1. Habitat prediction for southern bluefin tuna spatial management Alistair Hobday Klaas Hartmann Hobday and Hartmann (2006) Pelagic Fisheries and Ecosystems CSIRO Marine & Atmospheric Research

2. Breakthrough Technology I “Physical Observations” In situ coverage is patchy in space and time –Climatologies, no interannual variation Satellite data provides surface features –Platform coverage, clouds, space Ocean models (3D, multivariable, space/time coverage) –Short-term –Season/annual –Long-term Allow new phase of fisheries oceanography

3. Bluelink product: synTS synTS (synthetic temperature and salinity) –statistical data product –derived using SSH, SST & climatology for Australian region => temperature at standard oceanographic depths => produced in near real time David Griffin and team at CSIRO

4. Breakthrough Technology II “Smart Tags” Tag technology is making a difference to understanding fish movements and habitat use New insight into the basic biology of many species Improved the advice we can provide for management

5. Pop-up Satellite Archival Tags SBT track

6. Southern Bluefin Tuna “problem” World-wide stock at historical low (<10%) International catch agreement (quota) –Australia abides by this `

7. Bluefin tuna on the east coast Bycatch in a longline fishery –Limited quota held on the east coast –Fish are discarded if captured, because cannot be legally sold Management Goal –Avoid catching SBT (unless own quota)

8. Minimize non-quota holders catching bluefin tuna (Real-time spatial management) Zone east coast into 3 regions –Core SBT habitat: 4t quota required for access –Marginal SBT habitat zone: 0.5t quota required for access –Poor SBT habitat zone: no quota required for access Assist management by identifying present distribution of tuna habitat First example of using environment information for real-time “management” (in Australia, perhaps unique in world)

9. Method Analysis and habitat prediction tools Biological Data (tags) Habitat Preferences Physical Data (near-real time distribution of environment) Satellite data synTS (SSH & SST) Habitat Prediction Maps Management Support (sustainable use)

10. Biological Data: Tag temperatures (based on 45 tags for 2006) Distribution of temperatures is fish “preference” (e.g. SST) SST

11. 1.Generate distribution of surface habitat ( proportion of time fish spend in water colder than at each pixel )

12. 2. ….then do for sub-surface habitats (using the near-real time ocean model)

13. 3. Sum to create full habitat probability distribution (probability that fish are in water column “colder” than each spot)

14. Transferring predictions to management Management-selected habitat probabilities Core SBT habitat: 80% probability Marginal SBT habitat zone: 15% probability Poor SBT habitat zone: 5% probability Turn continuous habitat probabilities into 3 zones –Each pixel is classified into one of 3 types Send reports every 1-2 weeks to fishery managers –Decide on lines to divide these zones

15. 4. Convert to zones and add lines

16. Informing Stakeholders…Climatology short

17. Fisheries managers place lines Accepted approach by stakeholders…but how is management doing? Raw zones are complex shapes Simple lines needed (1, 2 or 3 segments) Subjective approach….subject to pressure from stakeholders…. No quota Limited quota Quota zone

18. Misclassified pixels Core: correct Core: incorrectBuffer: correct OK: correct Buffer: incorrect Core: incorrect OK: incorrect

19. Objective function… …seeks to balance the contribution of non-precautionary (blue) and precautionary (orange) misclassifications to the score.

20. Line placement 7 AFMA Optimiser

21. Classification success AFMA: Precautionary bias: red bar > aqua bar (n=2) AFMA: Non-precautionary bias: aqua bar > red bar (n=5) Optimizer: no bias yellow bar = brown bar (n=7) 1 2 3 4 5 6 7

22. Human vs Machine Correctly-classified habitat (~80% of habitat area) –Computer wins 5/7 placements –When management did better….strong bias to precautionary or non-precautionary No bias in line placement? –Computer 7/7 placements –Managers 0/7 Non-precautionary bias (disadvantage fish) –Managers: 5/7 times Precautionary bias (disadvantage fishers) –Managers: 2/7 times

23. Management Uptake YearReportsLine Adjustments Line Complexity 2003185 2004127 2005105 20061510

24. Summary Effective management support tool –Using biology + physics => management –Real time, adaptive…. This season (ended Oct 2006, next start May 2007) Include more tags (57) Provide computer line placement as a guide Encourage more rapid response to predictions Future Email daily habitat prediction (but lines can jump, QC) Continued validation (observer data in all zones) Habitat predictions for other species

25. Extra slides

26. Effect of delays in placement Zones moving north (1 week delay) Zones moving south (1 day delay) Model date Implementation date Model date Implementation date Disadvantage fish (by 7 days) Disadvantage fisher (but only by 1-day) SBT habitat can be fished SBT habitat with extra protection

27. Validation: How good is the prediction? Compare captures of SBT in each of the zones Cumulative probability of SBT habitat: –E.g. 20% sets in waters where cumulative prob of SBT presence is <70% SBT sets –e.g. 50% of the SBT were captured in waters where they are predicted to spend 50% of their time….. Future: catch per zone (f, wt)

28. May 29, 2006 (Line 1)

29. July 13, 2006 (Line 2)

30. July 27, 2006 (Line 3)

31. August 10, 2006 (Line 4)

32. August 24, 2006 (Line 5)

33. September 7, 2006 (Line 6)

34. September 21, 2006 (Line 7)

35. September 29, 2006 (Line 8)

36. October 12, 2006 (Line 9)

37. October 27, 2006 (Line 10)

38. Management maps Examples of maps distributed online to stakeholders