The few or the many: Evolution, predictors, and drivers of host specificity in African parasitic flatworms belonging to Cichlidogyrus (Monogenea, Dactylogyridae) (R-11518)

Species evolution is shaped not only by environmental factors but also interactions with other species. However, most species interactions are hardly recorded in historical records. One exception from this trend are host-parasite interactions, which are frequently preserved in natural history collections due to the close physical proximity of hosts and their parasites. Yet this diversity still remains widely underexplored. One of the more widely explored host-parasite systems of the last decades are cichlid fishes (Teleostei: Cichlidae) and their flatworm gill parasites belonging to the genera Cichlidogyrus and Scutogyrus (Platyhelminthes: Monogenea, Dactylogyridae). These parasites have been proposed as model systems for macroevolutionary studies due to the model system status of cichlids and a parasite species-richness that rivals the hosts. Most evolutionary studies to date have used simplified parameters such as host range and morphological categories. However, these quantifiers do not reflect the complexity and variability of these traits. Furthermore, meta-analytical studies face a study bias towards economically relevant host species, e.g. tilapia-like species, which are relevant protein source in many parts of Africa. This PhD project aims to establish the Cichlidogyrus-cichlid system as model system for evolutionary studies in host-parasite networks by increasing data availability and addressing knowledge gaps concerning the biodiversity and ecology. We would like to optimise outcome measures and data availability, and use the optimised systems to infer evolutionary patterns in host-parasite species networks. This optimisation involves assembling and offering morphological, genetic, geographical, and ecological data in established open-access online databases. The assembled data will enable us to close knowledge gaps, i.e. completely capture the host ranges of known species and explore the diversity of host species that have so far been overlooked. In particular, we would like to analyse the evolution of host specificity using modern statistical and computational methods including network analyses, multivariate phylogenetic comparative methods, and machine learning algorithms. Using the optimised system, we want to explore factors driving the host-parasite evolution and find potential markers correlating with host specificity based on genes coding for morphological and physiological adaptions.

Keywords:host-parasite interactions

Disciplines:Animal morphology, anatomy and physiology, Animal systematics and taxonomy, Invertebrate biology, Computational biomodelling and machine learning, Phylogeny and comparative analysis