Network link prediction: Ecological factors shape community
structure
Despite the uncertainty in the host repertoires, network link prediction
(NLP) models confirmed the influence of the hosts’ evolutionary history
on fish-monogenean community structure. The host phylogeny contributed
considerably to the performance of the plug-and-play algorithm.
However, host-parasite links appear to be mostly predicted by ecological
parameters as the basin/basin type–parameter (Table 1) contributed the
most (Fig. 5c). Therefore, ecological opportunity might play a major
role in the assembly of cichlid-Cichlidogyrus communities similar
to neotropical teleost-monogenean communities (Braga et al.2014), and these opportunities are predicted by host presence in rivers
and lakes.
The uncovered significance of opportunity is highly relevant for
aquaculture and fish conservation efforts. This study is the first to
quantify host-pathogen interactions in tilapias, Nile tilapia
(Oreochromis niloticus L.) being one of the most widely farmed
fish worldwide (FAO 2019). Introductions of infectious diseases can have
devastating effects on native ecosystems (Thompson 2013). Concerning
tilapia, co-introductions of the tilapia-lake virus have caused
significant economic losses (Eyngor et al. 2014; Fathi et
al. 2017). Moreover, introductions of tilapias have led to
co-introductions of their monogenean parasites in continental Africa
(Jorissen et al. 2020), Madagascar (Šimková et al. 2019),
Asia (Paperna 1960; Duncan 1973; Wu et al. 2006), Australia
(Wilson et al. 2019), and the Americas (Jiménez-García et
al. 2001; Azevedo et al. 2006), with occasional spillover to
native fishes (Jiménez-García et al. 2001; Šimková et al.2019), albeit with little changes to the respective meta-community
structures (Fig. 3). Our results suggest that anthropogenic
introductions might promote further host switches in the future. In this
context, network predictions could present key tools for understanding
and possibly minimising the risk of emerging diseases (Albery et
al. 2021).
Our results underline that NLP can verify traditional statistical
analyses and provide further insight into ecological and evolutionary
mechanisms shaping host-parasite interactions. For instance, we inferred
that life style, trophic level, and host size are among the more
informative predictors of cichlid-Cichlidogyrus interactions
whereas parasite phylogenetic relationships and morphological parameters
mostly failed to improved model performance (Fig. 5b). Therefore, host
switches might more likely occur between ecologically similar hosts and
emerging diseases in aquaculture could be avoided through culturing
native fishes (Ju et al. 2020; Nobile et al. 2020).
Previous studies on fish parasites have delivered inconclusive results
for the role of host and parasite traits on host-parasite community
composition. No studies investigated the effects of life style as
coded here (Table 1), but host habitat preference can affect parasite
communities (Locke et al. 2013). 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). Host sizewas suggested as important predictor for the community composition of
ectoparasitic monogeneans (Guégan et al. 1992; Sasal & Morand
1998; Sasal et al. 1999; Šimková et al. 2001; Desdeviseset al. 2002; Morand et 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 of attachment or reproductive organ morphologywith community composition was found for species of Cichlidogyrusunlike for species of 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) influence the results. NLP provides an
accessible path to start uncovering the role of various parameters (Fig.
5c) and predicting undetected interactions (Fig. 5b).