otolith weight as proxy for age, generalized age comp

[k-doering-NOAA]

Imported from redmine, Issue #35753 Opened by @RickMethot on 2017-06-30 Status when imported: In Progress

Our Panama City lab has done fairly extensive analyses demonstrating the relationship between otolith weight and age (see attached presentation) for GOM species. Beverly Barnett (copied), Guy Davenport, Gary Fitzhugh and to some extent Bonnie have expressed interest in better understanding the value of this information for stock assessment. I also believe the Panama City lab is interested in reducing the number of otoliths that are aged, and want to know if there is a cost-effective approach to substitute some age comp with otolith weight - age keys (see attached manuscript).

Short of full simulation study, do you have any suggestions for how we could test the value of this information for stock assessment? For example, can SS be configured to use an otolith weight at age key rather than an ALK, and if so, are there particular performance metrics that could be used to assess improved model performance? I know folks are loath to give up age comp data, but for some species we oversample age comp, and for others we have very few observations. Using otolith weight could be an attractive and low-cost option for our lower priority stocks.

Shannon

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You could enter otolith weight bins as the age bins, then to enter as the age transition info the mean and standard deviation of otolith weight for each true age.

The problem is that SS only allows for one set of age bins, so trying to do both otolith weights and regular ages in the same model might be tricky, but not impossible if the otolith weights were re-scaled to "look" like ages.

[k-doering-NOAA]

comment from @RickMethot on 2017-11-07: Consider doing this as generalized weight composition. But rather than create the weight composition as a slicing of the length comp, instead do a direct transform from age to otolith weight so need to define some estimable parameters (3) to define the shape and probably 2 more to define the variance then define otolith weight bins also need to enter conditional age' at otolith_weight_bin in order to calibrate the curve

internally, will need an oto_wt x true_age matrix to generate the expected oto_wt comp from the 5 parameter relationship then for a bin of that oto_wt comp need to be able to go expected true age comp then to expected age' comp for some ageing method