Spatial issues in WCPO stock assessments (bigeye and yellowfin tuna) Simon Hoyle SPC

Introduction Overview of WCPO fisheries and stock assessments Summary of spatial issues Deeper examination of several issues

WCPO

Complex fisheries Fishing methods – longline, purse seine, pole and line, other Fleets – JP DW LL, JP offshore LL, Korea, China, US, … Species – Skipjack, bigeye, yellowfin, albacore, swordfish, … Spatial – Oceanography, thermocline depth, seasonal changes, convergence zones, seamounts, migrations, age effects, EEZs,

Spatial stratification – bigeye tuna – yellowfin tuna

Model structure 6 regions. Qtrly age classes (e.g. 40 bigeye). Input data – Catch, CPUE, size frequency, tag recapture Population dynamics – Recruitment: overall regional proportion, temporal trend, regional deviates, B-H SRR. – Growth (estimated). – Age-specific natural mortality (fixed). – Movement dynamics (estimated). Fitting to data – LL CPUE index – shared catchability and selectivity among regions. – Size frequency, given selectivity by fishery – Tagging data

Selected spatial issues Regionalization of model – Choosing the right regions Regional scaling of CPUE Estimating movement among regions Spatial effects not included in the model

Criteria for defining regions Simplicity (“less is more”) Homogeneous abundance trend (CPUE) in region* Homogeneous size data in a region* Biogeography Enough data from region to reliably index abundance and size Specific management issues (e.g. ID) Consistency between assessments – fishery interactions

Spatial heterogeneity – YFT CPUE trends JP LL standardised CPUE. GLMs include 5*5 lat/long, HPB, bet_cpue, and no_hooks. Consistent local variation accounted for by lat-long GLM effects Different trends require separate regions

Spatial CPUE variation Yellowfin in region 3, one band of latitude per plot. Less pronounced decline north of 10N Steep decline in far west (of 120E), arguably deserves separate region

Spatial heterogeneity - Size Most size data 10 lat * 20 long blocks. Compare trends in median length/weight between 10*20 blocks. Minimal size data from far west (of 120E)

Decadal trends in median yellowfin size by 10*20 lat/long cell. Red fish small Yellow fish large JP LL size data. Consistent local effects may require separate fisheries Different trends may require separate regions weight length

LL 3 fishery subdivision – different sizes LL ALL 3 fishery (yft) changed to exclude area approx. PNG waters. New fishery with different selectivity, catchability. High catch in the s. Trends arguably different for 10S, 0N, and 10N (as for CPUE, but trends imply different things)

Region and fishery definitions Rule of thumb – If population trajectories differ, separate regions – If fish sizes differ, separate fisheries Pragmatic choice – Does it affect the results? May need more (smaller) regions where fish & effort are concentrated More data makes smaller regions possible

Scaling up size data Catch at size varies with – Time (cohorts moving through fishery) – Fishing method (even to vessel level) – Set (fish school by size) – Space (consistent local effects) Assume constant selectivity for fishery But effort moves around region through time, which affects size – Sampling is ad hoc, may not be representative of catch

Scaled size data Size data Catch

WCPO scheme for scaling up size data Catch data (numbers of fish) from a region are aggregated at the same spatial resolution as the size data (usually 20° longitude, 10° latitude cells). Check size data are available from all cells that cumulatively account for 70% of the total quarterly catch from the region. If not, reject the size data from that quarter. Check there are at least 20 fish sampled from each of the main cells fished and at least a total of 50 fish sampled per quarter. If not, reject the size data from that quarter. Combine the sample data from each cell weighted by the catch in each cell (number of fish). Scale the overall weighted sample to the total number of fish measured in the quarter.

‘Representative’ size data Size data changes may drive population biomass estimates Model ‘assumes’ size changes within fishery reflect changes in population + sampling error Should size data reflect the catch or the population? Length frequency data can strongly affect population trend (e.g. sth Pacific albacore assessment) Standard approach is to reflect catch, but this may be problematic if there is significant size variation in space If this occurs, – Downweight size data sample size to reflect heterogeneity – Define more fisheries ‘Standardize’ size data? – Also an issue for other effects on selectivity, such as LL set depth and gear type

Regional CPUE scaling Region specific CPUE index = relative abundance between regions i.e. region scaling factors.

Area weighted GLM index CPUE indices comparable between regions and reflect relative biomass in each region. 1.GLM model for each region. Data aggregated 5*5 lat/long, HBF, month. YR/qtr index. 2.Region scalar. Sum coefficients within region (at HBF=5). YR/QTR index multiplied by region scalar.

Relative YFT CPUE – from WCPO GLM. Region scaling factors.

YFT area weighted CPUE indices - WCPO

Total biomass trend dominated by biomass trend within Region 3. Total Biomass (yellowfin 2006)

Spatial variation in biology Reproduction – Maturity – Sex ratio – Fecundity at length Growth

Reproductive parameters may vary in space (e.g. bigeye maturity) Model assumes same reproductive output at age for all females. – Affects ‘spawning biomass’ reference points – Affects other ref pts when steepness < 1 L 50 = cm L 50 =135 cm L 50 =105 cm

Yellowfin growth (within WCPO) Region 3 growth estimated by using only region 3 data Slower initial growth within region 3 (western equatorial) compared to overall growth estimated by MFCL WCPO model. WCPO growth estimates strongly influenced by Region 1 size data. Change growth; change fixed M-at-age (length-based).

Spatial variation in yft growth Improved fit to the length frequency data from region 3 small fish fisheries when apply region 3 growth to WCPO model.

Model pars/outputs – biomass (yellowfin) Total and adult biomass. R3 and R4 account for most of the biomass.

Model pars/outputs (bigeye) – recruitment R3 and R4 account for most of the recruitment. Increase in R3 recruitment from early 1990s. Most recent recruitment approximates long- term average.

Model pars/outputs (bigeye) – fishery impact

Estimating movement Biological issues – Food, oceanography, spawning Effects and consequences – Fishing pressure variation – Biomass trends Data – Tagging data directly inform movement – Length frequency and CPUE data also affect estimates

Movement model Instantaneous movement at start of quarter Between regions that share common boundary Parameter indicates prop. in A that move to B Usually 4 seasonal movements each way across each boundary pair Other time effects not modelled Age effects usually not estimable

Bigeye movement – tagging data (> 1000 n. miles)

Model pars/outputs – movement (bigeye) Max. 4% per quarter

Movement issues Movements are difficult to estimate and data are not very informative – Implausible estimates seen – e.g. albacore assessment – Model uses movement and recruitment to account for lack of fit in age-based selectivity estimates. May be better to use biologically reasonable diffusion rates as prior distributions – Would also permit age-based movement estimates – Oceanography needs to be included

Conclusions Multiple spatial issues Work in progress…