Introduced species can alter the dynamics and structure of a native community. Network analysis provides a tool to study host–parasite interactions that can help to predict the possible impact of biological invasions or other disturbances. In this study, we used weighted bipartite networks to assess differences in the interaction patterns between hosts and helminth parasites of native (Sea of Japan) and invasive (Black Sea and Sea of Azov) populations of Planiliza haematocheilus (Teleostei: Mugilidae). We employed three quantitative network descriptors, connectance, weighted nestedness and modularity, to gain insight into the structure of the host–parasite networks in the native and invaded areas. The role of parasite species in the networks was assessed using the betweenness centrality index. We analyzed networks encompassing the whole helminth community and subsets of species classified by their transmission strategy. The analyses were downscaled to host individual-level to consider intraspecific variation in parasite communities. We found significant differences between networks in the native and invaded areas. The latter presented a higher value of nestedness, which may indicate a co-occurrence between parasite species with many connections in the network and species with fewer interactions within the same individual-host. In addition, modularity was higher in the native area’s networks than those of the invaded area, with subgroups of host individuals that interact more frequently with certain parasite species than with others. Only the networks composed of actively transmitted parasites and ectoparasites did not show significant differences in modularity between the Sea of Azov and the Sea of Japan, which could be due to the introduction of a part of the native community into the invaded environment, with a lower diversity and abundance of species. We show that network analysis provides a valuable tool to illuminate the changes that occur in host–parasite interactions when an invasive species and its parasite community are introduced into a new area.

Cophylogeny represents a framework to understand how ecological and evolutionary process influence lineage diversification. The recently developed algorithm Random Tanglegram Partitions provides a directly interpretable statistic to quantify the strength of cophylogenetic signal and incorporates phylogenetic uncertainty into its estimation, and maps onto a tanglegram the contribution to cophylogenetic signal of individual host-symbiont associations. We introduce Rtapas, an R package to perform Random Tanglegram Partitions. Rtapas applies a given global-fit method to random partial tanglegrams of a fixed size to identify the associations, terminals, and internal nodes that maximize phylogenetic congruence. This new package extends the original implementation with a new algorithm that examines the contribution to phylogenetic incongruence of each host-symbiont association and adds ParaFit, a method designed to test for topological congruence between two phylogenies, to the list of global-fit methods than can be applied. Rtapas facilitates and speeds up cophylogenetic analysis, as it can handle large phylogenies (100+ terminals) in affordable computational time as illustrated with two real-world examples. Rtapas can particularly cater for the need for causal inference in cophylogeny in two domains: (i) Analysis of complex and intricate host-symbiont evolutionary histories and (ii) assessment of topological (in)congruence between phylogenies produced with different DNA markers and specifically identify subsets of loci for phylogenetic analysis that are most likely to reflect gene-tree evolutionary histories.

Cophylogeny represents a framework to understand how ecological and evolutionary process influence lineage diversification. However, linking patterns to mechanisms remains a major challenge. The recently developed Random Tanglegram Partitions provides a directly interpretable statistic to quantify the strength of cophylogenetic signal, maps onto a tanglegram the contribution to phylogenetic signal of individual host-symbiont associations, and can incorporate phylogenetic uncertainty into estimation of cophylogenetic signal. We introduce Rtapas (v1.2), an R package to perform Random Tanglegram Partitions. Rtapas applies a given global-fit method to random partial tanglegrams of a fixed size to identify the associations, terminals, and nodes that maximize phylogenetic congruence. Rtapas extends the original implementation with a new algorithm that tests phylogenetic incongruence and adds ParaFit, a method designed to test for topological congruence between two phylogenies using patristic distances, to the list of global-fit methods than can be applied. Rtapas can particularly cater for the need for causal inference in cophylogeny as demonstrated herein using to two real-world systems. One involves assessing topological (in)congruence between phylogenies produced with different DNA markers and identifying the particular associations that contribute most to topological incongruence, whereas the other implies analyzing the evolutionary histories of symbiont partners in a large dataset. Rtapas facilitates and speeds up cophylogenetic analysis, as it can handle large phylogenies reducing computational time, and is directly applicable to any scenario that may show phylogenetic congruence (or incongruence).