Fig. 4

Distribution of \(\Delta A\) and the drivers of fitness increase. We used the distribution reported in [46] to generate \(\Delta E\) in order to obtain trajectories of expression level, E, and adaptive value, A, from which we infer values of \(\Delta A\). A, C Histograms of \(10^8\) \(\Delta A\) values each (pooled across all 1000 individuals in all 100 replicate populations for 1000 time-steps) with A high mutation rate dynamics and Chlamydomonas DFE parameters \([p,n,f,s]= [0.001,-0.01,0.75,0.3, 0]\) with an exponential fit (blue). C Low mutation rate dynamics and the most stringent conducive DFE parameters \([p,n,f,s] = [0.005,-0.005,0.5,0.1]\), with a fit to a power-law distribution (black). B, D Scatter plots of correlation of population-averaged fitness trajectories with trajectories of population-averaged expression level (x-axis) and trajectories of population-averaged adaptive value (y-axis), in populations following high (B) and low (D) mutation rate dynamics. The black (blue/green) points represent populations that cross (do not cross) the fitness threshold. Overall, each plot contains \(108 \times 100\) points representing all replicate populations across all parameter sets for \(N={1000}\) populations. Red lines demarcate regions where fitness change is driven by changes in expression level (bottom right), driven by changes in adaptive value (top left), or by both expression level and adaptive value (top right). As expected, replicates that cross the threshold (black points) are absent from the bottom left region, where trajectories of both adaptive value and expression level are negatively correlated with the fitness trajectory