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\begin{document}

\title{The role of phylogeny, ecological opportunity, and adaptive radiation in
host-parasite interactions: network metrics, host repertoire, and
network link prediction}



\author[1]{Armando Cruz-Laufer}%
\author[1]{Tom Artois}%
\author[2]{Stephan Koblmüller}%
\author[3]{Antoine Pariselle}%
\author[1]{Karen Smeets}%
\author[4]{Maarten Van Steenberge}%
\author[1]{Maarten Vanhove}%
\affil[1]{Hasselt University}%
\affil[2]{Karl-Franzens-Universitat Graz}%
\affil[3]{ISEM}%
\affil[4]{KU Leuven}%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Many species-rich ecological communities result from adaptive radiation
events. The effects of these explosive speciation events on community
assembly remain poorly understood. Here, we explore the well-documented
radiations of African cichlid fishes and interactions with their
flatworm gill parasites (Cichlidogyrus spp.) including 10529 reported
infections and 477 different host-parasite combinations collected
through a survey of peer-reviewed literature. We assess the
evolutionary, ecological, and morphological parameters on
meta-communities partially affected by adaptive radiation evens using
network metrics, host repertoire measures, and network link prediction
(NLP). The hosts' evolutionary history mostly determined host
repertoires. Ecological and evolutionary parameters predicted
host-parasite associations, but many interactions remain undetected
according to NLP. Parasite meta-communities under host adaptive
radiation are more specialised and stable while ecological opportunity
and ecological fitting have shaped interactions elsewhere. The
cichlid-Cichlidogyrus network is a suitable eco-evolutionary study
system but future studies should validate our findings in other
radiating host-parasite systems.%
\end{abstract}%



\sloppy


The role of phylogeny, ecological opportunity, and adaptive radiation in
host-parasite interactions: network metrics, host repertoire, and
network link prediction

Armando J. Cruz-Laufer\textsuperscript{1}, Tom
Artois\textsuperscript{1}, Stephan Koblm\selectlanguage{ngerman}üller\textsuperscript{2},
Antoine Pariselle\textsuperscript{3,4}, Karen Smeets\textsuperscript{1},
Maarten Van Steenberge\textsuperscript{5,6}, Maarten P. M.
Vanhove\textsuperscript{1,5}

\emph{1 UHasselt -- Hasselt University, Faculty of Sciences, Centre for
Environmental Sciences, Research Group Zoology: Biodiversity and
Toxicology, Agoralaan Gebouw D, 3590 Diepenbeek, Belgium.}

\emph{2 Institute of Biology, University of Graz, Universitätsplatz 2,
8010, Graz, Austria.}

\emph{3 ISEM, Université de Montpellier, CNRS, IRD, Montpellier,
France.}

\emph{4 Faculty of Sciences, Laboratory ``Biodiversity, Ecology and
Genome'', Research Centre ``Plant and Microbial Biotechnology,
Biodiversity and Environment'', Mohammed V University, Rabat, Morocco.}

\emph{5 Laboratory of Biodiversity and Evolutionary Genomics, KU Leuven,
Charles Deberiotstraat 32, B-3000, Leuven, Belgium.}

\emph{6 Operational Directorate Taxonomy and Phylogeny, Royal Belgian
Institute of Natural Sciences, Vautierstraat 29, B-1000 Brussels,
Belgium.}

Running title

Host-parasite interaction networks

Key words

\emph{Cichlidogyrus} , Cichlidae, ectoparasites, flatworms,
functional-phylogenetic distances, host niche, Lake Tanganyika, Lake
Victoria, Monogenea, species interactions.

Type of article

Letter

Number of words in abstract

149

Number of words in text

5000

Number of references

147

Number of figures

5

Number of tables

1

Corresponding author

Armando J. Cruz-Laufer, armando.cruzlaufer@uhasselt.be

Statement of authorship

AJCL conceptualised the study and conducted the literature survey. AJCL
performed all analyses and produced tables and graphs. AJCL produced
host phylogenies with input from SK. AJCL and MPMV wrote the manuscript
with input from TA, SK, AP, KS, and MVS.

Conflict of interest

The authors declare that they have no conflict of interest.

Data accessibility statement

Species interaction, host ecological, and community membership data as
well as DNA sequence alignments underlying this article are available in
Zenodo at www.zenodo.org, at https://dx.doi.org/10.5281/zenodo.4075171.
Abstract

Many species-rich ecological communities result from adaptive radiation
events. The effects of these explosive speciation events on community
assembly remain poorly understood. Here, we explore the well-documented
radiations of African cichlid fishes and interactions with their
flatworm gill parasites (\emph{Cichlidogyrus} spp.) including 10529
reported infections and 477 different host-parasite combinations
collected through a survey of peer-reviewed literature. We assess the
evolutionary, ecological, and morphological parameters on
meta-communities partially affected by adaptive radiation evens using
network metrics, host repertoire measures, and network link prediction
(NLP). The hosts' evolutionary history mostly determined host
repertoires. Ecological and evolutionary parameters predicted
host-parasite associations, but many interactions remain undetected
according to NLP. Parasite meta-communities under host adaptive
radiation are more specialised and stable while ecological opportunity
and ecological fitting have shaped interactions elsewhere. The
cichlid-\emph{Cichlidogyrus} network is a suitable eco-evolutionary
study system but future studies should validate our findings in other
radiating host-parasite systems.

Graphical Abstract

Many species-rich ecological communities result from adaptive radiation
events. Here, we investigate interactions of African cichlids and their
flatworm parasites belonging to \emph{Cichlidogyrus} (a) through network
analyses (b), host repertoire estimation, and network link prediction
(heatmaps) (c). The hosts' evolutionary history and environment
determine observed host repertoires and network structure (b). Cichlid
radiations in Eastern Africa have formed more specialised
meta-communities (c).

