N = 100
|
c
|
μ
|
t
N
|
t
c
|
t
μ
|
t
I
|
t
II
|
t
III
|
---|
A
|
0.0
|
10−6
|
159
|
1
|
2.6 × 106
|
1
|
1
|
2.6 × 106
|
B
|
0.8
|
10−6
|
159
|
25
|
2.6 × 106
|
27
|
27
|
2.6 × 106
|
C
|
0.97
|
10−6
|
159
|
159
|
2.6 × 106
|
174
|
177
|
2.6 × 106
|
D
|
0.99
|
10−6
|
159
|
529
|
2.6 × 106
|
464
|
498
|
2.6 × 106
|
E
|
1.0
|
10−6
|
159
|
∞
|
2.6 × 10
6
|
38,366
| ≫ 40,000 |
2.6 × 106
|
F
|
1.0
|
10−2
|
159
|
∞
|
263
|
234
|
138
|
264
|
G
|
1.0
|
10−1
|
159
|
∞
|
25
|
25
|
14
|
25
|
- Population size N = 100 throughout. Columns: c – rate of clonality, F
IS,∞ = 0 – mutation rate, t
N
– genetic drift maximal expected convergence time, t
c
– reproduction maximal convergence time, t
μ
– mutation maximal convergence time, t
I
– convergence time to the mean \( \overline{F_{IS,\infty }} \) based on the model in [13], t
II
– convergence time to the mean \( \overline{F_{IS,\infty }} \) based on our model, t
III
– convergence time to full final distribution of \( \tilde{{F_{IS}}_{,\infty }} \). Rows: example parameter sets (compare Fig. 4). Bold: min (t
c
, t
μ
)