Bayesian inference and assessment for rare‐event bycatch in marine fisheries: a drift gillnet fishery case study. Issue 2 (1st March 2015)

• Record Type: Journal Article
• Authors: Martin, Summer L.; Stohs, Stephen M.; Moore, Jeffrey E.
• Is Part Of: Ecological applications. Volume 25:Issue 2(2015)
• Page Start: 416
• Page End: 429
• Publication Date: 2015-03-01
• Subjects: Bayesian prediction -- California drift gillnet fishery -- endangered species -- fisheries bycatch -- humpback whale -- leatherback sea turtle -- marine megafauna -- Markov chain Monte Carlo -- model -- protected species -- rare events; Ecology -- Periodicals; Environmental protection -- Periodicals; Biology, Economic -- Periodicals; 577.05
• DOI: 10.1890/14-0059.1
• Languages: English
• ISSNs: 1051-0761

Abstract

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, with the highest levels occurring prior to a regulatory change. Area had the strongest effect on humpback bycatch. Cumulative bycatch estimates for the 20‐year period were 104–242 leatherbacks (52–153 deaths) and 6–50 humpbacks (0–21 deaths). The probability of exceeding a regulatory threshold under the U.S. Marine Mammal Protection Act (Potential Biological Removal, PBR) of 0.113 humpback deaths was 0.58, warranting a "medium bycatch impact" classification of the fishery. No PBR thresholds exist for leatherbacks, but the probability of exceeding an anticipated level of two deaths per year, stated as part of a U.S. Endangered Species Act assessment process, was 0.0007. The approach demonstrated here would allow managers to objectively and probabilistically classify fisheries with respect to bycatch impacts on species that have population‐relevant mortality reference points, and declare with a stipulated level of certainty that bycatch did or did not exceed estimated upper bounds.