Partial clonality is widespread across the tree of life, but most population genetics models are conceived for exclusively clonal or URLanisms. 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 under increasing rates of clonality (c), analysed their relationships with genotypic and genetic indices, and tested predictions of c from population genetics data through supervised machine learning. Two complementary behaviours emerged from the probability distribution of genotypic and genetic indices under increasing c. While the impact of c on genotypic descriptors (R and \beta ) was easily described by simple mathematical equations, it was noticeable only at the highest levels (c>0.95) on genetic indices (F_{IS . Consequently, genotypic richness 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 diversities 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, calling for a reappraisal of many interpretations hitherto proposed in the literature and mostly based on genotypic richness. We propose future avenues through which 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.
Partial clonality is widespread across the tree of life, but most population genetics models are conceived for exclusively clonal or URLanisms. 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 and genetic 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 genotypic descriptors (R and \b{eta ) was easily described by simple mathematical equations, its effects on genetic indices (FIS and linkage disequilibrium) were noticeable only at the highest levels (c>0.95) . Consequently, genotypic richness 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 diversities 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, calling for a reappraisal of many interpretations hitherto proposed in the literature , mostly based on genotypic richness. We propose future avenues through which 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.
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