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Bycatch of non-target species in many fisheries can contribute to mortality of discarded individuals and negative environmental impacts. The objective of this study was to predict the capability of commercial fishers to successfully obtain a targeted species of grouper, snapper, or porgy based on the fishing gear and set configuration employed. Data were collected by the Southeast Fisheries Science Center (SEFSC) Galveston Reef Fish Observer Program from 2006-2014 as mandated under the Gulf of Mexico Fishery Management Council (GMFMC) Reef Fish Fishery Management Plan. Binomial regression models using the complementary log-log link function (except red grouper, log odds link function) were constructed in R version 3.2.3 using backwards regression to predict target species fishing success using variables directly manipulated by fishers, including soak time, fishing depth, main line length, hooks deployed, gangion length, hook distance, and the temporal variables month and year. An ANOVA was used to calculate the significance of the final model against the null model, and McFadden’s R 2 (R 2 McF) was calculated to determine the proportional reduction in error variance from the null model. Significant models were generated for speckled hind (p< 0.01, R 2 McF = 0.204), red grouper (p< 0.01, R 2 McF = 0.023), scamp (p< 0.01, R 2 McF = 0.183), gag grouper (p< 0.01, R 2 McF = 0.090), red snapper (p< 0.01, R 2 McF = 0.059), mutton snapper (p< 0.01, R 2 McF = 0.330), jolthead porgy (p< 0.01, R 2 McF = 0.124), and red porgy (p< 0.01, R 2 McF = 0.120). These models ultimately serve as guidelines for fishers to adjust fishing practices to improve the likelihood of successfully obtaining the species targeted, which may reduce bycatch mortality of non-target species and its resulting environmental impacts.

Fishing Gear and Set Methodology Models for Target Species Fishing Success in Gulf of Mexico Longline Reef Fishing