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.

中文翻译：

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通过比较精细和粗规模渔业相关数据评估海上风电开发对渔业作业的潜在影响

气候变化将扰乱海洋环境的许多方面，预计会对美国东北部一半的渔业产生影响。为了减轻气候变化的影响，美国指定了 90,650 公里²（35,000 英里²) 用于海上风能开发，但这个不断发展的行业可能会影响该地区的渔业。因此，需要衡量捕鱼作业的空间分布以支持多个目标，包括空间规划和补偿性缓解。在美国东北部，国家海洋和大气管理局渔业部以前通过使用日志来开发捕鱼足迹。然而，日志足迹依赖于粗略的数据：一个单一的位置，日志中报告的钓鱼旅行的中心点。因此，我们通过限制航海日志足迹的大小以及根据在同一地区运营的参考船队收集的精细位置数据生成活跃捕鱼足迹来评估这些航海日志足迹中的偏差。活跃捕鱼足迹作为基准，近似于“真实”捕鱼足迹和暴露于风电场。我们专注于长鳍近海鱿鱼白花鲈渔业，包括 2016 年至 2019 年的 336 次旅行，以及新英格兰南部和大西洋中部海湾的 38 个风电场。与基准的主动捕鱼足迹相比，不受限制的日志足迹检测到所有暴露的行程。由于我们限制了日志足迹，日志分析未能检测到暴露的行程，但更好地估计了暴露的收入金额。最后，不受限制的日志足迹低估了高影响风电场的暴露收入，高估了低影响风电场的暴露收入，并且这种偏差随着日志足迹限制而下降。我们展示了在精细尺度数据不可用时，限制日志足迹如何改进依赖于粗略尺度数据的暴露分析。此外，我们的分析强调了粗尺度数据（即日志足迹）的局限性。因此，我们建议为自愿参与收集精细数据的计划提供额外奖励。这些激励措施应该被优先考虑，因为明智的、时间敏感的决策取决于在海上风电场建设之前收集的数据。