spatially structured stock assessment method.
• Ignoring spatial structure will lead to biases when the population is spatially-structured.
• Including multiple fleets reduces the biases, but not completely.

There is widespread recognition that spatial structure is important for fisheries stock assessment, and several efforts have been made to incorporate spatial structure into assessment models. However, most studies exploring the impact of ignoring spatial structure in stock assessments have developed population models with multiple subpopulations, rather than exploring the impact spatial dynamics may have on performance of non-spatially structured assessment methods. Furthermore, the data available for stock assessments usually do not include tagging or other data necessary to estimate movement rates. One solution to this problem is to include several fleets, each with a different selectivity pattern to represent availability, within a spatially-aggregated assessment method. In this study, the impacts of ignoring spatial structure, and the effectiveness of using multiple selectivity patterns as a proxy for spatial structure, are evaluated for the northern subpopulation of Pacific sardine (or California sardine; Sardinops sagax). A spatially-explicit operating model is used to explore three spatial factors: the existence of size-dependent seasonal migrations across large geographical areas, the influx of another stock into the area of the assessed stock, and the occurrence of recruitment outside the area where it is assumed to occur. Two other factors related to data were evaluated: data availability and data collection design. The assessment model (AM) is based on the 2010 stock assessment for Pacific sardine, implemented in Stock Synthesis, and includes two seasons per year and six fleets, each with a different selectivity pattern. Ignoring spatial structure is found to negatively impact estimation performance, with seasonal movement having the largest impact. The AM compensates for ignoring movement and spatial structure by adjusting the selectivity patterns, but selectivity alone is not able to account for all biases caused by spatial structure.