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Introduction to Integrated Stock Assessment using Stock Synthesis



Course format On-site
Date 2023-09-18 - 2023-09-22
Duration 1 week
Cost ICES Members: 750 euros; non-ICES Members: 1250

Stock Synthesis (SS3) is a highly flexible assessment working with varying amounts and types of age, length & abundance data.




The course will cover fisheries stock assessment principles and instruction on using the Stock Synthesis (SS3) modeling platform. The course will include theoretical and practical sessions, during which students will apply the model to datasets provided by the instructors. Time will be allocated for students to apply SS3 to their own data set.

Course outline:


  • concept of Integrated Analysis (IA) as implemented in SS3
  • relationship of SS3 to statistical CAA and data-limited tools
  • online resources


Overview of Basic Features:

  • catch and fishing mortality
  • surveys and catchability
  • age-composition data and age selectivity
  • recruitment and spawner-recruitment

Simple examples:

  • age-structured production model
  • data-limited equilibrium model
  • statistical catch-at-age

Running SS3:​​​​​​​​​​​​​​​

  • ​the Interface
  • command line
  • graphical interf​ace
  • R
  • run-time problem resolution
  • associated tools in R
  • viewing output with r4ss
  • diagnosing the fit

Intermediate features:

  • l​​ength-composition data and length selectivity
  • growth
  • log-likelihood and data weighting
  • parameters and priors
  • MSY based reference points (benchmarks)
  • forecasts

Intermediate examples:

  • length composition data but no ages (e.g. tuna)
  • age and length data, with growth estimated
  • demonstration of model internal consistency with flat time series


Participants should have a basic understanding of population dynamics, fisheries biology, quantitative data analysis, and stock assessment. Intermediate/advanced knowledge of R is desirable.  Knowledge of C++ and ADMB is not necessary.

Deadline for registration: 7 August 2023.


ISCED Categories

Scientific modelling