1. Albacore CPUE based on joint analysis Simon Hoyle, Yin Chang, Doo Nam Kim, Sung Il Lee, Takayuki Matsumoto, Kaisuke Satoh, and Yu-Min Yeh.

2. Objectives Improve analysis methods for Yellowfin and Bigeye CPUE. adjust for introduction of vessel ids in late-1970s, produce indices for temperate areas, produce confidence intervals. Extend methods to Albacore tuna. Develop CPUE indices to use in the 2016 stock assessments.

3. C ONCLUSIONS FROM 2015 WORKSHOP The WG NOTED that cluster analysis and related approaches (e.g. PCA methods) to identify effort associated with different fishing strategies, should be used when direct measures of directed effort (e.g. HBF) are unavailable or less effective. The WG RECOMMENDED that examining operation level data across all LL fleets (Korean, Japanese, and Taiwanese) will help us understand the fishery and stock, especially if effort varies among fleets and years, so we have a representative sample covering the broadest areas in the Indian Ocean. This will also avoid having no information in certain strata if a fleet were not operating there, and avoid combining two indices in that case. The WG NOTED that using filtered operational data from different fleets is generally appropriate as long as different catchability of the fleets is accounted for (e.g. using vessel id), rather than computing indices separately across fleets and then averaging them after the standardization process.

4. The WG NOTED that using vessel effects would enable estimation of historical change in catchability over time. The WG NOTED that vessel effect should be included in the standardization process in subsequent years, as in some cases these tend to change the trend of the series used in assessments, and can have a significant effect on the overall outcome of the assessment. The WG also NOTED that vessel effects is a surrogate variable until more direct measures of catchability changes attributed to fishing can be incorporated into the standardization process. The WG NOTED that a small resolution area effect (5*5 degree) should also be used in conjunction with the data examined, and that biases due to shifting effort concentration should be avoided by giving equal weight to data from each time-area stratum, by a combination of adjusting the statistical weights in the model, and/or randomly sampling an equal number of sets from each stratum.

5. F UTURE STEPS FOR FURTHER ANALYSIS It was NOTED that clustering approaches and other ways to define targeting should be further explored. The effect of these analysis in defining a subset of operational data (sets/hauls) and its effects on the standardization be tested. It was NOTED that time-area interactions within regions need further examination.. It was NOTED that using a subset of vessels to examine Vessel-Year interactions over time would be important to understand vessel- dynamics, and their reasons for their change in efficiency over time.

6. Approaches for 2016 1.Load, prepare, and check each dataset. 2.Conduct analyses to characterise the fisheries, including exploratory analyses of the data to develop understanding of factors likely to affect CPUE. 3.Subset data by fleet (3), species (3), regional structure (2-4 per sp), and region (1-5 per structure). a)Cluster analyses to separate fishing strategies. b)Select useful clusters from each data subset, then combine all fleets. c)Standardize data using generalized linear models. 3 index types: lognormal constant, delta lognormal, and negative binomial approaches 4 time series

7. 1. Data characterisation Develop an understanding of the data and the fisheries, update for 2016 Detailed results in IOTC-2015-WPTT17-INF07, IOTC-2015-WPTT17-INF08, and IOTC-2015-WPTT17-INF09 Key points JP 1952 - present. Vessel ids start in 1979. Sparse data before 1955 and since 2010. TW 1979 – present. HBF since 1995. KR 1978 – present. Sparse data before 1980 and since 2009 esp. in R2. TW ‘other’ fishery (oilfish) very important in sub-tropics in recent years. The following 8 slides are repeated from the 2015 WPTT presentation

8. Japanese time series Relative CPUE Year

9. Taiwane se time series Relative CPUE Year

10. Korean time series Relative CPUE Year

11. Data coverage by flag

12. Data coverage Logbook coverage < 40% for the Taiwanese fleet 1987 - 1996. These indices may be affected by lower sample sizes, and varying motives for data submission across the fleet, so may simply be less representative of the fleet than at times when coverage rates are higher.

13. 1971-2000 period Pre-1979 operational data unavailable for TW. Aggregated data based on operational data now lost. Coverage 1987-2000 < 40%, and at times as low as 4%. Representative data?

14. Data preparation Some issues identified in TW data Operational data not provided to analysts if submitted after ‘finalization’ Some data cleaning variables inconsistently defined (outlier flags) Data cleaning rules may have affected CPUE particularly outside equatorial areas

15. 4. Targeting analyses Aimed to: identify distinct fishing strategies in the data for each fleet and region assign effort to fishing strategies, so the clusters can be used in standardization. Methods Targeting analyses by flag Cluster analyses on species composition after aggregating by vessel-month. Applied multiple methods – Ward hclust, kmeans Results Cluster analysis approaches were successful in identifying strategies, especially outside tropical areas, e.g. TW oilfish fishery Clustering now possible before 1979 due to availability of logbook ids (thank you Japan!) In absence of logbook ids, using grid months also appeared successful Clusters dropped from dataset if they had very low catches of species of interest

16. Albacore regional structures

17. Example of hierarchical clusters for TW, JP and KR TWJPKR

18. Species composition – TW ‘other’ species (mostly oilfish and escolar)

19. Species composition by flag and cluster, structure A3, R3

20. Variables by flag and cluster, structure A3, R3

21. Maps of clusters by flag, structure A3, R3

22. CPUE standardization methods

23. 4 sets of indices ‘novess_allyrs’ – 1952-2014 without vessel effects ‘boat_allyrs’ – 1952-2014 with vessel effects ‘novess_5279’ – 1952-1979 without vessel effects ‘vessid_7914’ – 1979-2014 with vessel effects

24. Results Delta lognormal models were not successful Strata with either 100% positive or 0% positive, no parameter estimates for delta values Lognormal constant models were successful Negative binomial models also ran successfully with similar but slightly different trends. Chose to run with lognormal constant models since diagnostics were reasonable, and the lognormal constant models are commonly used.

25. Structure A3, regions 1, 2, 3 and 4

26. Structure A3, regions 1 and 2

27. Structure A3, regions 3 and 4

28. Structure A5

29. Structure A3, regions 1 and 2, annual

30. Structure A3, regions 3 and 4, annual

31. Structure A5, annual

32. Conclusions Cluster analysis identified multiple targeting strategies, which were included in CPUE standardization. Albacore CPUE indices were estimated successfully. Early CPUE declines are very steep, possibly due to target change not accounted for by cluster analysis. Recent CPUE may be affected by target change in the Japanese fishery.

33. Future work Distributions Improve modelling approach for strata where delta method doesn’t work Explore NB, ZINB, Tweedie Targeting Explore residual patterns by fleet and cluster, to better understand effects of target change Model residual patterns vs covariates Explore alternative cluster aggregations (e.g. vessel-week / vessel-month-HBF / month-HBF- cell) Examine SBT fishery open/close dates as extra aggregation boundaries Testing Check approaches using longline fishery data simulators Indices Reduce number of regional structures Estimate separate indices by fleet, as well as joint indices

34. Acknowledgments International Seafood Sustainability Foundation (ISSF) for funding the project IOTC for facilitating the project Thanks to the respective Fishery Agencies for granting access to their data Thanks to Ren-Fen Wu and Lisa Chang for their thoughtful contributions and organizational support