Network metrics and meta-community structure
The infection data assembled here originate from different ecosystems.
Therefore, we considered all communities inferred from these data as
meta-communities of cichlids and species of Cichlidogyrus . To
investigate the effects of adaptive radiation, we compared the
meta-communities of species infecting hosts from Lake Tanganyika (LT)
and the region inhabited by the Lake Victoria superflock (LV) (see
Verheyen et al. 2003) with the whole cichlid-Cichlidogyrusnetwork. We also inferred meta-communities through the Louvain community
detection algorithm, an approach based on optimisation of network
modularity (see Blondel et al. 2008) implemented in the Rpackage igraph v1.2.6 (Csardi & Nepusz 2006). The algorithm was
applied to the entire (natural and invasive) documented host ranges with
hosts and parasites treated equally as nodes. To characterise
meta-community structure, we inferred several network metrics that are
widely applied to weighted links, including the weighted nestedness
based on overlap and decreasing fill (NODFw)
(Almeida-Neto & Ulrich 2011), weighted connectance (Cw)
(Bersier et al. 2002), specialisation asymmetry (SA) (Blüthgenet al. 2007), interaction evenness (Ei) (Bersieret al. 2002), and the standardised interaction diversity
(H2’) (Blüthgen et al. 2006) using Rpackage bipartite v2.15 (Dormann et al. 2008, 2009;
Dormann 2011).
We calculated network metrics for meta-communities including more than
10 species (Fig. 2) both for the natural ranges and the full realised
host repertoire and geographical distribution (including the result of
anthropogenic translocations). To correct for varying sampling
intensity, we produced two null distributions (NM): Patefield’s
algorithm (Patefield 1981), which randomly redistributes rows and
columns of the interaction matrix (NM1) and the
redistribution algorithm proposed by Vázquez et al. (2007)
(NM2), which maintains the network connectance, i.e.
only realised interactions are redistributed. We generated 1000 null
matrices through the function nullmodel in bipartite and
assessed significance as proportion of null estimates greater than the
observed estimates.