Introduction

Evolutionary processes are a major factor in how ecological communities
are formed (Toju \emph{et al.} 2017) at both the ancient (Algar \emph{et
al.} 2009) and recent (Fussmann \emph{et al.} 2007) timescale. Many
species-rich communities are the result of adaptive radiations (Glor
2010), a form of explosive species formation. Adaptive radiation stem
from ecological opportunity arising from a great variety of newly
available ecological niches (Losos 2010). This mechanism has produced
several diverse and well-known species flocks including Darwin's finches
(Petren \emph{et al.} 2005), Caribbean anole lizards (Losos 2009), and
cichlid fishes (Salzburger \emph{et al.} 2014). Despite this species
diversity, ecological research has focused mostly on the feeding ecology
of radiating lineages (e.g. Guerrero \& Tye 2009; Takahashi \&
Koblmüller 2011) with few studies investigating parasitic (e.g. Karvonen
\& Seehausen 2012) or mutualistic interactions (e.g. Litsios \emph{et
al.} 2012).

Metazoan parasites can be of particular interest in this context due to
their intimate associations that profoundly affect host fitness (Kutzer
\& Armitage 2016) and shape biological communities (Gómez \& Nichols
2013). For instance, host range, a key characteristic of parasite
ecology (Poulin \emph{et al.} 2011), is influenced by environmental
factors as well as the hosts' evolutionary history (Poulin \emph{et
al.}2011). Integrative measures account for host ecology as well as
evolutionary history (Clark \& Clegg 2017), e.g. functional-phylogenetic
distance metrics (FPDist) (Cadotte \emph{et al.} 2013). However, the
frequency of recorded host switches (see Agosta \emph{et al.} 2010)
suggests that such metrics fail to fully grasp the niche limitations of
parasites. Host repertoires observed today have likely resulted from
alternating phases of host range expansions and isolation
(\emph{oscillation hypothesis} ) (Janz \& Nylin 2008). Parasites expand
their host range through their capacity to access novel resources
(\emph{ecological fitting} ) (Agosta \emph{et al.} 2010), i.e. host
species, and through the opportunity emerging from the rise and fall of
ecological barriers (D'Bastiani \emph{et al.} 2020), e.g. after
anthropogenic introductions (Brooks \emph{et al.} 2021).
Therefore,\emph{realised host repertoires} do not equate to the full
repertoires of host species that can potentially be infected
(\emph{fundamentalhost repertoires} ) (Braga \emph{et al.} 2020). The
oscillation of host repertoires resulting from ecological fitting and
opportunity has been termed the \emph{Stockholm Paradigm} (Brooks
\emph{et al.} 2019) and is considered a major source of parasite
biodiversity (Agosta \& Brooks 2020).

One of the aspects highlighted by the \emph{Stockholm paradigm} is the
potential of predicting future host-parasite interactions in the context
of emerging parasitic diseases. Understanding the mechanisms behind
these diseases is increasingly relevant in a world where environmental
degradation promotes host switches between previously unconnected hosts
(Brooks \emph{et al.} 2019). Host switches may present threats to human
health and food security (Fitzpatrick 2013; Jenkins \emph{et al.} 2015;
Ekroth \emph{et al.} 2019; Brooks \emph{et al.} 2021). To understand
parasitic interactions (Bogich \emph{et al.} 2013; Bordes \emph{et
al.}2017), ecological research has put forward network theory (Poulin
2010) through which species are represented as discrete interacting
units, e.g. in plant-pollinator (Soares \emph{et al.} 2017;
Vizentin-Bugoni\emph{et al.} 2018), predator-prey (Allesina \& Pascual
2008), and plant-mycorrhiza systems (Simard \emph{et al.} 2012).
Ecologists widely employ network analyses to characterise and visualise
species interactions (Pocock \emph{et al.} 2016). Furthermore,
increasing computational capacities have promoted the use of network
link prediction (NLP) algorithms to model undetected interactions. These
methods originating in social network studies (Wang \emph{et al.} 2015),
have lately been optimised for biological systems (Martínez \emph{et
al.} 2016) including ecological networks (Dallas \emph{et al.} 2017;
Zhao \emph{et al.} 2017; Fu \emph{et al.} 2019). Few recent studies on
the \emph{Stockholm paradigm} have integrated network analyses (but see
D'Bastiani \emph{et al.} 2020; Braga \emph{et al.} 2021). Instead, the
focus has remained on inferring ancestral host-parasite interactions
(Braga \emph{et al.} 2020, 2021) rather than predicting undetected
links. The distinction between undetected and unrealised links remains a
major hurdle for network studies as observed interactions will often
present an underestimation of the real interaction diversity (Fu\emph{et
al.} 2019). Furthermore, previous studies (Braga \emph{et al.}2020,
2021) treated interactions as discrete states, e.g. as non-hosts,
potential hosts, and real hosts, despite the literature on network
analyses substantiating that some host-parasite interactions are more
prevalent than others (Blüthgen \emph{et al.} 2008; Poulin \emph{et
al.}2011). Many of the metrics describing the structure of species
networks, such as nestedness, connectance, and specialisation, have been
optimised to account for interaction strength, i.e. the frequency of
interactions (see Blüthgen \emph{et al.} 2008). Undetected links and
interaction strengths could be addressed through NLP as the algorithms
account for both of these issues (Dallas \emph{et al.} 2017; Fu \emph{et
al.} 2019).

