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

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

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

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

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?

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

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

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

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

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

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).

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

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

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.

e.g. Vessel ID

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

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

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. 2015 shark indicator status report (SC11-2015/EB-WP-04) Example: Start with 102311 fishing records Remove rows with missing hooks between floats (HBFs), hooks, lon/lat: 94926 records left Remove sets with mismatch b/w observed and logsheet no of hooks: 93509 records left Remove sets with unrealistic # hooks : 79105 records left Remove sets with unrealistic HBFs: 75805 records left i.e. had to discard 26500 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..

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

CPUE Example 2: Species composition From: 2015 South Pacific Albacore stock assessment (SC11-2015/SA-IP-03)

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

ALB BET YFT SWO

ALB BET YFT SWO

ALB BET YFT SWO

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

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

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)

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…?

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

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

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