Bayesian inference and assessment for rare-event bycatch in marine fisheries: a drift gillnet fishery case study

Martin, Summer L.; Stohs, Stephen M.; Moore, Jeffrey E.

doi:10.1890/14-0059.1

Cited by 41Turtle Biology and ConservationMarine and fisheries researchMarine animal studies overview

Bayesian inference and assessment for rare-event bycatch in marine fisheries: a drift gillnet fishery case study

Martin, Summer L., Stohs, Stephen M., Moore, Jeffrey E.·2015·Ecological Applications

DOI 10.1890/14-0059.1

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Abstract

Fisheries bycatch is a global threat to marine megafauna. Environmental laws require bycatch assessment for protected species, but this is difficult when bycatch is rare. Low bycatch rates, combined with low observer coverage, may lead to biased, imprecise estimates when using standard ratio estimators. Bayesian model-based approaches incorporate uncertainty, produce less volatile estimates, and enable probabilistic evaluation of estimates relative to management thresholds. Here, we demonstrate a pragmatic decision-making process that uses Bayesian model-based inferences to estimate the probability of exceeding management thresholds for bycatch in fisheries with < 100% observer coverage. Using the California drift gillnet fishery as a case study, we (1) model rates of rare-event bycatch and mortality using Bayesian Markov chain Monte Carlo estimation methods and 20 years of observer data; (2) predict unobserved counts of bycatch and mortality; (3) infer expected annual mortality; (4) determine probabilities of mortality exceeding regulatory thresholds; and (5) classify the fishery as having low, medium, or high bycatch impact using those probabilities. We focused on leatherback sea turtles (Dermochelys coriacea) and humpback whales (Megaptera novaeangliae). Candidate models included Poisson or zero-inflated Poisson likelihood, fishing effort, and a bycatch rate that varied with area, time, or regulatory regime. Regulatory regime had the strongest effect on leatherback bycatch…

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Observer coverage for the catch dataset was approximately 15% of all sets within the fishery for 1990-2011.
“Observer coverage for the catch dataset was approximately 15% of all sets within the fishery for 1990-2011 (Martin et al., 2015).”
From Oceanographic determinants of ocean sunfish (Mola mola) and bluefin tuna (Thunnus orientalis) bycatch patterns in the California large mesh drift gillnet fishery (2017) · cites this paper as Martin et al., 2015

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Using Bayesian Models to Estimate Humpback Whale Entanglements in the United States West Coast Sablefish Pot Fishery
Jannot, J. E., Ward, E. J., Somers, K. A., Feist, B. E., Good, T. P., Lawson, D., & Carretta, J. V. (2021) · Frontiers in Marine Science · doi:10.3389/fmars.2021.775187 · Cited by 3 · see citing works
MODELING SPATIAL PATTERNS IN FISHERIES BYCATCH: IMPROVING BYCATCH MAPS TO AID FISHERIES MANAGEMENT
Sims, M., Cox, T. M., & Lewison, R. L. (2008) · Ecological Applications · doi:10.1890/07-0685.1 · Cited by 61 · see citing works
Bayesian prediction of fishery biological impacts from limited data: A deep-set buoy gear case study
Stohs, S. M., & Harmon, K. M. (2022) · Fisheries Research · doi:10.1016/j.fishres.2022.106228 · Cited by 4 · see citing works
Self-citation

Authors: Martin, S. L., Stohs, S. M., & Moore, J. E.
Affiliations: NOAA National Marine Fisheries Service (US)
Corresponding author: Martin, Summer L.
ORCID: Martin, Summer L.
Stohs, Stephen M.
Moore, Jeffrey E.
Venue: Ecological Applications
Publisher: Wiley
Published: 2015-03
Volume: 25
Issue: 2
Pages: 416-429
ISSN: 1051-0761 (ISSN-L)
1939-5582
DOI: 10.1890/14-0059.1
OpenAlex: W2155734453
Cited by: 40
References: 45
Citations by year: 2026: 2 · 2024: 6 · 2023: 2 · 2022: 4 · 2021: 6 · 2020: 3 · 2019: 3 · 2018: 5 · 2017: 4 · 2016: 3 · 2015: 2
FWCI: 2.82
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Ecological Applications (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Environmental Science › Nature and Landscape Conservation › Turtle Biology and Conservation
Physical Sciences › Environmental Science › Global and Planetary Change › Marine and fisheries research
Physical Sciences › Environmental Science › Ecology › Marine animal studies overview
Keywords: bycatch, Dermochelys coriacea, humpback whale, Bayesian inference, zero-inflated Poisson regression, management thresholds, potential biological removal, observer data, Endangered Species Act
MeSH terms: Animals, Bayes Theorem, California, Conservation of Natural Resources, Environmental Monitoring, Fisheries, Models, Biological, Pacific Ocean, Turtles, Models, Statistical, Humpback Whale

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Bayesian inference and assessment for rare-event bycatch in marine fisheries: a drift gillnet fishery case study

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