Network link prediction
Overall, the NLP algorithm performed solidly (plug-and-play : mean
AUROC = 0.72, full model AUROC = 0.85; Poisson N-mixture :
mean AUROC = 0.62) despite missing data (Fig. 5a). Several input
parameters improved performance of the plug-and-play algorithm
significantly (Fig. 5c) including the basin/basin type, host
phylogenies, trophic level, and life style whereas the parameters for
parasite morphology and phylogeny decreased the performance. A
substantial amount of species interactions remains undetected, albeit
less for LT and LV (Fig. 5b; Appendix S6). For LV ,
model performances were slightly better (0.78, 0.87; 0.76) but with the
host phylogeny as the most important predictor. For LT , the
models showed little discriminatory power (0.41, 0.87; 0.62).
Discussion
We investigated the patterns of host-parasite interaction of African
cichlid fishes and their gill parasites belonging toCichlidogyrus , a proposed model system for macroevolutionary
research (Pariselle et al. 2003; Vanhove et al. 2016).
This study is the first to empirically investigate the effects of
adaptive radiation events on species interactions [but see Maynardet al. (2018) for simulations]. The size of this species
network (10529 infections, 477 interactions) is comparable to widely
used host-parasite datasets in terms of species richness, e.g. the
Global Mammal Parasite Database (GMPD) (Nunn & Altizer 2005), the
Sevillata interaction network (Dallas & Presley 2014), and other
fish-parasite (Lima Jr et al. 2012; Bellay et al. 2015)
and plant-arthropod systems (López-Carretero et al. 2014; de
Araújo et al. 2020; Oliveira et al. 2020; de Araújo &
Maia 2021). The system is also the first to encompass closely related
parasite species infecting a host system that is a model for speciation
research (Seehausen 2006). Therefore, our dataset could be an asset for
comparative studies in network ecology and ecological parasitology.