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In the process of solving the unknown quantities, a new parameter substitution method is applied.
Certain distributions, considered as special cases of other distributions, can be derived from the original distribution by parameter substitution.
That is, the probability that equivalent-effect parameter substitution can generate non-uniqueness in a modeling system may be inversely proportional to the complexity of the system.
Replacing of one set to another set of parameter values to generate the same modeling result is called equivalent-effect parameter substitution.
But for the integral inversion, when product effect is considered, parameter substitution to the original distribution may not produce accurate results.
The explanation may be that, in a system complex enough as in the 3D stratigraphic modeling, non-uniqueness caused by the equivalent-effect parameter substitution is less probable, compared to a simpler system as in the 2D model case.
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Taken individually, most of the parameter substitutions have little effect on the overall model performance, although a handful have quite large effects, and are investigated more thoroughly.
The neighbor-joining method was used with 1000 bootstrap replications (Kimura 2-parameter Substitution Model).
The Kimura 2-parameter substitution model with different rates among sites (α = 0.8) was used for EF1-α.
Recombination was checked by the Bootscan method [41] using neighbor-joining and Kimura (two-parameter) substitution models over a sliding window of 500 bp with 80-bp increments.
Finally, we used the Kimura 2-parameter substitution model assuming uniform rates among sites for ITS2; gaps and missing data were deleted for pairwise calculations.
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