  Partial clonality is widespread across the tree of life, but most population
genetics models are designed for exclusively clonal or sexual organisms. This
gap hampers our understanding of the influence of clonality on evolutionary
trajectories and the interpretation of population genetics data. We performed
forward simulations of diploid populations at increasing rates of clonality
(c), analysed their relationships with genotypic (clonal richness, R, and
distribution of clonal sizes, Pareto \beta) and genetic (FIS and linkage
disequilibrium) indices, and tested predictions of c from population genetics
data through supervised machine learning. Two complementary behaviours emerged
from the probability distributions of genotypic and genetic indices with
increasing c. While the impact of c on R and Pareto \beta was easily described
by simple mathematical equations, its effects on genetic indices were
noticeable only at the highest levels (c>0.95). Consequently, genotypic indices
allowed reliable estimates of c, while genetic descriptors led to poorer
performances when c<0.95. These results provide clear baseline expectations for
genotypic and genetic diversity and dynamics under partial clonality.
Worryingly, however, the use of realistic sample sizes to acquire empirical
data systematically led to gross underestimates (often of one to two orders of
magnitude) of c, suggesting that many interpretations hitherto proposed in the
literature, mostly based on genotypic richness, should be reappraised. We
propose future avenues to derive realistic confidence intervals for c and show
that, although still approximate, a supervised learning method would greatly
improve the estimation of c from population genetics data.
