Some Questions on dadi-cli

[RAYIS T R]

Hi all,
I recently started using dadi-cli to infer demographic history from ddRAD-seq data. We generated site frequency spectra (SFS) using easySFS and used these as input for dadi-cli for a set of five species. For each species, we are testing both two-population and three-population demographic scenarios. We used the following set of Portik models to test different scenarios (each model with 100 runs of optimisations) and calculated the AIC using the equation AIC = 2*number of parameters - 2*Log Likelihood. We plan to select the model with the lowest AIC as the best supported scenario.

Models used:
2D models: no_mig, sym_mig, asym_mig, anc_sym_mig, anc_asym_mig, sec_contact_sym_mig, sec_contact_asym_mig
3D models: split_nomig, split_symmig_all, split_symmig_adjacent

Parameter bounds used:
nu: 1e-3 to 100
m: 0 to 10
T: 0 to 20

I had the following doubts that I would appreciate some input on:

1. During the runs, we encountered the following warnings:
   “WARNING:Inference:Model is < 0 where data is not masked.
   WARNING:Inference:Number of affected entries is 25. Sum of data in those entries is 89.7342:”
   Should this be concerning?  If so, how can we overcome this?
   
2. When I checked for convergence, only a few of the runs were converged. Would you recommend increasing the number of optimisation replicates, and if so, what would be a reasonable number to aim for?
   
3. We got the warning:
   “WARNING: The converged parameters are close to the boundaries”
   The values appear to have converged closer to the lower bounds (near 0). We expect our system to have a recent divergence. Should we be concerned about these warnings?
   
4. Does the AIC-based model we follow make sense, or should we use another method to determine the best-fit model?
5. We are using VCFs thinned to retain SNPs at least 10kb apart. Is this appropriate for our analysis, or would you suggest LD pruning or using the non-thinned VCF?

Thanks & Regards,

Rayis.