Here, we investigate host-parasite networks of multiple host lineages
evolved through adaptive radiation using network theory and NLP. As
model system, we selected one of the best-known examples for explosive
speciation: African cichlid fishes. The approximately 2000 species
residing in the East African Great Lakes are the result of multiple
adaptive radiation events (Salzburger \emph{et al.} 2014). Cichlid
science has been at the forefront of evolutionary (e.g. Salzburger 2018;
Ronco \emph{et al.} 2021) and behavioural (see Koblmüller \emph{et
al.}2019) research. Outside of feeding behaviour (e.g. Cooper \emph{et
al.}2010; Hulsey \emph{et al.} 2019), and fish-fish (e.g. Bla\selectlanguage{polish}ž\selectlanguage{english}ek
\emph{et al.} 2018; Marshall 2018) and human-fish interactions (Irvine
\emph{et al.} 2019), studies on interactions of cichlids with
non-cichlid organisms have focused mostly on parasitic interactions
(Cruz-Laufer\emph{et al.} 2021a). One parasite lineage infecting African
cichlids, monogenean flatworms belonging to \emph{Cichlidogyrus}
Paperna, 1960 sensu Wu \emph{et al.} (2007) (including \emph{Scutogyrus}
Pariselle \& Euzet, 1995), is particularly species-rich. Currently, 143
species are described that infect the gills of 139 cichlid and five
non-cichlid species (see Cruz-Laufer \emph{et al.} 2021a). Monogenean
parasites of cichlids were proposed as model system for host-parasite
interaction studies (Pariselle \emph{et al.} 2003; Vanhove \emph{et al.}
2016) (Fig. 1).

We explore cichlid-\emph{Cichlidogyrus} interactions comparing
meta-communities of the African Great Lakes that are strongly shaped by
adaptive radiation events to those outside these lakes. First, we use
network metrics to characterise the structure of the observed networks
and meta-communities. Second, we assess host repertoires considering
both functional and phylogenetic host diversity (Poulin \emph{et
al.}2011; Esser \emph{et al.} 2016) and discuss the limitations of this
traditional approach to host repertoires. Third, we assess the
performance of two recently proposed NLP models for predicting
host-parasite interactions. We aim to address the following questions on
the ecology and evolution of parasites using the
cichlid-\emph{Cichlidogyrus} network as a model system: (i) Do observed
host repertoires correlate with functional or phylogenetic host
diversity, (ii) what can network link prediction models reveal about
predictors of these interactions, (iii) how are
cichlid-\emph{Cichlidogyrus} meta-communities structured when hosts
evolve through adaptive radiation?

Materials \& methods

\emph{Data assembly: Infection data, niche modelling, phylogenies}

We assembled infection data through a survey of peer-reviewed
literature. This survey resulted in an updated version (Supporting
Information) of the list published by Cruz-Laufer \emph{et al.} (2021a).
For abundance weighting, we also assembled infection parameters
including the number of examined hosts, infected hosts, and parasites.
Yet if no infection parameters were reported (59\% of reports, 61\% of
interaction), we considered these reports singular observations to take
them into account but minimise their impact on downstream analyses
(eventually constituting 9.6\% of infections).

We built host niche dendrograms based on ecological, geographical, and
morphological data (Table 1) accessed on FishBase (Froese \& Pauly 2000)
through the \emph{R} package \emph{rfishbase} (Boettiger \emph{et
al.}2012). Missing trophic level and habitat data were added through a
literature survey (see Supporting Information). Dendrograms were built
through hierarchical clustering in \emph{R} (Pavoine \emph{et al.} 2009)
based on a Gower's distance matrix (Gower 1971). Gower's distances were
calculated using the function \emph{dist.ktab} in the \emph{R}
package\emph{ade4} v1.7.16 (Pavoine \emph{et al.} 2009). As in Clark \&
Clegg (2017), we accounted for uncertainty of the host niche by applying
several clustering algorithms implemented in the function
\emph{hclust}in \emph{R} (incl. \emph{ward.D2} , \emph{single} ,
\emph{complete} ,\emph{average} , \emph{mcquitty, median} , and
\emph{centroid} ) (R Core Team 2022). We tested for topological
congruence of the resulting dendrograms using the congruence among
distance matrices (CADM) test (Legendre \& Lapointe 2004; Campbell
\emph{et al.} 2011) in the package\emph{ape} v5.4 (Paradis \& Schliep
2019).

As no previous phylogenetic study covers all the species known to host
members of \emph{Cichlidogyrus} , we conducted a new analysis (see
Appendix S1.1) based on DNA sequence data accessed on GenBank (Appendix
S2) to infer phylogenetic distances between hosts. For the parasites, we
included morphometric and phylogenetic data from Cruz-Laufer \emph{et
al.} (2021b), i.e. morphological measurements and 100 randomly sampled
Bayesian tree topologies from the post-burn in fraction.

\emph{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 \emph{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 \emph{et al.} 2003) with the whole
cichlid-\emph{Cichlidogyrus}network. We also inferred meta-communities
through the Louvain community detection algorithm, an approach based on
optimisation of network modularity (see Blondel \emph{et al.} 2008)
implemented in the \emph{R}package \emph{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
(NODF\textsubscript{w}) (Almeida-Neto \& Ulrich 2011), weighted
connectance (C\textsubscript{w}) (Bersier \emph{et al.} 2002),
specialisation asymmetry (SA) (Bl\selectlanguage{ngerman}üthgen\emph{et al.} 2007), interaction
evenness (E\textsubscript{i}) (Bersier\emph{et al.} 2002), and the
standardised interaction diversity (H\textsubscript{2}') (Blüthgen
\emph{et al.} 2006) using \emph{R}package \emph{bipartite} v2.15
(Dormann \emph{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 (NM\textsubscript{1}) and the
redistribution algorithm proposed by Vázquez et al. (2007)
(NM\textsubscript{2}), which maintains the network connectance, i.e.
only realised interactions are redistributed. We generated 1000 null
matrices through the function \emph{nullmodel} in \emph{bipartite} and
assessed significance as proportion of null estimates greater than the
observed estimates.

