Fasciola gigantica, F. hepatica and Fasciola intermediate forms: geometric morphometrics and an artificial neural network to help morphological identification.

Abstract

BackgroundFasciola hepatica and F. gigantica cause fascioliasis in both humans and livestock. Some adult specimens of Fasciola sp. referred to as “intermediate forms” based on their genetic traits, are also frequently reported. Simple morphological criteria are unreliable for their specific identification. In previous studies, promising phenotypic identification scores were obtained using morphometrics based on linear measurements (distances, angles, curves) between anatomical features. Such an approach is commonly termed “traditional” morphometrics, as opposed to “modern” morphometrics, which is based on the coordinates of anatomical points.MethodsHere, we explored the possible improvements that modern methods of morphometrics, including landmark-based and outline-based approaches, could bring to solving the problem of the non-molecular identification of these parasites. F. gigantica and Fasciola intermediate forms suitable for morphometric characterization were selected from Thai strains following their molecular identification. Specimens of F. hepatica were obtained from the Liverpool School of Tropical Medicine (UK). Using these three taxa, we tested the taxonomic signal embedded in traditional linear measurements versus the coordinates of anatomical points (landmark- and outline-based approaches). Various statistical techniques of validated reclassification were used, based on either the shortest Mahalanobis distance, the maximum likelihood, or the artificial neural network method.ResultsOur results revealed that both traditional and modern morphometric approaches can help in the morphological identification of Fasciola sp. We showed that the accuracy of the traditional approach could be improved by selecting a subset of characters among the most contributive ones. The influence of size on discrimination by shape was much more important in traditional than in modern analyses. In our study, the modern approach provided different results according to the type of data: satisfactory when using pseudolandmarks (outlines), less satisfactory when using landmarks. The different reclassification methods provided approximately similar scores, with a special mention to the neural network, which allowed improvements in accuracy by combining data from both morphometric approaches.ConclusionWe conclude that morphometrics, whether traditional or modern, represent a valuable tool to assist in Fasciola species recognition. The general level of accuracy is comparable among the various methods, but their demands on skills and time differ. Based on the outline method, our study could provide the first description of the shape differences between species, highlighting the more globular contours of the intermediate forms.