# Presentation: 328746

## Improving Stock  Assessments by Explicitly Including FATE Products

-           Michael J. Schirripa                                                               Jim J. Colbert
-      NOAA Fisheries Service                                                 Oregon State University
- Northwest Fisheries Science Center                                                    CIMRS

**Notes:**
I work with the NWFS
One my major responsibilities is the assessment of the sablefish stock off the US West Coast
Information on ecosystem and environmental effects is incorporated into the models, when possible, to improve the interpretation of historical information and the precision of forecasts. 
I want to talk to you today about explicitly incorporating environmental change into the stock assessment model

## Motivation for Proposal

- The mission statement of FATE states that the program is to produce ecological and oceanographic products for the purpose of improving fishery stock assessments (FATE Science Plan 2005).
- While significant progress has been made towards developing biological and leading indicators, very little work has been directed at determining exactly how best to use the FATE products in the current stock assessment framework
- In other words, now that we have these indicators, how do make the best use of them? Answer: via simulation.

## Standard Approach To 

**SAMPLE / DATA**

**ANALYSIS**

**CONCLUSION**

**POPULATION**
**  ****(unknown?)**

**Standard Approach To **
**Gaining Inference About a Population**

## Simulation Approach To 

**Simulation Approach To **
**Gaining Inference About a Method**

**SAMPLE / DATA**

**SIMULATED**
**POPULATION**
**  ****(known attributes)**

**A1**

**C1**

**A2**

**A3**

**C2**

**C3**

## FSIM (v4.0) – Fisheries Simulator

**FSIM (v4.0) – Fisheries Simulator**
**An Independent Platform**

## FSIM - Defined Biological Inputs

**FSIM - Defined Biological Inputs**

**  ****Growth**
**                   ****Fecundity**
**    **
**              **
**                              *****Environmental Effect**

_**FISHERY DEFINED**_
**Selectivity**
**Seasonality**
**Min-max size**
**Quota**

_**SURVEY DEFINED**_
**Selectivity**
**Seasonality**
**Q**
**Numbers/Wt**
**Fishery Association**

## Coupling FSIM and SS2

- FSIM was modified to allow variation in annual recruitment due to a defined environmental effect
- A flexible program was written to read FSIM output files and create the complimentary SS2 data input file
- This program was compiled into an executable form so that it could be run within batch files
- This work was used in the recent Groundfish Harvest Policy Workshop, La Jolla, CA, December 18-20, 2007

## Objective of the Groundfish Harvest Policy WS Study

- Evaluate alternative methods to estimate proxies for initial or unfished biomass (*B**0*) and the biomass that produces maximum sustainable yield (*B**MSY*) and provide recommendations on their use
- How to  deal with the reduced recruitment sigma-r and subsequent reduced bias correction that is exists between the “data poor” and “data rich” periods
- How to deal with the reduced recruitment sigma-r and subsequent bias correction that is created in order to properly model an environmental effect on recruitment

## Recruitment Deviation is Broken Down into

** ****Two Parts: Known and Unknown**

- Recruits

**Notes:**
However, this model does not account for a significant portion of the total variability

## Annual Variation Accounted for by [Spawning Stock Biomass] +/- [ENV] 

**Where the estimated parameters are:**
**	*****h*****   =  steepness***
**	*****R******0***** =  virgin / initial recruitment***
**	*****S******0***** =  virgin / initial spawning biomass**
**	*****S******y*****  = Spawning stock biomass in year t**
**	*****α***** = environment link parameter**
**	*****Vy***** = value of environmental time series (z-score)**

**Notes:**
SS2 uses the following equation to link SSB and Recruitment

## For Years where Recruitment Deviations are Estimated

**Where:**
***σ****** R  *****is the standard deviation for recruitment in log space **
**     ****is the lognormal recruit deviation in year *****y***
***     *****(an estimated parameter)**
** **

## Basis of the Problem:

** ****Mean**

** ****Median**
**(Bias adjusted)**
**ML tends to **
**draw devs to **
**this level**

_**Basis of the Problem:**_
**Increased sigma-r results in a decreased **
**median recruitment due to bias adjustment**

## Modeling an Environmental effect on rec devs requires proper partitioning of total deviation

- The problem is SS2 is that the sigma-r is assumed to be the total sigma-r, even when a certain amount of the variation in recruitment is accounted for by the environmental link.  Consequently, using the above equation will result in a sigma-r which causes SS2 to under-estimate the bias adjustment and thus the median recruitment