\emph{Host repertoire}

We investigated the effects of host environment and phylogeny on the
host repertoire of the parasites. Host repertoires were quantified as
mean pairwise distance (MPD) and mean nearest taxon distance (MNTD) of
host species to investigate ancient and recent evolutionary
relationships respectively (Clark \& Clegg 2017). We calculate MPD and
MNTD from functional-phylogenetic distance (FPDist)
matrices\emph{.}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 = (aPDist\textsuperscript{p} + (1 -
a)FDist\textsuperscript{p})\textsuperscript{1/p} (Cadotte \emph{et
al.}2013) with p = 2 to calculate Euclidean distances (see Cadotte
\emph{et al.} 2013; Burbrink \emph{et al.} 2017; Clark \& Clegg 2017).
Finally, we applied 100 incremental increases from 0 to 1 to the
weighting parameter \emph{a} . We calculated FPDist through the
function\emph{FPDist} in the \emph{R} package \emph{funphylocom} v1.1
(Walker 2014). For the null model (NM\textsubscript{3}), we generated
null distributions by randomly redistributing the labels of the FPDist
matrices resulting from 1000 random draws from FDist and PDist matrices.
We calculated MPD, MNTD, and 1000 null estimates through the
functions\emph{mpd} , \emph{mntd} , and \emph{taxaShuffle} in the
\emph{R} package\emph{picante} v1.8.2 (Kembel \emph{et al.} 2010). For
interpretation of the FPDist plots, we followed Cadotte \emph{et al.}
(2013).

\emph{Network link prediction}

We applied the following network link prediction (NLP) algorithms:

The \emph{plug-and-play} algorithm (Dallas \emph{et al.} 2017) predicts
missing links based on conditional probability estimation and was
developed to infer the probabilities of unobserved links being
undetected through sets of input parameters.

The \emph{Poisson} N\emph{-mixture} link prediction model (Fu \emph{et
al.} 2019) combines the Poisson N-mixture model used in ecological
research with a low-rank collaborative filtering approach. Poisson
N-mixture models are used in ecological research to account for
imperfect detection in field observations (Royle 2004). Meanwhile,
low-rank matrix completion--based collaborative filtering methods are a
popular approach for NLP in social network studies. Missing entries in a
data matrix are completed based on a low number of known entries (low
rank matrix), e.g. to predict consumer preferences (Candes \& Plan
2010).

We provided ecological, morphological, and phylogenetic input parameters
to these models (Table 1). Both models do currently not account for
phylogenetic uncertainty. Therefore, we included only the majority-rule
consensus host and parasite BI phylogenies and the
\emph{ward.d2}dendrograms (Murtagh \& Legendre 2014), one of the most
widely used clustering algorithms (Murtagh \& Legendre 2014). To avoid
overfitting, we reduced the number of input variables per parameter
through principal coordinate analyses (PCoA) of the distance matrices of
each parameter resulting in 9 input parameters consisting of 24
variables (Table 1). Distance matrices were computed through the
\emph{cophenetic} function in \emph{R} applied to dendrograms that were
built for each parameter separately through the clustering methods also
employed for the host niche dendrograms. We imputed missing data through
a k-nearest neighbour algorithm (kNN) in the \emph{R} package
\emph{caret} (Kuhn 2008, 2020).

We assess model performance through the Area Under the Receiver
Operating Characteristic curve (AUROC) and 10-fold cross validation.
Each time, the algorithms were trained on 80\% of the interaction matrix
to predict the remaining 20\%. We implemented the models in
\emph{R}v4.1.2 (R Core Team 2022) and \emph{MATLAB} v9.11.0 (MathWorks,
Natick, USA) using published codes (doi: 10.6084/m9.figshare.4965038;
https://github.com/Hutchinson-Lab/Poisson-N-mixture). Following
Dallas\emph{et al.} (2017), we assessed variable importance of
the\emph{plug-and-play} algorithm by measuring the reduction in model
performance resulting from 500-fold permutation of each parameter. For
variable importances and link prediction, the algorithm was trained on
the full dataset. These analyses were not performed for
the\emph{Poisson} N-\emph{mixture} model due to lacking implementation.

\emph{Graphing}

We plotted the species network through the R package \emph{igraph}v1.2.6
(Csardi \& Nepusz 2006), chord diagrams through \emph{circlize}v0.4.13
(Gu \emph{et al.} 2014), dendrograms and phylogenetic trees through
\emph{ggplot2} v3.3.5 (Wickham 2016) and \emph{ggtree} v3.2.1 (Yu
\emph{et al.} 2017, 2018), and heatmaps in \emph{ComplexHeatmap}v2.10.0
(Gu \emph{et al.} 2016).

Results

\emph{Data assembly}

Across their natural and anthropogenically expanded host and
geographical ranges, we found reports of 10529 infected host specimens
and 477 different host-parasite interactions in 183 publications. The
resulting species network comprises 144 host and 143 parasite species
(including species not officially described yet, i.e. cheironyms). We
assembled environmental and morphometric data for 133 and DNA sequence
data for 117 host species (Appendix S1.1). Niche dendrograms resulting
from the clustering algorithms (Appendix S3) were congruent (CADM,
Kendall's W = 0.66, \selectlanguage{greek}χ\selectlanguage{ngerman}² = 34944, p \textless{} 0.01). The BI host tree
topology showed no deviation from recent phylogenetic hypotheses likely
to affect downstream analyses (see discussion in Appendix S1.1).

