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An analysis of the population structure and genetic diversity for any organism often depends on one or more molecular marker techniques. Nonetheless, these techniques are not absolutely reliable because of various sources of errors arising during the genotyping process. Thus, a complex analysis of genotyping error was carried out with the AFLP method in 169 samples of the oil seed plant Plukenetia volubilis L. from small isolated subpopulations in the Peruvian Amazon. Samples were collected in nine localities from the region of San Martin. Analysis was done in eight datasets with a genotyping error from 0 to 5%. Using eleven primer combinations, 102 to 275 markers were obtained according to the dataset. It was found that it is only possible to obtain the most reliable and robust results through a multiple-level filtering process. Genotyping error and software set up influence both the estimation of population structure and genetic diversity, where in our case population number (K) varied between 2–9 depending on the dataset and statistical method used. Surprisingly, discrepancies in K number were caused more by statistical approaches than by genotyping errors themselves. However, for estimation of genetic diversity, the degree of genotyping error was critical because descriptive parameters (He, FST, PLP 5%) varied substantially (by at least 25%). Due to low gene flow, P. volubilis mostly consists of small isolated subpopulations (ΦPT = 0.252–0.323) with some degree of admixture given by socio-economic connectivity among the sites; a direct link between the genetic and geographic distances was not confirmed. The study illustrates the successful application of AFLP to infer genetic structure in non-model plants.

A combined approach in the determination of Haemonchus nematodes from sheep was applied in this trial. Using selected morphological characters 90.2 % females and 84.2 % males of Haemonchus contortus and 9.8 % females and 15.8 % males of Haemonchus placei were identified. Although cluster analysis based on morphological identification clearly separated two Haemonchus species, H. contortus was exclusively detected in all specimens using restriction cleavage of the ITS-2 region with FspBI endonuclease as well as through the sequencing analysis. Because sheep from both farms have never had contact with other ruminants, and the farmers apply only closed flock turnover, we assume that only H. contortus mono-infection occurred on both farms. This opinion is also supported by molecular data. The most striking result of our study was the finding which indicates that the discriminant function is not able to accurately identify Haemonchus males at the species level.

The aim of this study was to identify Trichuris species in wild ruminants from 32 localities in the Czech Republic using morphological and molecular methods (ITS1-5.8S RNA-ITS2 region polymorphisms). Trichurids were obtained from 176 wild ruminants (roe deer, sika deer, red deer, fallow deer and mouflons) that were culled between 2009 and 2017. Trichuris discolor is the predominant trichurid of all of the above-mentioned wild ruminants, whereas Trichuris ovis was identified less frequently in roe deer, fallow deer, sika deer and mouflons. Red deer were parasitised exclusively by T. discolor. Young hosts under 1 year of age were more intensively infected by trichurids than were adults (χ = 32.02, p = 0.00). Trichurid prevalence results obtained through coprological methods and those based on parasitological dissections differed significantly (χ = 16.26, p = 0.00). The regression analysis indicated that the eggs per gram (EPG) threshold (20 EPG) was exceeded only if the host was parasitised by more than 7 trichurid females. Full concordance between the positive results obtained by the coprological methods and those obtained via direct dissections was achieved when the number of trichurid females per host exceeded 51.

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