Studying isolation by distance can provide useful demographic information. To analyze isolation by distance from molecular data, one can use some kind of genetic distance or coalescent simulations. Molecular markers can often display technical caveats, such as PCR-based amplification failures (null alleles, allelic dropouts). These problems can alter population parameter inferences that can be extracted from molecular data. In this simulation study, we analyze the behavior of different genetic distances in Island (null hypothesis) and stepping stone models displaying varying neighborhood sizes. Impact of null alleles of increasing frequency is also studied. In stepping stone models without null alleles, the best statistic to detect isolation by distance in most situations is the chord distance D. Nevertheless, for markers with genetic diversities H<0.4-0.5, all statistics tend to display the same statistical power. Marginal sub-populations behave as smaller neighborhoods. Metapopulations composed of small sub-population numbers thus display smaller neighborhood sizes. When null alleles are introduced, the power of detection of isolation by distance is significantly reduced and D remains the most powerful genetic distance. We also show that the proportion of null allelic states interact with the slope of the regression of F/(1-F) as a function of geographic distance. This can have important consequences on inferences that can be made from such data. Nevertheless, Chapuis and Estoup's FreeNA correction for null alleles provides very good results in most situations. We finally use our conclusions for reanalyzing and reinterpreting some published data sets.

BackgroundPathogens and their vectors are organisms whose ecology is often only accessible through population genetics tools based on spatio-temporal variability of molecular markers. However, molecular tools may present technical difficulties due to the masking of some alleles (allelic dropouts and/or null alleles), which tends to bias the estimation of heterozygosity and thus the inferences concerning the breeding system of the organism under study. This is especially critical in clonal organisms in which deviation from panmixia, as measured by Wright’s FIS, can, in principle, be used to infer both the extent of clonality and structure in a given population. In particular, null alleles and allelic dropouts are locus specific and likely produce high variance of Wright’s FIS across loci, as rare sex is expected to do. In this paper we propose a tool enabling to discriminate between consequences of these technical problems and those of rare sex.MethodsWe have performed various simulations of clonal and partially clonal populations. We introduce allelic dropouts and null alleles in clonal data sets and compare the results with those that exhibit increasing rates of sexual recombination. We use the narrow relationship that links Wright’s FIS to genetic diversity in purely clonal populations as assessment criterion, since this relationship disappears faster with sexual recombination than with amplification problems of certain alleles.ResultsWe show that the relevance of our criterion for detecting poorly amplified alleles depends partly on the population structure, the level of homoplasy and/or mutation rate. However, the interpretation of data becomes difficult when the number of poorly amplified alleles is above 50%. The application of this method to reinterpret published data sets of pathogenic clonal microbes (yeast and trypanosomes) confirms its usefulness and allows refining previous estimates concerning important pathogenic agents.ConclusionOur criterion of superimposing between the FIS expected under clonality and the observed FIS, is effective when amplification difficulties occur in low to moderate frequencies (20-30%).

A longitudinal study assessed the chemoresistance to isometamidium chloride (ISM) and diminazene aceturate (DA) in the region of the Boucle du Mouhoun in Burkina Faso. A preliminary cross-sectional survey allowed the identification of the 10 villages with the highest parasitological prevalences (from 2.1% to 16.1%). In each of these 10 villages, two herds of approximately 50 bovines were selected, one being treated with ISM (1mg/kg b.w.) and the other remaining untreated as control group. All animals (treated and untreated herds) becoming infected were treated with DA (3.5mg/kg b.w.). In total, 978 head of cattle were followed up. Fortnightly controls of the parasitaemia and PCV were carried out during 8 weeks. The main trypanosome species was T. vivax (83.6%) followed by T. congolense

Glossina palpalis palpalis remains the major vector of sleeping sickness in Côte d'Ivoire. The disease is still active at low endemic levels in Bonon and Sinfra foci in the western-central part of the country. In this study, we investigated the impact of a control campaign on G. p. palpalis population structure in Bonon and Sinfra foci in order to adapt control strategies. Genetic variation at microsatellite loci was used to examine the population structure of different G. p. palpalis cohorts before and after control campaigns. Isolation by distance was observed in our sampling sites. Before control, effective population size was high (239 individuals) with dispersal at rather short distance (731 m per generation). We found some evidence that some of the flies captured after treatment come from surrounding sites, which increased the genetic variance. One Locus, GPCAG, displayed a 1000% increase of subdivision measure after control while other loci only exhibited a substantial increase in variance of subdivision. Our data suggested a possible trap avoidance behaviour in G. p. palpalis. It is important to take into account and better understand the possible reinvasion from neighboring sites and trap avoidance for the sake of sustainability of control campaigns effects.