\emph{Network metrics and community structure}

We identified 42 meta-communities in the
cichlid-\emph{Cichlidogyrus}network through the Louvain algorithm.
Several detected meta-communities that included only few species and
were completely unconnected to the other communities formed part of
\emph{LT} (Fig. 2a). Four meta-communities other than \emph{LT} and
\emph{LV} (Fig. 2b) included 10 or more species (Fig. 2c; Appendix S4):
the `\emph{Coptodon zillii} ' (\emph{CZ} ), `\emph{Oreochromis
niloticus} ' (\emph{ON} ), `\emph{Hemichromis} ' (\emph{He} ), and
`\emph{Tilapia sparrmanii} ' (\emph{TS} ) clusters. These names reflect
the host taxa accounting for a majority of reported infections (Appendix
S1.2). Network structure differed between meta-communities but
anthropogenic introductions had only a limited effect (Fig. 3; Appendix
S1.2).

\emph{Host repertoire}

Functional-phylogenetic distances (FPDist) were calculated for all
species of \emph{Cichlidogyrus} with more than one host species
(Appendix S5) with the weighting parameter \emph{a} between 0 and 1.
Plots for some species are shown in Fig. 4 to illustrate main patterns
observed throughout the lineage. Generally, FPDist decreased with
increasing phylogenetic weight (MPD or MNTD) as indicated by the falling
trend of FPDist estimates (Fig. 4). Observed MPD and MNTD showed a
significant pattern of underdispersion (clustering) compared to 1000
random simulations in some cases. Specifically, MPD metrics of FPDist
became mostly significant if the weight of phylogenetic distances
(PDist) increased (i.e. \emph{a} \selectlanguage{english}- 1) (Fig. 4). In contrast, MNTD
estimates were largely non-informative regardless of \emph{a} . One
exception is \emph{Cichlidogyrus} sp. `nyanza' (Gobbin \emph{et
al.}2021) with MNTD estimates being lowest for \emph{a} \selectlanguage{english}- 0 (Fig. 4).

\emph{Network link prediction}

Overall, the NLP algorithm performed solidly (\emph{plug-and-play} :
mean AUROC = 0.72, full model AUROC = 0.85; \emph{Poisson}
N-\emph{mixture} : mean AUROC = 0.62) despite missing data (Fig. 5a).
Several input parameters improved performance of the
\emph{plug-and-play} algorithm significantly (Fig. 5c) including the
basin/basin type, host phylogenies, trophic level, and life style
whereas the parameters for parasite morphology and phylogeny decreased
the performance. A substantial amount of species interactions remains
undetected, albeit less for \emph{LT} and \emph{LV} (Fig. 5b; Appendix
S6). For \emph{LV} , model performances were slightly better (0.78,
0.87; 0.76) but with the host phylogeny as the most important predictor.
For \emph{LT} , the models showed little discriminatory power (0.41,
0.87; 0.62).

Discussion

We investigated the patterns of host-parasite interaction of African
cichlid fishes and their gill parasites belonging to\emph{Cichlidogyrus}
, a proposed model system for macroevolutionary research (Pariselle
\emph{et al.} 2003; Vanhove \emph{et al.} 2016). This study is the first
to empirically investigate the effects of adaptive radiation events on
species interactions {[}but see Maynard\emph{et al.} (2018) for
simulations{]}. The size of this species network (10529 infections, 477
interactions) is comparable to widely used host-parasite datasets in
terms of species richness, e.g. the Global Mammal Parasite Database
(GMPD) (Nunn \& Altizer 2005), the Sevillata interaction network (Dallas
\& Presley 2014), and other fish-parasite (Lima Jr \emph{et al.} 2012;
Bellay \emph{et al.} 2015) and plant-arthropod systems (Lopez-Carretero
\emph{et al.} 2014; de Araujo \emph{et al.} 2020; Oliveira \emph{et al.}
2020; de Araujo \& Maia 2021). The system is also the first to encompass
closely related parasite species infecting a host system that is a model
for speciation research (Seehausen 2006). Therefore, our dataset could
be an asset for comparative studies in network ecology and ecological
parasitology.

\emph{Host repertoire: Host evolution shapes community structure}

We inferred two main predictors of
cichlid-\emph{Cichlidogyrus}interactions: host phylogenetic
relationships and host environment. The hosts' evolutionary history can
have a significant impact on the structure of host-parasite networks
(e.g. Mouillot \emph{et al.} 2008a; Braga \emph{et al.} 2014, 2020).
Here, we show that the realised host repertoire of most species of
\emph{Cichlidogyrus} is determined more by the hosts' evolutionary
history than their environment. First, host repertoires differed more
regarding the ecological niche than the phylogenetic relationships as
evidenced by a reduction of the mean functional-phylogenetic distances
(FPDist) with increasing phylogenetic weight (Fig. 4). Second, FPDist
estimates rarely differed from the null distribution for increasing
functional weight (but see \emph{C.} sp. `nyanza\emph{'} ; Fig. 4).
Third, estimates outside the null distribution were underdispersed
(clustered), i.e. lower than expected at random at both the ancient and
recent evolutionary scale (measured as MPD and MNTD respectively). The
strong phylogenetic influence and underdispersion are associated with
co-divergent evolution (Clark \& Clegg 2017). Co-divergence assumes that
host and parasite phylogenies are phylogenetically congruent (Page 2003;
Hoyal Cuthill \& Charleston 2012), a pattern that has already been
observed for a number of species of \emph{Cichlidogyrus} (Vanhove
\emph{et al.} 2015). Congruence might arise especially in younger
parasite lineages that have experienced a phase of isolation resulting
in co-divergence (Agosta \& Brooks 2020), e.g. species of
\emph{Cichlidogyrus} infecting tropheine cichlids (Vanhove \emph{et al.}
2015), which arose 4.5--6.5 MYA (Schedel \emph{et al.} 2019).

