Host repertoire
We estimated the realised host repertoire as the structural and
phylogenetic specificity of species of Cichlidogyrus (Esseret al. 2016). Structural specificity was quantified through the
specialisation index di’ with significance levels
inferred from NM1 and NM2. This index
measures the deviation from a perfectly nested network and is derived
from Shannon’s entropy, which takes diversity, abundance, and evenness
of interactions into account (Blüthgen et al. 2006, 2008).
Phylogenetic specificity was quantified as mean pairwise distance (MPD)
and mean nearest taxon distance (MNTD) of host species to investigate
ancient and recent relationships respectively (Clark & Clegg 2017). For
the null model (NM3), we randomly redistributed the
species labels of the host phylogenetic distance matrix to test if
phylogenetic distances differed significantly from a random
distribution. We calculated MPD, MNTD, and 1000 null estimates through
the functions mpd , mntd , and taxaShuffle in theR package picante v1.8.2 (Kembel et al. 2010) for
each parasite tree. We assessed the MPD and MNTD through the Z-scores
provided by these functions. Negative Z-scores indicate a greater
phylogenetic distance than expected under NM3, positive
scores a smaller distance. Significance was assessed as proportion of
MPD and NTD values smaller (negative Z-scores) or greater (positive
Z-scores) than for the observed network.
Next, we investigated the effects of host environment and phylogeny on
the host repertoire of the parasites. We calculate MPD and MNTD from
functional-phylogenetic distance (FPDist) matrices. These
matrices are derived from functional (FDist) and phylogenetic (PDist)
distance matrices of the host species infected by each parasite species.
We inferred the FDist matrices from the host niche dendrograms and the
PDist matrices from the host phylogenetic trees and scaled the matrices
by dividing the values through the respective maximum distance. We
accounted for uncertainty in the host niche/tree topology by drawing
random samples of dendrograms/trees every time we calculated FPDist =
(aPDistp + (1 -
a)FDistp)1/p (Cadotte et al.2013) with p = 2 to calculate Euclidean distances (see Cadotte et
al. 2013; Burbrink et al. 2017; Clark & Clegg 2017). Finally,
we applied 100 incremental increases from 0 to 1 to the weighting
parameter a . We calculated FPDist through the functionFPDist in the R package funphylocom v1.1 (Walker
2014). We generated null distributions by randomly redistributing the
labels of the FPDist matrices resulting from 1000 random draws from
FDist and PDist matrices. We applied the same redistribution algorithm
as for NM3. For interpretation of the FPDist plots, we
followed Cadotte et al. (2013).