Recruitment

[e-perl-NOAA]

Discussion for questions about recruitment. Please subscribe to this discussion so that you get notifications about discussion posts.

[e-perl-NOAA]

For recruitment, we generally advocate starting as early as catch becomes significant, not when other data start.

We also are increasingly seeing merit in using:
2 #do_recdev: 0=none; 1=devvector; 2=simple deviations
to give more flexibility to the recruitment time series, especially in cases where recruitment is not declining in accordance with a spawner-recruitment curve.

[e-perl-NOAA]

From VLAB conversation about recdevs:

The situation in which use of early recruitment devs is most useful is when:

• long time series of catch and sorter time series of data that are informative about recdevs
• desire to include uncertainty about early recdevs even if they cannot be estimated individually
• desire to use the recdev option 1 for later recdevs so their mean value will closely match conditions of the estimated MSY

That lead to creation of the early recdevs which are a simple dev vector without a sum to zero constraint. An advantage of use of early recdevs is that they can be turned on in a later phase to capture their variance without slightly extending runtime associated with estimating them in early phase.

Over time, we have learned, but not definitely demonstrated, that the option 1 dev-vector has disadvantages, particularly when doing MCMC, and that the sum-to-zero constraint can produce illogical patterns in the recdevs.

The current status is that I generally recommend using option 2 for all rec_devs and not using early recdevs.

[e-perl-NOAA]

From Adam Langley in response to the VLAB discussion:
I am currently undertaking two assessments and have been exploring a number of these issues. The species (NZ snapper) is relative long lived (M ~ 0.07). Both stocks have shown a marked increase in productivity over the last 10 years. The stocks have a good time series of age composition data from the mid 1970s onwards. There was a substantial amount of catch from the preceding decades although the magnitude of the catch is not well known.

Previously, we conducted a full catch history model starting from unexploited conditions. However, the change in productivity means that the B0 is no longer considered appropriate for management purposes. Starting the model under exploited conditions (in 1975) means that we no longer need to include the less reliable large catches from the earlier years.

Previously, we have tended to estimate recruitments with the constrained dev vector (option 1). Some simulation work indicated that this approach had the potential to bias estimates of stock status. I also encountered issues with the stock crashing when running MCMC - when the stock was at very low biomass the constrained rec devs were unable to generate enough recruits (for a relatively small proportion of the MCMC draws, but frustrating). This was resolved when switching to unconstrained devs (option 2). Other comparisons did not result in appreciable differences in the estimated recruitment/biomass from the two options.

Starting the model in 1975, I am estimating an initial equilibrium fishing mortality - informed by the early age composition data. However, those data exhibit considerable variability in recruitment over the 20-30 y age range. So, I have also been estimating early rec devs to better fit those age comps and incorporate the uncertainty in the initial F. If those data aren't very informative then you can get confounding between the initial F and the early rec devs. The attached plot of the rec devs for the two periods (early and main) is attached. The very early (1960-65) recruitments are highly uncertain, while the 1970-74 deviates are quite well informed by the age comp data - there is good progression of some very strong year classes. Estimating the early rec devs allowed a good fit to those data, but didn't greatly change the estimate of initial F or initial biomass.

I hope these observations are useful - this could serve as a potential case study.