Additionally, phylogenetic tracking might explain the role of the host
phylogeny on host repertoires. Even within the same ecosystem, parasites
are constrained to compatible ecological niches created by host
speciation (Russo \emph{et al.} 2018). Consequently, phylogenetic
relationships are determinants of neotropical (Braga \emph{et al.} 2014)
and Mediterranean (Desdevises \emph{et al.} 2002) monogenean-fish
communities but also plant-pollinator, plant-frugivore (Rezende \emph{et
al.} 2007), plant-mycorrhiza (Jacquemyn \emph{et al.} 2011), and other
host-parasite networks (Mouillot \emph{et al.} 2008). Yet natural (Birgi
\& Euzet 1983; Birgi \& Lambert 1986) and invasion-induced
(Jimenez-Garcia \emph{et al.} 2001; \selectlanguage{polish}Š\selectlanguage{english}imkov\selectlanguage{ngerman}á \emph{et al.} 2019;
Jorissen\emph{et al.} 2020) host switches of species of
\emph{Cichlidogyrus}highlight that fundamental host repertoires might
differ considerably from the realised host repertoires estimated here.
Monogeneans have the capacity to infect new hosts and adapt to new
environments (Braga\emph{et al.} 2014; Messu Mandeng \emph{et al.} 2015;
Nack \emph{et al.}2016). Thus, conclusions drawn based solely on
analyses of host repertoires should be treated with caution.

\emph{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 \emph{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-\emph{Cichlidogyrus} communities similar
to neotropical teleost-monogenean communities (Braga \emph{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
(\emph{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 \emph{et al.} 2014; Fathi \emph{et
al.} 2017). Moreover, introductions of tilapias have led to
co-introductions of their monogenean parasites in continental Africa
(Jorissen \emph{et al.} 2020), Madagascar (\selectlanguage{polish}Š\selectlanguage{english}imkov\selectlanguage{ngerman}á \emph{et al.} 2019),
Asia (Paperna 1960; Duncan 1973; Wu \emph{et al.} 2006), Australia
(Wilson \emph{et al.} 2019), and the Americas (Jiménez-García \emph{et
al.} 2001; Azevedo \emph{et al.} 2006), with occasional spillover to
native fishes (Jiménez-García \emph{et al.} 2001; \selectlanguage{polish}Š\selectlanguage{english}imkov\selectlanguage{ngerman}á \emph{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 \emph{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-\emph{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 \emph{et al.} 2020; Nobile \emph{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 \emph{life style} as
coded here (Table 1), but host habitat preference can affect parasite
communities (Locke \emph{et al.} 2013). Parasite community composition
correlated with the host \emph{trophic level} in some cases, e.g. for
shelf fish off Buenos Aires (Timi \emph{et al.} 2011), but not in
others, e.g. for freshwater fish in Canada (Locke \emph{et al.} 2013)
and marine fish in Finland (Locke \emph{et al.} 2014). \emph{Host
size}was suggested as important predictor for the community composition
of ectoparasitic monogeneans (Guégan \emph{et al.} 1992; Sasal \& Morand
1998; Sasal \emph{et al.} 1999; \selectlanguage{polish}Š\selectlanguage{english}imkov\selectlanguage{ngerman}á \emph{et al.} 2001;
Desdevises\emph{et al.} 2002; Morand \emph{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 \emph{attachment} or \emph{reproductive organ
morphology}with community composition was found for species of
\emph{Cichlidogyrus}unlike for species of \emph{Dactylogyrus} (\selectlanguage{polish}Š\selectlanguage{english}imkov\selectlanguage{ngerman}á
\emph{et al.} 2001; Jarkovský \emph{et al.} 2004). Instead, the
morphology mostly reflects phylogenetic relationships of the parasites
(Vignon \emph{et al.} 2011; Cruz-Laufer \emph{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 \emph{et al.} 2016) (Fig.
5a) and character coding (Pavoine \emph{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).

\emph{The East African lakes: specialised and saturated communities?}

Explosive speciation in cichlids has resulted in distinctively
structured cichlid-\emph{Cichlidogyrus} communities in Eastern Africa.
Meta-communities in Lake Tanganyika (\emph{LT} ) and the Lake Victoria
region (\emph{LV} ) are more specialised, and less nested and connected
than those inferred for species elsewhere (Fig. 3; Appendix S1). Few
additional interactions were predicted (Fig. 5b) suggesting that most
are already known for the sampled species. This saturation of
interactions might be expected as new ecological opportunities, an
important determinant of interactions at the global scale, arise less in
comparatively stable lake ecosystems such as \emph{LT}
(Salzburger\emph{et al.} 2014). Newcomers to these systems might find
little opportunity to radiate (e.g. Koch \emph{et al.} 2007).
Furthermore, simulations suggest that specialisation in a network can be
a by-product of adaptive radiation (Maynard \emph{et al.} 2018).

