Oceanographic Determinants of Bycatch Patterns in the California Drift Gillnet Fishery: Building an EBFM Tool for Sustainable Fisheries.

Nick Hahlbeck1,2, Kylie L Scales3, Elliott L. Hazen3 and Steven James Bograd3, (1)University of Miami, Rosenstiel School of Marine and Atmospheric Science, Coral Gables, FL, United States, (2)NOAA Ernest F. Hollings Undergraduate Scholarship Program, United States, (3)NOAA Southwest Fisheries Science Center, Environmental Research Division, Monterey, CA, United States
The reduction of bycatch, or incidental capture of non-target species in a fishery, is a key objective of ecosystem-based fisheries management (EBFM) and critical to the conservation of many threatened marine species. Prediction of bycatch events is therefore of great importance to EBFM efforts. Here, bycatch of the ocean sunfish (Mola mola) and bluefin tuna (Thunnus thynnus) in the California drift gillnet fishery is modeled using a suite of remotely sensed environmental variables as predictors. Data from 8321 gillnet sets was aggregated by month to reduce zero inflation and autocorrelation among sets, and a set of a priori generalized additive models (GAMs) was created for each species based on literature review and preliminary data exploration. Each of the models was fit using a binomial family with a logit link in R, and Aikake‚Äôs Information Criterion with correction (AICc) was used in the first stage of model selection. K-fold cross validation was used in the second stage of model selection and performance assessment, using the least-squares linear model of predicted vs. observed values as the performance metric. The best-performing mola model indicated a strong, nearly linear negative correlation with sea surface temperature, as well as weaker nonlinear correlations with eddy kinetic energy, chlorophyll-a concentration and rugosity. These findings are consistent with current understanding of ocean sunfish habitat use; for example, previous studies suggest seasonal movement patterns and exploitation of dynamic, highly productive areas characteristic of upwelling regions. Preliminary results from the bluefin models also indicate seasonal fluctuation and correlation with environmental variables. These models can be used with near-real time satellite data as bycatch avoidance tools for both fishers and managers, allowing for the use of more dynamic ocean management strategies to improve sustainability of the fishery.