Conclusion
To conclude, our results showed a large impact of PC on the genotypic composition of natural populations across the whole spectrum of all possible rates of clonality, supporting its strong influence on the tuning of evolutionary forces acting on these populations at different spatial and temporal scales, even at low values of c , as conjectured by Lewis (1987). By affecting the main path of emergence of new variants (somatic mutations rather than recombination), the targets of natural selection and migration (“… the entity that persists and evolves is the clonal lineage …”; Ayala, 1998), and the influence of drift (through the potentially much longer-term retention of polymorphism; Reichel et al., 2016; Yonezawa et al., 2004; and the present results), PC has the potential to profoundly influence both the short-term dynamics and the evolutionary trajectories of natural populations, even at a modest rate of clonality. Unravelling the occurrence of clonality and understanding its extent are thus of paramount importance for reconstructing, understanding and forecasting the demography, ecology and evolution of the vast number of (possibly including some that often remain undiagnosed) partially clonal species across the tree of life.
Unfortunately, given the present state of knowledge and existing analytical tools, the possibilities of inferring the rates of clonality using one episode of population genotyping are remote. These results also clarify the paradox of the often reported (but also often overlooked) combination of high genotypic diversities, suggesting both significant rates of sexual reproduction and significant heterozygote excess, supporting nearly strict clonality (Dia et al., 2014; Orantes et al., 2012). Many partially clonal organisms studied to date may rely on a much higher prevalence of clonal reproduction than initially thought, but clonal richness in these organisms may be overestimated due to the limited sampling power at hand. This work thus calls for a reappraisal of previously published data and conclusions on a broad range of clonal organisms. Perspectives on how to infer the importance of clonality using one episode of genotyping may, however, exist and can be summarised with the following guidelines:
1) PC can be detected or quantified with the usual sampling power and existing methods, mostly when the rate of clonality exceeds 95%.
2) Departure from HWE towards heterozygote excess, particularly together with a large variance in FIS across loci, indicates the occurrence and likely prevalence of clonality.
3) The joint examination of genotypic and genetic descriptors is often necessary when PC detection is still needed (a recommendation reminiscent of the ones formulated a long time ago for human pathogens (Tibayrenc et al., 1991; see also Tibayrenc & Ayala, 2012 but seldom followed in ecological studies).
4) Considering both families of parameters may help better estimate the extent of clonal reproduction but may require accepting a large uncertainty, particularly when the rate of clonal reproduction is not very high.
5) As such departures are expected due to clonality,F IS should not be used
a- for the estimation of psex (as initially offered by Douhovnikoff & Dodd, 2003 and relayed by Arnaud-Haond et al., 2007), as it may be in most cases due to clonality rather than non-random pairing of gametes. b- (perhaps not as strictly) when filtering next-generation sequencing (NGS) data based on possible PC. Such filters, failing to fit in the case of partial PC, would lead to at best a very large number of informative loci being discarded and at worst complete ignorance of the occurrence of PC in the dataset. c- to detect technical artefacts such as null alleles and correct data or select loci using models based on pure sexuality, including those implemented in software, such as Micro-Checker (Van Oosterhout, Hutchinson, Wills, & Shipley, 2004).
6) Finally, due to the observed but faint signature of c slightly below 95% in the second and further moments ofFIS and to a lesser extentrd , which remains visually undetectable but can be detected by machine learning methods, improvement is expected to result from using machine learning based on informed databases corresponding to the broadest possible range of scenarios. Such development represents a promising avenue and will require large and versatile databases to accommodate the diversity of life history traits associated with clonality and subsampling to account for sampling effects.