Specialisation was particularly high for \emph{LT} . Notably, host
repertoires were frequently not estimated for these parasite species as
many infect only single host species. These restricted host repertoires
(Fig. 5b) might also be an explanation for the poor performance of NLP
for the \emph{LT} network as the input variables are by design identical
for duplicate interactions. The algorithms performed well for
\emph{LV}likely because of the higher nestedness (Fig. 3). For instance,
only one recorded monogenean species infects deepwater cichlids from
the\emph{LT} tribe Bathybatini (Kmentová \emph{et al.} 2016) whereas
co-infections for 36 host species in the \emph{LV} community are
reported (Fig. 2b, Appendix S1.2). Species of \emph{LV} might be more
interconnected than \emph{LT} due the younger age of the host radiation
(0.4 M years; see, Salzburger \emph{et al.} 2014), which left the
network less saturated. The Lake Victoria region has also experienced
rapid ecosystem degradation over the past century including the
introduction of a novel predator, \emph{Lates niloticus} (L.), and
eutrophication causing the decline and extinction of many native fishes
(Marshall 2018). Environmental disturbance might have promoted the
expansion of host repertoires as new ecological opportunities arise and
parasites increase their change of survival through ecological fitting
(Brooks \emph{et al.} 2019). For instance, \emph{Cichlidogyrus} sp.
`nyanza' is one of the few species with a host repertoire more
determined by ecological parameters than the host evolution at least at
the recent evolutionary scale (Fig. 4), although this pattern might
emerge from the comparatively low genetic differentiation of
the\emph{LV} radiation (Salzburger \emph{et al.} 2014). In conclusion,
cichlid-\emph{Cichlidogyrus} communities in the East African lakes
appear more stable and saturated than those elsewhere. Yet the younger
age and environmental degradation might make \emph{LV} a less stable
system than \emph{LT} . Limitations of the dataset call for more
extensive sampling of host-parasite communities in these lakes to better
understand these interactions. Other lake systems, e.g. Lake Malawi
could be of particular interest for future comparative studies mirroring
the research conducted on the cichlid radiations in this region (e.g.
Duponchelle \emph{et al.} 2008).

\emph{Opportunities and limitations: sampling bias, missing data, and
databases}

We produced the most extensive study on species interaction of cichlid
fishes or any other lineage with adaptive radiations to date. Patterns
of community structure were inferred through a series of network
analytical methods ranging from more traditional to new approaches.
Limitations could be addressed through the following measures:

\begin{itemize}
\tightlist
\item
  Because of the sampling bias in
  cichlid-\emph{Cichlidogyrus}interactions studies towards economically
  relevant hosts (Cruz-Laufer\emph{et al.} 2021a), the data likely give
  an incomplete picture as confirmed by NLP (Fig. 5b). Null models can
  account for this issue, but taxonomic research remains ultimately
  essential for closing knowledge gaps. Data generated from such studies
  should be gathered in online databases, e.g. the Global Interaction
  Database GLOBI (Poelen\emph{et al.} 2014), to improve access to
  interaction data for research communities worldwide (Molloy 2011;
  Upham \emph{et al.}2021).
\item
  The basin/basin-type parameter analysed here only summarises the
  entire geographical range of the hosts. Future studies should also
  account for geographical distribution as geocoordinates to infer local
  interaction patterns as we expect climates to vary across basins and
  species ranges.
\item
  New models for NLP are being developed and employed in an increasing
  number of fields (Martínez \emph{et al.} 2016). We suggest that a
  streamlined software package or library targeted at ecological
  research could simplify implementation for ecologists.
\item
  The NLP algorithms applied here differentiate between true (impossible
  or `forbidden' links) and false negatives (undetected links)
  (Dallas\emph{et al.} 2017; Fu \emph{et al.} 2019) among unobserved
  interactions. Wildlife host-parasite infection data regularly include
  prevalence data, i.e. ratios of uninfected host specimens. This
  information could be incorporated into future models.
\end{itemize}

Generally, the cichlid-\emph{Cichlidogyrus} data serves as a study
system eco-evolutionary studies because of a substantial amount of
interaction, molecular, and morphological data for hosts and parasites.
Addressing the limitations listed above might increase this potential.
We were able to detect key mechanisms of ecology and evolution. First,
the realised host repertoire is phylogenetically constrained as host
range parameters are determined more by the host evolutionary history
than by ecological parameters. However, recent host switches indicate
that fundamental host repertoires might be more extensive than currently
known. Second, network link prediction algorithms show that network
structure is shaped by ecological opportunity induced by habitat sharing
but host evolution, life style, and trophic level are also influential
factors. Third, adaptive radiations of host lineages in Eastern Africa
have created more specialised and potentially saturated
meta-communities. Future studies should investigate whether our findings
also apply in other host-parasite systems shaped by adaptive radiation.
Therefore, we encourage researchers to reuse data provided here to
diversify the portfolio of host-parasite interaction research in the
future.

Acknowledgements

We would like to thank Walter A. Boeger for his extensive comments and
thoughts on the manuscript. Data collection started within the BRAIN-be
Pioneer Project BR/132/PI/TILAPIA (Belgian Federal Science Policy
Office) under the supervision of Tine Huyse and Jos Snoeks and the
Knowledge Management Centre project CiMonoWeb (Royal Museum for Central
Africa) under the supervision of Tine Huyse with the kind help of Wouter
Fannes. Part of the research leading to results presented in this
publication was carried out with infrastructure funded by the European
Marine Biological Research Centre (EMBRC) Belgium, Research Foundation
-- Flanders (FWO) project GOH3817N. AJCL (BOF19OWB02) and MPMV are
funded by the Special Research Fund of Hasselt University (BOF20TT06).
We thank the anonymous reviewers for suggesting improvements to the
manuscript.

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List of table headers

Table 1. Evolutionary, ecological, and morphological parameters of hosts
and parasites used for calculation of host habitat niche dendrogram and
network link prediction (NLP) models. Host parameters were accessed in
FishBase (Froese \& Pauly 2000) and parasite parameters were reused from
Cruz-Laufer \emph{et al.} (2021b). To avoid overfitting NLP models,
variable numbers per parameter were reduced through principial
coordinate analyses (\emph{PCoA} ) based on distance matrices of
phylogenetic trees or \emph{dendrograms} built through clustering
methods (see number of PCoA axes used for NLP and their proportion of
parameter variation in brackets).

