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 . We
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 R packageigraph v1.2.5 (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 calculated a range of widely used network metrics
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) (Blüthgen et al. 2007), interaction
evenness (Ei) (Bersier et al. 2002), and the
standardised interaction diversity (H2’) (Blüthgenet al. 2006) using R package bipartite v2.15
(Dormann et al. 2008, 2009; Dormann 2011). We tested if
meta-community membership was correlated to the parasite phylogeny
through phylogenetic signal detection using the R packagegeiger (Pennell et al. 2014) as previously reported
(Cruz-Laufer et al. 2021b).
We calculated network metrics for the ten most species-rich
meta-communities (number of species > 10) (Fig. 2)
separately for the full realised host repertoire and geographical
distribution (including the result of anthropogenic translocations) and
the natural ranges. 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 inbipartite and assessed significance as proportion of null
estimates greater than the observed estimates.