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 March 2013

2 Empirical estimates of selectivity Hook size experiments Mesh size experiments Paired trawl experiments, closed cod end ROV/Acoustic studies coupled with survey sampling

3 Selectivity is product of several processes S: 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)?

5 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

6 Ways to treat empirical estimates within integrated models 1. Functional form (shape or PDF) 2. PDF, starting values, informative min/max 3. PDF, Bayesian priors 4. 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 blocks 7. Others? Increasing Belief suggestion Gospel

7 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

8 Results – Model Estimates Patterson et al Bull Mar Sci.

9 exponential logistic  double normal parms

10 Basic Gulf of Mexico Red Snapper SS model structure Ages: Years: Season 2 Areas (east/west) Age and length comp 14 fleets, 8 fishery dependent CPUE indices, 10 Surveys Time-varying recruitment distribution, Several selectivities mirrored, reduces parms Retention and growth estimated Age-varying natural mortality Currently 1052 parameters

11 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

12 SEDAR 31 Red snapper SS model preliminary results Caveat: these results may be subject to change and imply no generality

13 selectivity MRIP E 2. Using informed min- max values improved model fit Increasing strong treatments do little to change estimates

14 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 fixed parms, estimate age selectivity with RW starting values, estimate age selex TOTAL Length_comp Age_comp Age sel MRIP E Age sel MRIP W

15 8. Time blocking selectivity Pre and post circle hooks (2008) no blocksblock TOTAL Length_comp Age_comp No blocks Time block at 2008

16 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

17 Vary final selectivity (Parm 6) of double normal PDF in SS3 MRIP selectivity Toggling gives asymptotic or dome-shape selex

18 value of fishery independent information improves ability to estimate ‘dominess’ of MRIP fleet

19 Some conclusions and caveats to empirical estimates of selectivity 1.Functional form (shape or PDF) - beware of forcing such a shape when availability could vary 2. PDF, starting values, informative min/max - can allow setting more appropriate bounds 3. PDF, Bayesian priors - entertains estimates, when no information may be estimates 4. 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 fitting 6. 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.