Somewhere I Belong: Phylogenetic Comparative Methods and Machine Learning to Investigate the Evolution of a Species-Rich Lineage of Parasites

AO_SCPLOWBSTRACTC_SCPLOWMetazoan parasites encompass a significant portion of the global biodiversity. Their relevance for environmental and human health calls for a better understanding as parasite macroevolution remains mostly understudied. Yet limited molecular, phenotypic, and ecological data have so far discouraged complex analyses of evolutionary mechanisms and encouraged the use of data discretisation and body-size correction. In this case study, we aim to highlight the limitations of these methods and propose new methods optimised for small datasets. We apply multivariate phylogenetic comparative methods (PCMs) and statistical classification using support vector machines (SVMs) to a data-deficient host-parasite system. We use continuous morphometric and host range data currently widely inferred from a species-rich lineage of parasites (Cichlidogyrus incl. Scutogyrus - Platyhelminthes: Monogenea, Dactylogyridae) infecting cichlid fishes. For PCMs, we modelled the attachment organ and host range evolution using the data of 135 species and an updated multi-marker (28S and 18S rDNA, ITS1, COI mtDNA) phylogenetic reconstruction of 58/137 described species. Through a cluster analysis, SVM-based classification, and taxonomic literature survey, we infered the systematic informativeness of discretised and continuous characters. We demonstrate that an update to character coding and size-correction techniques is required as some techniques mask phylogenetic signals but remain useful for characterising species groups of Cichlidogyrus. Regarding the attachment organ evolution, PCMs suggest a pattern associated with genetic drift. Yet host and environmental parameters might put this structure under stabilising selection as indicated by a limited morphological variation. This contradiction, the absence of a phylogenetic signal and multicollinearity in most measurements, a moderate 73% accordance rate of taxonomic approach and SVMs, and a low phylogenetic informativeness of reproductive organ data suggest an overall limited systematic value of the measurements included in most species characterisations. We conclude that PCMs and SVM-based approaches are suitable tools to investigate the character evolution of data-deficient taxa.