Journal
HYDROBIOLOGIA
Volume 848, Issue 16, Pages 3847-3863Publisher
SPRINGER
DOI: 10.1007/s10750-020-04426-4
Keywords
Cichlid parasites; Dactylogyridae; Monogenea; Host-parasite network; Taxonomic bias; Data reporting
Categories
Funding
- BRAIN-be Pioneer Project [BR/132/PI/TILAPIA]
- Knowledge Management Centre project CiMonoWeb (Royal Museum for Central Africa)
- European Marine Biological Research Centre (EMBRC) Belgium
- Research Foundation: Flanders (FWO) [GOH3817N]
- Hasselt University
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Species interactions, particularly driven by parasites, play a crucial role in shaping species communities and impacting biosecurity and public health. Utilizing recent interacting host-parasite radiations as macroevolutionary models can provide insights into the evolution of species interactions, but it is important to address issues such as data resolution, sampling bias, and reporting quality to enhance understanding in this field.
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 toCichlidogyrus(Dactylogyridae, Monogenea), as macroevolutionary model of species interactions. The cichlid-Cichlidogyrusnetwork 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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