1. Planning the next generation general population assessment model Mark Maunder (IATTC) and Simon Hoyle (SPC)

2. Outline Why we need a new general model Advantages of a general model Existing general models Important features of the next generation general model Features required for protected species Issues with developing a general model Summary

3. Recent advances Improved computer performance Parallel processing and distributed computing Automatic differentiation and MCMC Convergence of approaches towards integrated population dynamics modeling

4. Why we need a new general model Too many populations to assesses Not enough qualified analysts Common language Current models are reaching their limitations Fit to data

5. Common language Facilitates discussions Easier to review –use of SS2 in west coast STAR panel process and Pacific cod assessment Comprehensive analysis and testing to develop best practices Focuses development Reduces duplication

6. Advantages of a general model Less development time Tested code Familiarity Diagnostics and output

7. Existing general models Stock assessment –Coleraine –MULTIFAN-CL –SS1/SS2 –CASAL –Gadget –Xsurvivers –ADAPT

8. Table of model comparisons A-SCALAMFCLSS2CASAL ApproachADMBAUTODIFFADMBBETADIFF Normal approxYes Automatic profile likelihood YesNo Yes BayesianMCMCNoMCMC MCMC practical for tunaNoNANo Model uncertainty MCMCNo BootstrappingNo Automatic ReviewDual programming, comparisons with MFCL, publication review Publication review, comparison with A-SCALA (no spatial or tagging) Independent expert review, intensive reviews of applications, comparisons with other models, simulation tests Comparison with Coleraine and other models, applications reviewed by independent experts AssessmentsIATTC Assessments (YFT, BET, SKJ) and comparisons with WCPO WCPO YFT BET, ALB, SKJ, BUM, SOW, Blue shark, Lobster Atlantic BET ALB 15 west coast and Alaska groundfish assessments, SEPO swordfish From 10 to 20 stocks in NZ and CCAMLR, fin fish and Shellfish Max parameters estimated in application 2000 (RE)3000 (RE)200 Time required for tuna app4 hrs 40 min 4 Not evaluated

9. Model Structure StructureA-SCALAMFCLSS2CASAL SpatialNoYes Fishing mortalityEffort devs Pope’s/catch EqPope’s SeasonsRestrictedGeneral Modeling of discardsNo YesNo Sex structuredNoUnder developmentOptional Growth morphsNo Yes multi-speciesNoUnder development/no predator prey NoYes/no predator prey SelectivitySmoothness penaltiesFunctional forms, smoothness penalties, splines Functional forms and nonparametric Functional forms, smoothness penalties Selectivity basisAge, length penaltyAge or LengthAge, length, and sexAge, length, partition Time varying parametersCatchability All parametersLimited EnvironmentR and qRAll parametersR (untested) Stock-recruitment relationship B-H B-H, Ricker MFull age-structureFull age-structure with smoothness 2 breakpointsFull age-structure with smoothness MovementNATransfer rates with implicit time steps Transfer ratesTransfer rates, density dependent Aging errorNo Yes Variable length bin sizeNo Yes

10. Additional model structure QuestionA-SCALAMFCLSS2CASAL Recruitment deviates Penalized likelihood Penalized likelihood/MCMC UncertaintyNormal approximation but MCMC and profile likelihood possible but impractical Normal approximation profile likelihood by hand and limited in practice Normal approximation, MCMC, profile likelihood, bootstrap Covariate approach Fit to index or as relationship UndeterminedRelationship ProjectionsPoint estimates or likelihood based with normal approximation Likelihood based with normal approximation Likelihood based with normal approximation, MCMC MCMC, point estimates, parametric or nonparametric recruitment Weighting data sets Estimate process error component Spatial structureOnly in fisheriesIn fisheries and population dynamics, uses tagging data In fisheries and population dynamics, does not use tagging data In fisheries and population dynamics, uses tagging data

11. Data types DataA-SCALAMFCLSS2CASAL Catch-effortEffort dev Index Catch-at-age√√√ Catch-at-length√√√√ Abundance index√√√ Tagging√√ Catch-at-weight√ Age-length√√√ Average weight√ Discard (fit)√ Proportions mature √ Proportions migrating √ Age at maturity√

12. Existing general models Multi-species/Ecosystem –Ecopath/Ecosim Mark recapture –MARK –M-SURGE –Barker’s Mother of All Models Wildlife –St Andrews state-space framework PVA –ALEX –RAMAS –VORTEX –GAPPS –INMAT

13. Existing general models Multi-species –Similar to integrated models Ecosystem –Simple structure and data use Mark recapture –Generally limited to mark-recapture data Wildlife –Only a framework, not a general model PVA –Not fit to data

14. State-space models Models processes as probability distributions Not all SS models need to be integrated* or Bayesian Not all integrated* or Bayesian models have to be SS Most process variation is due to the environment not demographic processes –Random effects *Integrated in this context means use multiple data types

15. FLR (Fisheries Library in R) Collection of R tools that facilitate the construction of models representing fisheries and ecological systems. Focuses on evaluating fisheries management strategies Includes several models for stock assessment and simulation Some components are written entirely in R, while others use C++ or Fortran to accommodate existing programs or to recode programs for greater efficiency. (http://flr-project.org/doku.php, Kell et al. 2007)

16. Important features to consider for the next generation general model Integrated multiple data types Priors Include process error spatial structure Sub-population structure (as well as spatial structure) Covariates Age, length, stage, sex Multi-species Meta analysis Genetics Estimate uncertainty Model selection and averaging Simulate data for model testing and MSE Ability to include user defined functions Ability to run each component of the model separately MSE

17. Abundance –Absolute or relative Composition –Age, length, stage, sex, weight, otolith size Aggregated Mark-recapture Archival tags Mortality/catch Future types of data Data

18. Features required for protected species Alternative stock-recruitment curves (density dependence) mate pairing, widowing, skip breeding Density dependence in other processes –Survival –Movement Stage structure Small population sizes –Random variation in population processes Mark-recapture data Occupancy data Minimum counts Habitat data Individual characteristics

19. Management strategy evaluation Data to collect Method to analyze data Management rule Evaluation criteria Operating models

20. Output Management quantities –MSY –Extinction risk –Projections Impact plots Diagnostics –Not well developed for integrated models

21. Some issues with developing a general model Tradeoff between generality and computational efficiency Using the model incorrectly Weighting of data sets Missing data in covariates

22. How to get it done Open source and Free –Create a community for development, testing, training, and assistance Collaboration –Expertise scattered among countries, organizations, and disciplines –Efficient algorithms: statisticians and mathematicians –Efficient code: computer scientist –Appropriate statistical framework (e.g. likelihood functions): statisticians –Population dynamics: ecologists and biologists Funding –Who will pay –Who will get paid Some experts do not have their salaries covered

23. Summary A general model is needed to fulfill management’s increasing needs, and to focus and accelerate research It will take a well planned collaboration from diverse disciplines Organizations are willing to fund it