P. R. Crone, J. L. Valero, and K. T. Hill NOAA Fisheries

1. Age- vs. length-based selectivity for small pelagic fisheries: outside/inside model considerations for management P. R. Crone, J. L. Valero, and K. T. Hill NOAA Fisheries Center for the Advancement of Population Assessment Methodology (CAPAM) Southwest Fisheries Science Center 8901 La Jolla Shores, Dr., La Jolla, CA 92037, USA

2. Pacific mackerel assessment - selectivity evaluation • Phase 1 • Motivation: ongoing sensitivity analysis with stock assessment model for management • Question: choice of biological data and selectivity assumptions impact on management quantities? • e.g., MSY, SPBcurrent, Depletion (SPBcurrent/SPBunfished) • Evaluation summary • Outside model considerations • Fish vs. fishing dynamics • Inside model considerations • Examine alternative model scenarios involving combinations of biological data and selectivity • Biological data • Age compositions vs. length compositions • Selectivity • Age-based vs. length-based • Conduct simulations/estimations involving alternative model scenarios • Results • Examine central tendencies, precision, and bias of management statistics • Identify potential areas of parameter tension (misspecification) in assessment model • Phase 2 • Increasingly add process (estimated parameters) to assessment model and repeat evaluation • Evaluate other species/assessments (P. sardine and bigeye tuna)

3. Pacific mackerel assessment - selectivity evaluation • Outsidethemodelconsiderations • What underlying factors govern selection? • Extrinsic, e.g., gear design/operation (contact- and retention-selection) • Intrinsic, e.g., fish biology (available-selection) • Gear • Commercial: purse seine fisheries that operate seasonally across states/countries • Recreational: hook-and-line fishery operates year-round in CA • Biology • Driving mechanisms: size (length) vs. time (age) • Examine biological compositions for consistency • Fish grow rapidly and realize full selection in the fisheries by age 1

4. Pacificmackerel Outsidethemodelconsiderations OR-WA Distribution Spawning Area Fisheries Monterey San Pedro San Diego Ensenada Bahia Magdalena

5. Pacific mackerel assessment - selectivity evaluation • Outsidethemodelconsiderations • Consistency across biological compositions (or not)

6. Pacific mackerel assessment - selectivity evaluation • Insidethemodelconsiderations • Current assessment model • Stock Synthesis model • Multiple fisheries and indices of abundance • Age and length data • Age compositions and age-based selectivity • Sensitivity analysis is not robust • Tension between selectivity and growth/natural mortality/S-R relations • Assessment model simplified for Phase 1 evaluation • 1 composite fishery: USA (com and rec) / MEX • 1 index of abundance: recreational CPUE • Age or length data • Other data omitted and most other parameters fixed • Alternative model scenarios evaluated (biological data/selectivity combinations)

7. Pacific mackerel assessment - selectivity evaluation Insidethemodelconsiderations

8. Pacific mackerel assessment - selectivity evaluation • Insidethemodelconsiderations • Design and methods • Construct alternative model scenarios • 4 hypothesized ‘states of nature’ based on combinations of biological data and selectivity • Biological data • Age compositions vs. length compositions • Selectivity • Age-based vs. length-based • Each model scenario represents ‘true’ population and fishing dynamics • Produce 500 data sets from each model scenario (simulation) • Parametric bootstrap procedures (Stock Synthesis) • Evaluate each model scenario based on true (simulated) vs. assumed (estimated) selectivity • 4 (simulated) model scenarios x 2 (estimated) selectivity assumptions • Biological data / True selectivity / Assumed selectivity • Age / Age / Age • Age / Age / Length • Age / Length / Length • Age / Length / Age • Length / Length / Length • Length / Length / Age • Length / Age / Age • Length / Age / Length

9. Pacific mackerel assessment - selectivity evaluation Biological Data ‘True’ Selectivity Assumed Selectivity 500 Simulated models 500 Estimated models Age - AAA Age - AA Age - AA Length - AAL Age - A Length - ALL Length - AL Length - AL Age - ALA Length - LLL Length - LL Length - LL Age - LLA Length - L Age - LAA Age - LA Age - LA Length - LAL

10. Pacific mackerel assessment - selectivity evaluation Results– General selectivityforms AA LL

11. Pacific mackerel assessment - selectivity evaluation Results– General fitstobiologicalcompositions AA LL

12. Pacific mackerel assessment - selectivity evaluation Results Black: True Selectivity = Assumed Selectivity Red: True Selectivity ≠ Assumed Selectivity AAA AAL ALL ALA LAA LAL LLL LLA

13. Pacific mackerel assessment - selectivity evaluation Results Black: True Selectivity = Assumed Selectivity Red: True Selectivity ≠ Assumed Selectivity

14. Pacific mackerel assessment - selectivity evaluation Results AAA Black: True Selectivity = Assumed Selectivity Red: True Selectivity ≠ Assumed Selectivity AAL ALL ALA LAA LAL LLL LLA

15. Pacific mackerel assessment - selectivity evaluation Results Black: True Selectivity = Assumed Selectivity Red: True Selectivity ≠ Assumed Selectivity

16. Pacific mackerel assessment - selectivity evaluation Results Black: True Selectivity = Assumed Selectivity Red: True Selectivity ≠ Assumed Selectivity AAA AAL ALL ALA LAA LAL LLL LLA

17. Pacific mackerel assessment - selectivity evaluation Results Black: True Selectivity = Assumed Selectivity Red: True Selectivity ≠ Assumed Selectivity

18. Pacific mackerel assessment - selectivity evaluation • Preliminaryconclusions • An objective approach to evaluate ‘risk’ (uncertainty in management terms) of misspecification of assumed selectivity in the assessment model • Basic research that could contribute to a diagnostics/selectivity/management section in a good practices in stock assessment modeling guide • Age-composition data more robust to selectivity misspecification than length-composition data • Implication of using age compositions (vs. length) as the basis for the assessment is that growth parameterization is more certain, which may be misleading • No substitute for careful scrutiny of ageing techniques/consistency outside the model • Instability of some model scenarios can lead to poor convergence • Further develop assessment model and repeat evaluation and need to apply evaluation to other species/assessments