A roadmap to fill the gaps
Given the current state of knowledge, the characterisation of the genotypic (based on groups of clones) and genetic (based on allele and genotype frequencies at loci) compositions of populations is both the target and the proxy of population genetics studies aiming to understand the influence of clonal reproduction on the dynamics and evolution of natural populations. Reconciling the effects of PC on both the genotypic and genetic compositions of populations in a robust theoretical framework is thus necessary to illuminate the concomitant changes in their respective estimators depending on the rate of clonality.
Here, we propose a simulation-based exploratory approach both to enhance our understanding of the consequences of clonality and to improve our ability to reliably assess its rate within natural populations. We aim to provide the first exploration of the effect of increasing c on the genotypic and genetic compositions of populations to provide baseline expectations for the composition of natural populations depending on the extent of clonality. We used comprehensive forward individual-based simulations to obtain the theoretical distribution of genotypic (genotypic richness and size distribution of lineages) and genetic (departure from HWE and LD) parameters describing the population composition at increasing rates of clonality from 0 to 1, including all populations with some clonal reproduction, hereafter denoted PC for brevity, ranging from partial (0<c<1) to strict clonality (c=1), and populations with solely sexual reproduction (c=0), hereafter denoted sexual populations. We explored the temporal evolution of these populations along trajectories towards equilibrium and under various levels of genetic drift (population sizes spanning three orders of magnitude). To move from insights about the expected effects of PC on natural populations towards more reproducible and formalised arguments, we assessed the signature of PC in the genotypic and genetic index distributions using a classical and robust Bayesian supervised learning method. This method allowed the selection of descriptors that were more clearly affected to in turn develop sound estimates of the extent of clonality. Finally, we tested the robustness of the method in examining the influence of sample size on the accuracy of estimates and proposed further improvements based on realistic sample sizes.