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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

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

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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 - PowerPoint PPT Presentation

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Page 1: P. R. Crone, J. L. Valero, and K. T. Hill NOAA Fisheries

Age- vs. length-based selectivity for small pelagic fisheries: outside/inside model considerations for management

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

Center for the Advancement of Population Assessment Methodology (CAPAM)Southwest Fisheries Science Center

8901 La Jolla Shores, Dr., La Jolla, CA 92037, USA

Page 2: P. R. Crone, J. L. Valero, and K. T. Hill NOAA Fisheries

Phase 1• Motivation: ongoing sensitivity analysis with stock assessment model for management• Question: choice of biological data and selectivity assumptions impact on management quantities?

o e.g., MSY, SPBcurrent, Depletion (SPBcurrent /SPBunfished)

• Evaluation summaryo Outside model considerations

Fish vs. fishing dynamics o 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-basedo Conduct simulations/estimations involving alternative model scenarioso Results

Examine central tendencies, precision, and bias of management statistics Identify potential areas of parameter tension (misspecification) in assessment model

o Phase 2 Increasingly add process (estimated parameters) to assessment model and repeat evaluation Evaluate other species/assessments (P. sardine and bigeye tuna)

Pacific mackerel assessment - selectivity evaluation

Page 3: P. R. Crone, J. L. Valero, and K. T. Hill NOAA Fisheries

Outside the model considerations• What underlying factors govern selection?

o Extrinsic, e.g., gear design/operation (contact- and retention-selection)o Intrinsic, e.g., fish biology (available-selection)

• Gearo Commercial: purse seine fisheries that operate seasonally across states/countrieso Recreational: hook-and-line fishery operates year-round in CA

• Biologyo Driving mechanisms: size (length) vs. time (age)o Examine biological compositions for consistencyo Fish grow rapidly and realize full selection in the fisheries by age 1

Pacific mackerel assessment - selectivity evaluation

Page 4: P. R. Crone, J. L. Valero, and K. T. Hill NOAA Fisheries

Distribution

Spawning Area

FisheriesSan Pedro

BahiaMagdalena

Ensenada

OR-WA

Monterey

San Diego

Pacific mackerelOutside the model considerations

Page 5: P. R. Crone, J. L. Valero, and K. T. Hill NOAA Fisheries

Pacific mackerel assessment - selectivity evaluationOutside the model considerations• Consistency across biological compositions (or not)

Page 6: P. R. Crone, J. L. Valero, and K. T. Hill NOAA Fisheries

Inside the model considerations• Current assessment modelo Stock Synthesis modeloMultiple fisheries and indices of abundanceoAge and length dataoAge compositions and age-based selectivityoSensitivity analysis is not robustoTension between selectivity and growth/natural mortality/S-R relations

• Assessment model simplified for Phase 1 evaluationo 1 composite fishery: USA (com and rec) / MEXo 1 index of abundance: recreational CPUEoAge or length dataoOther data omitted and most other parameters fixedoAlternative model scenarios evaluated (biological data/selectivity combinations)

Pacific mackerel assessment - selectivity evaluation

Page 7: P. R. Crone, J. L. Valero, and K. T. Hill NOAA Fisheries

Pacific mackerel assessment - selectivity evaluation

Inside the model considerations

Data AA AL LL LACatch Age composition Length composition Index of abundance (CPUE)

ParameterizationTime period 1983-11 1983-11 1983-11 1983-11Initial equilibrium catch (F ) Fix Fix Fix FixNatural mortality (M ) Fix Fix Fix FixGrowth Fix Fix Fix Fix

Virgin recruitment (R 0) Est Est Est Est

σ-R Fix Fix Fix FixS-R steepness (B-H) Est Est Est Est

Selectivity (fishery) Age (asymptotic) Length (asymptotic) Length (asymptotic) Age (asymptotic)Selectivity (CPUE) Mirrors fishery Mirrors fishery Mirrors fishery Mirrors fisheryCPUE q Est Est Est EstVar. adj. factors (reweighting) No No No NoEst. parameters 68 68 68 68

Model scenario (operating model)

Page 8: P. R. Crone, J. L. Valero, and K. T. Hill NOAA Fisheries

Pacific mackerel assessment - selectivity evaluation

Inside the model considerations• Design and methods

o 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 dynamicso Produce 500 data sets from each model scenario (simulation)

Parametric bootstrap procedures (Stock Synthesis)o 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

