Fish & Ships: Using overlap between Atlantic fishing fleets and highly migratory pelagic species to reveal hotspots of potential management interest

Due to their large ecological niche and high commercial value, highly migratory species are exposed to fishing pressure across the Atlantic Ocean. Although international management collects spatial catch and effort data, accuracy and representativeness are difficult to verify. Fleet behavior can increasingly be monitored using ship tracking technologies. Simultaneously, electronic tagging provides location estimates as well as broader insights into seasonal habitat suitability. Here, we combined electronic tagging datasets from 778 satellite and archival tags with environmental data to model distributions for Atlantic bluefin tuna, blue sharks, swordfish, and blue marlin. We overlaid these maps with longline effort estimates derived from AIS data to identify times and regions of high overlap between species and fleet. Comparing with species-specific CPUEs reported to management, we identified regions where, for specific species and seasons, reported catches were low compared to species density predicted from presence data and habitat models. This data science approach may be useful to management enforcement agencies and compliance committees, potentially identifying regions where fleets may be under-reporting or other management assumptions may be invalid.

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Publication Details

Journal
Canadian Journal of Fisheries and Aquatic Sciences
Published
2026-07-23
DOI
https://doi.org/10.1139/cjfas-2025-0218
Primary Topic
Marine and fisheries research
Type
article
Field-Weighted Citation Impact
0.00
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article

Fish & Ships: Using overlap between Atlantic fishing fleets and highly migratory pelagic species to reveal hotspots of potential management interest

Simon R. Thorrold, Jonathan Dale, Barbara A. Block, John F. Walter et al.
Canadian Journal of Fisheries and Aquatic Sciences
Marine and fisheries research
article

Fish & Ships: Using overlap between Atlantic fishing fleets and highly migratory pelagic species to reveal hotspots of potential management interest

Simon R. Thorrold, Jonathan Dale, Barbara A. Block, John F. Walter, Emilius A. Aalto, Christian Jones, Pedro Afonso, Gregory Skomal, Timothy White, Eric Orbesen, Derke Snodgrass, Camrin Braun
article en

Abstract

Due to their large ecological niche and high commercial value, highly migratory species are exposed to fishing pressure across the Atlantic Ocean. Although international management collects spatial catch and effort data, accuracy and representativeness are difficult to verify. Fleet behavior can increasingly be monitored using ship tracking technologies. Simultaneously, electronic tagging provides location estimates as well as broader insights into seasonal habitat suitability. Here, we combined electronic tagging datasets from 778 satellite and archival tags with environmental data to model distributions for Atlantic bluefin tuna, blue sharks, swordfish, and blue marlin. We overlaid these maps with longline effort estimates derived from AIS data to identify times and regions of high overlap between species and fleet. Comparing with species-specific CPUEs reported to management, we identified regions where, for specific species and seasons, reported catches were low compared to species density predicted from presence data and habitat models. This data science approach may be useful to management enforcement agencies and compliance committees, potentially identifying regions where fleets may be under-reporting or other management assumptions may be invalid.

Canadian Journal of Fisheries and Aquatic Sciences
NOAA National Marine Fisheries Service (US), Universidade dos Açores (PT), World Water Watch (US), NOAA National Marine Fisheries Service Northeast Fisheries Science Center (US), Institute of Marine Research (PT), NOAA National Marine Fisheries Service Southeast Fisheries Science Center (US), Pacific University (US), Woods Hole Oceanographic Institution (US), Stanford University (US)
Life below water
Openalex Percentile: Top 11%
Marine and fisheries research
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