- Total sigma-r should reflect the deviation due to the environment plus the deviation due to other random noise as:

## Level 1 – Demonstrate that FSIM and SS2 can arrive at similar answers given relatively error free data. 

- FSIM *ENV* link = off,   sigma-r = 0.89
- SS2 *ENV* link = off,  sigma-r fixed at 0.89
- Minimal data error and very large sample size
- All ages fully selected by survey
- Fitted response variables: (growth), *R0*, steepness, *Q*, recruit devs, some fishery selectivity parms; compare to true values
- Compare various R0/B0 calculations to the R0/B0 from the burn-in period

## Total of 100 ages and 100 platoons (growth morphs)
FSIM seasonally adjusted to fit SS2 assumptions
M = 0.07
One fishery, one survey; Length/ages randomly sampled (5000 survey, 500  fishery per year)
B-H type recruitment steepness = 0.61, ln(R0) = 9.33 
   Recruit deviations can be driven in part by actual SSH data (50 years) and can be explicitly accounted for in SS2 
Each run (n=300) independent from all others within a level

- FSIM seasonally adjusted to fit SS2 assumptions
- M = 0.07
- One fishery, one survey; Length/ages randomly sampled (5000 survey, 500  fishery per year)
- B-H type recruitment steepness = 0.61, ln(*R0*) = 9.33
-    Recruit deviations _can be_ driven in part by actual SSH data (50 years) and _can be_ explicitly accounted for in SS2
- Each run (n=300) independent from all others within a level

**Simulation uses sablefish biological parameters from last assessment**

## Approach

**Level 1 **– Demonstrate that FSIM and SS2 can arrive at the same answers given relatively error free data.
**Level 2** – Introduce environmental forcing and test for    continued model agreement
**Level 3 **– Same as level 2 except estimate R1
**Level 4** – Same as Level 1 except estimate R1
**Level 5**–No *ENV* effect but assume there is (Level 1)
**Level 6** – Significant *ENV* effect but we do not explicitly model for it (Level 2)

## Typical simulated time series that includes an environmental effect

- “Burn-in” Period

**Typical simulated time series that includes an environmental effect**

## Level 1FSIM and SS2 are capable of  agreement given error free data 

## Level 2Reduced σR resulted in an over estimation of R0 and B0

## Conclusions

- FSIM is a viable tool for testing SS2
- Estimating R1 did not help with the reduced sigma-r problem
- A different approach needs to be devised in order to properly use the ENV data
- Use the ENV data more like a survey time series and *fit* the recruitment devs to that as data using the associated error around the data

## Status of the Sablefish Resource off the Continental U.S. Pacific Coast in 2007

**Status of the Sablefish Resource off the Continental U.S. Pacific Coast in 2007**

**Michael J. Schirripa**
**Northwest Fisheries Science Center**
**FRAM Division**
**Newport, Oregon**
**SSC Meeting - June 11, 2007**

## The Effects of Climate on Recruitment 

**are Increasingly Apparent**
**Regression of Rec Devs to SSH and TRANS**
**Strength and Timing of the Spring Transition**

- 2005  >

- 2005  >

## Regression of Rec Devs to 

**Regression of Rec Devs to **
**Northern and Southern Copepod Anomalies**

## “Leave One Out” Cross-Validation Regression between Observed and Predicted

** ****p-value = 0.0005 **
** ****Percent R-squared = 39.14 **
** ****Root Mean Square Error of Calibration = 0.4654**
** ****Root Mean Square Error from ANOVA = 0.4798**
** ****Root Mean Square Error of Cross Validation = 0.4955**

## New Method For Using ENV Data

**Observed and Estimated **
**Recruitment Deviations ****With and without ENV**

## Sea Surface Height for April-May 2007 is below Average

**Sea Surface Height for April-May 2007 is below Average**

## Where to in 2007-2008 ?

**  ****Simulation test the newer method of using the environment as a recruit “survey” **
**  ****Further investigate the use of the date of Spring Transition as a more integrated recruitment index (and the new S.Bograd Upwelling indices)**
** ****Construct Management Decision Tables that take into account possible environmental variability  **

## Management Decision Table

**Management Decision Table**

## Sablefish and It’s Relation to the

**Sablefish and It’s Relation to the**
**11 Dimensions of String Theory**