| one publication added to basket [354925] | The cichlid–Cichlidogyrus network: a blueprint for a model system of parasite evolution
Cruz-Laufer, A.J.; Artois, T.; Smeets, K.; Pariselle, A.; Vanhove, M.P.M. (2021). The cichlid–Cichlidogyrus network: a blueprint for a model system of parasite evolution. Hydrobiologia 848(16): 3847-3863. https://dx.doi.org/10.1007/s10750-020-04426-4 In: Hydrobiologia. Springer: The Hague. ISSN 0018-8158; e-ISSN 1573-5117, more |  |
| Keywords | Dactylogyridae Bychowsky, 1933 [WoRMS]; Monogenea [WoRMS]
Fresh water | | Author keywords | Cichlid parasites; Host–parasite network; Taxonomic bias; Data reporting |
| Authors | | Top | - Cruz-Laufer, A.J.
- Artois, T., more
- Smeets, K., more
| - Pariselle, A.
- Vanhove, M.P.M., more
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| Abstract | 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; and third, standardising data reporting to increase reporting quality and to facilitate data accessibility. |
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