Planning the next generation general population assessment model

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

9. Model Structure

10. Additional model structure

11. Data types

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