Estimation of age-specific migration in an age-structured population dynamics model of Eastern Bering Sea walleye pollock (Theragra chalcogramma) Sara E. Miller and Terrance J. Quinn II Juneau Center, School of Fisheries and Ocean Sciences University of Alaska Fairbanks James N. Ianelli Resource Ecology and Fisheries Management Division, Alaska Fisheries Science Center, NMFS

Outline Background Spatial Movement Model and Migration Estimation  Methods  Results  Future Work  Conclusions

Background  Why develop a migration model?  Spatial structure of the fishery can affect potential yields and impact fishing mortality  Add to the biological understanding of walleye pollock  Reduce uncertainty in the yearly EBS pollock stock assessments However, no estimates of movement rates from a mark-recapture experiment; Can migration be estimated from current assessment data? However, no estimates of movement rates from a mark-recapture experiment; Can migration be estimated from current assessment data?

Distribution Alaska Distribution Source: Mecklenburg et al Bering Sea Gulf of Alaska Eastern Bering Sea

Background Groundfish catch in the commercial fisheries in the Bering Sea/Aleutian Islands region off Alaska by species from 1989 to 2003 by round weight. Walleye pollock accounted for 76% (1.49 million t) of the total groundfish catch in 2003 in the BSAI fishery (Source: Hiatt et al. 2004).

Background  Current stock assessment model (standard model) - age-structured population dynamics model -standard catch equation -Ages-1+ -no seasonal movement -spatially aggregated -estimates values for entire population in EBS

Fishery Seasons:  “A season,” mainly for roe, opens on January 20th and lasts until mid-March or April  “B season,” mainly for surimi and fillets, opens mid to late June and extends until October or early November  Both depending on catch rates Background

 Current Stock Assessment Model (Standard Model) DATA: - bottom trawl survey -acoustic survey -fishery catch-at-age Spatial distribution from surveys has poor correspondence to the commercial catch (different times of the year) Spatial distribution from surveys has poor correspondence to the commercial catch (different times of the year)

Methods  Current Stock Assessment Model (Standard Model)  Ianelli et al  Spatial Age-Specific Movement Model (ASM Model)  Simplified  Ages-3 to 10+,  Extended the standard model  Stratified survey data into 2 areas (NW and SE EBS)  Fishery data (2 areas, 2 seasons)  Population parameters area-specific  Added movement between the two areas  Implemented in ADModel Builder  Spatial Non-Movement Model  Special case of spatial movement model, but NO movement included

 ASM Model 13 data sources: (1) (2) Bottom trawl survey NW and SE ( ) (3) (4) EIT NW and SE (1994, 1996, 1997, 1999, 2000, 2002) (5) (6) NW_A fishery numbers & yield ( ) (7) (8) NW_B fishery numbers & yield ( ) (9) (10) SE_A fishery numbers & yield ( ) (11) (12) SE_B fishery numbers & yield ( ) (13) Total catch yield ( ) Methods

 ASM Model 13 data sources: (1) (2) Bottom trawl survey NW and SE ( ) (3) (4) EIT NW and SE (1994, 1996, 1997, 1999, 2000, 2002) (5) (6) NW_A fishery numbers & yield ( ) (7) (8) NW_B fishery numbers & yield ( ) (9) (10) SE_A fishery numbers & yield ( ) (11) (12) SE_B fishery numbers & yield ( ) (13) Total catch yield ( ) Methods

 ASM Model 13 data sources: (1) (2) Bottom trawl survey NW and SE ( ) (3) (4) EIT NW and SE (1994, 1996, 1997, 1999, 2000, 2002) (5) (6) NW_A fishery numbers & yield ( ) (7) (8) NW_B fishery numbers & yield ( ) (9) (10) SE_A fishery numbers & yield ( ) (11) (12) SE_B fishery numbers & yield ( ) (13) Total catch yield ( ) Methods

Abundance and fishing mortality during the A season (A to )…. Age-specific fishing mortality with a logistic equation for fishery selectivity. Assumed: no natural mortality during fishing. Methods Ex. of logistic equation

Methods

Natural mortality and movement from end of A season ( ) to start of B season (feeding)… # in NW (B)= # that stay in NW x natural survival + # that move from SE→NW x natural survival

Methods

Modeling Movement: NW: Movement (age-3) estimated Movement (age a+1)= γ Movement (age a) SE: Movement (all ages) constant 4 estimated movement parameters ( ) The probability of moving (NW→SE)= 1-probability of staying in the NW. [Based on reasonable guess]

Methods Objective function:  Negative log likelihood -addition of fourteen components [13 data sources and penalty function (constrained parameters)] that assumed a lognormal distribution

Results Spatial non-movement model:  Non-sensical results Estimates of year-class abundance (NW and SE), and total beginning year biomass (ages-3+) much higher than ASM model and the 2005 stock assessment estimates (standard model). If movement not included in spatially-explicit model, can’t estimate realistic population parameters.

Results

Overall ASM model fitted data well (√): 1.Bottom trawl survey age-composition data (NW, SE) √ 1.Yearly bottom trawl survey data (NW, SE) √ 2.Acoustic survey age-composition data (NW, SE) √ 3.Yearly acoustic survey data (NW, SE) √ 4.Catch data in numbers and biomass (NW, SE) √ 5.Fishery age-composition data (NW_A, NW_B, SE_A, SE_B) √ Data Conflicts: Tradeoffs with individual data sources (i.e. certain years) Frequent in stock assessment

Survey-age composition (NW)

Results Estimates of recruitment from the standard stock assessment were usually somewhat lower than the ASM model though of the same order of magnitude. Estimates of beginning year biomass from the standard stock assessment were lower than the ASM model (similar pattern).

Results Currently…. One yearly total allowable catch (TAC) for the whole EBS divided by the 3 fishing sectors and 2 fishery seasons (A and B) by fixed percentages Advantage of ASM model: More in-depth information for fishery management and allocation of quota both spatially (NW and SE separately) and temporally (within the year)

Results Reasonable estimates of many population and movement parameters obtained from existing data disaggregated by area and season.  Yet, this configuration of ASM model overly simplistic case of migration estimation with only 4 estimated migration parameters.  More realistic migration estimation would vary by year and age.

Future Work 1.Combined age- and year-specific movements (cold versus warm year movements) 2.More areas (oceanographic domains, Steller sea lions) 3.Test the robustness of the ASM model by a simulation experiment with known population and migration parameters (e.g., Fu and Quinn 2000; Hilborn and Mangel 1997). 4.Management strategy evaluation -How should harvest be allocated by area and season in the presence of movement? Cold Year (more overlap) Warm Year (less overlap) Age-1 pollock Adult pollock cold pool Adults are distributed more NW, offshore during cold years (Wyllie-Echeverria and Wooster 1998; Kotwicki et al. 2005). Source: Wyllie-Echeverria and Wooster 1998

Conclusions *Key finding – more in-depth information on finer spatial and temporal scales are likely from spatially-explicit studies of EBS walleye pollock. Having additional information from tagging studies (movement studies) would help stabilize the model.*

Acknowledgments Reviewers: Dr. Brenda Norcross, Dr. Gordon Haas, Pete Hulson, Cindy Tribuzio Funding: North Pacific Research Board, Alaska Fisheries Science Center Population Dynamics Fellowship Data: Dan Nichol (AFSC) bottom trawl survey data, Taina Honkalehto (AFSC) EIT survey data, Jim Ianelli (AFSC) fishery data Pictures: Jenny Stahl (ADFG)

Any Questions? Ray Troll