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
)