Bayesian prediction of fishery biological impacts from limited data: A deep-set buoy gear case study

Predicting the biological impacts of new or expanding fisheries presents challenges due to limited data, high variability in catch rates, and the often low frequency of bycatch events. These issues arose in the case of the West Coast deep-set buoy gear (DSBG) fleet, which the Pacific Fisheries Management Council recommended in 2019 for authorization as a legal gear type. DSBG selectively targets swordfish (Xiphias gladius) with infrequent bycatch of other species. Limited effort and incomplete observer coverage result in a data-limited context for estimating the impacts of a fully authorized and expanded fishery. Recently, data analysts have explored Bayesian estimation for modeling rare-event bycatch in a manner that incorporates uncertainty and enables updating as more data become available. Here, we apply a Bayesian methodology to an integrated dataset of DSBG observer and logbook records to estimate bycatch rates under several plausible scenarios of DSBG authorization. We estimate posterior distributions of catch rates for three species caught in DSBG Exempted Fishing Permit (EFP) trials, and incorporate bootstrap samples of vessel-level effort to calculate posterior predictive distributions of catch counts under alternative management regimes. We discuss how our results can inform policy decisions about a new fishery with limited data, and how to extend this approach to other federal environmental actions. This approach allows policymakers to compare biological impacts of management alternatives while considering the uncertainty inherent in the predictions, and to determine whether the range of potential impacts is likely to significantly alter the affected environment.