Elliott L. Hazen

University of California
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Articles (11)

Accurate assessments of human-wildlife risk associated with industrial fishing are critical for the conservation of marine top predators. Automatic Identification System (AIS) data provide a means of mapping fishing and estimating human-wildlife risk; however, risk can be obscured by gaps in the AIS record due to technical issues and intentional disabling. We assessed the extent to which unseen fishing vessel activity due to AIS gaps obscured estimates of overlap between fishing vessel activity and 14 marine predators including sharks, tunas, mammals, seabirds, and critically endangered leatherback turtles. Among vessels equipped with AIS in the northeast Pacific, up to 24% of total predator overlap with fishing vessel activity was unseen, and up to 36% was unseen for some individual species. Waters near 10°N had high unseen overlap with sharks yet low reported shark catch, revealing potential discrepancies in self-reported datasets. Accounting for unseen fishing vessel activity illuminates hidden human-wildlife risk, demonstrating challenges and solutions for transparent and sustainable marine fisheries.

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2024

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2023

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2023

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2022

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2022

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2022

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2022

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2022

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2022

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2022

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2022

Collaborators (12)

Emanuele Di Lorenzo

Professor

Brown University

UNITED STATES
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Rebecca Selden

Assistant Professor

Wellesley College

UNITED STATES
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Nicole Lovenduski

University of Colorado Boulder

UNITED STATES
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Megan Cimino

University of California, Santa Cruz

UNITED STATES
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Alexa Fredston

Assistant Professor

University of California, Santa Cruz

UNITED STATES
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Shirel R. Kahane‐Rapport

-

UNITED STATES
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Jameal F. Samhouri

University of Washington

UNITED STATES
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Jeremy Goldbogen

Associate Professor

-

UNITED STATES
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Barbara Muhling

University of California, Santa Cruz

UNITED STATES
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Giulio De Leo

Professor

Stanford University

UNITED STATES
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Max Czapanskiy

Assistant Teaching Professor

University of California, Santa Barbara

UNITED STATES
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Desiree Tommasi

-

UNITED STATES
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