The use of selected statistical methods and Kohonen networks in the revision and redescription of parasites
Revisions and redescriptions of species and higher taxa have been known in parasitology since the first description of a parasite. Usually, they are based on standard morphometric methods or more modern genetic analysis. The former are not always sufficiently reliable, while the latter often require expensive equipment, pre-defined genetic markers, and appropriately prepared research material. They may be replaced by multivariate statistical methods, in
particular discriminant analysis and cluster analysis, and Kohonen artificial neural networks included in data mining. This paper presents the examples of specific applications of these methods for the verification of the affinity of nematodes. The discriminant analysis showed that it was possible to statistically significantly discriminate individual nematode species, both for males and females, based on morphometric variables. This confirmed the previously assumed division of the species complex Amidostomum acutum into three distinct species. Similarly, hierarchical cluster analysis, used for the determination of coherent groups of nematode parasites, allowed the identification of relatively homogeneous clusters of nematode species depending on their circle of hosts, and groups of hosts.
Laboratory of Biostatistics, West Pomeranian University of Technology, ul. Doktora Judyma 10, 71-466 Szczecin, Poland
Daniel
D
Zaborski
Laboratory of Biology and Ecology of Parasites, West Pomeranian University of Technology, ul. Doktora Judyma 20, 71-466 Szczecin, Poland
Katarzyna M
KM
Kavetska
Laboratory of Biostatistics, West Pomeranian University of Technology, ul. Doktora Judyma 10, 71-466 Szczecin, Poland
Wilhelm
W
Grzesiak
Laboratory of Biology and Ecology of Parasites, West Pomeranian University of Technology, ul. Doktora Judyma 20, 71-466 Szczecin, Poland
Katarzyna
K
Królaczyk
Laboratory of Biology and Ecology of Parasites, West Pomeranian University of Technology, ul. Doktora Judyma 20, 71-466 Szczecin, Poland
Emil
E
Dzierzba
D000818:Animals; D006801:Humans; D000069550:Machine Learning; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D010271:Parasites / Q000145:classification*; D013045:Species Specificity