Abstract
Proteocephalids are a cosmopolitan and diverse group of tapeworms (Cestoda) that have colonized vertebrate hosts in freshwater and terrestrial environments. Despite the ubiquity of the group, key macroevolutionary processes that have driven the group's evolution have yet to be identified. Here, we review the phylogenetic relationships of proteocephalid tapeworms using publicly available (671) and newly generated (91) nucleotide sequences of the nuclear RNA28S and the mitochondrial MT-CO1 for 537 terminals. The main tree search was carried out under the parsimony optimality criterion, analysing different gene alignments simultaneously. Interestingly, we were not able to recover monophyly of the Proteocephalidae. Additionally, it was difficult to reconcile the tree with host and biogeographical data using traditional character optimization strategies in two dimensions. Therefore, we investigated if host and biogeographical data can be correlated with the parasite clades in a multidimensional space-thus considering multiple layers of information simultaneously. To that end, we used random forests (a class of machine learning models) to test the predictive potential of combined (not individual) host and biogeographical data in the context of the proteocephalid tree. Our resulting models can correctly place 88.85% (on average) of the terminals into eight representative clades. Moreover, we interactively increased the levels of clade perturbation probability and confirmed the expectation that model accuracy negatively correlates with the degree of clade perturbation. Our results show that host and biogeographical data can accurately predict proteocephalid clades in multidimensional space, even though they are difficult to optimize in the parasite tree. These results agree with the assumption that the evolution of proteocephalids is not independent of host and biogeography, and both may provide external support for our tree.