Figures legends
Figure 1.Distribution of parameters
explored at equilibrium (104 generations of
quantitatively homogeneous evolution since the initial random
population) as a function of rates of clonality, c , at population
size N=105 (for N=103 and
N=104, see Figure S1): genotypic parameters: (a) R and
(b) Pareto β; and genetic parameters: (c) FIS mean, (d)
FIS variance, (e) FIS skewness, (f)
FIS kurtosis and (g) linkage disequilibrium. measured as
ṝd. The X-axis is linear from c =0 to c =0.9
and then non-linear for the last two boxes at c =0.99 andc =1.
Figure 2. Temporal
evolution of each parameter at a population size of
105 individuals per generation, as a function of the
number of generations elapsed from a fully random population at
generation 0. Genotypic parameters: (a) R and (b) Pareto β; genetic
parameters: (c) FIS mean, (d) FIS variance, (e) linkage disequilibrium
measured as ṝd. For smaller population sizes (N=103and N=104), see Figure S2 and S2b. Caution regarding
interpretation: all x-axes are non-linear, and the y-axis for\({\overset{\overline{}}{r}}_{d}\) and the mean and variance of theF IS distributions present one to two changes in
scaling.
Figure 3.Machine learning inferences ofc at N=105 and for each parameter used for
inference: genotypic parameters (a) R and (b) Pareto β and genetic
parameters (c) FIS and (d) ṝd, as well
as (e) the combination of all four parameters. The inferred values are
plotted against the simulated values, with the density gradient from
black to light grey indicating the most to least likely/probable.
Figure 4. Subsampling effects on the distributions of genotypic
indices (R and Pareto β) and genetic indices (mean and variance of the
FIS distribution and LD measured as ṝd),
depending on the sample sizes applied to the dataset, with
N=105 at equilibrium (generation g=10000). For smaller
population sizes (N=103 and N=104)
and for the combined effect of subsampling and a non-equilibrium state,
see supplementary Figure S2a and S2b. Caution regarding interpretation:
all x-axes are non-linear, and the y-axis for\({\overset{\overline{}}{r}}_{d}\) and the mean and variance of theF IS distributions presents one change in scaling.