Network link prediction: Community structure shaped by ecological
factors
Despite the uncertainty in the host range estimation, network link
prediction (NLP) models confirmed the influence of the host evolutionary
history on the structure of fish-monogenean communities. The host
phylogeny contributed considerably to the acceptable performance of theplug-and-play algorithm (AUROC: 0.72), which outperformed the
more complex Poisson N-mixture model. However,
host-parasite links appear to be mostly predicted by ecological
parameters as the ecosystem variable (Table 1) contributed the most to
model performance (Fig. 5c). Therefore, ecological opportunity might
play a major role in the assembly of cichlid-Cichlidogyruscommunities similar to neotropical fish-monogenean communities (Bragaet al. 2014), and these opportunities are likely created by host
geographical and habitat distribution.
The uncovered significance of opportunity is highly relevant for
aquaculture and fish conservation efforts. Introductions of infectious
diseases can have devastating effects on native ecosystems (Thompson
2013). For instance, economic consequences for tilapia aquaculture were
felt in some countries in association with the co-introduction of the
tilapia-lake virus (Eyngor et al. 2014; Fathi et al.2017). Moreover, introductions of Nile tilapia (Oreochromis
niloticus L.) and other large cichlids have led to co-introductions of
their monogenean parasites, e.g. in continental Africa (Jorissenet al. 2020), Madagascar (Šimková et al. 2019), Asia
(Paperna 1960; Duncan 1973; Wu et al. 2006), Australia (Wilsonet al. 2019), and the Americas (Jiménez-Garcia et al.2001; de Azevedo et al. 2006), and to occasional host switches to
native fishes (Jiménez-Garcia et al. 2001; Šimková et al.2019). Our results suggest that more of these host range expansions
might occur through anthropogenic introductions. Therefore, introduced
populations and their surrounding environments should continue to be
monitored.
Our results show that NLP can be a useful tool to verify traditional
statistical analyses and to gain further insight into ecological and
evolutionary mechanisms shaping host-parasite interactions. For
instance, we inferred that the trophic level of the host is one of the
more informative predictors of cichlid-Cichlidogyrusinteractions. Host size, life style, and parasite phylogenetic and
attachment organ morphological parameters also improved model
performance (Fig. 5b). In contrast, previous studies on fish parasites
have delivered inconclusive results for the role of host and parasite
traits on parasite community composition. Parasite community composition
correlated with the host trophic level in some cases, e.g. for
shelf fish off Buenos Aires (Timi et al. 2011), but not in
others, e.g. for freshwater fish in Canada (Locke et al. 2013)
and marine fish in Finland (Locke et al. 2014). No studies
investigated the effects of life style as coded here (Table 1)
but other studies suggest that host habitat preference can affect
parasite communities (Locke et al. 2013). Host size was
suggested as important predictor for the community composition of
ectoparasitic monogeneans (Sasal & Morand 1998; Sasal et al.1999; Šimková et al. 2001; Desdevises et al. 2002; Morandet al. 2002). However, these correlations might reflect
phylogenetic patterns of host size (Poulin 2002) explaining the variable
importance of host size here. Lastly, no correlation ofattachment or reproductive organ morphology with community
composition was found for species of Cichlidogyrus unlike for
other monogeneans, e.g. Dactylogyrus (Šimková et al. 2001;
Jarkovský et al. 2004). Instead, the morphology mostly reflects
phylogenetic relationships of the parasites (Vignon et al. 2011;
Cruz-Laufer et al. 2021b). The results of these studies highlight
the challenge of linking host and parasite traits with community
composition parameters and generalising observed patterns as sampling
biases (Fründ et al. 2016) (Fig. 5a) and character coding
(Pavoine et al. 2009) can influence the results. NLP can
complement these analyses by indicating possibly undetected interactions
(Fig. 5b) and assessing the predictive power of the ecological,
evolutionary, and morphological parameters (Fig. 5c).