R. Sharma*, A. Langley ** M. Herrera*, J. Geehan *

1. Indian Ocean Bigeye Tuna Stock Assessment: Weighting length composition versus CPUE data; Issues on Longline and Purse Seine selectivity R. Sharma*, A. Langley ** M. Herrera*, J. Geehan* * IOTC Secretariat ** IOTC Consultant

2. Overview • Data and trends • Stock Assessment Approach Used • Sensitivity on: • Aggregated fleets versus segregated fleets • Data weighting (Eff Sample Size issue) • LL selectivity by blocks • PS and LL selectivity by blocks • Implications on assessment • Size composition issue- How do we resolve this

3. Bigeye tuna: Catch (i)

4. Bigeye tuna: Catch (ii) 1970’s 1990’s 2000’s 2002-06 2010 2011

5. Bigeye tuna: Catch (iii) 2002/06 - Q1 2002/06 – Q2 2002/06 – Q3 2002/06 – Q4 2011 - Q1 2011 – Q2 2011 – Q3 2011 – Q4

6. Bigeye tuna: Average weight All fisheries Longline Taiwan,China Longline Japan Purse seine Free-school Large (>30 kg) Large (>30 kg) Large (>30 kg) Purse seine Associated school Medium (15-30 kg) Medium (15-30 kg) Medium (15-30 kg) Small (<15kg) Small (<15 kg) Small (<15 kg)

7. Bigeye tuna: Tagging data • 35,997 BET released to 5,740 recovered (representing ≈16% of the releases), the majority by PS in area 1.

9. Bigeye tuna: Data Summary • Total catches have been around 80,000 t in recent years, well below previous years catches • Marked drop in catches in main BET fishing grounds (Area 1) • Almost no longline BET catch in area 1 in 2010-11 (Piracy) • Increased contribution of coastal fleets to catches of bigeye tuna following the development of new fisheries using handlines, coastal longlines, and trolling (some around anchored FADs) • Higher contribution of surface fisheries to total catches  Drop in average weight

10. The Assessment Approach Used • SS-III 3 area and One Area Assessment • Focus on final model used (One Area Assessment). • Base model used LL (Area 1) Logistic • Base model used LL (Area 2 and 3) Double Normal. • Base model used PSLS (Double Normal). • CPUE and Length composition weighting issues.

13. Comparing the Aggregated model versus the segregated model-I Year Quarter 101 = 1952 (Q1) Aggregated Fisheries Segregated Fisheries

14. Comparing the Aggregated model versus the segregated model-II Aggregated Fisheries Segregated Fisheries

15. Diagnostics-III Aggregated Model

16. Segregated Model

18. Changing effective sample sizes-Segregated Model

19. Changing effective sample sizes-Segregated Model

22. Time Varying Selectivity-LL and PSLSHigher Eff SS

25. Marginal improvement in FL2, LL2 and PSLS1

30. Overall effects on assessment

36. SBMSY Max C ~ 140,000 T in 2002

37. How do we resolve these discrepancies • For now we are using the simple model without weighting length comp data. • Investigating issues on sampling and reporting data.

38. Taiwan, China – Big Eye size frequencies distribution (percentiles within each year) Low % High % Length (2 cm size classes) Taiwan, China

39. Taiwan, China • BE: spatial pattern of SF data • Analysis of the spatial pattern pre-2002 are limited by small sample sizes for these years. No overriding pattern can easily be seen. • Post-2002 show greater consistency in average weights in each area, with the highest values concentrated in the Arabian Sea area and north-west Indian Ocean. Size frequency data, 5 x 5 grid

40. Bigeye – size-frequency distribution 1980-2011 • Between 2001 until 2009 there is noticeable increase in the kurtosis values, caused by the relative narrowing range of size classes recorded by the samples. • The overall average weight for these years generally increases – peaking at around 55kg – as the percentage fish recorded in smaller length classes decreases, and the proportion of mid to larger fish increases.

41. Taiwan, China: Bigeye

43. Taiwan, China & Japan: Bigeye

44. Bigeye Size-frequency (SF) data: key points • A number of issues were identified with SF data for Bigeye, including: • low sample numbers for catch at size until around 2002; • for years where sampling numbers improve (2002-2009) a narrowing of the size-frequency distribution, with a general shift towards samples containing higher proportions of mid to large sized fish (see distribution heatmap on previous slide); • as a consequence of the shift in distribution, a relatively large increase in average weight from around 35kg in 2000 to over 55kg by 2006. • Size data collected after 2001 appear to have a systematically different size profile to data before 2001, and which needs to be explored more to understand the cause of the sharp rise in the trend of average weight. • One suggestion may be the effect of the introduction of quota (weight) limits, i.e., that only fish above a certain size suitable for export market are measured (compared to Catch and Effort which records all sizes); alternatively, issues with the conversion factors from gilled and gutted to live weight; or changes to the reporting system introduced around this time.

45. Possible issues in the data sets • Either the Length frequency data is biased & CPUE data is unbiased. • CPUE data is biased and LF unbiased. • Both CPUE and LF are biased. • More thorough examination of these datasets required for the assessment.

46. Overall conclusions • Selectivity not as important as the Effective SS estimated on length frequency of catches by fleets. • Selectivity is important as the shape used will effect the key reference points estimated. • More thorough Need: Resolve average size discrepancies between periods in the LL fisheries as these drive the assessment if weighed high. • Interactions with growth and natural mortality poorly understood, and more work needs to be conducted.

47. Acknowledgements • Rick Methot for developing the 2 stanza growth curve which we used in the assessment. • Ian Taylor for answering obscure questions about SS. • Adam Langley for constant advice on assessments. • IOTC countries for sharing their data with us. • Mark for letting me know about the meeting. • ISSF for funding travel.