1. TDW10: 25-29 April 2016, Noumea, New Caledonia
Stock assessments:
Behind the scenes
TDW10: April 2016, Noumea, New Caledonia

2. Objectives
1. Explain the main two outputs of stock assessments for WCPFC managers; 2. Showcase how stock assessments use variables other than catch and effort; 3. Highlight how data quality or coverage can impact our conclusions for three different types of logsheet variables

3. Two main goals of stock assessments for management by WCPFC countries
How many fish are left?
How hard are we fishing them?
The Scientific Committee makes recommendations based on estimates of those two quantities

4. The Majuro plot
How hard are we fishing ?
How many fish are left?

5. Aims of a stock assessment
Stock assessment estimates no./biomass of fish in every year
Can compare recent years to historical years – how depleted
Estimate impacts of fishing – sustainable???
Allow prediction of different management actions – how many should we catch in the future?

6. Data for SPC stock assessments
Wide variety of data used – together allow the best models to inform management of tuna, billfish and sharks
Catch / Effort
Size samples
Tagging data
CPUE analyses
Stock Assessment Model

7. Data for SPC stock assessments
Raised 5 x 5 estimates
Observer measurements
Port
sampling
Catch / Effort
Size samples
National tagging officers
Logsheet data
Tagging data
CPUE analyses
Stock Assessment Model
Tag seeding
Onboard recoveries

8. Stock assessment model
Different data types tell a different story about the fish
No./Biomass fish yr1
Survival – tagging data/cpue
Fish recruited – cpue/lengths
Die naturally – tagging data/cpue
Grow – length measurements
Move in – tagging/catch/lengths
Caught – catch data/tagging
No. fish yr2
Move away – tagging/catch/lengths
No. fish yr3
Etc. etc.
No. fish 2015

9. Stock Assessment Model
Data for SPC stock assessments
Wide variety of data used in stock assessments – together allow the best models to inform management of tuna, billfish and sharks
Raised 5 x 5 estimates
Very important for stock assessments
Catch / Effort
National tagging officers
Tagging data
CPUE analyses
Stock Assessment Model
Tag seeding
Main focus of talk
Onboard recoveries

10. CPUE = catch-per-unit-effort =
fishing effort
Relative abundance
Effort
(in hooks, # hours fished, sets, … )
Catch +
Time 1 –
Time 2

11. CPUE standardization
CPUE on its own is not always representative of abundance
CPUE standardization tries to correct the CPUE index so that it is a good representation of trends in species abundance over time
Ps:
Nominal CPUE is uncorrected, CATCH/EFFORT only;
Standardized CPUE is corrected by accounting for other variables (e.g. from the logsheets).

12. Why is CPUE standardization important?
Trends in CPUE over time tell the stock assessment (roughly) which way the stock is going …slight decline, big decline, slight increase, no change? Even small differences in standardized trends can impact the stock assessment

13. Why is CPUE standardization important?
Three examples drawn from SPC studies: 1. Vessel ID 2. Species composition 3. Artisanal effort metric

14. What we use in CPUE standardization
Example 1: Operational (fishing) variables
We rely heavily on logsheet variables to correct the index of abundance
Vessel ID
Vessel characteristics : (
Set time
Longline Purse-seine
Hooks-between-float Set type
Bait type FAD/not FAD
etc.

15. e.g. Vessel ID

16. e.g. Longline HBF HBF: Hooks between floats
The higher the HBFs, the deeper the hooks sink
Low HBFs =
more YFT
High HBFs =
more ALB
Image: Boris Colas

17. Missing fields/crazy values = : (
As soon as one variable is missing, we have to exclude the entire record, even if the other variables are there…
 HBFs are one of the most commonly missing or unreliable variables

18. Missing fields/crazy values = : (
As soon as one variable is missing, we have to exclude the entire record, even if the other variables are there…
e.g shark indicator status report (SC /EB-WP-04)
Example:
Start with fishing records
Remove rows with missing hooks between floats (HBFs), hooks, lon/lat: records left
Remove sets with mismatch b/w observed and logsheet no of hooks: records left
Remove sets with unrealistic # hooks : records left
Remove sets with unrealistic HBFs: records left
i.e. had to discard records that could have been used to improve the analysis… 25% of the dataset!
And we also have to filter over time and space
The less records, the more noise in the data, and the
less certain we are..

19. Example 2: Species composition
ie. what are other species are being caught?
We use catch data for non-target species to spot changes in targeting over time or space Signal is often clearer when looking at overall species composition vs. single species Ps. Targeting (or fishing strategy) = the main species that the vessels are trying to catch during the set

20. CPUE Example 2: Species composition From: 2015 South Pacific Albacore stock assessment (SC /SA-IP-03)

21. For each 5x5 square, the proportion of longline
sets in the square targeting: ALB, BET, YFT, SWO
ALB
BET
YFT
SWO

22. ALB
BET
YFT
SWO

23. ALB
BET
YFT
SWO

24. ALB
BET
YFT
SWO

25. ALB
BET
YFT
SWO
Trends in CPUE over time tell the stock assessment roughly which way the stock is going… slight decline, big decline, slight increase, no change? So small differences in standardized trends are important

26. C Example 3: Artisanal effort
CPUE is important for more than just stock assessments Case-study: Are there interactions between the artisanal and industrial fleet in French Polynesia? C

27. To find out, we need: 1. Industrial catch of artisanal target species (YFT) 2. Artisanal CPUE (catch rates) over time (# YFT caught/ artisanal effort) 3. Artisanal targeting data (vessel type)

28. To find out, we need: 1. Industrial catch of artisanal target species (YFT) 2. Artisanal CPUE (catch rates) over time (# YFT caught/artisanal effort) 3. Artisanal targeting data (vessel type)
Hours fished?
Or hours at sea? Or…?

29. Lots of noise when industrial YFT catch is low = ???
Low values when industrial YFT catch is high

30. Conclusion:
Small decline in artisanal catch rates with higher industrial YFT catch, but lots of uncertainty

31. Summary
1. Different types of data give us different clues about how the stock is doing and how hard we are fishing them 2. Need more than just catch and effort! 3. CPUE is one of the main tools for fisheries scientists (… and not just for stock assessments!) but we need other variables to correct it 4. Many records have to be discarded because field value is missing or improbable less records more noise uncertain results harder to give good recommendations C C C