Calibrating `marlin` to starting conditions from a stock assessment

[echelleburns]

Hi Dan, hope you're doing well!

I'd love to be able to calibrate my marlin model so that the total predicted spawning biomass (and potentially catch) under a business-as-usual (no MPA) matches a recent stock assessment and stays relatively close to this "equilibrium" value over time (assuming constant effort, constant habitat matrices, etc). I was wondering if you had any advice on which parameters/arguments to tweak to get this desired result.

I have already defined my critters using the life history parameters and population parameters presented in the stock assessment. However, I am running the model at a smaller spatial scale than the stock assessment area, but have set steepness to 1 and scaled r0, ssb0, and b0 to the proportion of biomass expected within the model region.

I've gone through the documentation a little bit and found the following parameters that might help me get to my end goal, but am unclear on whether these will actually help:

• increase the burn_years in Fish$new()? - I'm not sure this would help, since I specify ssb0, and the documentation here says that this would be used to tune population parameters without analytical solutions like ssb0
• set initial_conditions in simmar()? - I'm not exactly sure what this parameter is looking for; the documentation says simmar()[final step] but I'm not clear on what this means
• increase the steps and starting_step in simmar()? - would this tweak result in essentially creating a longer period of "burn years" for the population to each equilibrium?
• tweak the fished_depletion value in Fish$new()? - I'm currently tuning the fleets to the fished depletion value, which was also grabbed from the stock assessment, but maybe this should be changed to achieve the desired ssb output in the projected years?
• turning off tune_costs and/or fine_tune_costs in tune_fleets() function? - this might remove one additional piece of variation if fishing behavior isn't purely linked to the cost function we've chosen for the model?
• anything else I've missed?

Thanks in advance!