Planning the next generation general population assessment

model

Mark Maunder (IATTC)

and

Simon Hoyle (SPC)

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

Recent advances

• Improved computer performance

• Parallel processing and distributed computing

• Automatic differentiation and MCMC

• Convergence of approaches towards integrated population dynamics modeling

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

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

Advantages of a general model

• Less development time

• Tested code

• Familiarity

• Diagnostics and output

Existing general models• Stock assessment

– Coleraine– MULTIFAN-CL– SS1/SS2– CASAL– Gadget– Xsurvivers– ADAPT

Table of model comparisonsA-SCALA MFCL SS2 CASAL

Approach ADMB AUTODIFF ADMB BETADIFF

Normal approx Yes Yes Yes Yes

Automatic profile likelihood Yes No No Yes

Bayesian MCMC No MCMC MCMC

MCMC practical for tuna No NA No No

Model uncertainty MCMC No No No No

Bootstrapping No No Automatic Automatic

Review Dual 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

Assessments IATTC 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) 200 200

Time required for tuna app 4 hrs 4 hrs 40 min4 Not evaluated

Model StructureStructure A-SCALA MFCL SS2 CASAL

Spatial No Yes Yes Yes

Fishing mortality Effort devs Effort devs Pope’s/catch Eq Pope’s

Seasons Restricted General General General

Modeling of discards No No Yes No

Sex structured No Under development Optional Optional

Growth morphs No No Yes Yes

multi-species No Under development/no predator prey

No Yes/no predator prey

Selectivity Smoothness penalties Functional forms, smoothness penalties, splines

Functional forms and nonparametric

Functional forms, smoothness penalties

Selectivity basis Age, length penalty Age or Length Age, length, and sex Age, length, partition

Time varying parameters Catchability Catchability All parameters Limited

Environment R and q R All parameters R (untested)

Stock-recruitment relationship

B-H B-H B-H, Ricker B-H, Ricker

M Full age-structure Full age-structure with smoothness

2 breakpoints Full age-structure with smoothness

Movement NA Transfer rates with implicit time steps

Transfer rates Transfer rates, density dependent

Aging error No No Yes Yes

Variable length bin size No No Yes Yes

Additional model structureQuestion A-SCALA MFCL SS2 CASAL

Recruitment deviates

Penalized likelihood

Penalized likelihood

Penalized likelihood/MCMC

Penalized likelihood/MCMC

Uncertainty Normal 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

Normal approximation, MCMC, profile likelihood, bootstrap

Covariate approach

Fit to index or as relationship

Undetermined Relationship Relationship

Projections Point 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 structure Only in fisheries In 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

Data typesData A-SCALA MFCL SS2 CASAL

Catch-effort Effort dev Effort dev Index 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 √

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

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

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

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)

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

• Abundance– Absolute or relative

• Composition– Age, length, stage, sex, weight, otolith size

• Aggregated

• Mark-recapture

• Archival tags

• Mortality/catch

• Future types of data

Data

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

Management strategy evaluation

• Data to collect

• Method to analyze data

• Management rule

• Evaluation criteria

• Operating models

Output

• Management quantities– MSY– Extinction risk– Projections

• Impact plots

• Diagnostics– Not well developed for integrated models

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

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

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