Sea turtle bycatch by the U.S. Atlantic pelagic longline fishery: A simulation modeling analysis of estimation methods

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dc.contributor.authorBarlow, Paige Fithianen
dc.contributor.committeechairBerkson, James M.en
dc.contributor.committeememberSmith, Ericen
dc.contributor.committeememberRichards, Paulen
dc.contributor.committeememberKelly, Marcella J.en
dc.contributor.departmentFisheries and Wildlife Sciencesen
dc.date.accessioned2014-03-14T20:42:43Zen
dc.date.adate2009-09-01en
dc.date.available2014-03-14T20:42:43Zen
dc.date.issued2009-07-22en
dc.date.rdate2013-05-20en
dc.date.sdate2009-08-04en
dc.description.abstractThe 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.en
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-08042009-120628en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-08042009-120628/en
dc.identifier.urihttp://hdl.handle.net/10919/34342en
dc.publisherVirginia Techen
dc.relation.haspartBarlowPF_MS_Thesis.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectpelagic longline fisheryen
dc.subjectsea turtleen
dc.subjectsimulation modelen
dc.subjectbycatchen
dc.subjectdelta-lognormal estimationen
dc.subjectgeneralized linear modelen
dc.titleSea turtle bycatch by the U.S. Atlantic pelagic longline fishery: A simulation modeling analysis of estimation methodsen
dc.typeThesisen
thesis.degree.disciplineFisheries and Wildlife Sciencesen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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