The (potential) value and use of empirical estimates of selectivity in integrated assessments
John Walter, Brian Linton, Will Patterson and Clay Porch
CAPAM Selectivity workshop 11-14 March 2013
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Empirical estimates of selectivityHook size experimentsMesh size experimentsPaired trawl experiments, closed cod
endROV/Acoustic studies coupled with
survey sampling
http://www.acoustics.washington.edu/current_research.php
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Selectivity is product of several processesS: Gear or contact selectivity (Millar 1994)- fraction
of animals at size/age encountering gear that are retained.
A: Availability- fraction of animals at size/age available to the fishery. Often a spatial/biological process
S x A = Vulnerability or the probability of a fish being captured is a product of S and A.
Does knowing shape of contact selectivity inform shape of vulnerability?
More formally:
If vulnerability is the product of two vectors, when is the gradient of this product positive or 1 (implying an increasing function and asymptotic selex)?
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Simple logistic form Y=a*exp(b*A)
If length selex is dome-shaped for vulnerability not to be dome shaped: rate of increase in age/stage selex >> decline in length selex
- Strong ontogenetic shifts
- Low plus group
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Ways to treat empirical estimates within integrated models
1. Functional form (shape or PDF)2. PDF, starting values, informative min/max3. PDF, Bayesian priors4. PDF, Fix length selex, assume age selex=1
5. PDF, Fix length selex, est. age selex as proxy for availability (eg. Gummy sharks; Pribac, Punt et al. 2005))6. Informative time blocks7. Others?
Increasing Belief
suggestion
Gospel
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Red Snapper Fishing Experiments to get hooking selectivity
- Fish size distribution surveyed using ROV- Then fished with bottom-rig similar to
recreational fishery with 2/0-15/0 circle hooks
- Catch size distribution conditioned on in situ distribution
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Results – Model Estimates
Total length mm
100 200 300 400 500 600 700 800 900
Selectivity
0.0
0.2
0.4
0.6
0.8
1.0
2/04/09/012/015/0
Total length mm
100 200 300 400 500 600 700 800 900
Selectivity
0.0
0.2
0.4
0.6
0.8
1.0
2/04/09/012/015/0
Total length mm
100 200 300 400 500 600 700 800 900
Selectivity
0.0
0.2
0.4
0.6
0.8
1.0
2/04/09/012/015/0
4/0
200 300 400 500 600 700 800 9000.00
0.05
0.10
0.15
0.20
9/0
200 300 400 500 600 700 800 900
Pro
po
rtion
at Size
0.00
0.04
0.08
0.12
0.16
Total length mm
12/0
200 300 400 500 600 700 800 9000.00
0.04
0.08
0.12
0.16
2/0
200 300 400 500 600 700 800 9000.00
0.05
0.10
0.15
0.20PredictObs
15/0
200 300 400 500 600 700 800 9000.00
0.04
0.08
0.12
0.16
Patterson et al 2012. Bull Mar Sci.
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exponential logistic double normal parms
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Basic Gulf of Mexico Red Snapper SS model structure
• Ages: 0-20+• Years: 1872-2011• 1 Season• 2 Areas (east/west)• Age and length comp• 14 fleets, 8 fishery dependent CPUE indices, 10 Surveys• Time-varying recruitment distribution, 1972-2011• Several selectivities mirrored, reduces parms• Retention and growth estimated• Age-varying natural mortality• Currently 1052 parameters
3
6
5
4
2
78
17
9
1618 15
20
11
1914
21
10
13
1
1212
95°0'0"W
95°0'0"W
90°0'0"W
90°0'0"W
85°0'0"W
85°0'0"W
25°0'0"N
25°0'0"N
30°0'0"N
30°0'0"N
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8 treatments of empirical selex estimates in RS model
1. Naïve min/max starting values
2. Starting, informed min/max
3. diffuse priors (sym.beta sd=0.2)
4. tight priors (sym. beta sd=5)5. fixed len parms
6. fixed len selex, est age selex with RW
7. Est len selex, est. age selex
8. Starting values, time block MRIP
Apply to MRIP (recreational
fleet) Assume 9/0 circle hooks are standard
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SEDAR 31 Red snapper SS model preliminary results
Caveat: these results may be subject to change and imply no generality
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selectivity MRIP E
2. Using informed min-max values improved model fit
Increasing strong treatments do little to change estimates
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7. Estimate length selectivity and age with Rand Walk age 0,1= zero and several ages linked
,
6. fixed length selectivity estimate age with age 0= zero, Rand Walk on 1-20
Age sel MRIP E
Age sel MRIP W
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8. Time blocking selectivityPre and post circle hooks (2008)
no blocks block
TOTAL 13194 12905
Length_comp 7741 7527
Age_comp 4713 4656
No blocks Time block at 2008
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What is the value of this information?
Presumably if we have strong intuition of the selectivity of one fleet, it should inform others
A simple sensitivity analysis to the effects of leaving out the NMFS bottom longline survey age and length composition data
Can a survey or index with known selectivity inform the functional form of another fleet?
Assumed logistic selectivity in 2004 assessment
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Vary final selectivity (Parm 6) of double normal PDF in SS3
MRIP selectivity
Toggling gives asymptotic or dome-shape selex
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value of fishery independent information
improves ability to estimate ‘dominess’ of MRIP fleet
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Some conclusions and caveats to empirical estimates of selectivity1. Functional form (shape or PDF)
- beware of forcing such a shape when availability could vary2. PDF, starting values, informative min/max - can allow setting more appropriate bounds3. PDF, Bayesian priors
- entertains estimates, when no information may be estimates4. PDF, Fix length selex, assume age selex=1
- likely too strong faith in estimates 5. PDF, Fix length selex, est. age selex as proxy for availability (eg. Gummy sharks Pribac, Punt et al. 2005))
- complicated selex fitting6. Informative time blocks
- Strong empirical basis for blocking
Acknowledgements
Thanks to CAPAM for hosting workshop. Steven Garner at University of South Alabama for pictures and slides.