Species interactions are a key aspect of evolutionary biology. Parasites, specifically, are drivers of the evolution of species communities, and impact biosecurity and public health. However, when using interaction networks for evolutionary studies, interdependencies between distantly related species in these networks are shaped by ancient and complex processes. We propose using recent interacting host-parasite radiations, e.g. African cichlid fishes and cichlid gill parasites belonging to Cichlidogyrus (Dactylogyridae, Monogenea), as macroevolutionary model of species interactions. The cichlid-Cichlidogyrus network encompasses 138 parasite species and 416 interactions identified through morphological characteristics and genetic markers in 160 publications. We discuss the steps required to develop this model system based on data resolution, sampling bias, and reporting quality. In addition, we propose the following steps to guide efforts for a macroevolutionary model system for species interactions:First, evaluating and expanding model system outcome measures to increase data resolution. Second, closing knowledge gaps to address underreporting and sampling bias arising from limited human and financial resources.Identifying phylogenetic and geographic targets, creating systematic overviews, enhancing scientific collaborations, and avoiding data loss through awareness of predatory journal publications, can accelerate this process. Third, standardising data reporting to increase reporting quality and to facilitate data accessibility.

Metazoan 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.

A substantial portion of biodiversity has evolved through adaptive radiation. However, the effects of explosive speciation on species interactions remain poorly understood. Metazoan parasites infecting radiating host lineages could improve our knowledge because of their intimate host relationships. Yet limited molecular, phenotypic and ecological data discourage multivariate analyses of evolutionary patterns and encourage the use of discrete characters. Here, we assemble new molecular, morphological and host range data widely inferred from a species-rich lineage of parasites (Cichlidogyrus, Platyhelminthes: Monogenea) infecting cichlid fishes to address data scarcity. We infer a multimarker (28S/18S rDNA, ITS1, COI mtDNA) phylogeny of 58 of 137 species and characterize major lineages through synapomorphies inferred from mapping morphological characters. We predict the phylogenetic position of species without DNA data through shared character states, a morphological phylogenetic analysis, and a classification analysis with support vector machines. Based on these predictions and a cluster analysis, we assess the systematic informativeness of continuous characters, search for continuous equivalents for discrete characters, and suggest new characters for morphological traits not analysed to date. We also model the attachment/reproductive organ and host range evolution using the data for 136 of 137 described species and multivariate phylogenetic comparative methods (PCMs). We show that discrete characters not only can mask phylogenetic signals, but also are key for characterizing species groups. Regarding the attachment organ morphology, a divergent evolutionary regime for at least one lineage was detected and a limited morphological variation indicates host and environmental parameters affecting its evolution. However, moderate success in predicting phylogenetic positions, and a low systematic informativeness and high multicollinearity of morphological characters call for a revaluation of characters included in species characterizations.

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