Abstract
Climate change will disrupt many aspects of the marine environment, with anticipated effects for half of northeastern U.S. fisheries. To mitigate effects of climate change, the United States has designated 90,650 km2 (35,000 mi2) of ocean for offshore wind energy development, but this growing industry could impact fisheries in the region. Hence, there is a need to measure the spatial distribution of fishing operations to support multiple goals, including spatial planning and compensatory mitigation. In the U.S. Northeast, National Oceanic and Atmospheric Administration Fisheries developed fishing footprints previously by using logbooks. However, logbook footprints rely on coarse data: a single location, the center point of fishing trips reported in logbooks. Therefore, we evaluated bias in these logbook footprints by restricting the size of logbook footprints and by generating active-fishing footprints from fine-scale location data collected by a reference fleet operating in the same region. Active-fishing footprints act as a benchmark approximating the “true” fishing footprint and exposure to wind farms. We focused on the longfin inshore squid Doryteuthis pealeii fishery, including 336 trips from 2016 to 2019, and 38 wind farms in southern New England and the Middle Atlantic Bight. Compared to the benchmark active-fishing footprints, unrestricted logbook footprints detected all exposed trips. As we restricted the logbook footprints, the logbook analysis failed to detect exposed trips but better approximated the amount of exposed revenue. Finally, unrestricted logbook footprints underestimated the exposed revenue for high-impact wind farms and overestimated the exposed revenue for low-impact wind farms, and this bias declined with logbook footprint restriction. We show how restricting logbook footprints could improve exposure analysis that depends on coarse-scale data when fine-scale data are unavailable. Furthermore, our analysis highlights the limits of coarse-scale data (i.e., logbook footprints). Therefore, we recommend additional incentives for voluntary participation in programs collecting fine-scale data. These incentives should be prioritized because informed, time-sensitive decisions depend on data collected prior to construction of offshore wind farms.