John Walter, Brian Linton, Will Patterson and Clay Porch CAPAM Selectivity workshop

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.