Title page for ETD etd-08042009-120628


Type of Document Master's Thesis
Author Barlow, Paige Fithian
Author's Email Address
URN etd-08042009-120628
Title Sea turtle bycatch by the U.S. Atlantic pelagic longline fishery: A simulation modeling analysis of estimation methods
Degree Master of Science
Department Fisheries and Wildlife Sciences
Advisory Committee
Advisor Name Title
Dr. Jim Berkson Committee Chair
Dr. Eric Smith Committee Member
Dr. Marcella J. Kelly Committee Member
Dr. Paul Richards Committee Member
Keywords
  • pelagic longline fishery
  • sea turtle
  • simulation model
  • bycatch
  • delta-lognormal estimation
  • generalized linear model
Date of Defense 2009-07-22
Availability unrestricted
Abstract
The U.S. pelagic longline fishery catches 98% of domestic swordfish landings but is also one of the three fisheries most affecting federally protected sea turtles (Crowder and Myers 2001, Witherington et al 2009). Bycatch by fisheries is considered the main anthropogenic threat to sea turtles (NRC 1990).

Accurate and precise bycatch estimates are imperative for sea turtle conservation and appropriate fishery management. However, estimation is complicated by only 8% observer coverage of fishing and data that are hierarchical in structure (i.e., multiple sets per trip), zero-heavy (i.e., bycatch is rare), and often overdispersed (i.e., larger variance than expected).

Therefore, I evaluated two predominant bycatch estimation methods, the delta-lognormal method and generalized linear models, and investigated improvements in uncertainty incorporation. I constructed a simulation model to evaluate bycatch estimation at two spatial scales under ten spatial models of sea turtle, fishing set, and observer distributions.

Results indicated that distributing observers relative to fishing effort and using the delta-lognormal-strata method was most appropriate. The delta-lognormal-strata 95% confidence interval (CI) was wider than statistically appropriate. The delta-lognormal-all sets pooled 95% CI was narrower but simulated bycatch was above the CI too frequently. Thus, I developed a bycatch estimate risk distribution to incorporate uncertainty in bycatch estimates. It gives managers access to the entire distribution of bycatch estimates and their choice of any risk level.

Results support the management agency’s observer distribution and estimation method but suggest a new procedure to incorporate uncertainty. This study is also informative for many similar datasets.

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