Shortfin mako hot sets - Defining high bycatch conditions as a basis for bycatch mitigation

Shortfin mako sharks, Isurus oxyrinchus, were listed under Appendix II of CITES in 2019 in part due to the results of the last stock assessment for the North Atlantic population, which determined the population is overfished and experiencing overfishing. With population numbers expected to continue to decline, the managing body, the International Commission for the Conservation of Atlantic Tunas (ICCAT), has called for efforts to reduce shortfin mako bycatch. We evaluate the potential for reducing mako bycatch by identifying mako hot sets, those with particularly high shortfin mako bycatch. Environmental conditions were evaluated for their influence on catch per unit effort (CPUE) of shortfin mako sharks. Standardized CPUEs were calculated from the US pelagic longline observer program (2004-2012) using a generalized additive model (GAM) with a delta-lognormal approach applied to the environmental variables sea surface height, sea surface temperature, bathymetry, and chlorophyll a concentration. Quantile regression (QR) was also performed to evaluate whether environmental variables can predict fishing conditions with high CPUE. The results of the GAM and QR methods were compared and assessed for their ability to predict and identify locations where shortfin mako CPUE is particularly high. The results suggest that using the binomial portion of the delta-lognormal model, the probability of positive bycatch, is the best basis to define an algorithm to avoid setting in conditions that might have high mako bycatch. Bycatch avoidance strategies built from probability of positive bycatch perform well enough at identifying hot sets to avoid half the shortfin mako bycatch with only a 20% reduction in effort.

Automated summary

Shortfin mako sharks were listed under Appendix II of CITES in 2019 due to overfishing in the North Atlantic population. Efforts are being made to reduce mako bycatch by identifying hot sets and evaluating environmental conditions that influence catch per unit effort. By establishing an algorithm based on a delta-lognormal model, strategies to avoid high mako bycatch have been effective in reducing bycatch with minimal effort reduction.