Gearing up: Methods for quantifying gear density for fixed-gear commercial fisheries in the U.S. Atlantic

Fixed-gear commercial fisheries are unique due to their occupancy nature, claiming areas of the ocean for discrete periods. As space conflicts arise from competing ocean uses, there is an increased need to understand and categorize fixed-gear fisheries to incorporate into marine spatial planning (MSP) efforts. We used fishery-dependent data and input from stakeholders to discern fleet dynamics of all gillnet and trap/pot fisheries in U.S. waters of the Northwest Atlantic Ocean. A Fixed-Gear Fishery Layer (FGFL) was developed combining fishery subgroups that were categorized around gear type, gear configuration, and species landed. Fishing effort from each subgroup was spatially allocated onto a 1 nm 2 (1.9 km 2 ) grid using methods that relied on the level of detail available from trip reporting and monitoring, each with differing degrees of spatial resolution. This stepwise process allowed trips reported with minimal spatial detail to be included while not compromising trips where greater spatial precision existed. We demonstrate how the FGFL has been used for two MSP projects focused on protected species conservation and wind energy development.

Authors

Institutions

Publication Details

Journal
Canadian Journal of Fisheries and Aquatic Sciences
Published
2024-10-18
DOI
https://doi.org/10.1139/cjfas-2024-0123
Citations
2
Primary Topic
Marine and fisheries research
Type
article
Field-Weighted Citation Impact
0.57
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Gearing up: Methods for quantifying gear density for fixed-gear commercial fisheries in the U.S. Atlantic

Burton Shank, Michael J. Asaro, Douglas B. Sigourney, Laura K. Solinger et al.
2 citations
Canadian Journal of Fisheries and Aquatic Sciences
Marine and fisheries research
0.57
article

Gearing up: Methods for quantifying gear density for fixed-gear commercial fisheries in the U.S. Atlantic

Burton Shank, Michael J. Asaro, Douglas B. Sigourney, Laura K. Solinger, Alicia S. Miller, Alessandra Huamani
article en
2 citations

Abstract

Fixed-gear commercial fisheries are unique due to their occupancy nature, claiming areas of the ocean for discrete periods. As space conflicts arise from competing ocean uses, there is an increased need to understand and categorize fixed-gear fisheries to incorporate into marine spatial planning (MSP) efforts. We used fishery-dependent data and input from stakeholders to discern fleet dynamics of all gillnet and trap/pot fisheries in U.S. waters of the Northwest Atlantic Ocean. A Fixed-Gear Fishery Layer (FGFL) was developed combining fishery subgroups that were categorized around gear type, gear configuration, and species landed. Fishing effort from each subgroup was spatially allocated onto a 1 nm 2 (1.9 km 2 ) grid using methods that relied on the level of detail available from trip reporting and monitoring, each with differing degrees of spatial resolution. This stepwise process allowed trips reported with minimal spatial detail to be included while not compromising trips where greater spatial precision existed. We demonstrate how the FGFL has been used for two MSP projects focused on protected species conservation and wind energy development.

Canadian Journal of Fisheries and Aquatic SciencesVol. 82
Integrated Statistics (United States) (US), NOAA National Marine Fisheries Service Northeast Fisheries Science Center (US)
Affordable and clean energy
Openalex Percentile: Top 33%
Marine and fisheries research
0.57
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.