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Partial clonality is widespread across the tree of life, but most populationgenetics models are designed for exclusively clonal or sexual organisms. Thisgap hampers our understanding of the influence of clonality on evolutionarytrajectories and the interpretation of population genetics data. We performedforward simulations of diploid populations at increasing rates of clonality(c), analysed their relationships with genotypic (clonal richness, R, anddistribution of clonal sizes, Pareto \beta) and genetic (FIS and linkagedisequilibrium) indices, and tested predictions of c from population geneticsdata through supervised machine learning. Two complementary behaviours emergedfrom the probability distributions of genotypic and genetic indices withincreasing c. While the impact of c on R and Pareto \beta was easily describedby simple mathematical equations, its effects on genetic indices werenoticeable only at the highest levels (c>0.95). Consequently, genotypic indicesallowed reliable estimates of c, while genetic descriptors led to poorerperformances when c<0.95. These results provide clear baseline expectations forgenotypic and genetic diversity and dynamics under partial clonality.Worryingly, however, the use of realistic sample sizes to acquire empiricaldata systematically led to gross underestimates (often of one to two orders ofmagnitude) of c, suggesting that many interpretations hitherto proposed in theliterature, mostly based on genotypic richness, should be reappraised. Wepropose future avenues to derive realistic confidence intervals for c and showthat, although still approximate, a supervised learning method would greatlyimprove the estimation of c from population genetics data.

The discernible and hidden effects of clonality on the genotypic and genetic states of populations: improving our estimation of clonal rates