List of figure captions

Figure 1. Ecological and evolutionary processes shape the structure of
the cichlid-\emph{Cichlidogyrus} network consisting of cichlid fishes, a
model system for explosive speciation research, and the parasitic
flatworms belonging to \emph{Cichlidogyrus} infecting the gills of
cichlid and few non-cichlid fishes. Species presented in the figure
are\emph{Coptodon guineensis} (Günther, 1862) and \emph{Cichlidogyrus
gallus} Pariselle \& Euzet, 1995.

Figure 2. Cichlid-\emph{Cichlidogyrus} species network. (A) Whole
network with unweighted links and Lake Tanganyika (\emph{LT} ), Lake
Victoria regions (\emph{LV} ), inferred species-rich communities (n
\textgreater{} 10) highlighted in colours. Circles indicate host species
and squares species of \emph{Cichlidogyrus} . Meta-communities were
detected using the Louvain cluster algorithm including the Lake Victoria
(LV), `\emph{Coptodon zillii} ' (CZ), `\emph{Oreochromis
niloticus'}(ON), `\emph{Hemichromis} ' (He), and `\emph{Tilapia
sparrmanii'} (TS) cluster. Many small unconnected clusters belong to
\emph{LT} . (B) Chord diagrams of the \emph{LT} and \emph{LV} clusters.
(C) Four other species-rich meta-communities involving species of
\emph{Cichlidogyrus}and \emph{Scutogyrus} with links weighted by number
of observed infections communities. Unlike \emph{LT} and \emph{LV} ,
meta-communities\emph{CZ, ON, He,} and \emph{TS} are characterised by
sampling bias towards few, economically relevant host species, e.g.
\emph{Coptodon zillii} , \emph{Oreochromis niloticus} ,
\emph{Hemichromis fasciatus,} and\emph{Tilapia sparrmanii} . Species
names were omitted from (B) and (C) but are included in Appendix S4.

Figure 3. Changes of network metrics when only including natural host
repertoires and geographical ranges of
cichlid-\emph{Cichlidogyrus}meta-communities including Lake Victoria
region (\emph{LV} ), ´\emph{Oreochromis niloticus} ' (\emph{ON} ),
`\emph{Hemichromis} ' (\emph{He} ), and `\emph{Coptodon zillii} '
(\emph{CZ} ). Most values of the weighted nestedness based on overlap
and decreasing fill (NODF\textsubscript{w}) (Almeida-Neto \& Ulrich
2011), weighted connectance (C\textsubscript{w}) (Bersier \emph{et al.}
2002), specialisation asymmetry (SA) (Blüthgen \emph{et al.} 2007),
interaction evenness (E\textsubscript{i}) (Bersier \emph{et al.} 2002),
and the standardised interaction diversity (H\textsubscript{2}')
(Blüthgen\emph{et al.} 2006) that differed significantly from the null
distributions (NM\textsubscript{1}, NM\textsubscript{2}) remained
unchanged (see Appendix S1.2 for detailed discussion).

Figure 4. Functional-phylogenetic distances (FPDist) inferred from host
repertoires of selected species of \emph{Cichlidogyrus} calculated as
mean pairwise distance (MPD) and mean nearest taxon distance (MNTD)
weighted by abundancy of interactions (blue). FPDist matrices are a
function of functional (FDist) and phylogenetic (PDist) distance
matrices of the host species weighted by the parameter \emph{a} . Shaded
areas (grey) indicate 5\% and 95\% quantiles of 1000 null distributions
resulting from taxon shuffling. If estimates fall outside the null
distribution, they can be considered informative. Smaller values
indicated higher functional-phylogenetic similarities of host
repertoires. A decreasing trend for FPDist estimates indicates that host
communities are more phylogenetically than ecologically similar. For
plots of other species infecting at least two host species, see Appendix
S6.

Figure 5. Network link prediction based on host {[}H{]} and parasite
{[}P{]} data in the cichlid-\emph{Cichlidogyrus} network, and Lake
Tanganyika (\emph{LT} ) and Lake Victoria regions (\emph{LV} )
subnetworks including missingness map of input variables for whole
networks (a), heat maps of host-parasite links (b), and bar plot of
variable importances (c) predicted by the \emph{plug-and-play} algorithm
(Dallas\emph{et al.} 2017). The missingness map illustrates significant
gaps in the taxon coverage of phylogenetic data and host standard
lengths. The heat maps shows that a large proportion of
cichlid-\emph{Cichlidogyrus}interactions likely remain undetected
(highlighted in colour) (for taxon labels, see Appendix S6) although
most interactions of the studied organisms are most likely known for
\emph{LT} and \emph{LV} . The variable importance graph indicates that
the basins/basin types inhabited by the hosts are the most important
predictor of cichlid-\emph{Cichlidogyrus} interactions, but less so for
\emph{LT} and\emph{LV.}Supporting information

Appendix S1. Other methods and results including phylogenetic
reconstruction and structure of species-rich meta-communities in the
cichlid-\emph{Cichlidogyrus} system.

Appendix S2. GenBank accession numbers of DNA sequences used to render
host phylogenetic distances.

Appendix S3. Host niche dendrograms resulting from different clustering
algorithms.

Appendix S4. Chord diagrams of meta-communities presented in Fig. 2 with
additional species labels. Host species names are abbreviated with the
first three characters of the genus name and the first four charactersof
the species epithet. Parasite species names are abbreviated with the
first character and first six characters respectively.

Appendix S5. Functional phylogenetic distance (FPDist) plots of host
repertoires of all species of \emph{Cichlidogyrus} not included in Fig.
4.

Appendix S6. Heat maps of links predicted by the
\emph{plug-and-play}algorithm with complete taxon labels. See Fig. 5c
for simplified version.

\textbf{Hosted file}

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