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  • 1. Interactions and space-use overlap between satellite- tracked blue sharks and longline fishing vessels NUNO QUEIROZ, NICK E. HUMPHRIES, GONZALO MUCIENTES, LARA L. SOUSA & DAVID W. SIMS CIBIO – UNIVERSITY OF PORTO | MARINE BIOLOGICAL ASSOCIATION OF THE UK | IMM – CSIC
  • 2. Background Pelagic longlines are probably the most widespread fishing gear in the world’s oceans; Longlines are known to interact with several marine predators, being linked with declines in targeted and bycatch species, including seabirds, turtles, tunas and sharks; The blue shark is the most commonly caught shark in longlines targeting swordfish and tuna species, with an estimated 10.7 million sharks killed as bycatch each year.
  • 3. Novel approach to assess bycatch mortality Blue sharks exhibit an extensive distribution and migratory movements which hint at the possibility that sharks are vulnerable to longline gear throughout a large part of their lives; Understand where and when interactions between sharks and longliners happen; Mapping regions higher bycatch/higher mortality could lead to the implementation of fishing restrictions.
  • 4. Methodology – shark tracking
  • 5. Methodology – boat tracking Vessel monitoring system (VMS) data from Spanish and Portuguese longliners greater than 15 m operating in the north-east Atlantic were obtained; Boat GPS positions spanned from January 2006 to December 2008 and movements between fishing locations were ignored, retaining only data relative to fishing activity; To quantify the shared space use between longliners and tracked sharks over time, the number of days with shared occupancy (i.e. presence of both boats and sharks in a grid cell) was recorded.
  • 6. Results – fishing fleet  Data belonging to a total of 103 longliners targeting large pelagics were analysed; longlines were deployed over a large area for a cumulative number of 17,853 days.
  • 7. Results – fishing fleet  Highly unbalanced fishing effort concentrated in three main regions: southwest of Ireland, west of the Iberian Peninsula and southwest of the Canary Islands.
  • 8. Results – fishing fleet
  • 9. Results – fishing fleet  Overall pattern showed a general decrease of fishing activity until late spring – early summer, with a subsequent increase before the end of the year.
  • 10. Results – blue sharks  A total of 32 blue sharks (21 females and 11 males) ranging in size from 90 to 200 cm fork length (FL) were satellite tagged; high space-use was observed in coastal areas.
  • 11. Results – blue sharks
  • 12. Results – shark/boat interactions Of the 17 blue sharks successfully tracked five (29.4%) spent at least 1 day-at-risk from longliners; One shark (#6 – tracked for 13 days) reached a maximum of 3 days-at-risk; The percentage of time-at-risk ranged from 0.9% (#17) to 23.1% (#6) of tracking time; 2 sharks (#7 and 10) were captured by surface longliners in the single day fish were at-risk; Overall, confirmed fishing mortality was around ~9.4%, with 3 sharks being caught by longliners during the relatively short tracking period (< 120 d).
  • 13. Results – shark/boat interactions
  • 14. Results – shark/boat interactions  Shark/boat interactions were observed at the northern approach of the Bay of Biscay shelf edge and off the south-western and western Iberia coast.
  • 15. Results – Shark/boat interactions
  • 16. Conclusions Swordfish are pelagic, highly migratory/broad distribution species; this seems to have influenced the distribution of the fishing fleet, namely the vast geographic extent occupied and unevenly distributed effort; Results suggest there is a large shark/boat spatial overlap namely in productive regions; High percentage of the tracked blue sharks at-risk during short tracking periods; Population structuring in blue sharks, coupled with our results of highly unbalanced fishing effort, suggests different segments of the population face differential risk from longlining effort; Extensive space-use of restricted core areas is another factor known to increase interactions with fisheries and exacerbate catch rates; Mapping/quantification of interactions can be a general utility tool applicable to management of pelagic species.
  • 17. Preliminary results – full Atlantic
  • 18. Acknowledgments I would like to thank Professor D.W. Sims and all the co-authors of the presented study; MBA (Marine Biological Association of the U. K.) Behavioural Ecology Laboratory; CIBIO (Centro de Investigação em Biodiversidade e Recursos Genéticos); This work was supported by FCT (Fundação para a Ciência e a Tecnologia) and the Save our Seas Foundation. www.mba.ac.uk/simslab/