SRA_scope(..., condition = 'catch')
or effort. If the model is conditioned on catch, then the SRA will generate predicted catches that match the observed. If conditioned on effort, the estimated fishing mortality in the model will be proportional to the observed effort. A full time series of the conditioning variable is needed, and length of the historical period OM@nyears
will be the length of the conditioned time series.SRA_scope
. The SRA will then attempt to estimate the initial depletion in the first year of the historical period. However, initial depletion may be generally difficult to estimate with precision (consider what data are informative to estimate initial depletion, perhaps an age or length sample from that first year that shows the truncation of the composition data relative to unfished conditions).SRA_scope
. One of these several data types in addition to catch or effort is generally needed to obtain depletion estimates. Availability of these data can be quite sparse over time, yet still informative. For example, an age composition sample from a single recent year that shows a very truncated age structure can be sufficient to imply a heavily depleted stock.OM@Linf, OM@K, OM@t0
(or alternatively, OM@cpars$Len_age
)OM@LenCV
only if length data are usedOM@a
and OM@b
(or alternatively, OM@cpars$Wt_age
)OM@M, OM@M2
or OM@cpars$M_ageArray
OM@L50, OM@L50_95
or OM@cpars$Mat_age
OM@Perr
or OM@cpars$Perr
OM@SRrel
OM@h
or OM@cpars$h
OM@L5
, OM@LFS
, and OM@Vmaxlen
. If there are no age or length compositions, then selectivity in the model is fixed to these values. Otherwise, these are used as starting values.OM@R0
as the starting value.class?OM
. If passing custom objects to the operating model that override default inputs (e.g., for time-varying parameters), then DLMtool::validcpars()
will be helpful for setting up and indexing the dimensions of the custom objects.OM@R0
, only if catch is provided.OM@D
OM@EffYears
, OM@EffLower
, OM@EffUpper
, and OM@cpars$Find
. The effort is equal to the apical fishing mortality when paired with the depletion values.OM@AC
which is estimated post-hoc from the recruitment deviation estimates.OM@cpars$Perr_y
. Historical recruitment are those estimated from the model, while future recruitment will be sampled with autocorrelation.OM@L5, OM@LFS, and OM@Vmaxlen
. If multiple fleets are modeled, then the F-at-age matrix is used to derive the effective selectivity and placed in OM@cpars$V
.OM@cpars$Perr_y
for the operating model are adjusted in order to produce the estimated abundance-at-age in the first year of the SRA model.OM@cpars$V
slot. The default assumption in the projection period of the closed-loop simulation is that the selectivity and relative F among fleets are identical to those in the last historical year. Fleet allocation in management procedures can be explored in multiMSE
, see vignette('multiMSE')
.OM@cpars
to ensure reproducibility. Time-varying parameters affect calculation of reference points, mostly importantly unfished depletion. In SRA_scope
(and DLMtool), depletion is the ratio of the spawning biomass in the terminal year and the unfished spawning biomass in the first year of the model. In this sense, depletion used to describe changes in the stock since fishing began. If life-history parameter are time-varying, then this definition may not necessarily reflect a management target.DLMtool::runMSE()
, the apical F may be re-scaled to ensure that specified depletion has been reached at the beginning and end of the historical period. For simple operating models, i.e. those with conditions identical to the SRA, the apical F’s in the MSE should be nearly identical to those from the SRA. To confirm that this is the case, one can run the plot
function on output returned by SRA_scope
:SRA_scope(..., mean_fit = TRUE)
is run)nsim
replicates. Then we call SRA_scope
with the resample
argument:resample = TRUE
, the function will generate a single model fit, placed in SRA@mean_fit
, and then sample the covariance matrix to populate the recruitment, fishing mortality, selectivity slots in the updated OM. If the model has difficulty estimating the stock size, then there should be a high variance in the R0
estimate. A wide range of historical biomass among simulations should then be seen in the conditioned OM. The markdown report contains separate panels for evaluating the conditioned OM as well as the single model fit.SRA_scope
will provide more information on the possible inputs for the model. The help file for the SRA class (obtained by typing class?SRA
into the R console) describes the outputs from the function. An additional vignette is available to describe set up of fleet and survey selectivity in the function call.