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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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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
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
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
Distribution
Spawning Area
FisheriesSan Pedro
BahiaMagdalena
Ensenada
OR-WA
Monterey
San Diego
Pacific mackerelOutside the model considerations
Pacific mackerel assessment - selectivity evaluationOutside the model considerations• Consistency across biological compositions (or not)
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
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)
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
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
Pacific mackerel assessment - selectivity evaluation
AA LL
Results – General selectivity forms
Pacific mackerel assessment - selectivity evaluation
Results – General fits to biological compositions
AA LL
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
ResultsPacific mackerel assessment - selectivity evaluation
Black: True Selectivity = Assumed SelectivityRed: True Selectivity ≠ Assumed Selectivity
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
-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
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
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
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
