Abstract
BackgroundThere are more than 300 species of capillariids that parasitize various vertebrate groups worldwide. Species identification is hindered because of the few taxonomically informative structures available, making the task laborious and genus definition controversial. Thus, its taxonomy is one of the most complex among Nematoda. Eggs are the parasitic structures most viewed in coprological analysis in both modern and ancient samples; consequently, their presence is indicative of positive diagnosis for infection. The structure of the egg could play a role in genera or species discrimination. Institutional biological collections are taxonomic repositories of specimens described and strictly identified by systematics specialists.MethodsThe present work aims to characterize eggs of capillariid species deposited in institutional helminth collections and to process the morphological, morphometric and ecological data using machine learning (ML) as a new approach for taxonomic identification. Specimens of 28 species and 8 genera deposited at Coleção Helmintológica do Instituto Oswaldo Cruz (CHIOC, IOC/FIOCRUZ/Brazil) and Collection de Nématodes Zooparasites du Muséum National d’Histoire Naturelle de Paris (MNHN/France) were examined under light microscopy. In the morphological and morphometric analyses (MM), the total length and width of eggs as well as plugs and shell thickness were considered. In addition, eggshell ornamentations and ecological parameters of the geographical location (GL) and host (H) were included.ResultsThe performance of the logistic model tree (LMT) algorithm showed the highest values in all metrics compared with the other algorithms. Algorithm J48 produced the most reliable decision tree for species identification alongside REPTree. The Majority Voting algorithm showed high metric values, but the combined classifiers did not attenuate the errors revealed in each algorithm alone. The statistical evaluation of the dataset indicated a significant difference between trees, with GL + H + MM and MM only with the best scores.ConclusionsThe present research proposed a novel procedure for taxonomic species identification, integrating data from centenary biological collections and the logic of artificial intelligence techniques. This study will support future research on taxonomic identification and diagnosis of both modern and archaeological capillariids.Graphical abstract
Highlights
There are more than 300 species of capillariids that parasitize various vertebrate groups worldwide
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Moravec (1982) proposed a new taxonomy classification for capillariids to serve as a foundation for future studies, raising the genera to family Capillaridae Neveu-Lemaire, 1936 (Nematoda: Trichocephalida), because of the difference in worm morphologies, the variety of infection sites and their definitive hosts
Summary
There are more than 300 species of capillariids that parasitize various vertebrate groups worldwide. Species identification is hindered because of the few taxonomically informative structures available, making the task laborious and genus definition controversial. Borba et al Parasites Vectors (2021) 14:230 identification is hindered because of the few taxonomically informative structures available, making the task laborious and the genus or species definition controversial. Its taxonomy is one of the most complex among Nematoda, which makes the identification at the genus or species level difficult. Moravec (1982) proposed a new taxonomy classification for capillariids to serve as a foundation for future studies, raising the genera to family Capillaridae Neveu-Lemaire, 1936 (Nematoda: Trichocephalida), because of the difference in worm morphologies, the variety of infection sites and their definitive hosts. Dividing the capillariids into 16 genera (12 redefined, 2 rescued and 2 created) was suggested [1]
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