Evaluating Potential Impacts of Offshore Wind Development on Fishing Operations by Comparing Fine‐ and Coarse‐Scale Fishery‐Dependent Data

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.

Spatial analysis of fishing tows with Automatic Identification System (AIS) data to inform offshore wind layouts

“…Allen‐Jacobson et al. (2023) have recently explored one such source. They examined data from the National Marine Fisheries Service Northeast Cooperative Research Study Fleet to develop fine‐scale location information and improve the understanding of overlaps between fishing locations and planned wind farms.…”

NOAA fisheries research geared towards climate-ready living marine resource management in the northeast United States

“…Additionally, not all fisheries on the U.S. NES use VTRs to report their fishing activity and are therefore underrepresented in certain analyses (e.g., Highly Migratory Species, American lobster, charter/party vessels, etc.). Therefore, Allen-Jacobson et al (2023) evaluated bias in VTR footprints using finer scale data collected by the Northeast Fisheries Science Center's Study Fleet's global positioning system (GPS) location data (ping/minute) annotated as active fishing or not fishing [160]. This research demonstrates how high-resolution fishery dependent data are needed to detect spatially explicit differences in fishing behavior, and their overlap with planned wind energy areas.…”

Enhancing Chub Mackerel Catch Per Unit Effort (CPUE) Standardization through High-Resolution Analysis of Korean Large Purse Seine Catch and Effort Using AIS Data

“…However, a significant limitation arises in the resolution of landing data, which can only be resolved to a daily level of fishing activity. This is due to the absence of per-haul recording, which restricts the spatial resolution of the data [9]. Consequently, the precise location of individual catches cannot be determined, limiting the ability to analyze fishing effort and catch distribution at a fine-scale spatial level.…”

“…The time required for deploying, hauling, and emptying fishing gear is influenced by factors such as catch quantity and can vary significantly. However, this crucial information remains unknown [9]. The variability in handling time introduces uncertainty in the estimation of fishing effort and further complicates the interpretation of CPUE data.…”