Overcoming challenges of harvest quota allocation in spatially structured populations

Ignoring spatial population structure in the development of fisheries management advice can affect population resilience and yield. However, the resources required to develop spatial stock assessment models that match the spatial scale of management are often unavailable. As a result, quota recommendations from spatially aggregated assessment models are commonly divided among management areas based on empirical methods. We developed a spatially explicit simulation model to 1) explore how variation in population structure influences the spatial distribution of harvest that produces maximum system yield, and 2) contrast the performance of empirical quota allocation methods in approximating ideal spatial harvest strategies. Spatial scenarios that included post-recruitment movement resulted in a broader range of spatial management options (e.g., setting regional total allowable catch) that achieved near maximum system yield compared to scenarios without movement. Stochastic projections showed that using the proportion of total survey biomass in each management area to spatially allocate quota performed best for maximizing system yield when the true spatial structure was unknown, considerably outperforming equal allocation and allocation based on a recruitment index. However, with all methods, area-specific harvest rates sometimes led to unintended depletion within management units. Improved data and understanding of spatial stock dynamics can reduce the need for ad hoc approaches for spatial harvest allocation, allow for a greater range of management options, and increase the efficacy of spatial management procedures.