ss3sim comes with a built-in Stock Synthesis (SS3) operating model (OM) and estimation model (EM) for a cod-like (slow-growing and long-lived) species. This cod-like species is based on North Sea cod (Gadus morhua; R. Methot, pers. comm.). The original assessment model was simplified to make it more generic for simulation testing and structured to work with ss3sim. Previous ss3sim simulation studies also used flatfish-like (fast-growing and long-lived) and sardine-like (fast-growing and short-lived) model set ups. These model setups were based yellowtail flounder (Limanda ferruginea; R. Methot, pers. comm.) and Pacific sardine [Sardinops sagax caeruleus; Hill et al. (2012)]. The specific details (e.g., many fleets, tagging data, seasons, etc.) of the original model setups were not important and removed to produce a set of models generic enough to represent different life-history types. Further details on these models are available in Johnson et al. (2015) and Ono et al. (2015). While these model setups are generic and cover a wide range of life-history types, they may not be suitable for all users. Therefore, in this section, we outline strategies for modifying the existing SS3 model available within the ss3sim package.
ss3sim is not designed for testing properties of specific SS3 models, but rather properties of assessment models in general. Currently, there are many features of SS3 that cannot be manipulated by ss3sim. For example, seasons, hermaphrodism, multiple areas, movement, growth morphs, and platoons. All features that have been removed from the cod-like species if they were present in the original assessment. It is possible to create new models that will work within the ss3sim framework. But, this task will be complicated and likely require extensive knowledge of SS3 and R. This process is described in more detail in Sections and . We recommend adapting the current built-in models to match your desired model setup because the configuration of this model has already been tested within ss3sim and is easily adaptable.
For example, you can modify the cod OM and EM files to have a
different maturity before exploring sampling schemes using the
sample_index
, sample_lcomp
, and
sample_agecomp
functions. The following steps provide a
basic guideline for how to accomplish this:
run_ss3sim
or ss3sim_base
, respectively. It is
probably best to do a shorter deterministic run (see the section on deterministic
runs) rather than a run with process error. After running, read in
the data and do visual checks for proper functionality.system.file(file.path("extdata", "models"), package = "ss3sim")
..dat
or
.ctl
files for the new model and run them manually with SS3
via the command line or a system
call within R if there is
any question if they might break the model.run_ss3sim
and verify it is
still working.