Global patterns in the bycatch of sharks and rays

• Main Authors: Oliver, Shelby, Braccini, Matias, Newman, Stephen J., Harvey, Euan S.
• Format: Article in Journal/Newspaper
• Language: unknown
• Subjects: North Atlantic
• Online Access: http://www.sciencedirect.com/science/article/pii/S0308597X14003546

This study comprised a meta-analysis of elasmobranch bycatch in commercial longline, trawl, purse-seine and gillnet fisheries in order to obtain a general perspective of bycatch patterns, and to expose knowledge gaps and identify management and research priorities. Two bycatch ratios were considered: the number and the weight of elasmobranch bycatch relative to that of the target species captured. Patterns were determined through machine learning algorithms with gear type, oceanic region, habitat and the presence or absence of bycatch management measures as candidate predictors. There are considerable information gaps. Most of the current information on elasmobranch bycatch is for the North Atlantic, which is not where the greatest fishing pressure is exerted, so several fisheries were largely under-represented. Overall for sharks, gear type was the most important predictor with pelagic longline fisheries in the South Atlantic displaying the highest bycatch ratios. No patterns were found for ray bycatch ratios. For the fisheries considered in this study, pelagic longlines, and deep-sea and coastal trawl fisheries had the largest total annual shark and ray bycatch, respectively. Blue sharks (Prionace glauca, Carcharhinidae) dominated the total annual bycatch of longline fisheries. For other fishing gears, the annual species-specific bycatch composition varied across oceanic regions. Many of the fisheries with the largest elasmobranch bycatch operate over large spatial scales and often in international waters. International management, mitigation and cooperation are an essential component for the sustainability of elasmobranch bycatch species. Data collection systems and data availability are required at a global scale to improve assessments of elasmobranch bycatch and this should be a high priority for ongoing management and monitoring. Fisheries discards; Elasmobranch bycatch; Machine learning; Management;