Quantifying fish range shifts across poorly defined management boundaries

Management regimes of marine resources that rely on spatial boundaries might be poorly adapted to climate change shifts in species distributions. This is of specific concern for the management of fish stocks that cross management jurisdictions, known as shared stocks. Transitioning to dynamic rules in spatial management has been suggested as a solution for mismatches between species distributions and the spatial boundaries. However, in many cases spatial boundaries are not clearly drawn, hampering such transitions. Here, we use black sea bass (Centropristis striata), summer flounder (Paralichthys dentatus) and scup (Stenotomus chrysops) as case studies to explore different approaches to designing spatial regulatory units to facilitate the adaptation of fisheries management to shifting distributions of shared stocks. First, we determine the yearly distribution of each stock within the United States Exclusive Economic Zone from 1951 to 2019 during Fall and Spring sampling seasons. Second, we explore two approaches for drawing regulatory units based on state waters and historical landings. Finally, we estimate each state's proportion of the stock's distribution and compare historical and recent values. We show that the distribution of all three stocks has changed relative to the years used to determine the current quota allocation across states, with an overall gain for central-northern states at the expense of the southernmost states. In terms of the distribution of allocation, we find that, while seasonal differences exist, the biggest differences in the proportion of the stock spatial distribution attributed to each state come from the method for designing regulatory units. Here, we show that the method used to define allocation units can have meaningful impacts on resulting adaptive policy. As climate change-driven conflicts in fishing resource allocation are expected to increase and deepen around the world, we provide a replicable approach to make an informed and transparent choice to support data-driven decision-making.