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Bycatch mitigation for commonly caught shark species in the Gulf of Mexico reef bottom longline fishery
by
Babcock, Elizabeth A.
, O'Farrell, Halie B.
, McCarthy, Kevin J.
in
Bayesian analysis
/ Bayesian theory
/ Binomial distribution
/ bottom longline fishery
/ Bycatch
/ bycatch mitigation
/ Carnivorous animals
/ Catch per unit effort
/ Commercial fishing
/ data collection
/ Ecology
/ Endangered & extinct species
/ Endangered species
/ Environmental conditions
/ Fecundity
/ Fish
/ Fisheries
/ Fisheries management
/ Fishing
/ Fishing effort
/ Fishing methods
/ Fishing pressure
/ generalized additive model
/ Gulf of Mexico
/ habitats
/ landscapes
/ longline fisheries
/ Longline fishing
/ Marine fish
/ Marine fisheries
/ Marine fishes
/ Marine molluscs
/ Mathematical models
/ National Oceanic and Atmospheric Administration
/ Observers
/ Overfishing
/ Prediction models
/ Probability theory
/ Reefs
/ Rhizoprionodon
/ shark bycatch
/ Sharks
/ species
/ Sphyrna lewini
/ Variables
/ Wildlife conservation
2024
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Bycatch mitigation for commonly caught shark species in the Gulf of Mexico reef bottom longline fishery
by
Babcock, Elizabeth A.
, O'Farrell, Halie B.
, McCarthy, Kevin J.
in
Bayesian analysis
/ Bayesian theory
/ Binomial distribution
/ bottom longline fishery
/ Bycatch
/ bycatch mitigation
/ Carnivorous animals
/ Catch per unit effort
/ Commercial fishing
/ data collection
/ Ecology
/ Endangered & extinct species
/ Endangered species
/ Environmental conditions
/ Fecundity
/ Fish
/ Fisheries
/ Fisheries management
/ Fishing
/ Fishing effort
/ Fishing methods
/ Fishing pressure
/ generalized additive model
/ Gulf of Mexico
/ habitats
/ landscapes
/ longline fisheries
/ Longline fishing
/ Marine fish
/ Marine fisheries
/ Marine fishes
/ Marine molluscs
/ Mathematical models
/ National Oceanic and Atmospheric Administration
/ Observers
/ Overfishing
/ Prediction models
/ Probability theory
/ Reefs
/ Rhizoprionodon
/ shark bycatch
/ Sharks
/ species
/ Sphyrna lewini
/ Variables
/ Wildlife conservation
2024
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Bycatch mitigation for commonly caught shark species in the Gulf of Mexico reef bottom longline fishery
by
Babcock, Elizabeth A.
, O'Farrell, Halie B.
, McCarthy, Kevin J.
in
Bayesian analysis
/ Bayesian theory
/ Binomial distribution
/ bottom longline fishery
/ Bycatch
/ bycatch mitigation
/ Carnivorous animals
/ Catch per unit effort
/ Commercial fishing
/ data collection
/ Ecology
/ Endangered & extinct species
/ Endangered species
/ Environmental conditions
/ Fecundity
/ Fish
/ Fisheries
/ Fisheries management
/ Fishing
/ Fishing effort
/ Fishing methods
/ Fishing pressure
/ generalized additive model
/ Gulf of Mexico
/ habitats
/ landscapes
/ longline fisheries
/ Longline fishing
/ Marine fish
/ Marine fisheries
/ Marine fishes
/ Marine molluscs
/ Mathematical models
/ National Oceanic and Atmospheric Administration
/ Observers
/ Overfishing
/ Prediction models
/ Probability theory
/ Reefs
/ Rhizoprionodon
/ shark bycatch
/ Sharks
/ species
/ Sphyrna lewini
/ Variables
/ Wildlife conservation
2024
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Bycatch mitigation for commonly caught shark species in the Gulf of Mexico reef bottom longline fishery
Journal Article
Bycatch mitigation for commonly caught shark species in the Gulf of Mexico reef bottom longline fishery
2024
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Overview
Objective The Gulf of Mexico (GOM) reef bottom longline fishery typically not only targets groupers and snappers but also interacts with 27 species of sharks, which are primarily discarded as bycatch. Slow growth, late maturity, and low fecundity in a landscape of increasing fishing pressure make sharks comparatively more susceptible to overfishing and endangered status than other fishes. The purpose of this study was to determine which gear and/or environmental variables best predict the shark catch per set for commonly caught shark species in the GOM reef bottom longline fishery. Methods We considered 12 commonly caught shark species that vary from the abundant Atlantic Sharpnose Shark Rhizoprionodon terraenovae to the critically endangered Scalloped Hammerhead Sphyrna lewini. Catch per set, effort, gear, and environmental data were taken from the National Oceanic and Atmospheric Administration National Marine Fisheries Service observer dataset for the GOM reef bottom longline fishery (2009–2017) and were used to fit generalized additive models. The Bayesian information criterion and 10‐fold cross‐validation were used to select the best set of variables that predicted catch per set to determine gear configurations, fisher activities, and environmental conditions contributing to higher shark catch per unit effort. We modeled each species individually, all species combined, and species grouped by similar ecology. Result Gear and fishing method variables were consistently included in the best predictive models across species and were the only potential basis for a single strategy that could decrease bycatch across all 12 species. Patterns of environmental variables were only consistent across species with similar ecology and habitat. Conclusion Sharks as a group should not be lumped together, as the effects of mitigation measures become confounded and directly managing trade‐offs between species when minimizing bycatch becomes impossible. Focusing on gear rather than environmental variables is the best apparent option to potentially reduce shark catch per set across commonly caught species while minimizing trade‐offs. Impact statement Sharks are slow‐growing species that give birth to very few offspring late in life, making them highly susceptible to overfishing. We used catch data from the Gulf of Mexico to determine gear and environmental conditions that contribute to high shark catch in an attempt to minimize incidental shark catch and prevent overfishing. Focusing on gear configurations is the best apparent option to potentially reduce shark incidental catch across commonly caught species.
Publisher
Oxford University Press
Subject
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