Use of Multiple Selectivity Patterns As a Proxy for Spatial Structure

Acknowledging spatial structure is important for fisheries stock assessments, and several efforts have been made to incorporate it. However, most studies exploring the impact of ignoring spatial structure in stock assessments have dealt with complex stock structure and multiple subpopulations rather than the impact spatial dynamics on model performance. Furthermore, fully specified spatially-explicit models can be difficult to develop.

One approach around this problem is to use several fleets with different selectivity patterns to represent availability. Here, the impact of ignoring spatial structure and the effectiveness of using multiple selectivity patterns as proxy for spatial structure are evaluated. The northern subpopulation of Pacific sardine (Sardinops sagax) is used as case study. 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. Ignoring spatial structure impacts the stock assessment model, with seasonal movement having the largest impact on estimation ability. SS compensates for ignoring movement and spatial structure by adjusting the selectivity patterns, but selectivity alone is not able to account for all variability.