By g = 8, the hybrid population is quite homogeneous in admixture, and the variance of the admixture fraction has decreased to near zero for all sets of founding parameters.
The best-found parameter set is detailed in Table 2.
Results demonstrate that the best-found parameter set was significantly better than a randomly chosen parameter set.
An overview of the best-found parameter set is shown in Table 6.> > -wrap-foot> Inumber number of autophagosomes, lysosomes and autolysosomes were chosen according to our biological data, all other parameters were chosen according to the parameter fitting.
As an index to evaluate the accuracy of this best-found parameter set, its fitness value was compared to the fitness values of 200 randomly generated parameter sets (Table 5).
To compare the accuracy of the fit for the best-found parameter set, the fitness values of 200 randomly chosen parameter sets (every parameter was chosen from a specified range of values) were calculated and the results are shown in Table 1.
Moreover, in Figure 4, considering three scenarios with identical founding parameter values (s1,0 and s2,0), but different values for the introgression parameters s1 and s2 in the subsequent generations with identical ratios, s1/ s2, the expected admixture fraction also approaches the same long-term limit.
Figure 6 shows that in three scenarios with different founding parameter values (s1,0 and s2,0), because the admixture process is constant over time and identical among the scenarios, the variance of the admixture fraction from one of the source populations approaches the same long-term limit.
Applying Equation 50, lim g → ∞ V [ H 1, g, δ ] = A 1 − (s 1 1 − h ) 2, (53 which does not depend on the founding parameters.
The limits over time of the means in Equations 19 and 20 depend only on the continuing contributions, not on the founding parameters.
The figure shows that if the continuing contributions are held constant, then the long-term limiting variance does not depend on the founding parameters.
