Evaluating methods for estimating rare events with zero-heavy data: a simulation model estimating sea turtle bycatchudin the pelagic longline fishery

摘要

Estimating rare events from zero-heavy data (data withudmany zero values) is a common challenge in fisheries science and ecology. For example, loggerhead sea turtlesud(Caretta caretta) and leatherback sea turtles (Dermochelys coriacea) account for less than 1% of total catch in theudU.S. Atlantic pelagic longline fishery. Nevertheless, the Southeast Fisheries Science Center (SEFSC) of the National Marine Fisheries Service (NMFS) is charged with assessing the effect of this fishery on these federally protected species. Annual estimates of loggerhead and leatherbackudbycatch in a fishery can affect fishery management and species conservation decisions. However, current estimatesudhave wide confidence intervals, and their accuracy is unknown. We evaluate 3 estimation methods, each at 2udspatiotemporal scales, in simulations of 5 spatial scenarios representing incidental capture of sea turtles byudthe U.S. Atlantic pelagic longline fishery. The delta-log normal method of estimating bycatch for calendar quarter and fishing area strata was the least biased estimation method in the spatial scenarios believed to be most realistic. This result supports the current estimation procedure used by the SEFSC.