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.