Table 3.
Least-squares fit of a Pareto function to the distribution of small effects on fitness
| Pareto[k, a] | % of mutations | |
|---|---|---|
| μ = 0.001, B Optimized | k = 0.0202 ± 0.0003 | 88.1 |
| a = 0.848 ± 0.014 | R2 = 0.998 | |
| μ = 0.004, B Optimized | k = 0.0210 ± 0.0010 | 88.6 |
| a = 0.812 ± 0.043 | R2 = 0.981 | |
| μ = 0.001, B Adapting | k = 0.0205 ± 0.0006 | 94.7 |
| a = 1.065 ± 0.048 | R2 = 0.988 | |
| μ = 0.004, B Adapting | k = 0.0210 ± 0.0011 | 94.6 |
| a = 0.960 ± 0.065 | R2 = 0.971 | |
| μ = 0.001, D Optimized | k = 0.0212 ± 0.0010 | 62.0 |
| a = 0.393 ± 0.015 | R2 = 0.987 | |
| μ = 0.004, D Optimized | k = 0.0233 ± 0.0016 | 70.2 |
| a = 0.446 ± 0.027 | R2 = 0.967 | |
| μ = 0.001, D Adapting | k = 0.0216 ± 0.0011 | 71.0 |
| a = 0.475 ± 0.020 | R2 = 0.983 | |
| μ = 0.004, D Adapting | k = 0.198 ± 0.0015 | 80.1 |
| a = 0.586 ± 0.036 | R2 = 0.967 | |
The Pareto probability distribution function fits the numerically obtained distributions of small effects in all situations studied. Here we show the parameters yielded by the least-squares fit, the R-squared value, and the fraction (in percent) of mutations which affect fitness up to 22% (small effect).