Page 9: P. R. Crone, J. L. Valero, and K. T. Hill NOAA Fisheries

Pacific mackerel assessment - selectivity evaluation

Biological Data ‘True’ Selectivity Assumed Selectivity

Age - A

Age - AAA

Length - AALAge - AA

Length - L

Length - ALLength - ALL

Age - ALA

Length - LLL

Age - LLA

Age - LAA

Length - LAL

Length - LL

Age - LA

Age - AA

Length - AL

Length - LL

Age - LA

500 Simulated models 500 Estimated models

Page 10: P. R. Crone, J. L. Valero, and K. T. Hill NOAA Fisheries

Pacific mackerel assessment - selectivity evaluation

AA LL

Results – General selectivity forms

Page 11: P. R. Crone, J. L. Valero, and K. T. Hill NOAA Fisheries

Pacific mackerel assessment - selectivity evaluation

Results – General fits to biological compositions

AA LL

Page 12: P. R. Crone, J. L. Valero, and K. T. Hill NOAA Fisheries

Results

0 50000 100000 150000 200000 250000

a$mids

a$de

nsity AA

0 50000 100000 150000 200000 250000

a$mids

a$de

nsity AL

0 50000 100000 150000 200000 250000

a$mids

a$de

nsity LA

0 50000 100000 150000 200000 250000

a$mids

a$de

nsity LL

MSY

MSY (MT)

Black: True Selectivity = Assumed SelectivityRed: True Selectivity ≠ Assumed Selectivity

AAAAAL

ALA

LAL

LLA

ALL

LAA

LLL

Pacific mackerel assessment - selectivity evaluation

Page 13: P. R. Crone, J. L. Valero, and K. T. Hill NOAA Fisheries

ResultsPacific mackerel assessment - selectivity evaluation

Black: True Selectivity = Assumed SelectivityRed: True Selectivity ≠ Assumed Selectivity

Page 14: P. R. Crone, J. L. Valero, and K. T. Hill NOAA Fisheries

ResultsPacific mackerel assessment - selectivity evaluation

0e+00 1e+05 2e+05 3e+05 4e+05 5e+05

a$mids

a$de

nsity AA

0e+00 1e+05 2e+05 3e+05 4e+05 5e+05

a$mids

a$de

nsity AL

0e+00 1e+05 2e+05 3e+05 4e+05 5e+05

a$mids

a$de

nsity LA

0e+00 1e+05 2e+05 3e+05 4e+05 5e+05

a$mids

a$de

nsity LL

SPB 2011

Biomass (MT)

Black: True Selectivity = Assumed SelectivityRed: True Selectivity ≠ Assumed Selectivity

AAAAAL

ALA

LAL

LLA

ALL

LAA

LLL

Page 15: P. R. Crone, J. L. Valero, and K. T. Hill NOAA Fisheries

-1 0 1 2 3 4

AAA

AAL

-1 0 1 2 3 4

ALL

ALA

-1 0 1 2 3 4

LAA

LAL

-1 0 1 2 3 4

LLL

LLA

SPB 2011

Bias

Black: True Selectivity = Assumed SelectivityRed: True Selectivity ≠ Assumed Selectivity

ResultsPacific mackerel assessment - selectivity evaluation

Page 16: P. R. Crone, J. L. Valero, and K. T. Hill NOAA Fisheries

ResultsPacific mackerel assessment - selectivity evaluation

0.5 1.0 1.5

a$mids

a$de

nsity AA

0.5 1.0 1.5

a$mids

a$de

nsity AL

0.5 1.0 1.5

a$mids

a$de

nsity LA

0.5 1.0 1.5

a$mids

a$de

nsity LL

Depletion 2011

SPB 2011/SPB Virgin

Black: True Selectivity = Assumed SelectivityRed: True Selectivity ≠ Assumed Selectivity

AAA

AAL

ALA

LAL

LLA

ALL

LAA

LLL

Page 17: P. R. Crone, J. L. Valero, and K. T. Hill NOAA Fisheries

ResultsPacific mackerel assessment - selectivity evaluation

Black: True Selectivity = Assumed SelectivityRed: True Selectivity ≠ Assumed Selectivity

-1 0 1 2 3 4

AAA

AAL

-1 0 1 2 3 4

ALL

ALA

-1 0 1 2 3 4

LAA

LAL

-1 0 1 2 3 4

LLL

LLA

Depletion 2011

Bias

Page 18: P. R. Crone, J. L. Valero, and K. T. Hill NOAA Fisheries

Pacific mackerel assessment - selectivity evaluation

Preliminary conclusions• 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 datao Implication of using age compositions (vs. length) as the basis for the assessment is that growth

parameterization is more certain, which may be misleadingoNo 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