1. A standardized CPUE analysis of the Japanese distant-water skipjack pole-and-line fishery in the western and central Pacific Ocean (WCPO), (SA WP 8)
Adam Langley1, Koji Uosaki2, Simon Hoyle1, Hiroshi Shono2, and Miki Ogura2
1 Secretariat of the Pacific Community
2 National Research Institute of Far Seas Fisheries (NRIFSF)

2. Introduction Japanese distant-water pole-and-line logsheet data.
Shono and Ogura Standardised CPUE analysis using GLM approach.
Principal indices of relative abundance in WCPO SKJ stock assessment model.
Preassessment workshop recommendation to refine analysis of PL logsheet data.
This study. Collaborative study NRIFSF and SPC (Shimizu May 2010).

3. Decline in DW effort in all areas.
Very low effort in northern fishery.
Higher catch rates since mid 1980s.

4. Proportion of zero catch fishing days high in northern areas (after removing ALB fishing).
Low proportion of zero catch days in equatorial region. Slight increase since about (from 5% to 15%).

5. Previous analyses
Shono and Ogura 1999, routinely updated for input into SKJ assessment.
GLM approach. CPUE = catch per pole per day. Lognormal distribution (zero catches assigned nominal value).
Logsheet data and vessel equipment survey (bird radar (1G and 2G), bait tank, NOAA SST, sonar).
Global model. Regional indices derived from interaction terms (region*yearquarter).
Included ALB category to model interaction with ALB fishery in the northern areas.
“Vessel effect” not included.

6. Previous indices (Shono & Ogura)
Equatorial regions. Indices increase during the 1980s and then remain at the higher level through the 1990s and 2000s.

7. Current study - scope GLM approach. JP DW PL logsheet data 1972-2009.
Separate model for each region. Not sharing parameters among regions.
Interaction with ALB fishery in north (excl. ALB trips).
Spatial variable – finer scale variation in CPUE (5*5 lat/long).
Individual vessel effect “core vessels” (categorical variable).
Dependent variable: encounter rate (binomial) or catch per day (lognormal). Treatment of zero catch. Combined index (delta-lognormal).

8. Modelling approaches GLM models. Binomial model (prob. of catch).
Lognormal model (non zero catch).
Delta-lognormal model (= combined index).
Other approaches to consider (Poisson, negative binomial).

9. Binomial model Encounter rate.
Probability of encountering a school is related to the number (density) of schools.

10. Lognormal model Catch rate (non zero).
Catch rate on a school is related to the size of the schools.

11. An illustrative example:
Initial biomass.
Total biomass = Number of schools * average school size.

13. Assumptions Encounter Rate ~ number of schools (binomial).
Catch rate ~ school size (lognormal).
ER and CR are independent (Delta-lognormal).
Violation of assumptions; e.g. CR remains constant despite a decline in school size (lognormal, Delta-lognormal); ER lower/higher for smaller/larger schools (binomial, Delta-lognormal); saturation of index (bin and logn).
GLM models can account for all major changes in operation of fishery; e.g. relationship between searching and introduction of new technology (but...variables often confounded and unbalanced observations).

14. JP Logsheet data
Region 5. High CR since 1990s, slight increase in proportion zero catches.

15. Logsheet data Daily record of fishing/searching.
Location (midday), total daily catch (by species), number of poles fished.
Location resolution = nearest degree. Not informative about area searched.
Zero SKJ catch records (proxy for ER).
Searching, did not locate fish.
Located school (or multiple schools), no catch.
SKJ Catch record.
Located at least one school and caught fish from at least one school.
Very limited data for the northern areas since late 1980s.

16. Region 5 – logsheet data
Entrance of new, larger vessels. Shift in distribution of fishing northwards during 1980s.
Individual vessels identified from 1982 onwards.

17. New Technology
Adoption of new technology through 1980s. No vessel specific data available from earlier period (prior to 1982). Haven’t included NOAA SST receiver.

18. GLMs
Lognormal, non zero. Record k Ln(skj catchk) ~ Intercept + YearQtry + Vesselidv + LatLongLL + f(NumPolesk) + BaitTankb + NOAAn + Sonars + BirdRadarr + Errork Binomial, proportion non zero. Record j Pr (skj catchj != 0) ~ Intercept + YearQtry + Vesselidv + LatLongLL + f(NumPolesj) + BaitTankb + NOAAn + Sonars + BirdRadarr + Errorj
Technology as categorical variable. 0 = unknown, 1 = absent, 2 = present.
Bird radar. 0 = unknown, 1 = absent, 2 = 1G, 3 = 2G.

19. MFCL Region 5 Binomial model
Spatial variation in probability of SKJ catch. Increase in the efficiency of new entrants (vessels) in the fishery.

20. MFCL Region 5 Binomial model
Bird radar 2G strong positive effect on probability of catching SKJ.
(very few records with Bait tank unknown).
Decline in probability of catch from mid 1970s, but indices are much more uncertain.

21. What is driving the decline in the indices?
Preliminary exploration.
Interaction between the entry of new vessels and adoption of new technology, in particular the 2nd generation bird radar.

22. MFCL Region 5 lognormal (non zero) model
Bird radar also influencing magnitude of catch. Selection of larger schools?
MFCL Region 5 lognormal (non zero) model

23. MFCL Region 5 lognormal (non zero) model
Decline in index tends to coincide with northern movement of fishery.
May not be adequately accounted for simply by lat*long variable.
MFCL Region 5 lognormal (non zero) model
Sharp decline in the late 1980s

24. MFCL Region 5 delta-lognormal indices
Main decline driven by the logn indices from late 1980s, more gradual decline in latter period from binomial

25. MFCL region 6 – eastern equatorial
Binomial index
Slight decline over period.
Periods of lower indices (1990, 2004, 2009).
LTLBT most influential tech effect.
Lognormal non-zero index
Indices generally higher in 1990s-early 2000s.
Periods of lower indices (1990, 2004, 2009).

26. MFCL Region 6 delta-lognormal indices
Slight decline in binomial in 1990s countered by an increase in logn indices.

27. Outstanding issues Higher SE for lower binomial indices (R5 and R6).
Some conflict in trends between binomial and lognormal indices. Delta-lognormal appropriate?
Displacement of the PL fleet due to interaction with PS fleet. Shift of equatorial fishery northwards.
Decline in size of PL fleet – reduction in searching capacity.
Lower precision of indices in latter period.
Low precision of binomial indices for northern fishing areas. Insufficient data to derive a reliable index.
Northern fishery. JP offshore PL fleet in MFCL region 2.

28. Reconfiguration of MFCL regions
Analysis by Kiyofuji san

29. OS fleet, northern region (1)
No data on fishing equipment.
Exclude trips where albacore the dominant species caught.
Otherwise, equivalent approach to analysis of DW PL data.
Separate paper Kiyofuji et al. (SA WP 9).

30. Conclusions
Improvement on previous approach and potential further refinement of analysis.
Equatorial regions: DW PL binomial and delta-lognormal indices best available indices for inclusion in SKJ stock assessment model.
BUT ....
Potential (major) sources of bias in indices.
Some conflict is CPUE signal btwn indexes (difference bin and logn and between regions).
Core area of equatorial region no longer “monitored” by PL fishery.